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Assessment of Landuse and Land Degradation in the North-Western Part of
Bangladesh Using Landsat Imagery
Shareful Hassan
Master’s of Science Thesis in Geoinformatics TRITA-GIT EX 09-013
Division of Geoinformatics
Royal Institute of Technology (KTH) 100 44 Stockholm, Sweden
June 2009
Assessment of Landuse and Land Degradation in the North-Western Part of
Bangladesh Using Landsat Imagery
Supervisor: Dr. Hans Hauska Examiner: Dr. Yifang Ban, Professor
Dedicated To My mother Mrs. Mina Rahman and Late father Md. Mokbular Rahman
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Acknowledgements
I would like to express my gratitude and special thanks to all staff of Geodesy and
Geoinformatics of KTH, Sweden. I am especially grateful to my supervisor, Dr. Hans
Hauska, for his comments, cordial help and valuable guidance in support of this
research. Special thanks also to Dr. Yifang Ban, Professor, Geoinformatics at KTH.
I want to express my sincere thanks to senior research scientist Erick Asenjo for his
help and valuable discussions.
Special hearty and warm thanks to my wife Sharmin Afroz Laboni for her constant
encouragement and moral support.
My special thanks to Mr. Lebu, Mrs. Shithi, Mr. Nowroz and Mrs. Raka for their love
and moral support during our stay in Stockholm, Sweden.
I would like to express warm thanks to my project partner and friend Jim Cory,
Founder-GIS Analyst, Horizon Mapping, USA, for his encouragement and reading my
thesis paper as well.
Shareful Hassan
Stockholm, December 2008
Sweden.
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Abstract
In recent years, the