Development of a method for forest type detection

FACULTY OF FOREST AND ENVIRONMENTAL SCIENCES

DEPARTMENT OF REMOTE SENSING AND LAND INFORMATION SYSTEMS (FELIS)

TENNENBACHERSTR.4. D79106 FREIBURG IM BREISGAU. GERMANY

________________________

Development of a method for forest type detection

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Monograph submitted in partial fulfillment of the requirements for the degree of PhD

By

Juan Ygnacio López Hernández

Student ID 2729448

Freiburg, November 2012

Dean of the Faculty: Prof. Dr. Jurgen Bauhus

First supervisor: Prof. Dr. Barbara Koch

Second reviewer: Prof. Dr. Carsten F. Dormann

Adviser: Dr. Claus-Peter Gross

Date of defence: 14 of November 2012

This document is available free from servers "FreiDok" at the University of Freiburg.

[http://www.freidok.uni-freiburg.de/]

Printed with the support of the German Academic Exchange Service

DAAD

Acknowledgements Development of a method for forest type detection Juan Ygnacio López Hernández

Acknowledgements

My thanks, I express first to God. He is the beginning and the end of all.

Prof. Dr. Barbara Koch. She supported me from before starting in Freiburg, dedicated to me

more time than she uses to guide me and advised with wisdom all of the directions that should

take the research during years. Her patience and mysticism inspired me to always reach to the

best results. With all my respect: Thank you!

The person who was my first contact with Freiburg and the second supervisor of my thesis,

Prof. Dr. Dr. hc. Albert Reif. I thank you guiding also my thesis with serious questions and

suggestions.

The University of Los Andes that granted me the permission to study and gave the money in

my scholarship in difficult Venezuelan times: Thank you!

Thanks to the German DAAD, that gave me more than support, the security of a serious

institution and kept the ALEVEN agreement alive without equilibrium from Venezuela.

Special thanks to Frau Metje, she was the best official support and the voice of DAAD for me.

Thanks to the Graduate School "Environment, Society and Global Change" (ESGC) and to

the International Graduate Academy. Both institutions helped to me in my personal walk to

the knowledge and sciences.

Dr. Mathias Dees, who made very useful contributions: Thank you.

Thanks to Dr. Claus-Peter Gross. He always made serious questions and provided valuable

support.

Thanks to Dr. Hooman Latifi and Dr. Ahmad Yousef. Both shared office, scientific questions,

procedures, ideas and jokes on daily basis.

Thanks to Fabian Faßnacht, the first reader and corrector of my work.

Dr. Johannes Heinzel, who addressed discussions about SVM and classification problems:

Thanks.

Thanks to the staff and friends in FeLis. Always ready in the work. The same if they have

fixed positions or exchange visitants: Thanks.

Thanks to Dr. Cristabel Duran, you were my personal adviser in Germany.

To Karl Heinz, who was always discussing methods and research questions and to the

Spanish-speaking group, which provided a secure and disinterested support. Thanks to all of

you. Pura vida, Victor, thank you very much. Thanks to the group of Portuñol, changing

members in the time and always appreciated.

I cannot remember all of the people that supported me to finish this research. To all of them,

that should be here and I forgot. Thank you.

Acknowledgements Development of a method for forest type detection Juan Ygnacio López Hernández

En Español va mi agradecimiento para ustedes:

A Norma, Moncho, Yami, Oscar y Wilma. Tan lejos y siempre sentados a mi lado. Gracias.

A Paito y Maita, en oración constante para que todo salga bién. Gracias.

A la familia Jiménez López. Gracias por cuidar y vigilar mis intereses en Mérida.

A Carmen Alicia, María del Pilar y Santiago Josué. El apoyo sentimental y tierno de cada día

en tierras lejanas, aprendiendo idioma y costumbres distintas. ¡Gracias!

Juan Ygnacio López Hernández Development of a method for forest type detection Contents

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Contents

Contents ....................................................................................................................................... i

Executive summary .................................................................................................................... v

Resumen ejecutivo ................................................................................................................... vii

Zusammenfassung: .................................................................................................................... ix

List of figures ............................................................................................................................ xi

List of tables ............................................................................................................................ xiv

List of acronyms ..................................................................................................................... xvii

1 Introduction ......................................................................................................................... 1

1.1 Objectives .................................................................................................................... 3

2 Basic Information ................................................................................................................ 5

2.1 Literature review .......................................................................................................... 5

2.1.1 Definition of forest ............................................................................................... 5

2.1.2 Forest and type of forest mapping with LANDSAT data .................................... 7

2.1.3 Variable selection methods ................................................................................ 11

2.1.4 Unsupervised classification ................................................................................ 12

2.1.5 Supervised classification .................................................................................... 15

2.1.6 Classification with machine learning tools ........................................................ 21

2.1.7 Tuning algorithms .............................................................................................. 22

2.1.8 Cross validation .................................................................................................. 23

2.1.9 Pixel-based image analysis (PBIA) of forest areas ............................................ 24

2.1.10 Object based image analysis (OBIA) ................................................................. 28

2.2 Software used ............................................................................................................ 33

3 Test site and data used ...................................................................................................... 35

3.1 Test site ...................................................................................................................... 35

Contents Development of a method for forest type detection Juan Ygnacio López Hernández

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3.2 Satellite data .............................................................................................................. 38

3.3 DEM .......................................................................................................................... 41

3.4 Reference data ........................................................................................................... 41

3.4.1 Orthorectified colour aerial orthophotos ............................................................ 41

3.4.2 ATKIS forest layer ............................................................................................. 42

3.4.3 Forest stands from an aerial photo interpretation ............................................... 42

3.4.4 Document about interpretation of aerial photographs of forest stands .............. 42

3.4.5 Inventory data ..................................................................................................... 42

3.4.6 Cartographic projection of the data .................................................................... 43

4 Methods ............................................................................................................................. 45

4.1 Pre-processing the national forest inventory (NFI) data ........................................... 45

4.1.1 Query .................................................................................................................. 46

4.1.2 PAM Clustering .................................................................................................. 47

4.1.3 Table to points conversion ................................................................................. 50

4.1.4 Buffering ............................................................................................................ 51

4.1.5 Automatic selection of training samples from the NFI plots ............................. 52

4.1.6 Calculating the weight of the classes ................................................................. 53

4.2 Pre-processing satellite scenes ................................................................................... 55

4.2.1 Conversion of format ......................................................................................... 55

4.2.2 Pre-processing DEM .......................................................................................... 56

4.2.3 Orthorectification ............................................................................................... 56

4.2.4 Topographic normalisation ................................................................................ 59

4.2.5 Cloud mask ......................................................................................................... 61

4.2.6 Haze suppression ................................................................................................ 62

4.3 Creating synthetic layers for the classifications ........................................................ 63

4.4 Pixel based analysis of the satellite data .................................................................... 70

4.4.1 Detection of forest .............................................................................................. 70

4.4.2 Detection of forest types .................................................................................... 72

Juan Ygnacio López Hernández Development of a method for forest type detection Contents

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4.4.3 Assessment data based on aerial photographs, satellite and NFI ....................... 73

4.4.1 Vectorization of pixel-based classification results ............................................. 74

4.5 Model for detection of types of forest ....................................................................... 79

4.6 Object Based Image Analysis (OBIA) ...................................................................... 80

4.6.1 Variable selection methods ................................................................................ 80

4.6.2 Segmentation with OBIA ................................................................................... 83

4.7 Statistical approaches ................................................................................................ 85

4.7.1 Definition of parameters for grid search ............................................................ 85

4.7.2 Machine learning classification algorithms ........................................................ 85

5 Results ............................................................................................................................... 87

5.1 The digital layer of forest types of Bavaria ............................................................... 87

5.2 Variable selection process ......................................................................................... 88

5.2.1 Criterion based ................................................................................................... 88

5.2.2 Feature space optimization ................................................................................. 90

5.2.3 Recursive feature selection ................................................................................. 90

5.3 Evaluation of PBIA ................................................................................................... 91

5.3.1 Detection of forest areas ..................................................................................... 91

5.3.2 Detection of forest types for the scene in path 192 row 026. ............................. 95

5.3.3 Evaluation of the pixel oriented classification of forest types for all of the scenes

96

5.4 Evaluation of OBIA ................................................................................................... 97

5.4.1 Subset of a scene ................................................................................................ 97

5.5 OBIA of the forest types for the whole scene ........................................................... 98

5.5.1 Classification of the forest types for the whole scene in path 192, row 026 ...... 98

5.5.2 Classification of 6 scenes ................................................................................. 100

5.5.3 Results of the application of the machine learning classification algorithms .. 103

5.5.4 Comparing classifiers ....................................................................................... 110

5.5.5 Assessment of forest type classification ........................................................... 111

Contents Development of a method for forest type detection Juan Ygnacio López Hernández

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6 Discussion ....................................................................................................................... 115

6.1 Definition of forest .................................................................................................. 115

6.2 Regarding the satellite scenes .................................................................................. 115

6.3 Preprocessing the scenes ......................................................................................... 116

6.4 Orthorectification ..................................................................................................... 116

6.5 Clouds and haze ....................................................................................................... 117

6.6 PBIA approach ........................................................................................................ 118

6.7 OBIA approach ........................................................................................................ 118

6.8 OBIA method with fast segmentation ..................................................................... 119

6.9 Variable selection methods ...................................................................................... 120

6.10 The best classifier .................................................................................................... 120

6.11 Comparing kNN and SVM ...................................................................................... 121

6.12 PAM clustering the NFI .......................................................................................... 122

6.13 The border effect ..................................................................................................... 122

6.14 Other predictors from the NFI ................................................................................. 123

6.15 The stratified approach ............................................................................................ 123

6.16 Automatization of the processing chain .................................................................. 124

7 References ....................................................................................................................... 125

Apendix 1 ............................................................................................................................... 139

Apendix 2 ............................................................................................................................... 142

Apendix 3 ............................................................................................................................... 145

Apendix 4 ............................................................................................................................... 150

Apendix 5 ............................................................................................................................... 152

Juan Ygnacio López Hernández Development of a method for forest type detection Executive Summary

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Executive summary

The definition of forest based on FAO (Food and Agriculture Organization of the United

Nations), but with 40 % of crown closure was used. A set of 7056 plots, from the National

Forest Inventory (NFI) database of Bavaria, in Germany, were used to train algorithms and

classify LANDSAT TM data taken between 2006 and 2007. The data from the inventory was

filtered using a buffer around the boundary of official forest cover layer of the state to avoid

the boundary effect. The NFI was then classified with unsupervised method, taking into

account the relative importance of species. Clustering techniques based on Partitioning

Around Medoids (PAM) were used to find the number of clusters from the NFI. The images

were preprocessed for orthorectification and clouds removal. Some other anomalies at the

boundaries of the scenes were also removed. The algorithms tested for PBIA classification

were: Maximum Likelihood (MxL) and Minimum Distance (MD); for OBIA classification

were: support vector machines (SVM), k nearest neighbour (kNN), multinomial logistic

regression (MNL), decision trees (DT) and random forest (RF). Recursive back wise variable

selection based on Random Forest (RF) was carried out to select the best predictors of forest

types with OBIA. Classification and regression with Machine Learning algorithms were tuned

and applied with 10-folds cross validation and 5 replications to the optical bands and synthetic

bands of standard deviation (SD), tasseled cap (TC), and texture (TXT). The results showed

that the OBIA approach was more accurate for the delineation than the PBIA approach. PBIA

was rejected based on differences with the official forest layer. Consideration of boundary

effect in NFI was determinant in finding the solution to classify in terms of accuracy and

kappa. Precise placement of physical plot for each medoid of cluster derived from the NFI

could be found by PAM method. Mixed behavior was found in the accuracy of the

classifications. In general the SVM was the best classifier with overall accuracy ranging from

70.6%, 71.2%, 73.2% to 82.5% and kappa 0.559, 0.569, 0.898 to 0.738 respectively for 4 of

the 6 images under evaluation. In other image with less mixture of forest classes, the best

classifier was kNN with 85.1% accuracy and 0.778 kappa. RF was found to be the best in one

image with 78.5% accuracy and 0.678 kappa. The results suggest that the unequal distribution

of forest in all images can explain the errors in the classification. The best models selected per

image were composed by 10 to 12 predictors when SVM was the best classifier. Only models

that used 1 or 9 predictors were applied for two images which had kNN or RF as the best

Executive Summary Development of a method for forest type detection Juan Ygnacio López Hernández

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classifier. Parts of the method are automated and can be applied sequentially in the steps for

clustering NFI and the classification, regression with final evaluation.

Keywords: LANDSAT TM, forest type classification, machine learning, OBIA, forest

inventory.


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Resumen ejecutivo

La definición de bosque basada en la FAO pero con 40 % de cobertura del dosel, la

información de la base de datos del Inventario Nacional Forestal (NFI) de Bavaria, Alemania,

contentiva de casi 7056 parcelas, fue usada para clasificar datos de LANDSAT TM de los

años 2006 y 2007. Los datos del inventario fueron filtrados usando la capa oficial con los

bordes de bosque del Estado. Se usó un análisis de buffer alrededor de estos límites para

excluir los puntos del NFI. El NFI fue clasificado de forma no supervizada considerando la

importancia de los grupos de especies. Se usaron técnicas de clasificación basadas en

Partitioning Around Medoids (PAM) para encontrar la cantidad de clusters presentes en el

NFI y su definición basada en Medoides. Las imágenes fueron preprocesadas con

ortorectificación, remoción de nubes y algunas otras anomalías presentes en los bordes. Dos

enfoques fueron probados para analizar las imágenes: basado en pixeles (PBIA) y basado en

objetos (OBIA). Una selección recursiva por eliminación basada en Random Forest (RF) fue

empleada para seleccionar los mejores predictores de los tipos de bosque. Técnicas de

clasificación y regresión con Aprendizaje de Máquinas fueron usadas para entonar y aplicar

validación cruzada basada en 10 pliegues y 5 repeticiones con las imágenes de satélite y

bandas sintéticas, tales como la desviación estándar (SD), transformación especial gorro del

mago (TC) y la textura (TXT). Los algoritmos probados para clasificar por PBIA fueron:

Maximum Likelihood (MxL) y Minimum Distance (MD); para clasificación por OBIA,

support vector machines (SVM), k nearest neighbour (kNN), multinomial logistic regression

(MNL), decission trees (DT) y random forest (RF). Los resultados muestran que el enfoque

OBIA fue más exacto en la delineación que el PBIA. Un comportamiento mixto fue

encontrado para la exactitud de las clasificaciones. En general, SVM resultó el mejor

clasificador con exactitudes que van desde 70.6%, 71.2%, 73.2% a 82.5% y kappa 0.559,

0.569, 0.898 a 0.738 respectivamente para 4 de las 6 imágenes evaluadas. En las otras

imágenes, con menos mezcla de clases de bosque, el mejor clasificador fue kNN con 85.1%

en exactitud y kappa de 0.678. El efecto de borde fue determinante para encontrar la solución

de la clasificación in términos de exactitud y kappa. Los resultados sugieren que la

distribución de los bosques no es igual en todas las imágenes. Algunas técnicas de imputación

del algoritmo de kNN consiguió mejor solución en una imagen y el RF en la otra. Las

variables más importantes fueron NDVI, B7, B5 y B6. Los mejores modelos seleccionados

por imagen estaban compuestos por 10 o 12 predictores cuando el SVM resultó el mejor

clasificador. Sólo cuando kNN y RF fueron los mejores clasificadores se seleccionaron 1 o 9

variables respectivamente. El método puede ser automatizado y aplicado secuencialmente en


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dos pasos: para la agrupación en clusters del NFI y para la clasificación, regresión con

evaluación final.

Palabras clave: LANDSAT TM, clasificación de tipos de bosque, machine learning, OBIA,

inventario forestal.


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Zusammenfassung:

Die vorliegende Studie verwendet 7056 Inventurpunkte der Bundeswaldinventur des

Bundeslandes Bayern zur Klassifizierung von Bestandestypen aus Landsat TM

Satellitenbilder der Jahre 2006 und 2007. Die verwendeten Satellitenbilder wurden

orthorektifiziert and Wolken sowie andere Anomalien wurden entfernt. Die Inventurdaten

wurden mit Hilfe von offiziellen Geodaten der Forstverwaltung verschnitten um alle

Inventurpunkte außerhalb des Waldes zu entfernen. Dabei wurde die Walddefinition der FAO,

aber mit einem minimalen Kronenschlussgrad von 40% als Kriterium verwendet. Zusätzlich

wurde ein Puffer verwendet um Inventurpunkte nahe der Waldgrenzen ebenfalls

auszuschließen. Die verbleibenden Inventurpunkte wurden einem statistischen „Clustering“-

Verfahren unterzogen. Dabei dienten die Grundflächenanteile der unterschiedlichen

Baumarten als Eingangsvariable. Für das „Clustering“ wurde die „Partitioning Around

Medoids (PAM)“-Methode verwendet um eine optimale Anzahl unterschiedlicher

Bestandestypen und deren Eigenschaften, welche durch die Medoide definiert sind,

auszuscheiden. Zwei unterschiedliche Verfahren wurden für die Klassifizierung der

Satellitenbilder getestet: Pixelbasierte Verfahren und objekt-orientierte Verfahren. Rekursive

Rückwärtsselektion basierend auf Random Forest (RF) wurde durchgeführt um die besten

Prädiktoren für die Unterscheidung der definierten Waldtypen zu bestimmen. Die

Klassifizierung der Satellitenbilder und die Regression der Inventurpunkte mit den

Sallitendaten wurde mit unterschiedlichen Verfahren des maschinellen Lernens durchgeführt.

Es wurde eine 10-fache Kreuzvalidierung mit 5 Wiederholungen angewandt. Als

Eingangsdaten wurden sowohl die originären Landsat-Bänder als auch synthetische Derivate

der Bänder (Standartabweichungen, Tasseled-Cup-Transformationen, Texturinformationen)

verwendet. Als pixelbasierte Verfahren kamen MaximumLikelihood und MinimumDistance

Klassifikatoren zum Einsatz. Für die Klassifizierung der Segmente, welche während des

objekt-orientierten Ansatzes definiert wurden, wurden Support Vector machines (SVM), k

nearest neighbour (kNN) , multinomial logistic regression (MNL), und Entscheidungsbaum-

basierte Verfahren (DT, RF) eingesetzt. Die Ergebnisse zeigen, dass der verwendete objekt-

orientierte Ansatz bessere Ergebnisse als der pixel-basierte Ansatz lieferte. Allgemein lieferte

SVM die besten Genauigkeiten, die für 4 der 6 Bilder zwischen 70.6% und 82.5% lagen

(kappa zwischen 0.559 und 0.738). In den zwei übrigen Bildern, die eine geringere Mischung

an verschiedenen Bestandestypen aufwiesen, waren die besten Klassifikatoren einmal kNN

mit 85.1% (kappa 0.778) und einmal RF mit 78.5% (kappa 0.678). Die Ergebnisse zeigen,

dass die Zusammensetzung der Bestandestypen in den unterschiedlichen Landsat-Szenen


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variiert und dass das optimale Klassifikationsverfahren von der Zusammensetzung der

Bestandestypen abhängt. Für die 4 Bilder, bei denen SVM-Klassifizierungen beste Ergebnisse

erzeugten, beinhalteten die Modelle zwischen 10 und 12 Prädiktoren. Die kNN- und RF-

Modelle der beiden anderen Bilder, welche beste Genauigkeiten lieferten beinhalteten einen

bzw. 9 Prädiktoren. Zusammenfassend kann gesagt werden, dass die vorgestellte Methode

autmatisierbar ist und die drei methodischen Schritte (Clustering der Inventurdaten,

Klassifikation und schlussendlich Regression und Validierung) sequentiell durchgeführt

werden können, um aus Landsat-Daten in Kombination mit Daten der Bundeswaldinventur

Bestandestypen zu klassifizieren.

Keywords: LANDSAT TM, Bestandestypenklassifikation, maschinelles Lernen,

Waldinventur, objektbasiertes Verfahren.

Juan Ygnacio López Hernández Development of a method for forest type detection List of figures

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List of figures

Figure 1: Contrasting maps of forest cover globally based on different definitions of

canopy cover. Source: Hansen et al. (2003) and Kirkup (2001), cited in Achard

(2009). ......................................................................................................................... 5

Figure 2: LANDSAT mission’s timeline (USGS, 2012). ........................................................... 8

Figure 3: Minimum distance classifier. Source: Campbell and Wynne (2011). ....................... 16

Figure 4: Maximum likelihood classification. Source: Campbell and Wynne (2011). ............. 17

Figure 5: k-nearest neighbours classifier. Source: Campbell and Wynne (2011). .................... 18

Figure 6: In the example of the classification with SVM, the groups can be separated by a

line when the axis is transformed. Source: Verplancke et al. (2008). ...................... 19

Figure 7: Representation of a network from the multilayer perception with a hidden layer.

From Witten et al. (2011). ........................................................................................ 21

Figure 8: Effect of modal filters after classification. ................................................................ 26

Figure 9: Example of the second classification using dual modal filtering (forest: green;

water: blue; grey: other classes). The black lines are the forest from the ATKIS

layer. ......................................................................................................................... 27

Figure 10: Illustration of the radiometric differences between two adjacent images. .............. 28

Figure 11: Illustration of the generation of segment objects by the image segmentation

method. ..................................................................................................................... 29

Figure 12: Subset of LANDSAT scene showing a false color composite (a) and the same

place with a subdivision using chessboard segmentation (b). .................................. 30

Figure 13: View with the segment objects created by multiresolution segmentation. .............. 31

Figure 14: Example of merging segment objects dissolving lines dividing the same class. ..... 32

Figure 15: The location of Bavaria relative to the other fifteen federal states in Germany

and the position of Germany in the world. ............................................................... 36

Figure 16: Natural regions of Bavaria. Source: Bundesamt für Naturschutz (2012). ............... 36

Figure 17: Overview of the best ESA satellite data ordered. .................................................... 39

Figure 18: Illustration of the satellite scenes used. At left with the path and row numbers

and at left a mosaic overlapping to avoid the clouds ................................................ 40

Figure 19: An example of the reference data used. ................................................................... 41

Figure 20. General flow of the methods applied. The preprocessing is also presented. ........... 45

List of figures Development of a method for forest type detection Juan Ygnacio López Hernández

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Figure 21: Histogram of the number of species in the National Forest Inventory (NFI) of

Bavaria. The groups of species were sorted based on abundance from most

frequent to less frequent. ........................................................................................... 47

Figure 22: Silhouette of the groups built during the definition of the optimum number of

clusters based on the mean standardised intercluster distance (ave), known as

dissimilarity. ............................................................................................................. 48

Figure 23: Cluster plot of the three groups identified by the PAM method. Each group,

identified with numbers 1 to 3, represent the clusters in Table 10. .......................... 49

Figure 24: The 7056 inventory plots for Bavaria contained in the NFI database illustrated

graphically after import into GIS. ............................................................................. 50

Figure 25: A close-up of the centre of Bavaria showing individual groups of plots. ............... 51

Figure 26: A buffer of 1.5 times the pixel size (right) was applied to the boundaries of the

forest (left) and used to select the NFI plots for the classification and the

validation of the images. ........................................................................................... 52

Figure 27: Pre-processing steps applied to the scenes. ............................................................. 55

Figure 28: The DEM before (left) and after (right) the pre-processing step. ............................ 56

Figure 29: Example 1 highlighting the accuracy of the rectification (green lines are

vectors of ATKIS forest layers). ............................................................................... 58

Figure 30: Example 2 highlighting the accuracy of the rectification (green lines are

vectors of ATKIS forest layers). ............................................................................... 59

Figure 31: Test areas for the calculation of the C-factor taken from a subset of the image

in path 192, row 026. ................................................................................................ 60

Figure 32: Overcorrection effects of topographic normalization. ............................................. 61

Figure 33: Subset of the image before (left) and after (right) masking of the clouds. .............. 62

Figure 34: The haze uncorrected image (left) and the haze corrected image (right). ............... 63

Figure 35: Subset of the NDVI. ................................................................................................ 64

Figure 36: Subset of the tasselled cap 1. This band is also called Brighness of the objects

in the visual channels (Crist and Cicone, 1984). ...................................................... 65

Figure 37: Subset of the tasselled cap 2. Called Greenness, is presenting the white most

areas related to the level of green for the human eye (Crist and Cicone, 1984). ...... 65

Figure 38: Subset of the tasselled cap 4. This layer is called Haze and it shows the hazy

areas with more intense levels of white. ................................................................... 66

Figure 39: Subset of the tasselled cap 6. This band is called sixt. ............................................ 66

Figure 40: Subset of the texture for band 1 of LANDSAT TM 5 data. .................................... 67

Juan Ygnacio López Hernández Development of a method for forest type detection List of figures

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Figure 43: Subset of the texture for band 4 for the LANDSAT TM 5 data. ............................. 68



Figure 46: General description of the workflow. ...................................................................... 70

Figure 47: The pixel based image analysis applied to complete scenes. .................................. 70

Figure 48: The training process of the MxL classification involved adding points to the

image. ........................................................................................................................ 72

Figure 49: Example of the selection of reference points in two aerial photographs. ................ 73

Figure 50: Matrix values of a classified scene. ......................................................................... 75

Figure 51: Graphical representation of the legend. ................................................................... 78

Figure 52: The original ortho-scene in (a) is classified with kNN algorithm in (b), the

erroneous classification of forest in (c) and after the application of noHaze

algorithm. .................................................................................................................. 84

Figure 53: An example of the suppression of holes within the clouds is presented. ................ 84

Figure 54: Flowchart presenting the evaluation of the results in the methodology. ................. 87

Figure 55: Graphical representation of the map layer produced. .............................................. 88

Figure 56: Sectors for the statistical evaluation. ....................................................................... 89

Figure 57: The first classification using the original bands. ..................................................... 92

Figure 58: Image sections (in 4 illustrations) distributed over Bavaria visualising the

results of the classification. ....................................................................................... 94

Figure 59: General view of the results of the PBIA classification. ........................................... 96

Figure 60: Results of tunning process with the accuracies obtained....................................... 101

Figure 61: Levels of accuracies found in the tuning process applied with SVM algorithm . . 102

Figure 62: Example of one decision tree for the image in path 192, row 126. ....................... 103

Figure 63: Diagram with the output of the ML algoritms applied. ......................................... 106

Figure 64: Trend of the accuracy by changing the sigma and C values for the SVM

algorithm. ................................................................................................................ 108

List of tables Development of a method for forest type detection Juan Ygnacio López Hernández

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List of tables

Table 1: Summary of the launch dates and the sensors on board the satellites. .......................... 8

Table 2: Selected algorithms and their tuning parameters. ....................................................... 22

Table 3: List of LANDSAT scenes ordered from ESA. ........................................................... 39

Table 4: LANDSAT TM 5 satellite images observed in the study. .......................................... 40

Table 5: The final 7 LANDSAT TM images used in the project. ............................................. 40

Table 6: Variables obtained from the NFI as they were stored in the files, including their

description. ................................................................................................................ 42

Table 7: Map projection spatial reference parameters used (Butler et al., 2010). .................... 43

Table 8: The original field names and their descriptions contained in the national forest

inventory (NFI) for Bavaria. ..................................................................................... 46

Table 9: Example of the table obtained after applying a query to total basal area per

species in each plot the National Forest Inventory (NFI) database for Bavaria. ...... 46

Table 10: Characteristics of the clusters found by PAM algorithm. ......................................... 49

Table 11: Experiment on SVM for seleccion of features and threshold the NDVI feature. ..... 53

Table 12: List of the orthorectified LANDSAT 5 TM images acquired. .................................. 58

Table 13: Example of the signature editing process and the color assigned per class. ............. 73

Table 14: Minimum Distance Classification. List of codes used for every class. .................... 76

Table 15: Maximum Likelihood Classification. List of codes used for every class. ................ 77

Table 16: The classification of raster data were recoded with this values ................................ 77

Table 17. Results of the variable selection using the Bayesian Information Criterion (BIC). .. 89

Table 18: Results from the FSO. Features selected for the classification with kNN. ............... 90

Table 19: Some of the results of the recursive feature selection. .............................................. 90

Table 20: Results of the evaluation of the classification for forest and non forest areas for

the image in path 192, row 126. ............................................................................... 91

Table 21: Results of the evaluation of the accuracy for forest and non forest areas with all

of the 6 images. ......................................................................................................... 91

Table 22. Contingency matrix comparing the inventory data with the result of the MxL

classification method. ............................................................................................... 95

Table 23. Contingency matrix comparing the inventory data with the result of the MD

classification method. ............................................................................................... 95

Juan Ygnacio López Hernández Development of a method for forest type detection List of tables

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Table 24: Results of the accuracy assessment for the classification per LANDSAT TM

image. ........................................................................................................................ 96

Table 25: Contingency table showing the results of the classification using MxL compared

with the inventory data. ............................................................................................ 99

Table 26: Contingency table showing the results of the classification using kNN compared

with the inventory data. ............................................................................................ 99

Table 27: Accuracies obtained for the scene in path 192 row 026 tuning the C and sigma

parameters for the support vector machines algorithm. .......................................... 108

Table 28: Contingency table for the scene in path 192, row 026 comparing the NFI plots

with the group obtained by SVM classification algorithm. .................................... 109

Table 29: Accuracies found applying SVM algorithm for classification with 4 replications

and 10 folds LGOCV for the scene in path 192, row 026. The minimum, first

quarter, median, mean, third quarter and maximum accuracy is shown. The

figures are in the scale from 0 to the worst accuracy and 1 to the best. ................. 110

Table 30: Differences in accuracy among classifiers applied to the scene in path 192, row

026. The accuracy and their differences is presented in the scale from 0 to 1. ...... 110

Table 31: Results of assessment the classifications for the image in path 192, row 026. ....... 111






Table 37: Accuracies obtained for the scene in path 192, row 026 tuning the C and sigma












List of tables Development of a method for forest type detection Juan Ygnacio López Hernández

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Table 43: Confussion table for the scene in path 192, row 026 comparing the NFI plots












Juan Ygnacio López Hernández Development of a method for forest type detection List of a

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List of acronyms

CLC2000 Corine Land Cover database 2000

DEM Digital elevation model

ETM+ Thematic Mapper Enhanced

FSO Feature Space Optimization

GIS Geographic information systems

GLS Global land survey

JRC Joint Research Centre of the European Commission (EC)

kNN k Nearest neighbours

LAI Leaf area index

LFU Bayerische Landesamt für Umwelt (Bavarian State Office for the Environment)

MD Minimum distance to means

ML Machine learning

MNL Multinomial logistic regression

MxL Maximum likelihood

NFI National forest inventory (in German BWI, Bundeswaldinventur)

OA Overall accuracy

PCA Principal component analysis

PDF Portable document format

REDD+ Reducing emissions from deforestation in developing countries and approaches to

stimulate action

SVM Support vector machines

TM Thematic Mapper

List of acronyms Development of a method for forest type detection Juan Ygnacio López Hernández

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Juan Ygnacio López Hernández Development of a method for forest type detection 1.- Introduction

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1 Introduction

Concern over the correct use and conservation of natural resources is on the increase globally.

Considering only the amount of published documents, recorded in the science direct database,

regarding conservation and natural resources in 2009 were 7428. That figure increased to

7703 in 2010, 9288 in 2011 and 11493 in 2012. The planning and evaluation of the use of

forest resources is largely based on map products. (Kennedy, et al. 2009; MacAlister et al.

2009, Raymond et al. 2009; Hooftman and Bullock, 2012) The mapping process is carried out

by means of the inventory and analysis of satellite images. (Brandt, J. et al. 2012; Ohmann, J.

et al. 2012 and Sedano, F. et al. 2012) Satellite data together with advances in mathematics,

statistics and the computing sciences applied to the data retrieved in order to develop and

standardise methods, and to produce maps in a more efficient way.

Often at the time of evaluations carried out in the past, no standard definition of forest was

agreed upon. Even though currently there is still no universal agreement, in the year 2000 the

FAO published a standard definition of forest that should be considered in every new study of

forest cover carried out anywhere across the globe. (Achard, 2009) Many earlier studies of

forest were carried out in small areas or with limited coverage, and the statistical methods

applied were based upon the supposition of a normal probability distribution; an assumption

not supported by the more comprehensive data sets provided using modern remote sensing

instruments. (Camps-Valls, G and Bruzzone, L. 2009) New non-parametric statistics provide

methods and tools producing results that approximate better the data collected from

measurements made in the field, than parametric statistics. (Herbrich, R. 2002)

Evaluation of forest areas require to cover broad areas in short time. For forest areas, the most

common data source for evaluation is based on satellite scenes. The remote sensing

community is producing constantly maps and assessment of the natural resources. Newly

developed remote sensing instruments orbiting the Earth can be used to calibrate, and even

validate, the results of old Thematic Mapper (TM) and Thematic Mapper (ETM+) products or

other remote sensing data. (Racoviteanu, A. et al. 2008) NASA, the US National

Administration of Space Agency, has a programme to continue the acquisition of new

imagery using new and improved instruments which will provide the scientific community

with even more up-to-date data, the LANDSAT Data Continuity Mission (LDCM). (NASA.

2012) The United Nations (UN) is currently implementing a programme on Reducing

Emissions from Deforestation and Degradation (REDD+) focused on the evaluation of natural

1.- Introduction Development of a method for forest type detection Juan Ygnacio López Hernández

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resources and on the accounting the CO2 emissions produced by third world countries. (FAO,

UNDP, UNEP. 2012) By conducing changes to land use practices addressed to reduce the

emissions of CO2 produced in deforestation and degradation, the countries could derive

profits and receive technical support. Most of the evaluations made as part of this programme

were based on the comparison of forest layer coverage at different dates. However, in the

absence of a standard method and a standard forest definition, the comparability of the data is

open to question and the results ambiguous. (Bock, M, et al. 2005)

For these reasons, the assessment of methods and their application to the evaluation of forest

cover is of considerable importance for coming generations. The process may reveal ways to

apply the results obtained to many activities; for example, in supporting the reduction of CO2

emissions, the steering of the actual forest cover to an optimal level, the restoration of

damaged forest areas, the preservation of ecological diversity and the provision of services to

communities. (Grainger, A, and M. Obersteiner. 2011)

Other policies focusing on conservation and sustainable development may be supported by

evaluations of natural resources. The process of mapping forest cover is an important part of

this analysis. In general, the management of forests is based on knowledge of the current

conditions so that they may be steered towards optimal productive and protective conditions.

(Corona, P. et al.2011, Hooftman, D. et al. 2012, Ohmann, J. et al. 2011 and Zaho, Y. et al.

2011)

With the use of a standardized method to develop in this work, the forest mapping either

forest map production, evaluations of degradation, carbon stock, related conservation and

management can be improved in terms of quality and resources to find internationally valid

products. The decision making process will be boosted when the information about actual

conditions of forest areas are well known.

Juan Ygnacio López Hernández Development of a method for forest type detection 1.- Introduction

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1.1 Objectives

The general aim of the research presented here focused on two main tasks:

1. The development of a method comparison to obtain forest maps for large areas

First separate the forest areas from other land cover types. The inductive approach can

ensure quality control in the processing chain.

Second within forest areas, classify forest types.

Third, evaluate the accuracy of the classical pixel based image analysis (PBIA) for

classification of forest areas against object based image analysis (OBIA).

2. Generate a digital layer comprising the forests of Bavaria.

1.- Introduction Development of a method for forest type detection Juan Ygnacio López Hernández

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Juan Ygnacio López Hernández Development of a method for forest type detection 2 Basic Information

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2 Basic Information

2.1 Literature review

2.1.1 Definition of forest

There is no broadly accepted basic definition of forest today. Rowntree (1984) stated that

different definitions of forest could be based only on changing the percent canopy cover.

Another study from the USA stated that canopy cover might range from 18 to 36 % (Marotz

and Coiner, 1973, cited by Rowntree, 1984, p. 4). Continuing in USA, other authors cited by

Rowntree (1984) used 7, 8, 17, 24, 34 and 39 % canopy cover to define forest from aerial

photos. This problem was reviewed in the United Nations Environment Programme (UNEP).

The authors of the study presented world maps of the forest cover and illustrated graphically

the variation in the global forest area depending on the different thresholds of canopy cover

(Achard, 2009), as shown in Figure 1.

Figure 1: Contrasting maps of forest cover globally based on different definitions of canopy cover. Source: Hansen et

al. (2003) and Kirkup (2001), cited in Achard (2009).

A working paper by the FAO indicated the need for a concerted global effort to standardise

the definition of forest (Neeff et al., 2006). Some authors have stated the need to incorporate

the ecological parameters of pioneer and non-pioneer species (Swaine and Whitmore, 1988).

However, this definition is useful only for tropical rain forest. Other authors have stressed the

2 Basic Information Development of a method for forest type detection Juan Ygnacio López Hernández

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need to consider the natural mixture of taxonomical classes. As the image processing chain

normally reduces the amount of information pertaining to ecosystems of a continuous nature,

this reduction results in uncertainties in terms of ecosystem mapping. (Rocchini et al., in

press) Sasaki and Putz (2009) highlighted the need for a new definition of forest that is

universally acceptable. They maintained that, “great quantities of carbon and other

environmental values will be lost when natural forests are severely degraded or replaced by

plantations but technically remain forests.” They also suggested that the classification as

forest should be based on a canopy coverage higher than 40 %. In this study, the definition

was modified in terms of canopy cover. The optical data obtained using remote sensing

instruments does not allow forests of low canopy cover to be distinguished readily. The Food

and Agriculture Organisation of the United Nations (FAO) provided a definition of forest as,

“land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of

more than 10 percent, or trees able to reach these thresholds in situ. It does not include land

that is predominantly under agricultural or urban land use” (FAO, 2010).

Certain approaches were developed to counter the loss of information. These approaches use

the landform classification based on hydrology and topography. In subdividing the Earth into

natural areas, the soil scientists and geologists introduced the term geomorphometric units

(Hengl & Reuter, 2009). The calculation of geomorphometric units is based on standard

terrain analysis. The geoinformatics principles are based mainly on DEM data and were

documented by Li et al. (2005). The uniform regions can be used as a first subdivision of the

geographic space, then either subdividing again or by directly applying the classification of

the scenes. An example of this approach was published by Pert et al. (2012). They developed

an index for anthropogenic threats of fire at a regional scale in a bioregion of Australia. In

their method they deemed that, “each sub-region has a characteristic climate, pattern of

geology and landform, and associated soils and vegetation” (Pert et al., 2012). A similar

method to address the continuum found in nature was used for a study of natural and land use

history in a mountain region of the USA and how these related to patterns of plant invasions

(Parks et al., 2005). The final product is a map that should be evaluated in terms of the

accuracy of the units presented (Seabrook et al., 2007; Fraser et al., 2009; Mellin et al., 2010).

A cloud of points is placed over the area according to certain sampling criteria, for example,

randomly, and the value obtained from the map evaluated compared to a reference. The

evaluation of the map is made and reported in terms of percent accuracy and the probability of

finding a point on the map for belonging to a selected class that is also in the same class in the

verification data (user accuracy), or the probability that a point in the verification data is also


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present in the corresponding class in the classification (producer accuracy) (Stehman, 1997;

Foody, 2002). The maps produced using remote sensing data are usually validated with data

from one of three different sources: inventory data (Thessler et al. 2005; Gillespie et al.,

2006), data from field measurements (Powell et al., 2010; Avitabile et al., 2012) or using

images with a higher resolution (Huang et al., 2008; Kirui et al., 2011).

The evaluation of natural resources, and particularly of forest, is frequently carried out using

remote sensing instruments. By definition, these instruments need not touch the objects being

measured in order to produce a measurement. “The remote sensing sensors can be classified

in two types: passive or active. Passive sensors measure radiation that reaches a detector

without the sensor first transmitting a pulse of radiation. Active sensors emit a pulse and later

measure the energy returned or bounced back to a detector. Both passive and active sensors

record the intensity of a signal within a wavelength interval, known as a ‘band’ or ‘channel,’

of specified width within the electromagnetic spectrum” (Turner et al., 2003). Another

classification of the instruments used for remote sensing is based on the number of bands

from the electromagnetic spectrum. Black and white cameras are examples of monospectral

instruments. Multispectral remote sensors are those with more than one band, such as those

producing colour photographs. Hyperspectral instruments usually have bands with a narrow

spectral width. Some bands in the sensors are capable of measuring the temperature of the

objects, and are referred to as thermal (Shaw and Burke 2003).

2.1.2 Forest and type of forest mapping with LANDSAT data

One of the most popular remote sensing systems in civil applications is the LANDSAT series.

The LANDSAT series of satellite systems began in July 1972 with the launch of LANDSAT

1. From that moment up to the present, the world is being recorded in images that have

similar characteristics in terms of geometry and radiometry. In Figure 2 the timeline of

LANDSAT missions is presented. Currently LANDSAT 5 TM is set to standby, in

preparation for decommissioning by the USGS. Since May 31st 2003 LANDSAT 7 has been

operating with a fault in the scan line corrector known as SLC-off. This fault produces

banding at the borders of the image. A summary of the dates and sensors on board the

satellites is provided in Table 1 and Figure 2.


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Table 1: Summary of the launch dates and the sensors on board the satellites.

LANDSAT Launch date Sensors Imaging up to

1 July 23, 1972 RBV, MSS January 6, 1978

2 January 22, 1975 RBV, MSS February 25, 1982

3 March 5, 1978 RBV, MSS March 31 of 1983

4 July 16, 1982 MSS, TM December 14, 1993

5 March 1, 1984 MSS, TM August 1995 MSS

May 8, 2012 TM

6 October 5, 1993 ETM Did not achieve orbit

7 April 15, 1999 ETM+ Present

8-LDCM January 24, 2013 OLI -

Figure 2: LANDSAT mission’s timeline (USGS, 2012).

Access to this historic database is open and can be attained through the internet without

restriction, copyright of the USGS. New advances in mathematics and statistics have opened

broad avenues to apply grouping techniques to the classification of satellite images. A branch

of machine learning algorithms has produced techniques to classify numerical data free from

distribution assumptions (Yang, 2010). The success of non-parametric methods is frequently

reported in remote sensing research papers (Tokola et al., 1996; Chirici et al., 2008; Shimatani

et al., 2008; Chang et al., 2010).

These characteristics make the LANDSAT satellite series the most popular reference for

mapping purposes. Even the new remote sensing instruments, using similar or contrasting

resolutions, are commonly used to validate or calibrate TM and ETM+ data (Cohen et al.,

2010; Huang et al., 2010; Propastin and Erasmi, 2010).

The assessment accuracy of the maps has increased with time. Wolter et al. (1995) produced

maps containing a classification of forest in the USA. They reported 83.2 % OA based on

verification using aerial photos and maps. Fooddy and Hill (1996) reported 84 to 94 % OA.


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They based their validation on contextual information and ancillary information, particularly

on topography. Hyyppä et al. (2000) based a validation of a forest map produced from remote

sensing data on forest inventory data. They used SAR, LANDSAT TM, ERS-1/2 SAR PRI

and SLC, JERS-1 SAR as a source of data and reported the accuracy in terms of error. The

result obtained for random error of the mean height of the trees of 2 m with a standard

deviation of 0.57 m. They determined a random error of the basal area of 3.7 m2/ha with a

corresponding standard deviation of 0 m2/ha. The result in relation to volume exhibited a

random error of 41.0 m3/ha, with a standard deviation of 19.3 m

3/ha. This large number of

sources of remote sensing data is not available for locations all around the world, and would

cost too much for conservation programmes and for the evaluation of forest degradation.

Heikkilä et al. (2002) used LANDSAT TM and aerial photographs as sources of remote

sensing data for the purposes of evaluating defoliation. They ascertained a 60.1 % OA for

sample plots that changed due to defoliation. They also reported that the most useful variables

to estimate defoliation are TM channels 4 and 5. However, the OA was 56 % for plot and

stand level, with a kappa coefficient of 0.12. Dorren et al. (2003) fused LANDSAT TM, IRS

and DEM data for the purposes of forest mapping, used object-based classification and

compared the results against the pixel classification. They used colour-infrared orthophotos to

build ground truth polygons to be used in the evaluation. Their results showed that the pixel-

oriented classification was the best approach, with 73 % OA. The object-oriented

classification achieved only 70 % OA. The most important features used in the analysis were

DEM, TM4, TM5, TM4 - TM2. Jiang et al. (2004a) used digital orhto-quad images in the

verification of maps of seral forest made from LANDSAT ETM+ images and attained 90 %

OA.

Salovaara et al. (2005) attained 85 % OA in the classification of Amazonian primary rain

forest vegetation. They used line transects of 500 m to make the floristic assessment. Kennedy

et al. (2007) obtained 90 % OA in trajectory-based change detection for the automated

characterisation of forest disturbance dynamics. The evaluation was carried out by comparing

delineations of the change in forest conditions made visually by the interpreter against the

findings of the automated approach. The same level of accuracy (90 %) was found by

Pekkarinen et al. (2007) in the pan-European forest map. They used supervised classification

and validated on the basis of three sources: field plots, visual interpretation of very high

resolution imagery and official national statistics. The highest error of 5 % was found in the

national statistics. In the USA, Triepke et al. (2008) used LANDSAT TM+ data to map forest

alliances and associations. They achieved 60 % accuracy using a regional database of plot


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records. Thessler et al. (2008) evaluated two statistical grouping techniques to classify rain

forest types in Costa Rica with LANDSAT TM images. They achieved 91 % OA validating

using three different field data sets: 41 plots in an old growth forest, 11 permanent plots in an

old growth forest and 52 plots in a young forest. Pu et al. (2008) reported 71 % OA mapping

forest crown closure and leaf area index from LANDSAT ETM+ data. They also used data

from other sensors, such as Hyperion and ALI. Both of the latter provided better results than

ETM+. The results of these studies show that the classification accuracy of LANDSAT TM

images is not the same for all locations. Most of the recent research was conducted using

more sources of data or using data of a higher spatial resolution. Using IKONOS data, Huang

et al. (2008) applied the concept of the dark object to automate forest change. They evaluated

the change in seven study areas representing different biomes across the world. The best OA

was over 90 % for five areas, compared to 89.4 % and 89.6 % for the remaining two.

A forest map of the whole of Europe has been produced (Pekkarinen et al., 2007; 2009), by

the Joint Research Centre (JRC) of the European Commission (EC). They employed a fully

automated procedure, verified using the Corine Land Cover database 2000 (CLC2000). The

method is one of the most widely accepted in the region but the minimum mapping unit

(MMU) was 25 ha and the degree of canopy cover 30%. Given these characteristics,

especially the MMU, many forest areas were excluded from the map. They applied

segmentation of single LANDSAT TM and ETM+ images, and built an adaptive spectral

representativity analysis tool to identify representative combinations of spectral and

informational classes of interest based on the CLC2000 database. Seebach et al. (2011)

reported that the adaptive spectral representativity analysis is the core part of the method to

properly select the CLC2000 data as training data.

Today new methods are waiting to be applied in forest evaluations. They are more than 100

methods are available from machine learning information theory and have been programmed

into the statistical language (Kuhn et al., 2012). However, only a dozen of these are reported

in the literature in the context of forest and land cover mapping applications (Moisen and

Frescino, 2002; Remm, 2004; Peters et al., 2007; Rogan et al., 2008; Sesnie et al., 2008;

Brenning, 2009; Ke et al., 2010; Chen et al., 2011). New methods such as robust linear

regression, quantile regression forests, quantile regression neural networks, stabilised linear

discriminant analysis and high dimensional discriminant analysis are just some of those

reported on and implemented in the statistical software (Kuhn et al., 2012). These methods are

waiting to be applied in forest mapping applications. All that is required for their application

is good data, some modelling foundations and careful analysis of the results.


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2.1.3 Variable selection methods

The main interest in modeling is to represent the world with the lowest amount of variables,

also known as features, predictors or descriptors. That principle is known as parsimony.

(Marsh and Hau, 1998 cited by Raykov and Marcoulides 1999) Three types of variable

selection methods were selected based on the availability of preprogrammed software

accessible for implementation.

2.1.3.1 Criterion based variable selection

Both Calculation Methods shares some propreties:

Base on the amount of information obtained from a set of variables.

Do not depend on normality of the input variables.

Can use either categorical or continuous variables.

Can use forward or backward stepwise variable selection.

Penalize some variables to promote others.

2.1.3.2 Feature space optimization (FSO)

According to Trimble (2011b) “the feature Space Optimization function offers a method to

mathematically calculate the best combination of features in the feature space.” When

classifying image objects using the Nearest Neighbor classifier, the recommended workflow

involves the next steps:

1. Load or create classes

2. Define the feature space

3 Define sample image objects

4. Classify, review the results and optimize the classification.

This option is available only in eCognition and no other classifiers can be tested.

2.1.3.3 Recursive feature selection

A tool already implemented for variable selection was chosen from caret package in R.

This method for variable selection is based on random forest. The algorithm uses backwards

variable selection. The process starts with all of the variables as predictors in the model. The


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predictors are ranked and the less important ones are sequentially eliminated prior to

modelling. The goal is to find a subset of predictors that can be used to produce an accurate

model. The metrics to select the predictors can be set from accuracy or the root mean square

error (Kuhn et al., 2012). Among the steps documented can be found the eliminating of zero

and near zero variance predictors, identifying and removing the correlated predictors,

removing linear dependent predictors, centering and scaling, imputation, transforming, data

splitting.

With the variables selected, an approach on classification can be applied. For remote sensing

data, the classification is divided in two types: unsupervised and supervised. A general

description of these two types of classification types is described in next sections.

2.1.4 Unsupervised classification

The classification algorithms applied can be presented in two types: Supervised and non-

supervised. Both of them builds groups called clusters. The principle is that the groups should

have all the elements with similar characteristics. The difference is for unsupervised, we do

not know ahead of time where the clusters are located or what they look like. This is the

difference with supervised learning or classification, where we attempt to assign data points to

pre-existing classes. (Janert 2011)

Clustering techniques can be used for classification with the definition of the classes made at

the end of the process. (Gan, G. et al. 2007). The list of algorithms reported in mathematical

and statistical sciences is increasing with the time. Two algorithms were selected for

classification of forest and non-forest areas and for clustering the reference data. The main

reason is that they were used in previously for forest mapping or were recommended to obtain

strong structures in clusters of the data. Both methods are based in central points to build the

clusters. Centroids are the objects located in the center of the cluster and are considered as

representative of it as in ISOCLASS. When partitioning, the other algorithm selected and

described in 2.1.4.2 (page 13), the objects in a cluster “show high degree of similarity, while

objects belonging to different clusters are as dissimilar as possible.” The dissimilarity is a

measure of distance in relative units from 0 to 1. A short description of both clustering

algorithms is presented next.


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2.1.4.1 ISODATA

“The Iterative Self-Organizing Data Analysis Technique (ISODATA) is considered an

iterative technique because it may make many passes through the data, rather than just two, in

order to develop an adequate final set of clusters.” (Khorram 2012, 49) This algorithm was

reported by Kaufman L., and Rousseauw, P. (2005). They citrated Ball and Hall (1965) and

presented the main features as follows:

“The method starts with a clustering into a given number k of clusters, according to the

method of Forgy

In a second step, outliers and very small clusters are eliminated; they are disaggregated for the

remainder of the method.

Then perform either a lumping (fusion) or splitting of one of the clusters. This is done

according to the following rules:

Perform a lumping if the current number of clusters is more than 2k.

Perform splitting if the current number of clusters is less than k/2.

Otherwise alternate between lumping and splitting.

Stop if the same clustering is obtained twice.

Return to the first step with the newly obtained number of clusters (which replaces k), unless

the user-specified maximum number of iterations is reached.” (Kaufman, L., Rousseeuw,

P.J., 2005)

The ISODATA algorithm was proven to be useful in the separation of forest and non-forest

areas as well as soil cover and crop evaluation. (Gumma et al. 2011; Dheeravath et al. 2010;

Kim and Ellis 2009; Biradar et al. 2009; Dorrough and Moxham 2005; Verhoeye and De

Wulf 2002; Chen, Tateishi, and Wang 1999; Cihlar, Ly, and Xiao 1996; Pope, Rey-Benayas,

and Paris 1994)

2.1.4.2 Partitioning around medoids (PAM)

The PAM method is termed also as k-medoids. (Kaufman and Rousseeuw 2005) “The

algorithm works in the same way as the k-means algorithm except that, instead of calculating

the new centroid, search through all points in the cluster to find the data point (the medoid)

that has the smallest average distance to all other points in the cluster.” (Venables and Ripley

2010) The k-means algorithm is linear in the number of data points; the k-medoids algorithm

is quadratic in the number of points. (Janert 2011) It results heavy to a single computer to find

the solution. The main reasons on the selection of PAM is that it can process categorical data,


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when strong clusters are present, k-means fails, the operator should set the k number of

clusters in k-means without a tool for assist in this point, in PAM the silhouette is built and

can be used to assess the most adequate value of k. (Janert 2011) The PAM method is also

“more robust to messy data, and will always return the same clusters. (Hengl 2007). In

IDAMS statistical package developed by UNESCO (2008) documents the algorithm and says

“It is more robust than k-means, because it minimizes a sum of dissimilarities instead of a

sum of squared Euclidean distances.”

As documented in Janert (2011), the algorithm select initial positions, in the feature space, for

the cluster medoid, calculate its distance (dissimilarities) from each cluster medoid, assign

each point to the nearest cluster and repeat the process. All the points are used as medoids in

this process. In this iteration a record of the mean dissimilarities and the number of clusters is

operated from k to 2 building the known silhouette and calculating the silhouette coeflcient

(SC). The operator should select the size of k based on in the maximum SC encountered. The

R implementation of PAM also includes other tools for graphic visualization of clusters in

standard cluster plot and silhouette representation. When the dataset are big, another package

called CLARA as acronym for Clustering for Large Applications is also available. It makes

possible to implement PAM algorithm for large datasets.

A short descriptions presented by Kaufman and Rousseeuw (2005), says that “the clustering

of a set of objects with CLARA is carried out in two steps. First a sample is drawn from the

set of objects and clustered into k subsets using the k-medoid method, which also gives k

representative objects (this is done with the same algorithm as in PAM). Then, each object not

belonging to the sample is assigned to the nearest of the k representative objects. This yields a

clustering of the entire data set. A measure of the quality of this clustering is obtained by

computing the average distance between each object of the data set and its representative

object. After five samples have been drawn and clustered, the one is selected for which the

lowest average distance was obtained. The resulting clustering of the entire data set is then

analysed further. For each cluster, CLARA gives its size and its medoid and prints a complete

list of its objects. Also a graphical representation of the clustering is provided. CLARA can

deal with much larger data sets than can PAM in the same computer.”

Other statistical approach different to clustering or partitioning is the supervised

classification. The nature and the procedures in those methods are very different and are

presented in the next section.


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2.1.5 Supervised classification

The term is also known as supervised learning, that assign each record to exactly one of a set

of predefined classes. In supervised classification the classes are known ahead of time and

don’t need to be inferred from the data. Algorithms are judged on their ability to assign

records to the correct class. (Janert 2011)

Continuing with Janert (2011) “first the user split the existing data set into a training set and a

test set. In the training phase, present each record from the training set to the classification

algorithm. Next compare the class label produced by the algorithm to the true class label of

the record in question; then adjust the algorithm’s “parameters” to achieve the greatest

possible accuracy or, equivalently, the lowest possible error rate. The results can be

summarized in a so-called confusion matrix whose entries are the number of records in each

category. Unfortunately, the error rate derived from the training set (the training error) is

typically way too optimistic as an indicator of the error rate the classifier would achieve on

new data—that is, on data that was not used during the learning phase. This is the purpose of

the test set: after we have optimized the algorithm using only the training data, we let the

classifier operate on the elements of the test set to see how well it classifies them. The error

rate obtained in this way is the generalization error and is a much more reliable indicator of

the accuracy of the classifier.”

A set of five algorithms for supervised classification will be evaluated. All they share similar

properties. They are distribution free. The Bayesian basis of the analysis is taking a

probabilistic (i.e., nondeterministic) view of classification. (Janert 2011) All of them were

reported previously applied to remote sensing data in small regions, with similar data and

reported good to very good accuracies in the validation process.

2.1.5.1 Minimum distance to means (MD) method

According to Navulur (2007), “the minimum distance classifier sets up clusters in

multidimensional space, each defining a distinct class. Each pixel within the image is then

assigned to that class it is closest to. This type of classifier determines the mean value of each

class in each band. It then assigns unknown pixels to the class whose means are most similar

to the value of the unknown pixel.” The method is presented as a robust estimator of unknown

distributions (Parr and Schucany, 1980). This method is “not widely used in remote sensing

work” as it “is not always accurate; there is no provision for accommodating differences in


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variability of classes, and some classes may overlap at their edges”. See Figure 3. (Campbell

and Wynne, 2011).

Figure 3: Minimum distance classifier. Source: Campbell and Wynne (2011).

In Figure 3, the small dots represent pixels from training fields and the crosses represent

examples of large numbers of unassigned pixels from elsewhere on the image. Each of the

pixels is assigned to the closest group, as measured from the centroids (represented by the

larger dots) using the distance measures discussed in the text.

2.1.5.2 Maximum likelihood (MxL)

The MxL method is “the most powerful classifier in common use” (Navulur, 2007). Navulur

(2007) stated that, “based on statistics mean, variance/covariance, a Bayesian probability

function is calculated from the inputs for classes established from training sites. Each pixel is

then judged as to the class to which it most probably belongs.” The MxL method is

implemented in many image processing programmes and is often reported to be a good

classifier for forest mapping (Fooddy and Hill, 1996; Bozdogan, 2000; Kokaly et al., 2007;

Ward, 2008; Couturier et al., 2009). As the classifier requires intensive calculations it has the

disadvantage of requiring more computer resources than most of the simpler techniques.

Another characteristic is that the method is sensitive to variations in the quality of training

data, even more so than most other supervised techniques. Computation of the estimated

probabilities is based on the assumption that both training data and the classes themselves

display multivariate normal (Gaussian) frequency distributions. That is the reason why the

training samples should present unimodal distribution, and often the data from remotely


- 17 -

sensed images do not strictly adhere to this rule (Campbell and Wynne, 2011). Figure 4

presents this principle.

Figure 4: Maximum likelihood classification. Source: Campbell and Wynne (2011).

These frequency distributions represent pixels from two training fields; the zone of overlap

depicts pixel values common to both categories. The relation of the pixels with the region of

overlap to the overall frequency distribution for each class defines the basis for assigning

pixels to classes. Here, the relationship between the two histograms indicates that the pixel

with the value ‘45’ is more likely to belong to the forest (‘F’) class rather than the crop (‘C’)

class.

2.1.5.3 Fuzzy k nearest neighbours (kNN) method

Fuzzy kNN is a method for classifying objects based on the closest training samples in the

feature space. The k-nearest neighbour algorithm is a non-parametric classification algorithm

that considers an object classified by a majority vote of its feature space neighbours with the

object being assigned the most common class among its k nearest neighbours (k is a positive

integer, typically small). If k = 1, then the object is simply assigned to the class of its nearest

neighbour (Shakhnarovich et al., 2006). Figure 5 presents this voting process.


- 18 -

Figure 5: k-nearest neighbours classifier. Source: Campbell and Wynne (2011).

kNN assigns candidate pixels according to a ‘vote’ of the k neighbouring pixels, with k

determined by the analyst. Mills (2011) compared the kNN to other newer methods, such as

learning vector quantisation and support vector machines. He found that kNN is slower in

both stages: training and classifying. Man et al. (2004) reported that, “k-nearest neighbours is

computationally efficient and is easy to visualise and understand.” The same kNN method

was used by Mäkelä and Pekkarinen (2004) to discriminate stand volumes from TM images

and field inventory data from stands. They concluded that, “the estimation results obtained are

not accurate enough for forest management purposes.” This conclusion may be inaccurate,

however, because they selected volume as a predicted variable, and the remote sensing data

does not include information about elevation of the canopy.

The k parameter is used when tuning kNN in caret package.

2.1.5.4 Support vector machines (SVM)

The SVM is a supervised learning kernel method that generates input-output mapping

functions from a set of labelled training data to create theoretical areas called maximum-

margin hyperplanes. The hyperplane (see Figure 6b) intersects the training samples in a n+1

dimensional feature space. The new dimension is automatically calculated by the algorithm.

In cases when given classes cannot be linearly separated in the original input space, the SVM

first (non-linearly) transforms the original input space into a higher dimensional feature space


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(Wang, 2005). The formulation of this process was also presented by Karatzoglou et al.

(2006) and Koutroumbas et al. (2006).

Figure 6: In the example of the classification with SVM, the groups can be separated by a line when the axis is

transformed. Source: Verplancke et al. (2008).

As presented in Figure 6, in (a) the true decision boundary (x1)2 +(x2)

2 ≤ 1 is also shown. (b)

The same data after mapping into a three-dimensional input space ((x1)2, (x2)

2, (2(x1)(x2) )

1/2

). The circular decision boundary in (a) becomes a linear decision boundary in three

dimensions (b).

“In searching for the best hyper-plane, SVMs find a set of data points which are most difficult

to classify. These data points are referred to as support vectors” (Yang, 2010). SVM finds a

linear separating hyperplane with the maximum margin in this higher dimensional space. The

penalty cost C and the gamma (some authors refers as sigma) parameters are unknown and

should be calculated for every single problem (Hsu et al., 2010). That calculation is referred

to as tuning for the modelling process. The grid search is an exhaustive tuning method to

search through the input feature space, or a subset, for the best values for these parameters.

The cross-validation procedure can prevent the overfitting problem. By dividing the training

set into v subsets of equal size, sequentially one subset is tested using the classifier trained on

the remaining v-1 subsets. Thus, each instance of the whole training set is predicted once so

the cross-validation accuracy is the percentage of data correctly classified (Hsu et al., 2010).

“Training the SVM with a Gaussian radial basis function requires setting two parameters: C is

a regularization parameter that controls the trade-off between maximizing the margin and

minimizing the training error, while sigma describes the kernel width”. (Knorn, et al. 2009)


- 20 -

In this case, the main application of SVM is in the classification of sets of objects selected

from the output of the segmentation. Every segment involves spectrally similar neighbour

pixels. Among those pixels basic statistical measurements were made. Tuning SVM sigma

and C were used in caret package.

2.1.5.5 Classification and decision trees (DT)

According with Janert (2011) “decision trees consist of a hierarchy of decision points (the

nodes of the tree). When using a decision tree to classify an unknown instance, a single

feature is examined at each node of the tree. Based on the value of that feature, the next node

is selected. Leaf nodes on the tree correspond to classes; once we have reached a leaf node,

the instance in question is assigned the corresponding class label.”

In every node a variable is used. The algorithm can be adapted to search in certain amount of

predictors in every node. This is the only value used when tuning the DT. In caret the name of

this parameter is mtry.

2.1.5.6 Classification with random forest (RF)

“RF apply specifically to decision trees. In this technique, randomness is introduced not by

sampling from the training set but by randomly choosing what features to use when building

the decision tree. Instead of examining all features at every node to find the feature that gives

the greatest gain ratio, only a subset of features is evaluated for each tree” (Janert, 2011).

Witten et al. (2011) says that RF builds a randomized decision tree in each iteration of the

algorithm, and often produces excellent predictors. The parameter mtry, as used in DT, was

used in caret for tuning the RF.

2.1.5.7 Classification with multinomial logistic regression (MNL)

MNL is a regression model which generalizes logistic regression by allowing more than two

discrete outcomes. The multinomial logistic regression is an extension of the logistic

regression for multiple responses (Wikipedia contributors, 2012). “Until the arrival of support

vector machines, it was the method of choice for many classification problems” (Janert,

2011). The logistic function takes on only positive values between 0 and 1.


- 21 -

In the application of MNL regression, the nnet package is used to search the best decay

parameter, which “adds to the error function a penalty term that consists of the squared sum of

all weights in the network, as in ridge regression. This attempts to limit the influence of

irrelevant connections on the network’s predictions by penalizing large weights that do not

contribute a correspondingly large reduction in the error” (Witten et al., 2011). The network is

the term used in the paradigm on the motivation for research into the supervised learning

problem in the fields of machine learning with biological analogies to the brain (Hastie et al.,

2009). This principle is presented in Figure 7.

Figure 7: Representation of a network from the multilayer perception with a hidden layer. From Witten et al. (2011).

Figure 7 presents the paradigm of the biological neural network in the brain connecting input

data to produce the output based on the connection among ideas.

The list of algorithms presented could be applied once and the results will bring a measure of

error and accuracies. From the machine learning tools, the principle is to apply many times

the same algorithm in different conditions. A description of this process is presented in the

following section.

2.1.6 Classification with machine learning tools

The list of methods available for classification under the machine learning approach is

extensive. In the caret package in R this list features 122 methods for classification, regression

only or a combination of both (Kuhn et al., 2012). A set of five classification and regression

algorithms was used for types of forest. The algorithms were selected based on their ability to


- 22 -

classify efficiently satellite images in forest mapping applications (Inglada, 2007; Balas,

2008; Brenning, 2009; Qiu et al., 2009; Chen et al., 2011). The algorithms selected and

compared were maximum likelihood (MxL), minimum distance to means (MD), k nearest

neighbours (kNN), multinomial logistic regression (MNL) and support vector machines

(SVM). The MxL and MD methods were applied for the classification of forest and non-forest

areas. The kNN, MNL and SVM methods were applied in classification and regression for

forest types in a subset of one scene only. All the supervised classification algorithms were

applied to the OBIA products. The classification of forest/non-forest areas was made using

the pixel-oriented approach and the classification of forest types was made using object-

oriented segmentation (Trimble, 2011a).

2.1.7 Tuning algorithms

The machine learning (ML) tools must be implemented in such a way that the model selected

is trained using the parameters selected. The precise value for an algorithm is not known a

priori. A measure of quality such as accuracy should be evaluated to find the best value for

the best parameters. Each algorithm has its own tuning parameters. From the manual for caret

(Kuhn et al., 2012), the list of parameters used is defined by the algorithm selected. In Table 2

the selected algorithms and their tuning parameters for classification and regression is

presented.

Table 2: Selected algorithms and their tuning parameters.

Model Family method Package Tuning Parameters

Random forest rf randomForest mtry

cforest party mtry

Support vector machines svmRadial kernlab sigma, C

k nearest neighbors knn caret k

Multinomial logistic regression multinom nnet decay

The random forest “is a substantial modification of bagging that builds a large collection of

de-correlated trees, and then averages them” (Hastie et al., 2009). “Each tree is learned using

a bootstrap sample obtained by randomly drawing N cases with replacement from the original

dataset, where N is the number of cases in that dataset. With each of these training sets, a

different tree is obtained. Each node of these trees is chosen considering only a random subset

of the predictors of the original problem. The size of these subsets should be much smaller

than the number of predictors in the dataset. The trees are fully grown, that is, they are

obtained without any post-pruning step” (Torgo, 2010). Witten et al. (2011) stated that this


- 23 -

“often produces excellent predictors”. For this reason, the random forest was used for variable

selection in this process. The parameter mtry, used both for decision trees and random forest,

is the only parameter that requires that some judgment be made. In the process of tuning, the

value that achieves the highest accuracy is the one to use.

The tuning parameters for SVM in the kernlab package are sigma and C. This is the only

algorithm that requires more than one parameter be tuned. In this case, the term grid search is

used for the selection of the optimum values of both parameters. Witten et al. (2011)

mentioned that it is performed over a pair of classifier options. Describing the same

technique, the same authors claimed, “it offers the ability to optimize parameters of a

classifier, a pre-processing filter, or one parameter from each.” In this process, according to

the same source, “the user specifies the lower and upper bounds for each parameter, and the

desired number of increments.” The process reports the best combination of sigma and C,

which corresponds to the highest accuracy and kappa values.

When applying the kNN algorithm, the parameter to tune is the k number of neighbors in the

evaluation of the class to be assigned.

2.1.8 Cross validation

The use of remote sensing data for mapping is based on statistical classification methods.

Some methods can be applied to either normally distributed or distribution-free data

(Kaufman & Rousseeuw, 2005). The information technology supports numerical description

for many of the basic principles. This technology has also provided many tools, algorithms

and systems applied in biology; a science known today as bioinformatics (Yang, 2010). The

open source community has made accessible tools with libraries expanding upon open

statistical languages like R. The list of methods provided by these tools was grouped in task

views. The machine learning task view for R holds 12 groups of tools, covering

computational statistics methods to GUI interfaces (Hothorn, 2012). The machine learning

methods commonly make the classification and can provide a re-sampling option. Normally

this option is provided under cross validation. A re-sampling can be made either from groups

of samples, called leave group out cross validation (LGOCV), or from each sample, called

leave one out cross validation (LOOCV). These re-sampling options and others can be found

in the caret package for R (Kuhn et al., 2012). During the cross-validation the reference data

is used in such way that all the records are used at least once as training samples and one as


- 24 -

validation data. Yang (2010, 102) says that “Data are randomly divided into k-folds. K

models are constructed. Each of them uses one fold of input vectors as test data while

the rest are used for model construction. The final model performance is estimated based on

these k sets of testing results.”

The algorithms presented for supervised classification can be applied either to pixel based or

object based image analysis. In next section, the classification of forest areas under PBIA is

presented.

2.1.9 Pixel-based image analysis (PBIA) of forest areas

“Multispectral satellite images that have been through preprocessing are then ready for

processing, which essentially means they are ready for image classification.” (Khorram 2012,

p 46). The classification of satellite scenes based on PBIA was described by many authors.

(Campbell and Wynne 2011, p 335; Khorram 2012, p 46; Purkis and Klemas 2011, p 92)

“The objective of image analysis is to create an accurate map of an area viewed by satellite

sensors.” (Purkis and Klemas 2011, p 84)

2.1.9.1 Detection of forest

The statistical approach employed was developed to solve the problem in two parts: forest

delineation and forest type identification. For the delineation of forest (forest and non-forest

areas) the ISODATA was applied as described in section 2.1.4.1 (page 13). This algorithm

was successfully used in the mapping of different types of forest cover (Carman and Merickel,

1990; Jiang et al., 2004b; Lang et al., 2008; Walsh et al., 2008; Makkeasorn et al., 2009;

Wang and Niu, 2009).

Two different classifications of forest areas were performed. One of the classifications was

based only on the original LANDSAT bands and the second using artificial bands such as

NDVI, TC1 to TC4 and texture bands, as described in section 0 (page 24). For the second

classification, two results were generated by the modal filter, described in section 2.1.9.2

(page 25), applied twice and once.

No other vegetation index was used based on the affirmation made by Campbell and Wynne

(2011) that says that “in practice there are few differences between the many VIs that have

been proposed”.


- 25 -

The detection of forest was based on the unsupervised approach. This approach was selected

on the basis of previous experience and reports in the literature (Holopainen et al., 2009;

Pekkarinen et al., 2009; Ren et al., 2009). The accuracy of the classification of forest areas

was greater than 85 % in most of the literature cited.

In preparation for the classification of each image, a statistical analysis containing features for

the classification needs to be carried out. The Bayesian information criterion (BIC) was

chosen for the selection of features because the tests showed this criterion to be most robust in

the selection of predictors in different images, and results in a smaller number of features

compared to other selection methods (López Hernández et al., 2010).

2.1.9.2 Application of modal filter

After completion of the classification, a modal filter was used to reduce the salt and pepper

effect in the areas classified (Figure 8).


- 26 -

Figure 8: Effect of modal filters after classification.

In Figure 8, at the top is the original image, in the middle is the forest ‘green class’ without a

filter and at the bottom is the forest with the stray pixels cleaned up by the 3 x 3 modal filter.

The modal filters clean stray pixels and so the number of polygons at the end is more

manageable. However, the filtering also results in a slight increase in large areas and a slight

shrinkage of small areas. Some very small areas even disappear. This will affect the final area

in each class. Products have been created in which the filtering process is performed either

once or twice. (Figure 9)


- 27 -

Figure 9: Example of the second classification using dual modal filtering (forest: green; water: blue; grey: other

classes). The black lines are the forest from the ATKIS layer.

The dual filtering resulted in a generalisation of forest areas and in an increase in already

large forest areas. To counteract this, a simple modal filter was used in the final version.

After filtering, all of the areas classified were converted from raster to vector, a process

referred to as vectorization. This is a very complex and computationally intensive process

during which all of the areas with common pixel values are delimited and stored as closed

polygons. This process was undertaken to facilitate further cartographic and GIS tasks. After

vectorization all forest areas smaller than 1 ha were filtered out and added to the non-forest

areas. This filtering ensures that many small polygons inside and outside of the forest will be

deleted and reduced the size of the final file. In ATKIS the size is 5 ha. A second filter is

needed to apply in order to make further comparisons.

The mosaicking of the classifications from all images was carried out at the end, because the

different images had strong radiometric differences, therefore each image was classified

individually (Figure 10).


- 28 -

Figure 10: Illustration of the radiometric differences between two adjacent images.

The filtering of the product is the final step in the pixel based image analysis (PBIA). In the

next section the approach based on image objects is presented.

2.1.9.3 Detection of forest types

The classification of forest types was based in two methods. The first method tested was the

Minimum Distance to Means (MD), described in section 2.1.5.1 (page 15), and the second

was Maximum Likelihood (MxL), described in section and 2.1.5.2 (page 16).

2.1.10 Object based image analysis (OBIA)

The PBIA generated a map of forest. The next steps were conducted in order to obtain the

same map by other means in order to compare the accuracies. Segmentation is the subdivision

of an area in zones (segments) with similar characteristics. The segments are also called

objects (Navulur, 2007). The same author says that “an object can be defined as a

grouping of pixels of similar spectral and spatial properties. Thus, applying the object-

oriented paradigm to image analysis refers to analyzing the image in object space rather than

in pixel space, and objects can be used as the primitives for image classification rather than

pixels”. The spatial properties, also called features, can be: size, spectral value, indexes,

textures, values of surrounding polygons etc. Figure 11 presents the general idea on creating

objects from a satellite scene.


- 29 -

Figure 11: Illustration of the generation of segment objects by the image segmentation method.

In Figure 11 (a) the original image in false color composition, in (b), the segment objects

generated drawn by their boundaries in blue and in (c) the result of the classification of the

segments.

The segments are polygonal objects derived from more or less uniform areas within the

images. As stated by Trimble (2011), the “image is cut in pieces, which serve as building

blocks for further analysis.” The generation of segments is an analysis commanded by the

parameters roughness, scale and shape calculated from the brightness values stored in the

image. This analysis can spread from only one band to the complete set of bands obtained by

the remote sensing instruments (Navulur, 2007). In every segment object, a descriptor can be

obtained and used as a feature for the learning process in the application of classifications.

More than 100 features can be obtained pre-programmed in eCognition (Trimble, 2011a). The

analysis using such large numbers of descriptors per polygon is frequently referred to as

object-based image analysis (OBIA).

Of numerous German exercises in forest mapping, the most recent focused on the riparian

forest of the Danube Floodplain National Park (Suchenwirth et al., 2012). The authors

identified meadows, reed beds and hardwood and softwood tree species using remote sensing

data from Ikonos-2 and a DEM created using LIDAR data. A spectral and knowledge-based

classification was performed with object-based image analysis. The authors’ objective was to

classify floodplain habitats, using OBIA to improve the accuracy of the classification of

vegetation cover mapping in central European floodplain habitats and in the estimation of the

carbon stored in floodplains. Their overall accuracy (OA) was 70 % with a kappa value of

0.64.

The OBIA can be resumed in three steps. The first step is importing the image. In this step the

coordinate system and the bands included in the analysis is made. The second step is the

a b c


- 30 -

application of the algorithm for segmentation. Various algorithms can be applied sequentially.

The last step is the classification of the segments and production of the final layer.

Every segmentation process generates a level, and all of the segments produced belong to that

level. A single segment can be subdivided following some rules and a new level would be

created below. Many different segments with the same attribute can also be merged in a new

level, that level would be above of the actual one. Characteristics from one to the other level

can be inherited, like level, super or sub objects values obtained from the image.

Some of the segmentation algorithms available were selected to complete the forest mapping

process. Every segmentation have their own parameters to set and will be presented next.

2.1.10.1 Chessboard segmentation (size of squares)

According to Trimble (2011), “Chessboard segmentation cuts the scene into equal squares of

a given size”. The chessboard segmentation creates a regular division of the area based on the

amount of pixels that will be used to build it. It is particularly useful for processing large

areas. It provides a framework before a multi-resolution segmentation is applied. This speeds

up the calculation of the second level segmentation. The chessboard segmentation method is

relatively new and no publications dealing with the optimal size of squares were found. A

subset of an image is presented with the result of the application of this segmentation

algorithm in Figure 12.

Figure 12: Subset of LANDSAT scene showing a false color composite (a) and the same place with a subdivision using

chessboard segmentation (b).

a b


- 31 -

Due to the size of the scenes (from 6000 to 8000 pixels in files and columns), the chessboard

segmentation algorithm was applied. The image was subdivided into small subsets of 600 x

600 pixels. With the subdivision, the next step in the segmentation required to calculate the

final segments was faster.

2.1.10.2 Multiresolution segmentation

Multi-resolution segmentation is an optimisation procedure that, for a given number of image

objects, minimises the average heterogeneity and maximises homogeneity (Trimble, 2011a).

The multi-resolution segmentation algorithm produces the segments that will be used for the

classification. It is called multi-resolution because the same algorithms can be used both to

subdivide and to merge segments.

Determining the parameter scale is one of the most important parts of this process. It is an

abstract definition of the size of the area to be used to select the information to create the

segments. The scale also depends on the size of the image, because large images can only

accommodate smaller scales. In Figure 13 an example of the output of this algorithm is

shown.

Figure 13: View with the segment objects created by multiresolution segmentation.

From Figure 13, some clouds and shadows are visible in white color. The clouds and their

shadows can be over forest and non-forest areas.

The scale of the multiresolution segmentation algorithm was set to 10. Bock et al. (2005)

presented a list of recommended scales to apply in REDD+ evaluations. When the algorithm

is set to a higher value, the segments tend to be bigger and include both forest an non-forest

classes based on the texture. When the value is smaller, the processing time tends to be longer


- 32 -

and the resulting segments are generally too small, producing a file too large for small

regions.

2.1.10.3 Merging segments

After segment objects are classified, some have the same class and share the same boundaries.

A rule can be applied to merge these segments. A copy of the first level is made and the

product is a new level of segmentation superior to the first, in which areas with similar

attributes are agglomerated. The resultant file requires less storage space and contains the

same spatial information as the first. In the figure below an example of the merging of

segments is shown.

Figure 14: Example of merging segment objects dissolving lines dividing the same class.

In this example conifer forest and broad leaved forest is classified and merged generating

polygons with two colors.


- 33 -

2.1.10.4 Object features

In the segmentation the image is subdivided into areas (segment objects) with similar

characteristics. Those segments are polygons enclosing groups of pixels. Different

segmentation programmes are able to record these polygons in either vector or raster format.

Calculations can be made for each polygon, such as the number of pixels in the polygon. Each

type of calculation produces a result called a feature.

In eCognition more than 100 features can be obtained for every segment or object. The

features per segment include, for example, the type of segment with two features (whether 3D

or connected), layer values with 20 features (mean, standard deviation, skewness, ratio,

minimum, maximum, mean of inner border, mean of outer border, border contrast, contrast to

neighbour, edge contrast, standard deviation of neighbours, circular mean, circular standard

deviation, circular standard deviation/mean), similar measurements to neighbours, to super

objects, to image, hue and saturation. There are anther 18 features related to the class of

segment, such as eight descriptors of relations to neighbour, five related to sub-objects of

the segment, relation to super-object, and five relations to the classification. Another three

features relate to linked objects, 53 image features, seven process related features, 13 region

features, two image registration features, three metadata and as many user defined

variables as the user defines (Trimble, 2011a).

Some of these features are used for other sciences, for instance, in microbiology (Li et al.,

2002; Kyan et al., 2005; Muthu Rama Krishnan et al., 2012), text analysis (Yang et al., 2011),

face recognition (Liau and Isa, 2011), etc. The features used to classify the satellite image

most commonly applied in the literature were incorporated in the model described previously

(Palubinskas et al., 1995; Triepke et al., 2008; Brenning, 2009; Heinl et al., 2009; Lier et al.,

2009; Petropoulos et al., 2011).

2.2 Software used

In the preprocessing steps, reprojection and converting formats of the original datas the next

licences of software was used:

Erdas Imagine 9.1 (Intergraph Geospatial)

ArcGIS 10 (ESRI)

eCognition 8.64 (Trimble)

SAGA GIS 2.0.8 (saga-gis.org)


- 34 -

Quantum GIS 1.7.0 (qgis.org)

A pair of programming languages was used for converting data and statistics.

Phyton 2.5 (python.org)

R 2.15. Extension packages: car, caret, cluster, DAAG, e1071, graphics, foreign and all of the

additional extensions required for each package. (cran.r-project.org)

RStudio 0.95.265 (rstudio.org)

Juan Ygnacio López Hernández Development of a method for forest type detection 3 Test site and data used

- 35 -

3 Test site and data used

3.1 Test site

The chosen test site was Bavaria as the availability of reference data was guaranteed. Bavaria

(Bayern in German) is located to the southeast of Germany (Figure 15). The total area is

70547.8 km2

(Meschede, 2004). The southernmost limit is situated at 47° 16’ and the north

eastern boundary at 50° 34’. The borders from east to west are situated at 8° 58’ and 13° 50’.

The elevation, in meters above sea level, ranges from 100 m in the international boundary in

Main up to 2962 m in the highest mountain of Germany. Bavaria shares borders with Austria,

the Czech Republic, Switzerland (across Lake Constance) and the neighbouring German

states Baden-Württemberg, Hessen, Thüringen and Sachsen. The Danube (known as the

Donau in German) and the Main are the two major rivers flowing through Bavaria. The

landscape of Bavaria has been subdivided into four major regions: a) the Alps, with the

Zugspitze the highest mountain in Germany at 2,962 metres; b) the Alpine foothills with their

numerous lakes; c) the eastern Bavarian central mountains, which host the first national park

established in Germany; and d) the Schwäbisch-Fränkische scarp landscape (BAYERN

TOURISMUS Marketing GmbH, 2012).

3 Test site and data used Development of a method for forest type detection Juan Ygnacio López Hernández

- 36 -

Figure 15: The location of Bavaria relative to the other fifteen federal states in Germany and the position of Germany

in the world.

The natural regions of Bavaria are presented in Figure 16.

Figure 16: Natural regions of Bavaria. Source: Bundesamt für Naturschutz (2012).


- 37 -

This figure shows in green lines the boundaries of the natural regions of Bavaria according

with the (BFN) Bundesamt für Naturschutz (2012). This division of Germany into major

natural regions takes account primarily of geomorphological, geological, hydrological and

pedological criteria in order to divide the country into large, physical units with a common

geographical basis. From south to north the Alps and Alpine foreland covers the Iller-Lech

Plateau (D64), Lower Bavarian Uplands (D65) and Isar-Inn Gravel Plateau (05-06), Southern

Alpine Foreland (D66), Swabian-Bavarian Foreland (D67) and the Northern Limestone Alps

(D68). At center of the state the Southwestern German Scarplands is covering the Swabian

Jura (D60), Franconian Jura (D61), Upper Palatinate-Upper Main Hills (D62), Swabian

Keuper-Lias Plains (D58), Franconian Keuper-Lias Plains (D59), Mainfranken Plateau (D56),

Odenwald, Spessart and South Rhön (D55). A region of the Eastern Central Uplands is

covered by the Upper Palatine-Bavarian Forest (D63) and the Vogtland (D17) in the north

most part of the state. A small region of the Western Central Uplands is covered by the East

Hesse Highlands (D47).

The geology of Bavaria is referred to as the southern German scarplands, a geological and

geomorphological natural region or landscape characterising the southern German states

Bavaria and Baden-Württemberg and also Switzerland. The landscape is characterised by

escarpments. The wooded scarps drop sharply to the west towards the Rhine Rift Valley and

the Rhine-Main Plain, whilst the slopes fall comparatively gradually towards the northeast

into the depressions beyond which lie the Thüringer Wald, Thüringer Schiefergebirge,

Frankenwald, Fichtelgebirge, Oberpfälzer Wald and Bayerische Wald. Similarly the

Schwäbische and Fränkische Jura descend gently to the southeast towards the Danube valley,

whilst the Schwäbische Jura, for example, drop very steeply to the north-northwest from the

Albtrauf, the top of the main scarp (Meynen, 1902; Dickinson, 1964; Geyer and Gwinner,

1986; Rothe, 2009).

The climate of Bavaria was reported as a transition between Continental and Atlantic

conditions. The south eastern region is closer to the continental due to the biggest differences

in temperature between the coldest and the warmest month. The annual mean temperature is

7.5 ºC, with the maximum in July and the minimum in January. The annual mean

precipitation surrounds the 850 mm. (Meschede, 2004)


- 38 -

3.2 Satellite data

The first step undertaken as part of the study was to explore a data set consisting of many

freely available satellite images from the past few years covering the Bavarian region.

LANDSAT data was used as the application had to be low cost. Only free satellite data with a

very high geometric resolution were considered. In order to carry out the evaluation within a

short period of time, LANDSAT 4 and 5 data was acquired. There are known problems with

LANDSAT TM 7, referred to as slc-off, that appeared in May 2003. This served to reduce the

area for which useable images were available by about 22 % (Roy et al., 2008). Only data

from the older LANDSAT TM 5 was selected for this method.

LANDSAT images from different sources provided the data used in the various treatment

stages. The three main sources of LANDSAT TM data at the time of writing are as follows:

1. Eurimage (www.eurimag.com). Eurimage is a company providing a wide range of

LANDSAT TM products. Additional information and products from many other satellite

sensors can also be purchased. However, the company works on a commercial basis, which

means that the data and products are not free of charge. Eurimage charge € 1500 for a

LANDSAT image. The nine images necessary to cover Bavaria would have cost € 13500. No

Eurimage data were used in this study.

2. Another source of data is the European Space Agency (ESA) (www.esa.int). The aim of the

ESA is to provide satellite data for the purposes of implementation. Therefore, it supports the

development of procedures for the application of satellite data in the form of free data. In

order to receive free information, however, a proposal must be formulated in advance and

submitted to a peer review process. This is likely to lead to significant delays in the start-up of

the research. Furthermore, ESA only provide LANDSAT data up to 2003 free of charge. All

data have cloud cover of less than 10 %. The Table 3 shown below provides an overview of

the data ordered from ESA.

3. The third organisation providing data is the United States Geodetic Survey (USGS)

(www.usgs.gov). The organisation offers worldwide LANDSAT data for free use. The data

can be ordered in various processing levels. The Table 4 lists part of the data ordered from

USGS. The cloud cover was less than 10 % for all of the most recent USGS LANDSAT data

available. These are LANDSAT TM 5 images. Most of the images were taken in the months

June, July and August; only two images were from September. Consequently, all of the

images depicted similar seasonal vegetation and in each the sun was high and the areas well

illuminated.


- 39 -

LANDSAT TM data available (Figure 17)

Of the LANDSAT images available, only those with a higher sun elevation were selected.

(Table 3)

Table 3: List of LANDSAT scenes ordered from ESA.

No Acquisition Path Row Azimuth Elevation zone

1 13.06.2006 192 26 142.357978 60.167360 33

2 13.06.2006 192 27 139.999279 61.080092 33

3 01.09.2006 192 27 150.716592 47.442644 33

4 22.07.2006 193 25 144.460102 55.967562 33

5 22.07.2006 193 26 142.432246 56.913072 32

6 24.09.2006 193 26 158.039958 38.507702 32

7 22.07.2006 193 26 142.432246 56.913072 32

8 22.07.2006 193 27 140.330468 57.826531 32

9 26.08.2007 193 27 148.779599 49.332557 32

10 11.06.2006 194 25 144.863185 59.141177 32

11 11.06.2006 194 26 142.618862 60.096287 32

12 11.06.2006 194 27 140.289196 61.008800 32

13 18.06.2006 195 25 144.051298 59.302303 32

14 20.07.2006 195 25 144.221858 56.322025 32

Another set of 14 LANDSAT scenes from GLS 2000 and 2005 were ordered to provide the

best available images (Table 4). The following LANDSAT TM 5 satellite images were used

in the study (Table 5).

Figure 17: Overview of the best ESA

satellite data ordered.


- 40 -

Table 4: LANDSAT TM 5 satellite images observed in the study.

Id Mission Sensor Date File name Path Row

1 Landsat-7 ETM+ 18/06/2000 LN7_TM_6259_25.zip 194 25







8 Landsat-5 TM 18/03/1990 LN5_TM_32153_26.zip 195 26












The images selected in this research are presented in the Table 5.

Table 5: The final 7 LANDSAT TM images used in the project.

Path Row Acquisition date

192 026 13/06/2006

192 027 01/09/2006

193 025 22/07/2006

193 026 24/09/2006

193 027 27/08/2007

194 025 11/06/2006

194 026 11/06/2006

Figure 18 shows that the images supplied were affected only very slightly affected by clouds.

Figure 18: Illustration of the satellite scenes used. At left with the path and row numbers and at left a mosaic

overlapping to avoid the clouds


- 41 -

The quality of the scenes can vary due to different atmospheric conditions and sun position.

This was considered in the subsequent classification.

3.3 DEM

The digital elevation model (DEM) was supplied by the LFU (Bayerische Landesamt für

Umwelt “Bavarian State Office for the Environment”) and its use was permitted for the

purposes of this project. It was used in the orthorectification process documented in the

section 4.2.3 (page 56).

A DEM in Geotiff format with a resolution of 50 m was used for the orthorectification. In

Figure 19 an example of the reference data used is presented.

In Figure 19 we can see in a) the DEM with the boundaries of Bavaria superimposed; b)

general overview of the aerial orthophotos (in each point there is at least one); c) detail of one

orthophoto; and d) other orthophoto in detail.

3.4 Reference data

The LFU provided the author of the study with the following reference data:

3.4.1 Orthorectified colour aerial orthophotos

A collection of 286 digital colour orthophotos in MrSid format with a resolution of 40 cm per

pixel was obtained. These orthophotos were used to select ground control points and in the

definition of training and verification samples. A set of 97 orthorectified aerial photographs

was used to select ground control points in the orthorectification of the LANDSAT image and

a set of 32 in the sampling of data for the validation process.

a c b d

Figure 19: An example of the reference data used.


- 42 -

3.4.2 ATKIS forest layer

A digital map of forest boundaries in ATKIS (authoritative topographic-cartographic

information system) vector shapefile format (ADV, 2003) was used to support the selection of

training samples and in the identification of clouds over forest areas.

3.4.3 Forest stands from an aerial photo interpretation

A file in vector format (shapefile) detailing forest stands in the foothills and alpine areas of

Bavaria from aerial photo interpretation was also used.

3.4.4 Document about interpretation of aerial photographs of forest stands

A file in .pdf format documenting the process of interpretation of aerial photographs for the

identification of forest stands in the foothills of the Alps was provided by the LFU. By

interpretation the aerial photographs the precise forest types could be identified.

3.4.5 Inventory data

A data set from the German national forest inventory (NFI) (‘Bundeswaldinventur,’ BWI)

provided by the Federal Ministry of Food, Agriculture and Consumer Protection

(Bundesministerium für Ernährung, Landwirtschaft und Verbraucherschutz, BMELV) and a

description of the data (BMELV, 2010) was used to validate the results of the classifications.

Whereas the classification of forest areas was validated based on the inventory data, forest

types were assessed based on the species information contained within the NFI sampling

plots. The groups of species considered are presented in table 4.

Table 6: Variables obtained from the NFI as they were stored in the files, including their description.

Variable Description

Ei [Oak] All species of oak (including red oak).

Bu [Beech] A genus (Fagus), broad leaved tree from the Fagaceae family.

ALH Other long-lived broad leaved trees: maple species, sycamore, sweet chestnut, ash,

lime species, walnut species, black locust, horse chestnut, wild service tree, holly,

elm species, white ash.

ALN Other short-lived broad leaved trees: birch species, service berry, alder species,

poplars, black cherry species, wild cherry, wild fruits, all other broad leaved tree


- 43 -

Variable Description

species not mentioned.

FI [Spruce] All spruce and other coniferous trees species except Douglas fir, pine,

larch, fir.

TA [Abies] White fir, grand fir and other fir trees.

DGL [Pseudotsuga] Douglas fir.

Ki All pine species.

LAE [Larix] Larch of all kinds.

3.4.6 Cartographic projection of the data

The original inventory data from the NFI were obtained with the datum in the standard EPSG

3396 and converted into EPSG 3397 (Butler et al., 2010). The ATKIS forest layer was also

obtained in the standard EPSG 3396 and re-projected into the standard EPSG 3397. (Table 7)

Table 7: Map projection spatial reference parameters used (Butler et al., 2010).

EPSG code 3396 3397

EPSG name PD/83 / Gauss-Kruger zone

3

PD/83 / Gauss-Kruger zone

4

Datum Potsdam 83 Potsdam 83

Spheroid Bessel 1841 Bessel 1841

Projection Transverse Mercator Transverse Mercator

Latitude of origin 0 0

Central_meridian 9 12

Scale_factor 1 1

False_easting 3500000 4500000

False_northing 0 0


- 44 -

Juan Ygnacio López Hernández A method for forest type detection 4 Methods

- 45 -

4 Methods

All methods applied, beginning with original images and finishing with the map of forest

types are presented in Figure 20.

Figure 20. General flow of the methods applied. The preprocessing is also presented.

The first part was the preprocessing. The NFI and the scenes received a preliminary

processing treatment that is described next.

4.1 Pre-processing the national forest inventory (NFI) data

When classification methods are applied to the data, they must be evaluated based on

reference data independent of the method. The NFI database was used in one approach only

as a validation, and in the other as training and in the validation of the results. This database

contains information relating to the plots assessed throughout the whole state and shows the

species, number of trees per hectare, basal area per hectare and the volume per hectare in each

plot.

The records containing information about gaps and open areas (BL > 0, or iBL > 0) were

excluded from sampling and validation.

For the evaluation of PBIA classifications the broadleaved and conifer forest were obtained

from the original records summarizing the species records.

The proportion of broadleaved forest by area was obtained from the following equation:

BROAD = Ei + Bu + ALH + ALN

The proportion of conifers by area, alternatively, was derived from the equation:

CONIFER = FI + TA + DGL + Ki + LAE

The original database contained information presented in only one table. Every row presents a

measurement made in a plot for a group of species.

Presented in Table 8 is a list of field names and their meaning according to the descriptions

stored in the original file.

4 Methods Development of a method for forest type detection Juan Ygnacio López Hernández

- 46 -

Table 8: The original field names and their descriptions contained in the national forest inventory (NFI) for Bavaria.

Field Description

BL State (9 = Bavaria)

TNR Tract no.

ENR Corner no. (‘wing area’)

RW Gauss-Krueger easting

HW Gauss-Krueger northing

BAGRNR Species group no.

BAGR Tree species group

NHA Number of stems per hectare (major and minor stand)

NHA_HB Number of stems per hectare (main stand)

BAF Stand area [ha] (main stand)

GHA Basal area [m2] per hectare (major and minor stand)

GHA_HB Basal area [m2] per hectare (main stand)

VHA Stock [m³] per hectare (major and minor stand)

VHA_HB Stock [m³] per hectare (main stand)

H_L Lorey height (mean height weighted proportional to basal area) (major and minor stand trees) (Loetsch et al.,

1973; Van Laar and Akça, 1997)

DG Quadratic mean diameter [cm] (major and minor stand trees)

DG_HB Quadratic mean diameter [cm] (main stand trees)

4.1.1 Query

The data was provided in an MS Access database and converted to ASCII format for use in

the statistical software.

Some queries were made to extract the information on the number of stems per ha, basal area

per ha, and volume per ha agglomerated per stand and per group of trees.

Table 9: Example of the table obtained after applying a query to total basal area per species in each plot the National

Forest Inventory (NFI) database for Bavaria.

The first two columns represent the coordinates of the plot in arbitrary units. The next two

columns (RW and HW) represent the latitude and longitude on a map. The remaining columns

present the total basal area per hectare in each plot. This information was converted into

ASCII format and imported into a GIS database as a point layer. A total of 7056 points were

imported into the database. The map coordinates of the database were Gauss-Krueger zone 3,

as can be seen from the easting coordinates.


- 47 -

4.1.2 PAM Clustering

As an ordination approach for grouping the NFI plots in clusters that represent species in the

field, the PAM algorithm was applied. The information contained in the NFI can be used to

find groups of species in the area on the basis of the natural distribution of the species. All of

the species are recorded in the inventory, but not all are attributed the same ecological

importance. This grouping process is called ordination and is used for the analysis of forested

areas (Guisan and Zimmermann, 2000; Austin, 2002; Thessler et al., 2005; Proisy et al.,

2007). The distances between clusters was measured based on normalised values of basal area

per ha, number of stems per ha and volume per ha.

The basal area per ha was chosen as the ordination variable, because it was found to have

relation with the values found in satellite scenes. (Holmgren et al. 2000; Reese et al. 2002;

Moisen et al. 2006) By other way the same order of importance was found in the number of

stems per ha and the volume per ha. The groups with the highest basal area were considered

to be the most important (Figure 21). The remaining groups had marginal representation for

basal area in the region. A histogram of frequencies was made to visualise this relative

importance of the groups.

Figure 21: Histogram of the number of species in the National Forest Inventory (NFI) of Bavaria. The groups of

species were sorted based on abundance from most frequent to less frequent.

The names of the species were presented in Table 6, page 42. The groups FI, KI and BU,

which represent species of spruce, pine and beech respectively, were selected to define the

Groups of species

Num

ber

of

spec

ies

Number of trees per group of species


- 48 -

ordination clusters. An exploration to define the number of clusters present in the area was

then made. Using the PAM package (Maechler, 2011), a,silhouette graph of the mean distance

between clusters was made (Kaufman and Rousseeuw, 2005). This diagram shows the mean

distance found after an unsupervised classification using the PAM method. Where there is too

much data for a desktop computer to handle, the package CLARA was used (Maechler, 2012).

Searching for a tentative number of clusters, the first grouping was made building a silhouette

with 30 clusters. The result of the clustering is shown in Figure 22.

Figure 22: Silhouette of the groups built during the definition of the optimum number of clusters based on the mean

standardised intercluster distance (ave), known as dissimilarity.

The silhouette was defined in section 2.1.4.2 (page 13). The best condition is when separation

among clusters get the highest values, which is given by the higher mean intercluster average


- 49 -

dissimilarity (ave). That property was found with fourteen clusters and the second best

separation was with three clusters. The higher the intercluster dissimilarity, the better the

number of clusters found. In Figure 22, the highest dissimilarity was found with ten clusters,

but it was considered inadequate to characterise the forest, especially when only three groups

of species were selected. The next was three clusters and 0.91 in mean dissimilarity which

corresponds to 3 clusters.

Only three main groups of species were selected from the NFI as it was expected that no more

than three clusters would be found. A new classification was performed with PAM with three

clusters were built. The clusters had the properties presented in the Table 10.

Table 10: Characteristics of the clusters found by PAM algorithm.

Cluster Plot Tract_Corner

Basal area per species Oak Pine Beech

1 4187_4 6 0 4 2 2914_4 44 0 0 3 15318_1 4 32 0

A scatter plot of these clusters was made along two axes, PCA 1 and 2. This diagram is

presented in Figure 23.

Figure 23: Cluster plot of the three groups identified by the PAM method. Each group, identified with numbers 1 to 3,

represent the clusters in Table 10.

The cluster 1 was represented by similar proportion of Oak and Beech trees. The total amount

of basal area is relatively low in this cluster. Cluster 2 is composed solely of Oak. No trees

Pine or Beech on it. Cluster 3 is composed mostly of pine. It also has some oak to a lesser


- 50 -

extent. Beech has not. Other species may be present or not, but they were not considered for

clustering. With the clusters made, and assigned to the records of the NFI, the next step is to

convert the table into special pints described in the next section.

4.1.3 Table to points conversion

The NFI table with the records was imported into the GIS. The EPSG codes were used to

make the transformation from GK zone 3 (3396) to GK zone 4 (3397). A general overview of

the plots imported into the GIS can be seen in Figure 24.

Figure 24: The 7056 inventory plots for Bavaria contained in the NFI database illustrated graphically after import

into GIS.

A close-up of the centre of the area shown in Figure 25 reveals the plots individually.


- 51 -

Figure 25: A close-up of the centre of Bavaria showing individual groups of plots.

The distance from one group of plots to the next is 4 km and between plots in the same group

is 150 m. In each plot are measured trees within a circle with a radius of 25 m. In the case of

Bavaria, inventory has greater density than the rest of the country. Only in western regions of

the state, they added a new group of four plots with the same characteristics of the standard

inventory at centre of the diagram. This increased density, so called enrichment, can be seen

at left in Figure 24.

With all the plots imported, the next step is to filter those plots that could affect the evaluation

of the classification due to irregularities compared with the rest of the records. A spatial

criterion was applied to avoid problems in classification. This criterion is described next.

4.1.4 Buffering

The forest is normally covering areas and a inventory plot can lay on the boundary of the

forest. Those records could bring flase information to the analysis if consider that the whole

area of the plot could have some portions without forest at al. The ATKIS forest layer was

used as a boundary of forest. All of the plots that were too close to the boundary of the forest

were filtered out. Only plots located at distances measured from the forest edge of greater

than the diagonal of one pixel were included. The distance considered was 45 m. A layer

4 Km

150 m

Group of 4 plots


- 52 -

defining all areas influenced by the distance to boundary of the forest was created. An

attribute was assigned to the NFI plots in those areas. All NFI plots located at a distance

greater than 45 m were selected for further use. A diagram with the boundary of forest and the

buffer around the boundary is presented in the Figure 26.

Figure 26: A buffer of 1.5 times the pixel size (right) was applied to the boundaries of the forest (left) and used to

select the NFI plots for the classification and the validation of the images.

The result of this step is a subset of the NFI database was obtained. The data is ready to be

used with satellite scenes. In the following steps, the preparation of the satellite scenes for the

analysis.

4.1.5 Automatic selection of training samples from the NFI plots

An experiment to evaluate the source and the number of training samples, and using a

threshold for the NDVI, was carried out over the image in path 192, row 026. The SVM

classification was carried out and evaluated with the National Forest Inventory (NFI) data.

Presented in table 33 is a list of the resulting parameters after application of the grid search

method to tune the parameters gamma and cost of SVM, described in 2.1.5.4 (page 18), the

best performance (BestP) achieved, the number of support vectors (SupportVec) and the

accuracy (Total Acc) evaluation of the training samples. The overall accuracy (OA) and the

respective kappa coefficient found are shown in the last two rows.


- 53 -

Table 11: Experiment on SVM for seleccion of features and threshold the NDVI feature.

G

rid

Sea

rch

Samples a b c d e f

Gamma 1.024 0.101 1.040 0.128 0.101 0.301

C 0.100 9.601 1.020 0.100 8.701 1.201

BestP 0 0.5 0.57 0 0.53 0.27

SupportVec 52.00 76.00 40.00 33.00 37.00 31.00

Total Acc 94.23 59 53 100 60 75.5

OA 38.6 55 51 40 59 75

Kappa 0.15 0.23 0.24 0.12 0.28 0.2

Samples selected in the following ways:

a Visual selection of training samples from Orthophotos and variables B2, B3,

B5, SD7 for bands (B) and Standard deviation (SD)

b Selecting 20 random training samples per class from NFI all scene features.

c Selecting 10 random training samples per class from NFI all scene features.

d Using all scene features NDVI as predictor.

e Selecting 20 random training samples per class from NFI all scene features.

f Selecting only NFI plots with NDVI > 50 as training samples and balancing

the weights per class.

As result of the experiment, all of the features from the image were selected and the NDVI

threshold was set to 50. This option is in column f of Table 11. This result conduced to adjust

the definition of forest.

The kappa coefficient was too low, indicating inconsistencies between the classes resulting

from the classification. In general, broadleaved forest is the class with the lowest user

accuracy. Data from the inventory were used in order to improve accuracy.

4.1.6 Calculating the weight of the classes

The selection of training samples for the classification of the segments always bring non-

balanced amount of training samples per class. The authors presented two methods to use

those unbalanced training samples for classification: a) repeat the smaller classes up to make

the same number as the biggest or b) to use some weighting factor in order to balance the

training.

The factor was applied by using the following equation:


- 54 -

Factorc = samplesc / max(samplesc)

Where

Factorc The weighting factor to be applied for each class c.

samplesc Is the amount of training samples in the class c.

max() A function to extract the highest value.

The overall accuracy of the classification increased from 59 to 75 %. Most of the errors in

classification are related to conifer forest.


- 55 -

4.2 Pre-processing satellite scenes

A general flow chart of the pre-processing steps applied to the scenes is presented in the

Figure 27.

Figure 27: Pre-processing steps applied to the scenes.

The data used were produced by different institutions, such as the USGS, BMELV and LFU.

Each has its own standards in the production of data. Those standards are usually different to

those of the final user. The files are optimised for transfer via the internet, for example, and

are usually in a standard exchange format but not in a form appropriate for use in geographic

information systems (GIS) software. During pre-processing, the user normally converts the

data from the original digital format to the format required by the software in use. Among

other tasks, the calibration and geodetic datum should be processed.

4.2.1 Conversion of format

The original format of the images provided was Geo-TIFF compressed to tar.gz by the USGS.

In this format, these images were originally projected in WGS84 zones 32 and 33, depending

on the position of the centre of each image.

The geodetic reference system was converted to the Gauss-Krueger reference system for the

selection of aerial orthophotos as a basis for the orthorectification. This conversion was

performed because all of the reference maps found was in Gauss-Kruger, the official

projection of Germany. The original satellite images obtained were referenced in the WGS84

system, were orthorectified based on control points selected from orthophotos.

The DEM was converted to a resolution of 30 x 30 m so that it could be registered with the

LANDSAT TM image.

In all processing steps, the Gauss-Krueger zone 4 projection system was used. The parameters

used for the projection transformation were:

Gauss-Krueger coordinate system

Datum: Potsdam

Spheroid: Bessel 1841


- 56 -

Projection: Transverse Mercator

Longitude of central meridian: 12 ° east

Latitude of origin: 0 ° north

Scale factor: 1

False easting: 4500000 m

False northing: 0 m

EPSG code: 3397

4.2.2 Pre-processing DEM

The DEM presented some abnormalities that could affect further steps like the Ortho-

rectification. The abnormalities found are presented in the following list:

The null value was set to -255. It was a problem introduced by the conversion of the

format from geotiff to IMG.

Some areas outside the test area and were clipped out from the raster.

A general view of the DEM before and after the pre-processing are presented in the .

Figure 28: The DEM before (left) and after (right) the pre-processing step.

4.2.3 Orthorectification

The satellite images to be orthorectified and the DEM were converted to compatible formats

so that the images could be opened in a viewer of the image processing system. To identify

the location of each ground control point (GCP) provided by the LFU, orthorectified USGS

images were used. These have a low spatial resolution, but as they are georeferenced. They

have been used to identify the location of the GCPs and relate each GCP to a certain aerial

photograph page number. The selected aerial photographs were then ordered from the LFU.


- 57 -

Every single checkpoint was then precisely identified in the satellite image and aerial

photographs. A total of at least 20 GCP were used per satellite image. At least five points are

required to calculate a model that is based on the equation of co-linearity. Then parameters of

the external orientation and the error (RMSE) can be calculated. The best results are achieved

when the control points are distributed uniformly over the image. For a more precise

orthorectification in hilly and montanious areas, the equeation of co-linearity was used. This

equation uses the altitude information from the DEM. The higher accuracy is found when

used high amount of GCP. The coarse resolution of satellite images complicated the

identification of suitable control points, especially in wooded rural areas.

The elimination of geometric distortion from an image is called orthorectification. The

geometric distortion of the image is caused by two basic reasons, the systematic and non-

systematic deformation present in the original scenes. Non-systematic deformations include

uneven terrain, whereas systematic deformations refer to the central perspective and incorrect

behaviour of the recording system. This is a special form of image georeferencing.

To geocode an image one requires the transformation equation, with which each pixel of the

input image can be transferred into the matrix of the output image. There are basically two

approaches to find this equation.

The recording geometry of the sensor is modelled in the DEM using parametric methods. The

pre-requisite is that the location and movement of the sensor is known and a DEM is

available.

GCP are used to establish the relationship to the reference system. A transformation equation

is applied to re-order the data of the input image into the matrix of the output image. With the

re-sampling, the grey value of the output image is calculated back from the input image. In

this study the nearest neighbour re-sampling method was used in order not to change the

original grey value, which is important for subsequent classification.

Height errors in the terrain model have an impact on the orthorectification, as position errors

in the orthorectified images, whereas the number of GCP used plays not so dominant role.

Studies have shown that a greater number of GCP produce no substantial improvement in the

resultant image. The quality of the DEM and of the GCP and the distribution serve to improve

quality considerably. The residuals expressed by the RMSE (residual error) resulting from the

orthorectification ultimately reflect the quality of the GCP (ERDAS Inc., 2010).


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For the orthorectification, the LANDSAT images were uniformly converted to a pixel size of

30 m x 30 m. The DEM was converted from 50 m x 50 m to 30 m x 30 m.

The following LANDSAT TM 5 images were orthorectified: (Table 12)

Table 12: List of the orthorectified LANDSAT 5 TM images acquired.

Path/Row Acquisition date

192/026 13/06/2006

192/027 01/09/06

193/025 22/07/2006

193/026 24/09/2006

193/027 27/08/2007

194/025 11/06/06

194/026 11/06/06

The accuracy achieved ranged from 1 to 1.5 pixels. This corresponds to a maximum deviation

of 30 to 45 m.

The two following figures illustrate the level of accuracy on the basis of the rectification of

the superimposed ATKIS data.

Figure 29: Example 1 highlighting the accuracy of the rectification (green lines are vectors of ATKIS forest layers).


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Figure 30: Example 2 highlighting the accuracy of the rectification (green lines are vectors of ATKIS forest layers).

In Figure 30 two adjacent images are presented. The differences in illumination conditions

can be seen at the centre of the image.

Once the image is orthorectified, the user can compare known topographic precision data

from different dates and even different sensors in one analysis.

4.2.4 Topographic normalisation

To compensate for the slope-related differences in lighting, a topographic normalisation was

applied to each satellite image. The C-factor method was selected for topographic

normalisation. This method was deemed particularly suitable, as it has been used successfully

in several forest inventories (Oehmichen, 2007).

The C-factor method is used to balance the brightness values of every pixel in the scene

depending on local sunlight illumination angle, varying with a compensation factor C, which

also takes into account the diffuse incident radiation at the sensor. The correction factor is

calculated by means of a regression between the individual bands of the sensor and the DEM

local solar incidence angle, and refers in this case to training areas selected as homogeneous

coniferous forests through visual interpretation of the satellite data (Figure 31). The


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topographic normalisation was performed using the SILVICS programme package ((c) Niall

McCormick, JRC-SAI, Ispra, 1995-1998) to determine the C-factor for all images. (Figure 31)

To avoid an erroneous correction of sharp edge slopes, the digital elevation model was

processed first with a 3 x 3 smoothed average filter.

Figure 31: Test areas for the calculation of the C-factor taken from a subset of the image in path 192, row 026.

The C-factor (correction factor) was calculated based on the homogeneous coniferous forest

areas. Once determined for all bands, the topographic normalisation was applied to the entire

image.

Contrary to the expectations for the topographic normalisation, however, artefacts of ridges

remained on corrected northern slopes (see Figure 32).

Therefore after a quality check the topographic normalization was discarded and not used for

the further processing.


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Figure 32: Overcorrection effects of topographic normalization.

The standard false colour composition of the original image is shown (left) and the same area

with overcorrections resulted from the topographic normalization (right). The topographic

normalization was not applied because of the observed over-corrections. The original

orthorectified images were used for further processing to ensure a better result in the

classification.

4.2.5 Cloud mask

The clouds were identified by a ratio and the setting of a threshold. For this, the value of each

pixel of the LANDSAT TM band 1 was divided by the corresponding value of the band

thermal channel 6. If the result was greater than the threshold 1, the corresponding pixel was

deemed a cloud and redefined as ‘not present’ (null value). The threshold was determined

interactively in multiple passes to ensure the best possible adaptation of the cloud mask to the

images.

Simultaneously, when band 4 was less than 25 and band 7 greatest than 15, the shadows were

identified. In those locations a null value was assigned. Some water bodies were assigned a


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null value with this process, but for the objectives of this research, this error was not

important.

The resulting data sets included only those areas not in the shadow of clouds and only those

with directly illuminated pixel values. Only at the edge of the clouds, where the clouds were

translucent, were adverse effects evident (see Figure 33).

Figure 33: Subset of the image before (left) and after (right) masking of the clouds.

4.2.6 Haze suppression

After orthorectification a haze correction was performed to minimise the impact of haze, and

to enhance the spectral differences between classes. The ‘haze reduction’ method was based

on the ‘tasselled cap coefficients’ method. Here, the proportions of vapour components are

identified and corrected in each pixel (Lavreau, 1991).

The results of the haze correction were checked for spectrally homogeneous land units. There

was a significant reduction in the noise detected by the correction (Figure 34). The haze

correction was, therefore, calculated for all images.


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Figure 34: The haze uncorrected image (left) and the haze corrected image (right).

Figure 34 shows significant improvement to the spectral differences between homogeneous

land units (water and vegetation) is clearly visible in the highlighted part of the images.

With the set of scenes prepared in the pre-processing steps, some variable selection methods

could be applied to choose the best predictors of the forest and forest types in the area. The

next section is dedicated to the variable selection.

4.3 Creating synthetic layers for the classifications

Before the application of the PBIA, some syntetic layers were made. The NDVI is proven to

be saturated and is quite sensible to seasonal changes (Huete et al., 2002; Moreau et al., 2003;

Inoue et al., 2008). In order to improve the classification result within the forest, the following

additional synthetic layers was created from the available data. The normalized difference

vegetation index (NDVI) was calculated. In Figure 35 is a subset of this index.


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Figure 35: Subset of the NDVI.

The special transformation Tasseled Cap (TC) was calculated and generated 4 components.

(Leica Geosystems Geospatial Imaging, LLC 2010) The TC values were obtained by the next

equations:

TC1 = Band1 * 0.2909 + Band2 * 0.2493 + Band3 * 0.4806 + Band4 * 0.5568 + Band5 *

0.4438 + Band7 * 0.1706 + 10.3695

TC2 = Band1 * -0.2728 + Band2 * -0.2174 + Band3 * -0.5508 + Band4 * 0.7220 + Band5 *

0.0733 + Band7 * -0.1648 - 0.7310

TC4 = Band1 * 0.8461 + Band2 * -0.0731 + Band3 * -0.4640 + Band4 * -0.0032 + Band5 *

-0.0492 + Band7 * 0.0119 + 0.7879

TC6 = Band1 * 0.1186 + Band2 * -0.08069 + Band3 * 0.4094 + Band4 * 0.0571 + Band5 *

-0.0228 + Band7 * 0.0220 - 0.0336

A subset of those components is presented in Figure 36, to Figure 39.


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Figure 36: Subset of the tasselled cap 1. This band is also called Brighness of the objects in the visual channels (Crist

and Cicone, 1984).

Figure 37: Subset of the tasselled cap 2. Called Greenness, is presenting the white most areas related to the level of

green for the human eye (Crist and Cicone, 1984).


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Figure 38: Subset of the tasselled cap 4. This layer is called Haze and it shows the hazy areas with more intense levels

of white.

Figure 39: Subset of the tasselled cap 6. This band is called sixt.

The texture of the bands was calculated based on the second moment gray level co-occurrence

matrix angular 2nd moment (Trimble, 2011b), and one more feature produced per band. In


- 67 -

total, seven new features were obtained. A subset of the texture for every band is presented in

the following six figures.

Figure 40: Subset of the texture for band 1 of LANDSAT TM 5 data.



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Figure 43: Subset of the texture for band 4 for the LANDSAT TM 5 data.


- 69 -



With the synthetic layers ready, the best predictor of forest and non-forest areas is selected in

the next step.


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4.4 Pixel based analysis of the satellite data

A general description of the workflow is presented in Figure 46.

Figure 46: General description of the workflow.

The preprocessing described in section 4.2 (page 55) was followed by two kinds of image

analysis. The approaches on pixel based image analysis (PBIA) and the object based image

analysis (OBIA) were tested. The processing steps are presented in the diagram of the Figure

47.

Figure 47: The pixel based image analysis applied to complete scenes.

4.4.1 Detection of forest

Two different classifications of forest areas were performed. One of the classifications was

based only on the original LANDSAT bands and the second using artificial bands such as

NDVI, TC1 to TC4 and texture bands, as described in section 0 (page 24). For the second

classification, two results were generated by the modal filter, described in section 2.1.9.2

(page 25), applied twice and once.

No other vegetation index was used based on the affirmation made by Campbell and Wynne

(2011) that says that “in practice there are few differences between the many VIs that have

been proposed”.


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In preparation for the classification of each image, a statistical analysis containing features for

the classification needs to be carried out.

An unsupervised classification method was selected for the classification of forest and non-

forest areas, as a supervised classification requires a variety of training areas which is time

consuming and needs reference data. The selection of training areas on the basis of existing

aerial photographs would have added significantly to the costs.

Based on the orthorectified and normalised data sets, the forest/non-forest unsupervised

classification was performed. A unsupervised classification is based solely on the statistical

values in the image, and classes are eliminated without thematic mapping. The thematic

mapping takes place after classes are formed.

An ISODATA algorithm was used for the classification. This is one of the most widely used

unsupervised classifiers in image processing (Jiang et al., 2004b; Lang et al., 2008; Walsh et

al., 2008; Makkeasorn et al., 2009; Wang and Niu, 2009). As the result of a classification

based on an ISODATA algorithm also depends on the initialisation parameters, these are

described here. These parameters were selected as follows:

- The first cluster is located on a diagonal axis of the first two principal components;

- The number of classes will start with 60 or 250 (classification 2). The mixture of classes is

analysed by the operator merging the most similar to forest;

- The thresholds are 20 repetitions and 95 % of convergence of the means. The repetitions

ensure that the stability of the classification could be found and the convergency of new

cluster centres with the previous ones ensures that not many clusters will have change in the

final stage;

- The classes will eventually be grouped into six final classes for thematic mapping because a

map with more classes could be confusing for the human eye.

The forest/non-forest classification using the ISODATA Classifier was performed twice. The

input features taken in the first approach were the six original bands of the LANDSAT TM 5.

In the second approach, the classification was made adding the six original types and features.

The NDVI, a texture feature (homogeneity) and the tasselled cap features TC 1, TC 2, TC 4

and TC 6. This approach explores the possibility to get more information from the synthetic

bands. According to Healey et al. (2005) cited by Campbell and Wynne (2011) in page 445

“recently cleared forest exhibits high brightness and low greenness and wetness in relation to

undisturbed forest.”


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4.4.2 Detection of forest types

The classification of forest types was based in two methods described in 2.1.9.3 (page 28).

The Minimum distance to means (MD) method and the Maximum likelihood (MxL) method

were applied as is documented in ERDAS, Inc. (2010).

Some areas containing pure forest communities were selected as samples. Those segments

were plotted over the image and related to the feature space of the bands 3 and 7. The spectral

signatures of the forest types were selected. The plots of every polygon in the samples were

observed and delimited. Figure 48 is an illustration of this process.

Figure 48: The training process of the MxL classification involved adding points to the image.

As can be seen in Figure 48, the user points to some pure pixels in the broadleaved forest

(left), and the system presents the exact position in the feature space, in this case, bands 3 and

7 (right). By pointing to many pixels, a precise area can be found where the specific class of

cover is present.

The spectral signatures for conifer and broadleaved forest were obtained. These spectral

signatures were used as a non-parametric estimator for the supervised classification. An

example of the signature editing process, the Table 13 is presented.


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Table 13: Example of the signature editing process and the color assigned per class.

Every class should have a similar sample size for the supervised classification. The value

column identifies the class trained and was re-labelled to 1 and 2 for broadleaves and conifer

forest. The classes were either conifer or broadleaved forest. The MxL algorithm for

classification was applied and the result is a map showing the cover of conifer and of

broadleaved forest in the area.

4.4.3 Assessment data based on aerial photographs, satellite and NFI

After classification of forest types a first evaluation took place to assess the classification

accuracy for forest and non-forest areas. The evaluation was based on a range of aerial

photographs covering the entire area. Points for verification were selected randomly in the

classified maps by the system (see Figure 49).

Figure 49: Example of the selection of reference points in two aerial photographs.


- 74 -

These verification points were visually assigned to aerial photographs and verified for the five

classes forest, cloud, shade, water and other surfaces. The classes cloud and shade were

relabelled ‘others’ in order to speed up the process.

4.4.1 Vectorization of pixel-based classification results

Each one of the 7 scenes in BIL format (see below) were classified with the minimum

distance producing files named (* _MD_40.bil) and with maximum likelihood producing files

named (* _MLN_40.bil) to vectorize.

192_26_POTSDAM_TMGER4_ATKIS_Maske_klassifiziert_MD_40.bil

192_26_POTSDAM_TMGER4_ATKIS_Maske_klassifiziert_MLN.bil













An illustration of the pixel values obtained after the classification of the image in path192,

row 027 is presented in Figure 50.


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Figure 50: Matrix values of a classified scene.

These raster data sets were first imported into the ERDAS image processing system. Each

raster data set contains classes corresponding to the predetermined values of 0-5.

To reduce the high variability within each classified scenes and generate a balanced picture of

the landscape in the vector data sets, the data were processed with a 3x3 pixel Majority filter.

Non-forest (pixel value 0) was not taken into account.

In comparison, the effect of filtering can be properly assessed (see figure 33).


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Figure 33: Classified scene (left) and filtered scene (right): the right scene shows balanced polygons with edges,

isolated pixels are eliminated.

To assign the correct class name of the individual pixel values, use was made of the BIL files

associated DBF tables in which the respective assignment was filed. The final pixel value

class - assignment can be found in the following 2 tables.

Table 14: Minimum Distance Classification. List of codes used for every class.

Scene No forest mixed broad leaved mixed conifers mixed broad leaved conifer

192_26 0 1 2 3 4 5

192_27 0 1 2 3 4 5

193_25 0 1 4 3 2 5

193_26 0 1 4 3 2 5

193_27 0 1 4 3 2 5

194_25 0 3 4 1 2 5

194_26 0 3 4 1 2 5

In Table 14 and Table 15 every scene were classified and coded in mixed, broad leaved forest

(broad leaved), mixed forest dominated by conifers (mixed conifers), mixed forest dominated

by broad leaved species (mixed broad leaved) and conifer forest (conifer). The 0 value

corresponds to no forest.


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Table 15: Maximum Likelihood Classification. List of codes used for every class.

Scene No forest mixed broad leaved mixed conifers mixed broad leaved conifer

192_26 0 1 2 3 4 5

192_27 0 1 4 3 2 5

193_25 0 2 4 3 1 5

193_26 0 2 4 3 1 5

193_27 0 3 4 1 2 5

194_25 0 3 4 1 2 5

194_26 0 3 4 1 2 5

To proceed with the subsequent analysis, classified scenes were recoded according to the

following table. (see Table 16)

Table 16: The classification of raster data were recoded with this values

Scene NoForest broad leaved

mixed:

broad leaved

dominated

mixed

mixed:

coniferous

dominated

coniferous

192_26 0 1 2 3 4 5

192_27 0 1 2 3 4 5

193_25 0 1 2 3 4 5

193_26 0 1 2 3 4 5

193_27 0 1 2 3 4 5

194_25 0 1 2 3 4 5

194_26 0 1 2 3 4 5

All raster files were converted into vector format in a vectorization procedure from GIS

software. (ESRI, 2010) In this process files were created in vector format (shapefile) and were

stored with the names listed in the list below:

192_26_potsdam_tmger4_atkis_maske_klassifiziert_md_40_bil_modal.shp

192_26_potsdam_tmger4_atkis_maske_klassifiziert_mln_modal.shp


192_27_potsdam_tmger4_atkis_maske_klassifiziert_mln_bil_modal.shp

193_25_potsdam_tmger4_atkis_maske_klassifiziert_md_40_modal.shp










Color.mxd (ArcGIS Project data with color schema / Legend)


- 78 -

After the vectorization was done, the resulting vector data sets were provided with the legend

shown in the following list and Figure 51.

Figure 51: Graphical representation of the legend.

In addition, two mosaics in vector format from the individual scenes were generated.

The following tables show a comparison of the surface distribution of the individual classes in

the various scenes for the Minimum Distance and Maximum Likelihood - Classification.

Here, the surfaces on the basis of the desired of the LFU classes have been computed,

1 broad leaved forest

2 mixed forest: broad leaved forest dominated

3 mixed forest

4 mixed forest: coniferous forest dominated

5 conifer forest

The areas were combined and produced the three major classes

1 broad leaved forest and mixed forest dominated by broad leaved forest

2 mixed forest

3 coniferous forest and mixed forest dominated by coniferous forest.

In evaluating the results is observed to remain there to the effect that the two classification

approaches radically different that the minimum distance method pixels that fit into no

category, unclassified, while the maximum likelihood approach in principle, all pixels are

assigned a class. This means that the maximum likelihood method is more area classified as

forest than the minimum distance method.


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4.5 Model for detection of types of forest

Some classification algorithms are based in models for predicting an output variable based on

independent input variables. This process is called modelling and the general model used is

described in this section.

In the case of the delineation within forest areas (types of forest), the model applied was:

Forest = function (Bi, NDVI, SDi, TCj, Txk, )

Where

Forest The presence of forest in one pixel of LANDSAT TM.

Bi The value of each pixel in every band of LANDSAT TM, i=1 for the blue up to

7 in the middle infra-red channel. Includes the thermal band i=6.

NDVI The normalised difference vegetation index value in one pixel scaled from -100

to 100. The equation used was: NDVI = 100 (B4-B3) / (B4+B3). Adapted from

Campbell and Wynne (2011).

SDi Standard deviation of the segment in every band i.

TCj The four channels produced by the special tasselled cap transformation. j ranges

from 1 to 4 (Crist and Cicone, 1984; Kauth and Thomas, 1976, cited by

Campbell and Wynne, 2011).

Txk Texture of each band based on the grey level co-occurrence matrix angular

second moment in all directions. This value is obtained from the following

parameters of the image: i the row number, j the column number, Pi, j the

normalized value in the cell i, j, N is the number of rows or columns. The

formula used was: (Trimble, 2011).

Two approaches were used in the delineation of forest types. One was based on pixel analysis,

making a supervised classification of areas within the forest. The other was the segmentation

approach. These approaches have been frequently reported upon in the literature and have

produced different results (Johansen et al., 2007; Gamanya et al., 2009; Watts et al., 2009;

Chirici et al., 2011). In the case of the segmentation approach, the classification was made by

the application of learning machine algorithms with use of cross validation. See section 4.7 on

page 85 regarding “Statistical approaches”.


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With the model for the delineation of types of forest selected, the segmentation and

classification of forest was made under OBIA.

4.6 Object Based Image Analysis (OBIA)

The OBIA was tested first classifying a subset of one image. When the result was acceptable,

the classification was applied to the complete scene, and then to the rest of the scenes

covering the complete area. For the classification of OBIA product, the next three feature

selection methods were tested: Criterion based, Feature Space Optimization (FSO) and

recursive feature selection.

The OBIA was applied in the order presented as follow:

4.6.1 Variable selection methods

Some classification methods result with higher accuracy with low amount of predictors

(Bacauskiene et al., 2009; Ekbal and Saha, 2011; Laliberte et al., 2011; Liau and Isa, 2011;

Luukka, 2011; Yang et al., 2011). Three types of variable selection methods were tested.

4.6.1.1 Criterion based variable selection

Some methods for variable selection are based in the normality distribution of the variables.

In an analysis of normality those variables it were found no normality distribution. Among the

methods for variable selection distribution free, some of them were found too intensive in

terms of computing processing.

The variable selection process followed the next steps

Consider all the input variables.

Apply an ISOCLASS algorithm.


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Obtain the vegetation map.

Select sample pixels.

Apply statistical GLM selection forward, backward, …)

o Build a model (variables)

o Find the maximum likelihood for prediction

o Measure residuals

Criteria for selecting the model of minimum residuals

o Akaike Information Criterion (AIC)

AIC(p') = n ln(SS(Res)p)+ 2p' - n ln(n)

o Bayes Information Criterion (BIC)

BIC(p') = n ln(SS(Res)p)+ [ln(n)]p' - n ln(n)

where:

n = number of data points

SS = sum of squares

p = number of parameters

The implementation of the variable selection was based in Geyer (2010) and McLeod and Xu

(2010).

In preparation for the classification, a statistical analysis of each image containing features for

classification was initially carried out. The Bayesian information criterion (BIC) was chosen

for feature selection because tests have shown this criterion to be robust in selection using

different images, resulting in a smaller number of features compared to other selection

methods (López Hernández, Juan Ygnacio et al., 2010).

4.6.1.2 Feature space optimization (FSO)

According to Trimble (2011b) “the feature Space Optimization function offers a method to

mathematically calculate the best combination of features in the feature space.” When

classifying image objects using the Nearest Neighbor classifier, the recommended workflow

involves the next steps:

1. Load or create classes

2. Define the feature space


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3 Define sample image objects

4. Classify, review the results and optimize the classification.

This option is available only in eCognition and no other classifiers can be tested.

4.6.1.3 Recursive feature selection

Another tool already implemented for variable selection was chosen from caret package in R.

This method for variable selection is based on random forest. The algorithm uses backwards

variable selection. The process starts with all of the variables as predictors in the model. The

predictors are ranked and the less important ones are sequentially eliminated prior to

modelling. The goal is to find a subset of predictors that can be used to produce an accurate

model. The metrics to select the predictors can be set from accuracy or the root mean square

error (Kuhn et al., 2012). Among the steps documented can be found the eliminating of zero

and near zero variance predictors, identifying and removing the correlated predictors,

removing linear dependent predictors, centering and scaling, imputation, transforming, data

splitting,

The recursive variable selection implemented in caret was applied. The preprocessing steps

were data splitting, centering and scaling the data. No near zero predictor, neither linear

dependent transformation was applied. As the satellite bands are correlated (Campbell and

Wynne, 2011), there was no elimination of correlated variables.

The variable selection was performed backwards stepwise, with the options presented next:

The classes of the samples were weighted.

A number of 10 folds cross validation were set.

Preprocessing the input values by Centering and scaling.

The metrics for selection of the models was the accuracy.

All of the 19 predictors were used for the selection and the iteration evaluated the

relative importance removing predictors one by one.

This method resulted in acceptable accuracies and kappa coefficient. For all the classifications

this variable selection was used.


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4.6.2 Segmentation with OBIA

The segmentation was applied with the chessboard method assigning 400 pixels in size. The

next segmentation method applied was multiresolution segmentation with scale parameter of

10. That makes the smaller polygon could have 10 pixels. The relation brightness / shape was

set to 1, in order to consider the brigness and the shape of the polygons should follow the

boundary of the polygons.

The haze masking filter was applied to the object as a procedure for removing areas

surrounding the clouds that normally had problems for distinguish the soli cover type as

described in the next step.

4.6.2.1 Automatic suppression of haze (haze out) by segmentation

Despite of the haze corrections applied to the images, some of them presented the effect of

haze. An object based rule was designed to suppress segments with haze. Clouds and their

shadows are clearly visible in the scene. Sometimes haze is present around the clouds. This

haze was reported as transparent and classified as cirrus by (Watmough et al. 2011). In those

areas, often no class is assigned or a misclassification occurs. An automated approach was set

to eliminate the haze areas. Previous experiments on land cover and change analysis using

OBIA (Ernst et al. 2010; Malinverni et al. 2010), integrated automatized haze suppression

techniques (Watmough et al. 2011). Even when those techniques can solve the problem

concerning clouds, in some other areas algorithms like the automatic cloud cover assessment

(ACCA) (Irish et al. 2006) can affect the spectral response. Watmough et al. (2011) modified

the ACCA algorithm and applied filters to identify clouds. In the case of the scenes under

consideration, some points were found to be affected with artifacts. For this reason an OBIA

approach was selected to solve this problem. In the rule for segmentation, the presence of a

non-forest polygon sharing a boundary with a cloud polygon was the main condition for a

class change, irrespective of the size of the boundary. This rule was applied three times and it

was enough to classify the erroneous non-forest polygons in the vicinity of clouds as clouds.

An outcome of this procedure was the enlargement of the size of the cloud class but only in

the areas surrounding cloud, where a correct classification usually fails. It solved the problem

of determining new sampling points in areas in the vicinity clouds, which can result in a

misclassification of other areas in different places as forest. An example of this effect can be

seen in Figure 52.


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Figure 52: The original ortho-scene in (a) is classified with kNN algorithm in (b), the erroneous classification of forest

in (c) and after the application of noHaze algorithm.

In Figure 52 the areas surrounded by clouds (1) are erroneously classified as non-forest.

4.6.2.2 Suppression of island classes within clouds

In the preprocessing step described in section 4.2.5 page 61, all clouds in the scenes were

detected and a mask was build. Some clouds contain small holes. In order to avoid

uncertainty, a segment object rule was set to classify all areas within clouds as cloud. Thus,

this error was eliminated. Figure 53 is an illustration of the effect of this rule.

Figure 53: An example of the suppression of holes within the clouds is presented.

In Figure 53, these areas show the classification of forest, in green, and non-forest in blue-

grey affected by clouds in white. On the left the classification product is presented and island

suppression within clouds is illustrated on the right.

a b c

1


- 85 -

After the suppression of island in clouds, only the forest areas´ segments were obtained

4.7 Statistical approaches

The classification of the scenes was made under the machine learning approach. Under this

approach all the reference data is divided randomly into training samples and verification

samples. This division is called fold. Then, a new fold is created by selecting randomly again.

The process is called cross fold validation was made 10 folds cross validation and repeated 4

times.

4.7.1 Definition of parameters for grid search

The technique for grid search and preprocessing suggested by Duro et al. (2012) and Kuhn et

al. (2012) was adapted to the area. At least 400 NFI plots were randomly selected per scene.

In the first step the feature selection was applied. Then it was configured the LGOCV as

validation method, with 10 folds and was replicated 4 times. Even when some studies

reported LOOCV, the method is time consuming and can grow similar accuracies as LGOCV.

With 10 folds the algorithm for learning machines applies a resampling process that chooses

10 groups of BWI plots with 1/10 of the plots in every subset. The first time the first group of

the plots was used as verification samples and the other groups were left as training samples

for the classification. Then, the process is repeated for the second group of plots using the rest

as training samples. This process is repeated 10 times in the classification. By this way, the

LGOCV algorithm ensures that all of the plots are used as training samples and as validation

at least once in every repetition. Under this definition of learning machines, the algorithms for

support vector machines (SVM), random forests (RF), multinomial logistic regression MNL,

k-nearest neighbor (kNN) and Conditional Inference Trees (condInfTree) were used for

classification and regression.

4.7.2 Machine learning classification algorithms

In the first steps, the boundaries of the forest detected were used to clip the scenes. A feature

selection was applied to the set of features available in the segmentation process. Now the

implementation of the machine learning algorithms was made in R language for statistics.

According with the vignettes of caret package, the code was written to apply the five


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algorithm tested. All the algorithms were set to use Leave Group Out cross validation

approach with 10 folds and 5 replications. The training percentage of samples for leave-group

out cross-validation was 2/3 of the total amount of NFI plots in every scene. All the values

were centered and scaled from -1 to 1. All the resampled summary metrics should be saved.

The specific parameters for every algorithm were set as follows:

For the Decision trees

The method used in R was ctree2.

The maxdepthparameter was set from 3 to 11.

For Random Forests

The method used in R was cforest.

The tuning parameter for mtry was set from 1 to the amount of descriptors.

For the multinomial logistic regression

The method used in R was multinom.

The decay parameter was set from 1 to 2-5

.

For the support vector machines

The method used in R was svmRadia.l

The metric used for grid search were the combination of

o c in these values: 1.25, 2.50, 5, 10, 20, 40, 80, 160

o sigma with values of: 0.125, 0.250, 0.500, 1, 2, 4, 8, 16

For kNN method

The method used in R was knn.

the k parameter was evaluated in the values of 3,5,7,9,10 and 11.

The fuzzy classifier was set to a threshold that ensures the selection of only those

classes assigned in the sampling process.

Using the features selected, the kNN classification was applied and resulted in a map of forest

types in the area selected. Contingency tables were created to evaluate classification results

compared with the clusters derivate from NFI data.

Juan Ygnacio López Hernández A method for forest type detection 5 Results

- 87 -

5 Results

The results of the classifications will be presented separated per image analysis type. The

flowchart presented in Figure 54 is showing the step of the evaluation of the results.

Figure 54: Flowchart presenting the evaluation of the results in the methodology.

5.1 The digital layer of forest types of Bavaria

A digital layer in shapefile format was produced comprising the forest types of the state of

Bavaria in Germany. A graphical view of this layer is presented in the Figure 55.

5 Results Development of a method for forest type detection Juan Ygnacio López Hernández

- 88 -

Figure 55: Graphical representation of the map layer produced.

5.2 Variable selection process

5.2.1 Criterion based

The criterion based variable selection was made in every scene. The scene was considered a

sector for the analysis and the order of the sectors is presented in the next figure.


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Figure 56: Sectors for the statistical evaluation.

The final selection of variables is presented in the next table:

Table 17. Results of the variable selection using the Bayesian Information Criterion (BIC).

Sector Variables selected

1 B1 + B2 + B4 + B7 + NDVI + TC1 +TXT1 + TXT2 + TXT7

2 B2 + B4 + B5 + B7 + NDVI + TXT1 + TXT3

3 B1 + B2 + B3 + B4 + B7 + NDVI + TC3 + TXT1 + TXT4

4 B1 + B2 + B3 + B4 + B7 + NDVI + TC1 + TXT2 + TXT4

5 B1 + B2 + B3 + B4 + B7 + NDVI + TC1 + TXT1 + TXT2 + TXT5 + TXT7

6 B1 + B2 + B3 + B4 + B5 + B7 + NDVI + TXT2 + TXT4

7 B1 + B2 + B3 + B4 + B5 + B7 + NDVI + TXT2 + TXT4

For every scene the minimum distance with a threshold value of 4 times the standard

deviation of basal area, and a classification with maximum likelihood for each scene was

carried out.

For the set of variables selected in every scene, a classification based on SVM algorithm was

applied. The results raised overall accuracies below 30 % and was rejected this procedure.


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5.2.2 Feature space optimization

K-nearest neighbour (kNN) classification was applied using the variables recommended by

the feature space optimisation (FSO) process, a set of 4 and 5 variables was selected. The

Table 18 present the results on the FSO.

Table 18: Results from the FSO. Features selected for the classification with kNN.

Features Features selected

4 Bands 1, 3, 4, and 5

5 Bands 4 and 7, standard deviation of band 4 and maximum difference

With the five variables selected, the kNN classification was applied and resulted in a map of

forest types in the area selected. After applying the evaluation by contingency matrix, the

result was 50.35 in overall accuracy and 0.3589 for kappa. This result was considered non

acceptable.

5.2.3 Recursive feature selection

The results of the variable selection are presented in the Table 19.

Table 19: Some of the results of the recursive feature selection.

Path

Row

Variables Accuracy Kappa Predictors

192 026 12 0.7410 0.6116 NDVI + B7 + B1 + B3 + B5 + B4 + B6 + SD7 + SD3

+ SD6 + SD2 + SD4

192 027 11 0.8397 0.7595 B5 + B6 + B7 + NDVI + B4 + B1 + SD6 + SD4 +

SD1 + SD7 + SD2

193 025 1 0.9022 0.8538 B2

193 026 12 0.7539 0.6308 NDVI + B5 + B4 + B7 + B2 + B6 + B1 + B3 + SD5 +

SD7 + SD1 + SD4

193 027 9 0.7902 0.6852 B5 + B7 + NDVI + B4 + B2 + SD1 + B3 + B1 + B6

194 025 12 0.7576 0.6364 NDVI + B4 + B7 + B5 + SD5 + B2 + B3 + SD7 + B1

+ B6 + SD4 + SD2

The predictors selected are presented in order of relative importance. The accuracy is

presented as fraction from 0 to 1. All the classification and evaluations were processed with

these variables selected by recursive feature selection.


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5.3 Evaluation of PBIA

The classification of forest and non-forest areas was found with very high accuracy when the

reference data was the aerial photographs. But a close look into some areas revealed that they

were many places misclassified.

5.3.1 Detection of forest areas

The contingency tables produced for the classification of PBIA are presented next. A total of

80 points were evaluated. Another selection of reference points was carried out to reinforce

the evaluation result. (Table 20)

Table 20: Results of the evaluation of the classification for forest and non forest areas for the image in path 192, row

126.

Reference

Classification Forest Water Other Total User Accuracy

Forest 41 41 100

Water 4 33 1 38 86.8

Other 1 1 100

Total 45 33 2 80

Producer Accuracy 91.1 100 50 OA 93.75

Contingency table shows a comparison of the classes forest, water, and other. The total points

and accuracies were evaluated. The acronyms are: UA for user's accuracy, producer accuracy

for PA and OA for overall accuracy. Visual evaluation of the classification was performed but

was dimed out because the reference was the satellite image and no real verification would be

provided. The evaluation should follow a standard statistical approach; the NFI was used to

evaluate the forest type map.

The goal was to have 800 reference points for the evaluation of the forest/non-forest

classification. The procedure used only ortho-photos and the evaluation was made only with

114 points. (Table 21)

Table 21: Results of the evaluation of the accuracy for forest and non forest areas with all of the 6 images.

Reference

Classification Forest Water Other total User Accuracy

Forest 65 1 66 98.484

Water 1 1 100

Other 5 42 47 89.364

total 70 1 43 114

Producer Accuracy 92.86 100 97.67 OA 94.74


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With this result the classification of forest and non-forest areas were finished.

The visual evaluation of the forest areas is presented in the following figure:

Figure 57: The first classification using the original bands.

In Figure 57, the colours represent green: forest; grey and light blue: other land. The yellow

lines are forest from the ATKIS layer. A simple analysis was made in order to verify the

forest and non-forest areas and it was found errors that were not quantified in the contingency

table. Figure 58 contains a series of illustrations outlining various results. From these, the

remaining problems become clear:

There is a generalisation of forest land, whereby non-forest islands within forest are

erroneously classified as forest.

An area classified as agricultural land is in fact forest.

Misclassifications occur where the reflection of non-forest land is very similar to that

of forest areas.

Water is often classified as forest.

On the other hand, areas classified as forest in the ATKIS forest layer are shown as

non-forest, but forest or wooded areas are clearly present.


- 93 -

Illustration 1: ATKIS borders (left) and area classified forest (right). The smaller forest areas delimited at right by

the classification are confusion with crop lands not presente in ATKIS at left.

Illustration 2: ATKIS borders (left) and area classified forest (right). Small crop areas, right image, were classified

erroneously as forest.


- 94 -

Illustration 3: ATKIS borders (left) and area classified forest (right). The blue areas in the central upper part of the

scene were erroneously classified as forest in the right image.

Illustration 4: ATKIS borders (left) and area classified forest (right). In areas with small polygons at the centre of the

image was erroneously classified as forest. They are only present in the classified image at right.

Figure 58: Image sections (in 4 illustrations) distributed over Bavaria visualising the results of the classification.

The class boundaries are shown only as white lines in order to provide a better estimation of

the results. To the left are the limits of the ATKIS forest layers and to the right the

classification.


- 95 -

5.3.2 Detection of forest types for the scene in path 192 row 026.

The classification of forest and non-forest areas was evaluated by comparing the inventory

NFI points and randomly generated points outside of ATKIS polygons. The results for both

Maximum Likelihood (MxL) and Minimum Distance (MD) methods are presented in the next

two tables.

Table 22. Contingency matrix comparing the inventory data with the result of the MxL classification method.

Inventory

MxL Forest NonForest Total User %

Forest 142 3 145 98

Non Forest 8 147 155 95

Total 150 150 300

Producer % 95 98 96.33

Overall Accuracy 93.33 kappa 0.93 Table 23. Contingency matrix comparing the inventory data with the result of the MD classification method.

Inventory

MD Forest NonForest Total User %

Forest 116 2 118 98

Non Forest 35 148 183 81

Total 151 150 301

Producer % 77 99 88

Overall Accuracy 87.71 kappa 0.75

The total accuracy for the MxL method was 8 % higher than that of the MD method. The user

accuracy (User %) and producer accuracies (Producer %) were similar. A general illustration

of the results of PBIA classification is presented in Figure 59.


- 96 -

Figure 59: General view of the results of the PBIA classification.

5.3.3 Evaluation of the pixel oriented classification of forest types for all of the

scenes

All NFI plots were used in the evaluation of the classification. The evaluation of the

classification based on pixel analysis provided the results presented in Table 24.

Table 24: Results of the accuracy assessment for the classification per LANDSAT TM image.

Complete

MxL

MD

Scenes id n OA k OA k

192 026

694

27.52 0.09

22.33 0.08

192 027

469

38.38 0.09

47.33 0.17

193 025

147

21.09 0.04

29.93 -0.03

193 026

1047

21.20 0.07

13.75 0.04

193 027

909

26.95 0.05

57.65 0.18

194 025 1310 30.69 0.13 62.37 0.11

id Image identification by path and row (ppp rrr) from LANDSAT standard

worldwide reference system 2.


- 97 -

n Number of reference points in forest used for the evaluation.

OA Overall accuracy in percent.

k kappa coefficient.

The accuracy of the classification of forest areas was deemed low. As the ATKIS data

contains the official boundaries of forest for Germany, it was decided to continue with the

classification of types of forest using ATKIS as mask to extract the values of the features

selected.

The ATKIS forest layer was the verification to be used for further classification within forest

only, after analysis of the reference data. The ATKIS areas approach was used because the

boundaries are considered ‘safe’ forests. Even where these boundaries do not correspond with

the actual images, they correspond to the official specification of forest.

5.4 Evaluation of OBIA

The evaluation was made based on the values of overall accuracy and kappa found for every

classifier.

5.4.1 Subset of a scene

A test of the classification made using OBIA was conducted for the subset of the scene in path

192 row 026. The objective was to evaluate whether this classification of segment objects

produced a higher level of accuracy than the pixel-based approach, as reported in the

literature.

From every classification algorithm a report of the procedure was obtained. The most

important information from those reports is presented here.

SVM

In the case of the application of the SVM, the accuracy estimated by the library e1071 was

obtained. The number of support vectors used was 23 and the estimated accuracy was 91.4%

in the automatic evaluation process. The levels of accuracy reported in this table are related to

10 cross fold validation processes based on bootstrapping of the training samples.


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kNN

The training with exhibited a misclassification rate of 52.75 %. And was considered too low.

MNL

The MNL regression reported residuals for the classification. The residual deviance was

0.0001253361 and the AIC was 68. The pixel-based MxL classification provided better

results than the OBIA classification method.

Results of classifications in a subset of the scenes were ready, and then the classification of

the complete scenes can be made. Results of this classification are presented in the following

section.

Contingency matrixes were made to evaluate the classification of forest types. The result per

classification algorithm is shown next.

5.5 OBIA of the forest types for the whole scene

A new classification was carried out in the forest. The boundary of forest was the one detected

in the section 4.4.1 (page 70). This classification is based on pixel analysis in one approach

and OBIA tools for the other approach.

5.5.1 Classification of the forest types for the whole scene in path 192, row 026

The output of the PBIA Maximum Likelihood (MxL) classification of forest and non-forest

areas was used as a mask for the complete image. The result was employed using the two

discrimination methods within forest classes. The methods MxL and kNN provided the best

results in the previous classification. The User and Producer accuracies are presented for each

classification for forest types. (Table 25 and Table 26)


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Table 25: Contingency table showing the results of the classification using MxL compared with the inventory data.

Inventory

MxL Broad Conifer Total User %

Broad 4 16 20 20

Conifer 14 128 142 90.14

Total 18 144 162

Producer% 22.22 88.89


Table 26: Contingency table showing the results of the classification using kNN compared with the inventory data.

Inventory

kNN Broad Conifer Total User%

Broad 11 5 16 68.75

Conifer 7 139 146 95.21

Total 18 144 162

Producer 61.11 96.53


The amount of evaluation samples per class of forest was not identical. There may be

differences in the total number of evaluation samples per class but they should be similar. In

this case the broad leaved forest is 10 % compared with the area of conifer forest. With those

unbalanced data the higher class would present a false overall accuracy. There are two options

available to solve this problem: either reduce the higher more frequent class or increase the

smaller less frequent class. The first option was selected because the number of pure

broadleaved forest plots, the less frequent class, was limited in most regions. A random

sample of a size similar to this less frequent class was selected.

The algorithm for the segmentation of complete LANDSAT satellite images functions well

but requires large computer processing capabilities. In order to make it available to smaller

processing configuration systems, the FastWay segmentation approach was developed. The

core aspect of this algorithm is that after the classification, a feature space optimisation

process provides a list of the layers that are really needed and the kNN classification approach

can be applied by class, or by pairs of classes, with a nested interactive procedure. The

classification is set first to the most important classes, with the remaining classes classified

afterwards in order to illustrate the surrounding areas not centrally important to the evaluation

process. The product can be set to raster rather than vector polygons, and the time required to

export the product can be reduced from 50 minutes to 3 minutes. Using this method, a


- 100 -

complete process consisting of importing, segmenting and classifying a single LANDSAT

image can take about two hours.

An adaptation of the method presented by Trimble (2011) a segmentation method using

training samples from ESRI shapefiles was implemented. The training samples were stored as

a level in the project for segmentation and classification. Each training sample was set by

reading one attribute from a shapefile to the kNN classifier. This approach accelerates the

identification of the most suitable training samples necessary to train the classifier using

Trimble’s eCognition modules. Some visually homogeneous training samples may contain

different polygons obtained in the segmentation, due to differences in either spectral response

or in texture. At this point the operator can choose which segments best represent the class to

train.

The FastWay method for segmentation and classification of forests was applied in the

discrimination of forest types from LANDSAT TM images. The kNN classifier was trained

using samples selected visually and applied to the whole image. The evaluation was made

using selected plots from the German NFI.

Another approach is to apply the set of machine learning algorithms for classification and

regression to every scene. That approach is described in the next section.

5.5.2 Classification of 6 scenes


- 101 -

Results of tuning process with the accuracies obtained are presented in the Figure 60.

Figure 60: Results of tunning process with the accuracies obtained.

The accuracies found tuning parameters for Decision Tress (DT), multinomial logistic

regression (MNL), k Nearest Neighbour (kNN) and Random Forest (RF) clasiifiers is

presented. The objective is to find the tuning parameter, in x axis, that could reach the highest

accuracy, in y axis, for every case. For every scene similar calculation was made.

The accuracy was below 70 % in each classification method presented in the Figure 60, when

tunned the Support Vector Machines (SVM) algorithm the accuracy continued very low.

(Figure 61)


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Figure 61: Levels of accuracies found in the tuning process applied with SVM algorithm .

The kNN provided good results for one image, but was not successful overall. The value

found for the fuzzy classifier was 0.60. Each segment was labelled according to a class only

when the fuzzy value of 60 % certainty was reached. This value should be assigned an image

basis in order to ensure the correct classification of the segments. SVM classification was

implemented within forest areas. SVM classifier, with a radial function, tuned for c (cost) and

gamma (some authors refer to sigma) parameters, was applied to the training samples and, as

an example, one of the images provided the same results as the image with path 193 and row

025. Using 10-fold cross-validation on a training data set, the total accuracy was 100 % in the

training phase. The individual accuracy per fold was 100 %. This indicates that the

classification adjusted very well to the training samples selected. A similar output was found

in the next images for the classification with SVM.

As described in the section 4.7.2 (page 85), the classification algorithms were applied under a

machine learning approach. The result of this process is presented in the following section.


- 103 -

5.5.3 Results of the application of the machine learning classification

algorithms

Every ML algorithm was applied 50 times on the data. The output is an object with the

specifications of the results for every step. A summary of one result for SVM in every scene

is presented in the Appendix 1.

In the case of DT, an illustration of a decision tree is presented in Figure 62.

Figure 62: Example of one decision tree for the image in path 192, row 126.

Following is a list of one execution of DT. With the regression of the trees produced all the

segments were classified. After the validation, contingency tables were created for DT

algorithm.

Confusion Matrix and Statistics

Reference

Prediction 1 2 3

1 32 4 2

2 25 38 12

3 11 14 16

Overall Statistics

Accuracy : 0.5584

95% CI : (0.4763, 0.6383)

No Information Rate : 0.4416

P-Value [Acc > NIR] : 0.002339

Kappa : 0.3331

Mcnemar's Test P-Value : 7.933e-05

The result is presented below for RF:


- 104 -


Reference

Prediction 1 2 3

1 47 8 4

2 16 41 5

3 5 7 21

Overall Statistics

Accuracy : 0.7078

95% CI : (0.6292, 0.7782)


P-Value [Acc > NIR] : 2.173e-11

Kappa : 0.5453

Mcnemar's Test P-Value : 0.3748

The MNL resulted as follows in the list.


Reference

Prediction 1 2 3

1 41 8 4

2 14 29 9

3 13 19 17

Overall Statistics

Accuracy : 0.5649

95% CI : (0.4828, 0.6445)



Kappa : 0.3441


For the kNN algorithm resulted as shown in the next list.


Reference

Prediction 1 2 3

1 44 7 2

2 13 36 7

3 11 13 21

Overall Statistics

Accuracy : 0.6558

95% CI : (0.5751, 0.7304)



Kappa : 0.4777



- 105 -

For the SVM algorithm resulted as shown in the next list.


Reference

Prediction 1 2 3

1 43 8 1

2 19 39 7

3 6 9 22

Overall Statistics

Accuracy : 0.6753

95% CI : (0.5953, 0.7485)



Kappa : 0.501


The process for tuning the SVM under grid search generated more statistical results that were

analyzed and evaluated in order to find the best combination of C and sigma for the highest

accuracy. In the Apendix 2 (page 142) the results for the scene in path 192, row 026 are

presented.

All the confusion matrixes presented are related to the last run of the machine learning

method. A stronger evaluation could be made considering all the executions of the algorithm

to have an idea of the errors. The classifiers can be compared by using all confusion matrix

produced. The values of overall accuracy were plotted and a distribution of the errors can be

evaluated. As an example, the overall accuracy (OA) values obtained are presented in the next

box plot diagram.


- 106 -

Figure 63: Diagram with the output of the ML algoritms applied.

In Figure 63 the mean accuracy and the variation across the 4 repetitions with 10 folds

LGOCV approach are shown. The accuracy is presented from 0 to 1 in the x axis. The ML

algorithms presented are svm as Support Vector Machines, rf as Random Forests, mnl as

Multinomial Logistic Regression, kNN as k Nearest Neighbour and CondInfTree as

Conditional Inference Trees. For illustration proposes the table with the results of the SVM

algorithm is presented next.

Y ~ NDVI + B7 + B1 + B3 + B5 + B4 + B6 + SD7 + SD3 + SD6 + SD2 + SD4

792 samples

12 predictors

3 classes: '1', '2', '3'

Pre-processing: centered, scaled

Resampling: Repeated Train/Test Splits (10 reps, 0.66%)

Summary of sample sizes: 525, 525, 525, 525, 525, 525, ...

Resampling results across tuning parameters:

C sigma Accuracy Kappa Accuracy SD Kappa SD

1.25 2 0.685 0.528 0.0335 0.0502

1.25 3.5 0.704 0.557 0.0316 0.0473


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1.25 5 0.709 0.563 0.0312 0.0469

1.25 6.5 0.707 0.56 0.0303 0.0454

1.25 8 0.707 0.561 0.0242 0.0363

2.19 2 0.7 0.549 0.0368 0.0552

2.19 3.5 0.71 0.565 0.0277 0.0416

2.19 5 0.709 0.563 0.0288 0.0433

2.19 6.5 0.704 0.556 0.0294 0.0441

2.19 8 0.707 0.561 0.0257 0.0385

3.12 2 0.7 0.549 0.0339 0.0508

3.12 3.5 0.712 0.569 0.0258 0.0386

3.12 5 0.708 0.562 0.0277 0.0416

3.12 6.5 0.704 0.557 0.0297 0.0446

3.12 8 0.707 0.561 0.0261 0.0391

4.06 2 0.7 0.55 0.0318 0.0477

4.06 3.5 0.712 0.567 0.0256 0.0384

4.06 5 0.708 0.562 0.0277 0.0416

4.06 6.5 0.704 0.557 0.0297 0.0446

4.06 8 0.707 0.561 0.0261 0.0391

5 2 0.704 0.556 0.0321 0.0481

5 3.5 0.711 0.566 0.0233 0.0349

5 5 0.709 0.563 0.0273 0.0409

5 6.5 0.704 0.557 0.0297 0.0446

5 8 0.707 0.561 0.0261 0.0391

Kappa was used to select the optimal model using the largest value.

The final values used for the model were C = 3.12 and sigma = 3.5.

Support Vector Machine object of class "ksvm"

SV type: C-svc (classification)

parameter : cost C = 3.125

Gaussian Radial Basis kernel function.

Hyperparameter : sigma = 3.5

Number of Support Vectors : 529

The final model selected for this scene was Y ~ NDVI + B7 + B1 + B3 + B5 + B4 + B6 +

SD7 + SD3 + SD6 + SD2 + SD4. Some of the predictors were rejected and the accepted

ones are presented in order of importance for every model. For this scene 729 samples were

used in total. Centering and Scaling was used to standardize the input data. The grid search

method was applied in two levels for finding the best combination of cost (C) and Sigma

parameters (described in section 2.1.5.4, page 18) that ensures the higher accuracy value. The

main problem is that both parameters C and sigma are unknown and should be found in every

scene. When in some scene, with the first pass the best combination of C and sigma

parameters was in the boundary of the searching grid, the boundaries were readjusted and the

search was repeated. Every result is the best combination found by this process. Now the

results show both OA and kappa values and their standard deviation during the replication of

the process.


- 108 -

The classification was set to Radial Basis Kernel function that is the most appropriated for

nominal output.

The next tables show the results of the tuning process for all of the scenes. The figures were

rounded up to 3 decimals.

Scene 192 026

Final Model Y ~ NDVI + B7 + B1 + B3 + B5 + B4 + B6 + SD7 + SD3 + SD6 + SD2 + SD4

Table 27: Accuracies obtained for the scene in path 192 row 026 tuning the C and sigma parameters for the support

vector machines algorithm.

Accuracy C

sigma 1.25 2.19 3.12 4.06 5.00

2.00 0.685 0.700 0.700 0.700 0.704

3.50 0.704 0.710 0.712 0.712 0.711

5.00 0.709 0.709 0.708 0.708 0.709

6.50 0.707 0.704 0.704 0.704 0.704

8.00 0.707 0.707 0.707 0.707 0.707

This table shows the trend of the accuracy by changing the sigma and C values for the SVM

algorithm. The same data is presented in Figure 64.

Figure 64: Trend of the accuracy by changing the sigma and C values for the SVM algorithm.


- 109 -

Similar tables of accuracy were obtained for every scene under consideration. The complete

list of tables for SVM is presented in the Apendix 1 (Page 139).

Another set of BWI plots were used for verification of the results. With this set of plots new

contingency table was build. The results obtained are presented next:

Table 28: Contingency table for the scene in path 192, row 026 comparing the NFI plots with the group obtained by

SVM classification algorithm.

kappa: 0.6503

Similar results were found for the rest of the scenes. The best classifier, its correspondent tuning parameters and the accuracy evaluation are presented in Apendix 5.

192026 Inventory Clusters

SVM 1 2 3 Total UA%

1 93 14 1 108 86.11

2 14 55 0 69 79.71

3 7 14 31 52 59.62

Total 114 83 32 229

PA% 81.58 66.27 96.88

OA% 78.17


- 110 -

5.5.4 Comparing classifiers

With the best classification found, the next logical question to answer is how good are the

other classification algorithms? In order to answer that question a calculation of the

differences among algorithms was calculated and the Bonferroni test of adjustments for

multiple tests based in the Bayesian theory was conducted.

The accuracy per method is presented in the next table:

Table 29: Accuracies found applying SVM algorithm for classification with 4 replications and 10 folds LGOCV for

the scene in path 192, row 026. The minimum, first quarter, median, mean, third quarter and maximum accuracy is

shown. The figures are in the scale from 0 to the worst accuracy and 1 to the best.

Accuracy Min. 1stQu. Median Mean 3rdQu. Max.

svm 0.6292 0.6891 0.7041 0.7056 0.7303 0.764

rf 0.5356 0.6217 0.6386 0.6353 0.6554 0.6891

kNN 0.5131 0.5609 0.573 0.5737 0.5843 0.6217

CondInfTree 0.4831 0.5693 0.5843 0.5841 0.6105 0.6442

mnl 0.5206 0.5393 0.5618 0.5612 0.5777 0.603

Models: svm, rf, kNN, CondInfTree, mnl

Number of resamples: 891000

The box plot diagram made to show graphically the mean accuracy among classifiers was

shown in Figure 63, on page 106.

In the next table, the differences in accuracy, with scale from 0 designing the worst to 1 for

the best, among classifiers are presented in the upper diagonal and the lower diagonal present

the p value from the Bonferroni test. The p-value is measuring the estimate of the difference

found could be by chance.

Table 30: Differences in accuracy among classifiers applied to the scene in path 192, row 026. The accuracy and their

differences is presented in the scale from 0 to 1.

svm rf kNN CondInfTree mnl

svm

0.07034 0.13193 0.1215 0.14441

rf < 2.2e-16

0.06159 0.05116 0.07407

kNN < 2.2e-16 < 2.2e-16

-0.01042 0.01248

CondInfTree < 2.2e-16 < 2.2e-16 < 2.2e-16

0.02291

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16

p-value adjustment: bonferroni

Upper diagonal: estimates of the difference

Lower diagonal: p-value for H0: difference = 0

Similar tables found for every scene were made and can be found in the Apendix 4 (page

150).


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5.5.5 Assessment of forest type classification

The assessment was made considering only those plots that are more than 45 m away from

forest boundaries. By this condition, possible misregistration errors could have no influence in

the analysis.

The NDVI in the plots for the evaluation was set to values higher than 0.50. This threshold

considers that the density of trees in every place for the evaluation should be high. The

influence of soil reflectance is minimized by this way.

This assessment generated new contingency tables based on one third of the samples reserved.

The complete output of the evaluation for the image in path 192, row 126 can be found in the

Apendix 3. In the following tables the results of the tuning process per image is presented.

Table 31: Results of assessment the classifications for the image in path 192, row 026.

Scene 192 026

Algorithm OA kappa BestParameters

MxL 0.275 0.090

MD 0.223 0.079

SVM 0.704 0.569 C = 3.12; sigma = 3.5

kNN 0.573 0.375 k = 3

mnl 0.562 0.249 decay = 1

RF 0.639 0.483 mtry = 10

DT 0.584 0.404 maxdepth = 10

Model: Y ~ NDVI + B7 + B1 + B3 + B5 + B4 + B6 + SD7 + SD3 + SD6 + SD2 + SD4


Scene 192 027


MxL 0.384 0.095

MD 0.473 0.171

SVM 0.799 0.738 C = 1.25; sigma = 1

kNN 0.738 0.630 k = 5

mnl 0.685 0.535 decay = 0.0312

RF 0.783 0.693 mtry = 10

DT 0.741 0.630 maxdepth = 10

Model: Y ~ B5 + B6 + B7 + NDVI + B4 + B1 + SD6 + SD4 + SD1 + SD7 + SD2


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Scene 193 025


MxL 0.211 0.043

MD 0.299 -0.030

SVM 0.567 0.569 C = 160; sigma = 16

kNN 0.748 0.778 k = 3

mnl 0.431 0.121 decay = 0.125

RF 0.684 0.527 mtry = 1

DT 0.702 0.683 maxdepth = 11

Model: Y ~ B2


Scene 193 026


MxL 0.212 0.073

MD 0.138 0.035

SVM 0.712 0.598 C = 1.25; sigma = 2

kNN 0.602 0.456 k = 3

mnl 0.612 0.424 decay = 0.25

RF 0.672 0.521 mtry = 5

DT 0.597 0.409 maxdepth = 7

Model: Y ~ NDVI + B5 + B4 + B7 + B2 + B6 + B1 + B3 + SD5 + SD7 + SD1 + SD4


Scene 193 027


MxL 0.270 0.053

MD 0.577 0.182

SVM 0.765 0.671 C = 1.25; sigma = 1.75

kNN 0.727 0.596 k = 3

mnl 0.632 0.465 decay = 0.0312

RF 0.779 0.678 mtry = 3

DT 0.734 0.622 maxdepth = 5

Model: Y ~ B5 + B7 + NDVI + B4 + B2 + SD1 + B3 + B1 + B6


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Scene 194 025


MxL 0.307 0.130

MD 0.624 0.108

SVM 0.706 0.559 C = 3.12; sigma = 3.5

kNN 0.574 0.499 k = 9

mnl 0.561 0.547 decay = 0.0312

RF 0.635 0.557 mtry = 9

DT 0.584 0.467 maxdepth = 7

Model: Y ~ NDVI + B4 + B7 + B5 + SD5 + B2 + B3 + SD7 + B1 + B6 + SD4 + SD2


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Juan Ygnacio López Hernández A method for forest type detection 6 Discussion

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6 Discussion

The developed method is carried out in several steps. Because some steps resulted better than

others, the discussion is presented.

6.1 Definition of forest

The definition of forest was adapted considering NDVI > 50. Carreiras et al. (2006) found

linear regression between NDVI and tree canopy cover for oak using aero photos and

LANDSAT TM images obtaining results with high correlation values when spectral bands

only where the predictors. The same authors showed the patter of canopy cover and NDVI is

close to linear on high NDVI values. Knyazikhin et al. (1998) made an algorithm for the

estimation of the vegetation canopy leaf area index and fraction of absorved photosintetically

active radiation that were based on 70 % of canopy closure. Carlson and Ripley (1997)

reported that NDVI and the fraction of canopy cover close to 100 % was well predicted using

simple radiative transfer model. They also found that the preprocessing with scaling

eliminated the need of rigorous atmospheric corrections. Even when the method developed

does not measure leaf area index (LAI) and they are no fixed relationship between NDVI and

canopy closure in forest (Gamon et al., 1995), according to the linear regression published by

Larsson (1993) for acacia wood lands, considering NDVI > 50 ensures canopy closure at least

40 %. When were fixed the lower NDVI value to 50 in the selection of inventory plots for

classification and evaluation, classification accuracies were improved by 10-20% and kappa

values raised above 0.5.

6.2 Regarding the satellite scenes

The use of Landsat TM 4 or 5 images in the classification of forest types is advisable for

reasons of both economy, given the free access to the data, and the low level of data

processing required.

Satellite data are readily available today and in the future will increasingly be available free of

charge. These data may also be included in this analysis in future. Much information can be

collated from a visual evaluation of the data, such as forests, their variation, and distribution

of broadleaf and conifer dominated areas. If, as in this study, the information is derived from

an automatic classification, it should be noted that the quality of the result depends not only

on the quality of satellite data, but also on the quality of the terrain model and the available

6 Discussion Development of a method for forest type detection Juan Ygnacio López Hernández

- 116 -

reference points. Only when a high resolution terrain model is available and a sufficient

number of reference points is defined for all classes, evenly distributed over all of the satellite

images available, can a good result be achieved. In addition, different classifiers should be

tested for large-scale approaches because the results revealed that, using the same input data,

different classifiers provide significantly different results.

6.3 Preprocessing the scenes

The conversion of formats ensures the quality of the input data. The process for format

conversion y the software used is controlled internally and the user is not able to know how

accurate is this step. The evaluation of the import tasks was made visually over each file

processed. The quality was acceptable in every step.

The DEM provided by the LFU was in ASCII format and the null value found in that file was

-32000 and the coordinates was in Gaus-Krugger system, zone 4. The boundaries of the DEM

extended away from the boundary of Bavaria state, which was conveniently used in the

orthorectification process. Some small areas had the elevation value of -89. A reclassification

option was used to mask out those values with elevation lower than 0. This mask ensured that

nosy values could not affect the orthorectification.

6.4 Orthorectification

In the orthorectification the use of at least 20 GCP well distributed per scene ensured that the

images were correctly orthorectified and the systematic and non-systematic errors were solved

up to the size of the diagonal of the original pixel size in the LANDSAT scenes, which is 45

m. A test was made overlaying the ATKIS forest boundaries over de orthoscenes and the

visual evaluation of the boundaries of forest compared with the boundaries present in some

forest areas of the scenes had very good match.

After analyzing the scenes, the effects of the topography, mainly in the Alpine region, the

need of topographic normalization was reconsidered. Instead of applying the correction for

the whole scene, only some hilly areas were processed and the results are not visible, but the

effect in normalization of the values is evident in a profile of the image in those sectors. The

effect of the normalization was a homogenization of areas with different illumination


- 117 -

conditions caused by the topographic position. This processing ensured the quality of the data

for input in the classification algorithms selected.

A few displacements were found following the orthorectification of the images. The

evaluation of the classification was carried out using data from inventory plots selected

randomly or on the basis of an NDVI value. Using an NDVI greater than 50, the selection of

NFI points produced good evaluation results.

6.5 Clouds and haze

The main difference among scenes is the amount of clouds and shadows of clouds in the

scenes. Some of them have almost no clouds but others have scattered clouds all over the

scene, or over the forest.

The adaptation of the ACCA algorithm covered all of the dense clouds in the area. That mask

was used also for removing those NFI plots covered by clouds and avoiding false results in

the posterior evaluations of the classifications.

Despite that the clouds can be suppressed, they are no problem themselves; some clouds are

usually surrounded by haze. That haze not always can be found and their treatment is limited

without atmospheric records per pixel in the day of the scene. That condition can explain the

low agreement presented by the kappa coefficient in some of the results.

The identification of haze in the scenes was useful for masking out those areas with problems

in the values obtained by LANDSAT TM and those areas were removed both from the

classification and the evaluation of the classification. Other no visible haze could be present

and that could be a source of errors in the classification.

Eliminating island inside clouds by the object oriented approach helped to remove more noisy

areas and ensures the quality of the data used for the classification.

All those preprocessing steps were carried out conveniently. Only validation of the reflectance

values was not carried out. It was recommended by USGS personal in telephonic interview

that the scenes do not need to be modified because in their processing chain the validation

was made.


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6.6 PBIA approach

In the processing on the PBIA of the scenes, the evaluation for both, a sector of the scene in

path 192, row 126 resulted with enough high accuracy and in this case, even better than the

Machine Learning algorithms applied to the OBIA data.

The classification of complete scenes, applied on the pixel oriented approach, the training

samples were selected from areas in the original LANDSAT TM scene. This process ensured

homogeneity of the training samples for both algorithms ISOCLASS, Maximum Likelihood

and Minimum Distance. But the selection of the validation samples was made by the operator

and a bias in the results were introduced. This effect was confirmed with a review of the

forest areas with ATKIS forest layer.

6.7 OBIA approach

For the classification of complete scenes, applied on the OBIA approach, the training samples

were selected fist from the ortho-fotos provided by LFU. The results were not successful and

then, two tests were made: a) for selecting the training samples from visual selection of the

segments, fitting the algorithms and applying a regression to the rest of the objects from the

scene, b) the second option was to train the Machine Learning (ML) approach with

information from the NFI plots. Under this second option best results were found. A reason

why the OBIA approach resulted in lowest accuracies than the pixel oriented approach is that

the NFI plots are reporting trees with very low canopy crown closure. In some cases, the

values obtained in the scenes suggest that they are no forest, but a plot in the inventory is

present. Those points were not removed from the NFI database, but a filter on NDVI > 0.50

was applied and the accuracy increased up to the values reported.

When the classification applied was using kNN algorithm, the overall accuracy obtained has a

maximum of 84 % and minimum of 58%. This result was obtained using unbalanced

evaluation samples.

The evaluation of the classifications resulted with different behavior per scene. It suggests

that they are steps missing in the preprocessing or the model applied is not the most adequate

for the data used. However, the kappa values were greater than 0.5 for every best algorithm

per image. The lowest kappa value was 0.559 for the scene in path 194, row 025. That image

is not too greatly affected by clouds and was situated at the northern boundary of the state. In

that region, corresponding to the north most part of the Southwestern highlands Scarps and


- 119 -

south most of the Western highlands, hosts the largest proportion of broadleaved forest and

the clustering made of the NFI data had the worst agreement between the classification and

the evaluation of this areas.

The ISODATA algorithm resulted relatively well in the delineation of forest / non-forest

areas. But this algorithm was not totally evaluated. The accuracy of the classification by this

algorithm was not measured after discussing that the boundaries of the forest detected were

not coincident with ATKIS forest layer. From that point, the ATKIS forest layer was used as

boundary of forest and started the evaluation of the other algorithms.

The best result in accuracy and kappa was found with the OBIA approach. The scene located

in path 192, row 025 obtained an accuracy of 85.1 % and kappa 0.778. This scene is located

just at east of the scene with the worst results in classification. These results are relatively low

compared with Knorn et al. (2009) who found more than 90 %, but they used other optical

scenes as verification without forest inventory data. In bigger areas Gjertsen (2007) found

79.6 % in overall accuracy for spruce species only. That was the best accuracy found in those

studies with multisource forest inventory. Dees et al. (2000) found errors from 15 to 46.3 % in

classification of satellite images based in forest inventory data for large stand. They classified

in a stand level with 10 stands in the analysis. That results cannot be compared with the

classification of more than 6000 plots.

Focusing in the automatization of the production of the forest cover layer, the OBIA

segmentation with eCognition is susceptible to be automated. The import of the original

scenes and export of the segments in formats raster or vector can be placed in a processing

chain that only ask for the name of the input data and writes the products with similar names

and the parameters for the segmentation like scale and roughness.

6.8 OBIA method with fast segmentation

A new FastWay method for the segmentation of complete images was implemented in this

process. The method reduced the computing time from 2 hours to 20 minutes in the worst

case. The time required to achieve a complete image classification was approximately 2

hours. Most of this time (about 50 minutes) comprised converting the results to vector

shapefile format.


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6.9 Variable selection methods

The criterion based variables selection was developed initially for numerical output variables,

for that reason, the application of this tool for categorical variables is not totally accepted. In

this point the intention was to get an indicator of the variables to use in a classification

process, not useful for regression proposes.

The FSO present in eCognition proposed a set of variables for the subset of the scene in path

192, row 126. When used, that set of variables did not improve the results in accuracy. For

that reason the recursive backwards stepwise feature selection was applied.

In four of the 6 scenes under consideration 11 or more predictors were selected. The NDVI

was among the first 4 most important predictors. The bands 5 and 7 share relatively the same

importance. The bands 1, 3, 4 and 6 were sometimes selected among the first 4 most variables

for the whole set of scenes.

The feature space optimization (FSO) was combined with a visual selection of bands to

increase the accuracy of the classification of forest and non-forest areas from 80 % to more

than 92 % with unbalanced samples. That tool for variable selection proved very useful in the

training set selection of the classification employing the kNN method. Murtaugh (2009)

compared the performance of several variable selection methods, but the FSO was not

considered.

6.10 The best classifier

In four of the six images processed, the SVM was the best algorithm on the basis of the

accuracy and kappa values obtained. In each of these images, the best C value was below 2

and the best sigma value below 4. The kNN proved best for the image in the path 193, row

025. The best k value was 3 and the degree of accuracy was 15 % higher than in the case of

the SVM. This was the image with the smallest model of all (Y ~ B2), with the classification

of forest depending only on the data in the green band. It was reported in the reference that the

kNN is a good classifier for data with low amount of predictors. (Díaz-Uriarte and De Andres,

2006; Deegalla and Boström, 2007)

The SVM is a classifier that can be adapted to all of the restrictions in place, but in some

images the position of the NFI plots was not representative of all of the forest communities in

the image; for example, situated on steep slopes or covered by clouds.


- 121 -

In general, the basal area is not an optimal predictor of forest type for the data provided by

LANDSAT TM when applying simple linear models. However, the accuracy values obtained

suggest that it should be used when modelling forest inventory data using images. This

finding is similar to Meng et al. (2009) who found good predictor the basal area in a

geostatistical approach for classification of Landsat ETM+ scenes.

The mixed effects models (MEM), generalised additive models (GAM) and Gaussian mixed

models (GMM) were not considered in this study.

When compared to one another, the SVM proved the best classifier. The mean overall

accuracy was 70.41%, about 10 % higher than the closest alternative classifier, the RF. The

difference was 7 % with a statistically high level of significance indicated by p < 2.2e-16. A

work made by Knorn et al. (2009) found a good classifier in SVM but recommended to test

other classifiers. Duro et al. (2012) did similar research with tree classifiers and lower

amount of scenes, used SPOT data and only multiresolution segmentation in their approach.

The conditional inference trees algorithm was the next best approach. The worst was MNL

followed by the kNN.

Compared with MNL and SVM in the first variant of the methods applied, the MxL resulted

with a high degree of accuracy in the discrimination of forest areas. It was found 81,48 %

accurate, but not acceptable after evaluation of the map produced with the ATKIS layer.

6.11 Comparing kNN and SVM

When the tests were performed over a subset of the image in path 192, row 026, the kNN

classifier proved best in this evaluation in terms of accuracy (92.59 %). The SVM classifier

tuned for c and the gamma parameter was similar to kNN. The accuracy of both classifiers

rose to 75 % for all images with unbalanced samples. When the weighting process was

applied, the accuracy of the kNN classifier dropped below that of the SVM and RF. But RF

was found as good predictor only under certain conditions and the properties of RF

computation makes the best algorithm in low resources requirement and speed. (Verikas et al.,

2011)

When kNN is applied in eCognition, the fuzzy operator should be tuned on an image basis to

a level at which the class can be classified correctly. Just one fuzzy threshold value for all

classes is enough for a good separation between classes in the training process. For the


- 122 -

evaluation phase, the fuzzy membership of 80% was found well separating the classes was

found to separate the classes well. This procedure is useful for the evaluation without NFI

data. The disadvantage of using this approach is that there is no possibility to tune the k

parameter with this programme. Hu et al., (2008) reported SVM as the best classifier for

experimental data. Now with diverse real conditions the superiority of SVM is demonstrated

as non universal.

6.12 PAM clustering the NFI

The use of PAM algorithm in the unsupervised classification of the NFI data obtained the

precise plots that are the centroids of the cluster. The Table 10 (page 49) presents the tract and

the corner for each cluster. Such plots cannot be found when other unsupervised clustering

methods are applied, such as ISOCLASS or Kmeans. With this information, the forest service

is able to use those plots for research with special proposes beyond the forest inventory. The

separation of the clusters was not perfect, but it is useful for classification proposes. As was

used by Seidl et al. (2007) who clustered forest inventory data to assess the trade-offs between

carbon sequestration and timber production. Similar approach was applied by Middleton et al.

(2012) in an ordination approach to classify biotopes with hyperspectral remote sensing data.

Overall accuracies they found are comparable with the accuracies found in this research.

Based on the large values in dissimilarity obtained in the clusters from the NFI, two special

groups of mixtures in conifer forests were found and correctly identified in the images. That

identification is not easy to obtain by visual interpretation.

6.13 The border effect

The level of accuracy doubled, generally rising from 30 % to 60 %, when a buffer of 1.5

pixels around the forest boundary was used to exclude the NFI plots situated close to the

forest edge.


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6.14 Other predictors from the NFI

The number of individuals per plot was considered. This information was available in the data

set obtained. However, no appreciable effect on the accuracy was attained using this feature in

the validation process. The general histogram of the number of trees per ha, and stock in

m3/ha revealed behaviour similar to basal area (m2/ha) for this data set.

Some other reports suggest that it should be good to use LANDSAT scenes from multiple

dates (scene stacks) in order to identify precisely the type of forest in question, independent of

the weather conditions (Huang, Goward, et al., 2009; Huang, Li, et al., 2009; Waske and

Braun, 2009; Huang, Goward, et al., 2010; Thomas et al., 2011; Pflugmacher et al., n.d.).

These authors also used other sources of data, such as SAR, Lidar and high resolution optical

sensors.

Not all of the NFI data plots were suitable for the evaluation of the classification. Some were

located at the boundaries of forest classes and others were in areas where the degree of

canopy cover was very low. It was necessary to select those plots with enough canopy cover

to compare with LANDSAT TM data.

6.15 The stratified approach

The classification of forest areas and subsequent classification of within forest resulted a good

way to avoid typical confusions among some forest types with crops. Dees et al. (2000) found

easy find similar accuracies in predictions of forest stands with reduction of grid size in

sampling pattern. They used also multisource remote sensing data.

The definition of forest, when based only in LANDSAT TM data, should be based in 40% of

canopy cover. With lowest canopy cover, the influence of the reflectance of the soil can affect

the accuracy of the classification. With the use of another source of remote sensing data, such

as LIDAR this definition could be modified to lower canopy cover boundary and low NDVI

values.

The species data for the state showed that some regions have 100 % of conifer plots, but the

broad leaved forest does not account for 100 % canopy cover for all of the scenes.


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6.16 Automatization of the processing chain

The procedure described is based in the automated selection of NFI data plots based on

clustering by PAM algorithm and machine learning classification. Many of the steps

described in the method for processing the NFI database can be automated from the import

into the statistical software up to the production of the silhouette. Even in small desktop

computer or laptops those steps can be carried out.

Other steps that could be automated are the classification based on NFI data under ML

algorithms, evaluation of the classifications and the regression to the complete area. Those

steps are in the caret package in R and are susceptible to be automated. Some authors have

already automatized forest mapping processes Pekkarinen et al. (2009), but the procedure

depends on databases from numerous countries and is not produced frequently.

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Juan Ygnacio López Hernández A method for forest type detection Apendix 1

- 139 -

Apendix 1

Results found in the tuning process for all of the scenes. The figures were rounded up to 3

decimals.

Scene 192 026

Final Model Y ~ NDVI + B7 + B1 + B3 + B5 + B4 + B6 + SD7 + SD3 + SD6 + SD2 + SD4

Table 37: Accuracies obtained for the scene in path 192, row 026 tuning the C and sigma parameters for the support


Accuracy C

sigma 1.25 2.19 3.12 4.06 5.00

2.00 0.685 0.7 0.7 0.7 0.704

3.50 0.704 0.71 0.712 0.712 0.711

5.00 0.709 0.709 0.708 0.708 0.709

6.50 0.707 0.704 0.704 0.704 0.704

8.00 0.707 0.707 0.707 0.707 0.707

Scene 192 027

Final Model Y ~ B5 + B6 + B7 + NDVI + B4 + B1 + SD6 + SD4 + SD1 + SD7 + SD2



Accuracy C

sigma 1.25 2.5 5 10 20 40 80 160

0.125 0.757 0.773 0.778 0.79 0.795 0.802 0.805 0.804

0.250 0.776 0.794 0.805 0.809 0.812 0.81 0.81 0.81

0.500 0.804 0.821 0.812 0.812 0.813 0.813 0.813 0.813

1.000 0.825 0.82 0.819 0.819 0.819 0.819 0.819 0.819

2.000 0.807 0.809 0.809 0.809 0.809 0.809 0.809 0.809

4.000 0.789 0.789 0.789 0.789 0.789 0.789 0.789 0.789

8.000 0.785 0.785 0.785 0.785 0.785 0.785 0.785 0.785

16.000 0.782 0.782 0.782 0.782 0.782 0.782 0.782 0.782

Apendix 1 Development of a method for forest type detection Juan Ygnacio López Hernández

- 140 -

Scene 193 025

Final Model Y ~ B2



Accuracy C

sigma 1.25 2.5 5 10 20 40 80 160

0.125 0.453 0.468 0.474 0.481 0.497 0.504 0.507 0.503

0.250 0.497 0.502 0.507 0.507 0.507 0.511 0.51 0.512

0.500 0.508 0.51 0.512 0.513 0.518 0.526 0.534 0.547

1,000 0.523 0.53 0.542 0.554 0.555 0.56 0.561 0.561

2,000 0.559 0.557 0.56 0.557 0.546 0.547 0.555 0.561

4,000 0.552 0.546 0.563 0.595 0.622 0.634 0.648 0.66

8,000 0.594 0.618 0.638 0.635 0.645 0.645 0.669 0.682

16,000 0.639 0.642 0.664 0.671 0.684 0.696 0.701 0.709

Scene 193 026

Final Model Y ~ NDVI + B5 + B4 + B7 + B2 + B6 + B1 + B3 + SD5 + SD7 + SD1 + SD4



Accuracy C

sigma 1.25 2.5 5 10 20 40 80 160

0.125 0.655 0.67 0.687 0.702 0.707 0.71 0.712 0.708

0.250 0.679 0.697 0.712 0.718 0.716 0.716 0.716 0.716

0.500 0.71 0.725 0.722 0.721 0.723 0.723 0.723 0.723

1,000 0.729 0.728 0.727 0.727 0.727 0.727 0.727 0.727

2,000 0.732 0.731 0.731 0.731 0.731 0.731 0.731 0.731

4,000 0.713 0.713 0.713 0.713 0.713 0.713 0.713 0.713

8,000 0.694 0.694 0.694 0.694 0.694 0.694 0.694 0.694

16,000 0.692 0.692 0.692 0.692 0.692 0.692 0.692 0.692


- 141 -

Scene 193 027

Final Model Y ~ B5 + B7 + NDVI + B4 + B2 + SD1 + B3 + B1 + B6



Accuracy C

sigma 1.25 2.19 3.12 4.06 5

0.125 0.733 0.749 0.759 0.764 0.764

0.219 0.769 0.766 0.760 0.758 0.758

0.312 0.770 0.763 0.761 0.760 0.757

0.406 0.770 0.761 0.761 0.755 0.754

0.5 0.770 0.763 0.758 0.754 0.751

Scene 194 025

Final Model Y ~ NDVI + B4 + B7 + B5 + SD5 + B2 + B3 + SD7 + B1 + B6 + SD4 + SD2



Accuracy C

sigma 1.25 1.56 1.88 2.19 2.5

0.250 0.696 0.7 0.701 0.7 0.7

0.438 0.703 0.703 0.706 0.703 0.697

0.625 0.701 0.698 0.694 0.692 0.691

0.812 0.698 0.698 0.698 0.696 0.698

1.000 0.699 0.701 0.7 0.7 0.696


- 142 -

Apendix 2

Results found in the verification of the classification with SVM algorithm. The figures were

rounded up to 2 decimals. The output of the classification with Support Vector Machines

(SVM) is compared with the National Forest Inventory (Nfi) data is used as reference. The

Over All accuracy (OA) User Accuracy (UA) and the Producer Accuracy (PA) per class was

calculated in percent.

Table 43: Confussion table for the scene in path 192, row 026 comparing the NFI plots with the group obtained by


kappa: 0.6503



kappa: 0.7632

192026

Nfi

SVM 1 2 3 Tot UA%

1 93 14 1 108 86.11

2 14 55 0 69 79.71

3 7 14 31 52 59.62

Tot 114 83 32 229

PA% 81.58 66.27 96.88

OA% 78.17

192027

Nfi

SVM 1 2 3 Tot UA%

1 62 10 0 72 86.11

2 6 69 0 75 92.00

3 2 2 5 9 55.56

Tot 70 81 5 156

PA% 88.57 85.19 100

OA% 87.18


- 143 -



kappa: 0.7632



kappa: 0.4562



kappa: 0.5941

193026

Nfi

SVM 1 2 3 Tot UA%

1 179 32 0 211 84.83

2 29 74 0 103 71.84

3 13 3 31 47 65.96

Tot 221 109 31 361

PA% 81.00 67.89 100.00 OA% 78.67

193025 Nfi

SVM 1 2 3 Tot UA %

1 14 0 0 14 100

2 4 11 1 16 68.75

3 11 1 4 16 25.00

Tot 29 12 5 46

PA % 48.28 91.67 80.00 OA% 63.04

193027 Nfi

SVM 1 2 3 Tot UA%

1 103 43 0 168 74.40

2 22 120 0 142 84.51

3 0 0 3 3 100.00

Tot 147 163 3 313

PA% 85.03 73.62 100 OA% 79.23


- 144 -



kappa: 0.6503

194025 Nfi

SVM 1 2 3 Tot UA%

1 93 14 1 108 86.11

2 14 55 0 69 79.71

3 7 14 31 52 59.62

Tot 114 83 32 229

PA% 81.58 66.27 96.88 OA% 78.17


- 145 -

Apendix 3

Messages and contingency table presented by the R statistical language after validation of

SVM for classification for all the scenes.

Scene path 192, row 026






Objective Function Value : -423.0207 -180.4852 -270.6232

Probability model included.


Reference

Prediction 1 2 3

1 93 14 1

2 14 55 0

3 7 14 31

Overall Statistics

Accuracy : 0.7817

95% CI : (0.7225, 0.8334)


P-Value [Acc > NIR] : < 2.2e-16

Kappa : 0.6503


Statistics by Class:

Class: 1 Class: 2 Class: 3

Sensitivity 0.8158 0.6627 0.9688

Specificity 0.8696 0.9041 0.8934

Pos Pred Value 0.8611 0.7971 0.5962

Neg Pred Value 0.8264 0.8250 0.9944

Prevalence 0.4978 0.3624 0.1397

Detection Rate 0.4061 0.2402 0.1354

Detection Prevalence 0.4716 0.3013 0.2271





- 146 -


Hyperparameter : sigma = 1





Reference

Prediction 1 2 3

1 62 10 0

2 6 69 0

3 2 2 5

Overall Statistics

Accuracy : 0.8718

95% CI : (0.809, 0.9199)


P-Value [Acc > NIR] : <2e-16

Kappa : 0.7632




Sensitivity 0.8857 0.8519 1.00000

Specificity 0.8837 0.9200 0.97351

Pos Pred Value 0.8611 0.9200 0.55556

Neg Pred Value 0.9048 0.8519 1.00000

Prevalence 0.4487 0.5192 0.03205

Detection Rate 0.3974 0.4423 0.03205




parameter : cost C = 160







Reference

Prediction 1 2 3

1 14 0 0

2 4 11 1

3 11 1 4

Overall Statistics

Accuracy : 0.6304

95% CI : (0.4755, 0.7679)




- 147 -

Kappa : 0.4562




Sensitivity 0.4828 0.9167 0.80000

Specificity 1.0000 0.8529 0.70732

Pos Pred Value 1.0000 0.6875 0.25000

Neg Pred Value 0.5312 0.9667 0.96667

Prevalence 0.6304 0.2609 0.10870

Detection Rate 0.3043 0.2391 0.08696











Reference

Prediction 1 2 3

1 179 32 0

2 29 74 0

3 13 3 31

Overall Statistics

Accuracy : 0.7867

95% CI : (0.7408, 0.8278)



Kappa : 0.6085




Sensitivity 0.8100 0.6789 1.00000

Specificity 0.7714 0.8849 0.95152

Pos Pred Value 0.8483 0.7184 0.65957

Neg Pred Value 0.7200 0.8643 1.00000

Prevalence 0.6122 0.3019 0.08587

Detection Rate 0.4958 0.2050 0.08587






- 148 -







Reference

Prediction 1 2 3

1 125 43 0

2 22 120 0

3 0 0 3

Overall Statistics

Accuracy : 0.7923

95% CI : (0.7431, 0.8359)



Kappa : 0.5941

Mcnemar's Test P-Value : NA



Sensitivity 0.8503 0.7362 1.000000

Specificity 0.7410 0.8533 1.000000

Pos Pred Value 0.7440 0.8451 1.000000

Neg Pred Value 0.8483 0.7485 1.000000

Prevalence 0.4696 0.5208 0.009585

Detection Rate 0.3994 0.3834 0.009585











Reference

Prediction 1 2 3

1 93 14 1

2 14 55 0

3 7 14 31

Overall Statistics

Accuracy : 0.7817

95% CI : (0.7225, 0.8334)



Kappa : 0.6503


- 149 -




Sensitivity 0.8158 0.6627 0.9688

Specificity 0.8696 0.9041 0.8934

Pos Pred Value 0.8611 0.7971 0.5962

Neg Pred Value 0.8264 0.8250 0.9944

Prevalence 0.4978 0.3624 0.1397

Detection Rate 0.4061 0.2402 0.1354



- 150 -

Apendix 4

Differences in accuracy found among classifications made with ML algorithms for the scenes

under consideration. The figures are presented as fraction in the scale from 0 to the worst to 1

for the best accuracy.

#######################





Accuracy


svm 0.07034 0.13193 0.12150 0.14441

rf < 2.2e-16 0.06159 0.05116 0.07407

kNN < 2.2e-16 < 2.2e-16 -0.01042 0.01248

CondInfTree < 2.2e-16 < 2.2e-16 < 2.2e-16 0.02291

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16

#######################






svm 0.0242989 0.0615149 0.0613276 0.1121391

rf < 2.2e-16 0.0372160 0.0370287 0.0878402

kNN < 2.2e-16 < 2.2e-16 -0.0001873 0.0506242

CondInfTree < 2.2e-16 < 2.2e-16 3.283e-11 0.0508115

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16

#######################





Accuracy


svm -0.11698 -0.18045 -0.13540 0.13594

rf < 2.2e-16 -0.06347 -0.01842 0.25292

kNN < 2.2e-16 < 2.2e-16 0.04504 0.31639

CondInfTree < 2.2e-16 < 2.2e-16 < 2.2e-16 0.27134

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16


- 151 -

#######################





Accuracy


svm 0.037946 0.108329 0.112928 0.097967

rf < 2.2e-16 0.070384 0.074982 0.060022

kNN < 2.2e-16 < 2.2e-16 0.004598 -0.010362

CondInfTree < 2.2e-16 < 2.2e-16 < 2.2e-16 -0.014960

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16

#######################





Accuracy


svm -0.019735 0.032851 0.024050 0.127233

rf < 2.2e-16 0.052586 0.043785 0.146968

kNN < 2.2e-16 < 2.2e-16 -0.008801 0.094382

CondInfTree < 2.2e-16 < 2.2e-16 < 2.2e-16 0.103184

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16

#######################





Accuracy


svm 0.0038379 0.0418302 0.0581640 0.0038152

rf <2e-16 0.0379923 0.0543260 -0.0000227

kNN <2e-16 <2e-16 0.0163337 -0.0380150

CondInfTree <2e-16 <2e-16 <2e-16 -0.0543487

mnl <2e-16 <2e-16 <2e-16 <2e-16


- 152 -

Apendix 5

List of messages presented by R language for processing classifiers with

the best accuracy.

***************************************************************************

nfi_full_pam_z4_xyNg_img_192_26

***************************************************************************

******** Resumen ********



Accuracy

Min. 1st Qu. Median Mean 3rd Qu. Max.

svm 0.6292 0.6891 0.7041 0.7056 0.7303 0.7640

rf 0.5356 0.6217 0.6386 0.6353 0.6554 0.6891

kNN 0.5131 0.5609 0.5730 0.5737 0.5843 0.6217

CondInfTree 0.4831 0.5693 0.5843 0.5841 0.6105 0.6442

mnl 0.5206 0.5393 0.5618 0.5612 0.5777 0.6030

Kappa


svm 0.4438 0.5337 0.5562 0.5585 0.5955 0.6461

rf 0.3034 0.4326 0.4579 0.4530 0.4831 0.5337

kNN 0.2697 0.3413 0.3596 0.3606 0.3764 0.4326

CondInfTree 0.2247 0.3539 0.3764 0.3762 0.4157 0.4663

mnl 0.2809 0.3090 0.3427 0.3419 0.3666 0.4045

******** Diferencias en Kappa

Call:

summary.diff.resamples(object = diferencias)




Kappa


svm 0.10551 0.19789 0.18225 0.21662

rf < 2.2e-16 0.09238 0.07674 0.11111

kNN < 2.2e-16 < 2.2e-16 -0.01564 0.01873

CondInfTree < 2.2e-16 < 2.2e-16 < 2.2e-16 0.03436

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16

******** Diferencias en Exactitud

Call:

summary.diff.resamples(object = diferenciasA)



- 153 -



Accuracy


svm 0.07034 0.13193 0.12150 0.14441

rf < 2.2e-16 0.06159 0.05116 0.07407

kNN < 2.2e-16 < 2.2e-16 -0.01042 0.01248

CondInfTree < 2.2e-16 < 2.2e-16 < 2.2e-16 0.02291

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16

************ Tuning SVM ************

Y ~ NDVI + B7 + B1 + B3 + B5 + B4 + B6 + SD7 + SD3 + SD6 + SD2 +

SD4













************* Regresión *************

************* Tablas de contingencia *************


Reference

Prediction 1 2 3

1 93 14 1

2 14 55 0

3 7 14 31

Overall Statistics

Accuracy : 0.7817

95% CI : (0.7225, 0.8334)



Kappa : 0.6503




Sensitivity 0.8158 0.6627 0.9688

Specificity 0.8696 0.9041 0.8934

Pos Pred Value 0.8611 0.7971 0.5962


- 154 -

Neg Pred Value 0.8264 0.8250 0.9944

Prevalence 0.4978 0.3624 0.1397

Detection Rate 0.4061 0.2402 0.1354


***************************************************************************

nfi_ForestBoudBuf45_xy_img192027Segm

***************************************************************************

******** Resumen ********



Accuracy


svm 0.7228 0.7828 0.7978 0.7985 0.8165 0.8502

rf 0.6854 0.7556 0.7828 0.7742 0.8015 0.8240

kNN 0.6854 0.7219 0.7378 0.7370 0.7537 0.8127

CondInfTree 0.6704 0.7191 0.7416 0.7372 0.7603 0.7903

mnl 0.6517 0.6742 0.6854 0.6864 0.6938 0.7191

Kappa


svm 0.5843 0.6742 0.6966 0.6978 0.7247 0.7753

rf 0.5281 0.6334 0.6742 0.6613 0.7022 0.7360

kNN 0.5281 0.5829 0.6067 0.6055 0.6306 0.7191

CondInfTree 0.5056 0.5787 0.6124 0.6058 0.6404 0.6854

mnl 0.4775 0.5112 0.5281 0.5296 0.5407 0.5787


Call:





Kappa


svm 0.0364484 0.0922724 0.0919915 0.1682087

rf < 2.2e-16 0.0558240 0.0555431 0.1317603

kNN < 2.2e-16 < 2.2e-16 -0.0002809 0.0759363

CondInfTree < 2.2e-16 < 2.2e-16 3.283e-11 0.0762172

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16


Call:






- 155 -

Accuracy


svm 0.0242989 0.0615149 0.0613276 0.1121391

rf < 2.2e-16 0.0372160 0.0370287 0.0878402

kNN < 2.2e-16 < 2.2e-16 -0.0001873 0.0506242

CondInfTree < 2.2e-16 < 2.2e-16 3.283e-11 0.0508115

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16

************ Tuning SVM ************

Y ~ B5 + B6 + B7 + NDVI + B4 + B1 + SD6 + SD4 + SD1 + SD7 + SD2




The final values used for the model were C = 1.25 and sigma = 1.









************* Regresión *************



Reference

Prediction 1 2 3

1 62 10 0

2 6 69 0

3 2 2 5

Overall Statistics

Accuracy : 0.8718

95% CI : (0.809, 0.9199)


P-Value [Acc > NIR] : <2e-16

Kappa : 0.7632




Sensitivity 0.8857 0.8519 1.00000

Specificity 0.8837 0.9200 0.97351

Pos Pred Value 0.8611 0.9200 0.55556

Neg Pred Value 0.9048 0.8519 1.00000

Prevalence 0.4487 0.5192 0.03205


- 156 -

Detection Rate 0.3974 0.4423 0.03205


***************************************************************************

pam_BoudBuf45_img193025seg

***************************************************************************

******** Resumen ********



Accuracy


svm 0.4175 0.5084 0.5556 0.5670 0.6263 0.7475

rf 0.6431 0.6726 0.6818 0.6840 0.7020 0.7407

kNN 0.6364 0.7029 0.7306 0.7475 0.7946 0.8788

CondInfTree 0.4882 0.6566 0.7189 0.7024 0.7677 0.8316

mnl 0.3872 0.4242 0.4293 0.4311 0.4478 0.4714

Kappa


svm 0.08539 0.2633 0.3364 0.3534 0.4442 0.6234

rf 0.45830 0.5016 0.5169 0.5208 0.5471 0.6071

kNN 0.46650 0.5603 0.6024 0.6267 0.6957 0.8194

CondInfTree 0.20500 0.4947 0.5750 0.5555 0.6515 0.7466

mnl 0.05321 0.1115 0.1192 0.1205 0.1453 0.1840


Call:





Kappa


svm -0.16738 -0.27330 -0.20204 0.23291

rf < 2.2e-16 -0.10592 -0.03466 0.40029

kNN < 2.2e-16 < 2.2e-16 0.07126 0.50621

CondInfTree < 2.2e-16 < 2.2e-16 < 2.2e-16 0.43495

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16





Accuracy


svm -0.11698 -0.18045 -0.13540 0.13594

rf < 2.2e-16 -0.06347 -0.01842 0.25292

kNN < 2.2e-16 < 2.2e-16 0.04504 0.31639

CondInfTree < 2.2e-16 < 2.2e-16 < 2.2e-16 0.27134

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16

************ Tuning kNN ************

Y ~ B2


- 157 -



Accuracy was used to select the optimal model using the largest value.

The final value used for the model was k = 3.

3-nearest neighbor classification model

************* Regresión *************


Training set class distribution:

1 2 3

333 282 264


Reference

Prediction 1 2 3

1 20 0 0

2 4 11 0

3 5 1 5

Overall Statistics

Accuracy : 0.7826

95% CI : (0.6364, 0.8905)



Kappa : 0.6464




Sensitivity 0.6897 0.9167 1.0000

Specificity 1.0000 0.8824 0.8537

Pos Pred Value 1.0000 0.7333 0.4545

Neg Pred Value 0.6538 0.9677 1.0000

Prevalence 0.6304 0.2609 0.1087

Detection Rate 0.4348 0.2391 0.1087


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nfi_Buf45_img193026

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******** Resumen ********



Accuracy


svm 0.6180 0.6957 0.7116 0.7103 0.7303 0.7640

rf 0.5843 0.6554 0.6779 0.6723 0.7004 0.7191

kNN 0.5468 0.5730 0.5918 0.6019 0.6292 0.6667

CondInfTree 0.4944 0.5730 0.5993 0.5973 0.6217 0.6742

mnl 0.5618 0.5908 0.6105 0.6123 0.6339 0.6554

Kappa



- 158 -

svm 0.4270 0.5435 0.5674 0.5654 0.5955 0.6461

rf 0.3764 0.4831 0.5169 0.5085 0.5506 0.5787

kNN 0.3202 0.3596 0.3876 0.4029 0.4438 0.5000

CondInfTree 0.2416 0.3596 0.3989 0.3960 0.4326 0.5112

mnl 0.3427 0.3862 0.4157 0.4184 0.4508 0.4831





Kappa


svm 0.056919 0.162494 0.169392 0.146951

rf < 2.2e-16 0.105575 0.112473 0.090032

kNN < 2.2e-16 < 2.2e-16 0.006898 -0.015543

CondInfTree < 2.2e-16 < 2.2e-16 < 2.2e-16 -0.022441

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16





Accuracy


svm 0.037946 0.108329 0.112928 0.097967

rf < 2.2e-16 0.070384 0.074982 0.060022

kNN < 2.2e-16 < 2.2e-16 0.004598 -0.010362

CondInfTree < 2.2e-16 < 2.2e-16 < 2.2e-16 -0.014960

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16

************ Tuning SVM ************

Y ~ NDVI + B5 + B4 + B7 + B2 + B6 + B1 + B3 + SD5 + SD7 + SD1 +

SD4




The final values used for the model were C = 1.25 and sigma = 2.









************* Regresión *************



Reference

Prediction 1 2 3


- 159 -

1 179 32 0

2 29 74 0

3 13 3 31

Overall Statistics

Accuracy : 0.7867

95% CI : (0.7408, 0.8278)



Kappa : 0.6085




Sensitivity 0.8100 0.6789 1.00000

Specificity 0.7714 0.8849 0.95152

Pos Pred Value 0.8483 0.7184 0.65957

Neg Pred Value 0.7200 0.8643 1.00000

Prevalence 0.6122 0.3019 0.08587

Detection Rate 0.4958 0.2050 0.08587


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nfi_BoudBuf45_img193027seg

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******** Resumen ********

MModels: svm, rf, kNN, CondInfTree, mnl


Accuracy


svm 0.7266 0.7528 0.7678 0.7653 0.7790 0.8090

rf 0.7378 0.7640 0.7790 0.7787 0.7940 0.8127

kNN 0.6891 0.7116 0.7266 0.7267 0.7416 0.7790

CondInfTree 0.6367 0.7228 0.7341 0.7355 0.7566 0.7828

mnl 0.5918 0.6142 0.6273 0.6323 0.6517 0.6779

Kappa


svm 0.5899 0.6292 0.6517 0.6480 0.6685 0.7135

rf 0.6067 0.6461 0.6685 0.6680 0.6910 0.7191

kNN 0.5337 0.5674 0.5899 0.5900 0.6124 0.6685

CondInfTree 0.4551 0.5843 0.6011 0.6032 0.6348 0.6742

mnl 0.3876 0.4213 0.4410 0.4484 0.4775 0.5169





Kappa


svm -0.01998 0.05802 0.04482 0.19959

rf < 2.2e-16 0.07800 0.06479 0.21957

kNN < 2.2e-16 < 2.2e-16 -0.01320 0.14157

CondInfTree < 2.2e-16 < 2.2e-16 < 2.2e-16 0.15478

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16


- 160 -





Accuracy


svm -0.013318 0.038679 0.029878 0.133061

rf < 2.2e-16 0.051998 0.043196 0.146380

kNN < 2.2e-16 < 2.2e-16 -0.008801 0.094382

CondInfTree < 2.2e-16 < 2.2e-16 < 2.2e-16 0.103184

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16

************ Tuning RF ************

Y ~ B5 + B7 + NDVI + B4 + B2 + SD1 + B3 + B1 + B6



Accuracy was used to select the optimal model using the largest value.

The final value used for the model was mtry = 3.

************* Regresión *************



Reference

Prediction 1 2 3

1 102 45 0

2 42 108 0

3 3 10 3

Overall Statistics

Accuracy : 0.6805

95% CI : (0.6257, 0.7318)



Kappa : 0.3965




Sensitivity 0.6939 0.6626 1.000000

Specificity 0.7289 0.7200 0.958065

Pos Pred Value 0.6939 0.7200 0.187500

Neg Pred Value 0.7289 0.6626 1.000000

Prevalence 0.4696 0.5208 0.009585

Detection Rate 0.3259 0.3450 0.009585


***************************************************************************

nfi_Buf45_img194025segm

***************************************************************************

******** Resumen ********



- 161 -


Accuracy


svm 0.6292 0.6891 0.7041 0.7056 0.7303 0.7640

rf 0.5356 0.6217 0.6386 0.6353 0.6554 0.6891

kNN 0.5131 0.5609 0.5730 0.5737 0.5843 0.6217

CondInfTree 0.4831 0.5693 0.5843 0.5841 0.6105 0.6442

mnl 0.5206 0.5393 0.5618 0.5612 0.5777 0.6030

Kappa


svm 0.4438 0.5337 0.5562 0.5585 0.5955 0.6461

rf 0.3034 0.4326 0.4579 0.4530 0.4831 0.5337

kNN 0.2697 0.3413 0.3596 0.3606 0.3764 0.4326

CondInfTree 0.2247 0.3539 0.3764 0.3762 0.4157 0.4663

mnl 0.2809 0.3090 0.3427 0.3419 0.3666 0.4045


Call:





Kappa


svm 0.10551 0.19789 0.18225 0.21662

rf < 2.2e-16 0.09238 0.07674 0.11111

kNN < 2.2e-16 < 2.2e-16 -0.01564 0.01873

CondInfTree < 2.2e-16 < 2.2e-16 < 2.2e-16 0.03436

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16


Call:





Accuracy


svm 0.07034 0.13193 0.12150 0.14441

rf < 2.2e-16 0.06159 0.05116 0.07407

kNN < 2.2e-16 < 2.2e-16 -0.01042 0.01248

CondInfTree < 2.2e-16 < 2.2e-16 < 2.2e-16 0.02291

mnl < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16

************ Tuning SVM ************





************* Regresión *************




- 162 -

Reference

Prediction 1 2 3

1 93 14 1

2 14 55 0

3 7 14 31

Overall Statistics

Accuracy : 0.7817

95% CI : (0.7225, 0.8334)



Kappa : 0.6503




Sensitivity 0.8158 0.6627 0.9688

Specificity 0.8696 0.9041 0.8934

Pos Pred Value 0.8611 0.7971 0.5962

Neg Pred Value 0.8264 0.8250 0.9944

Prevalence 0.4978 0.3624 0.1397

Detection Rate 0.4061 0.2402 0.1354


Documents

Development of a method for forest type detection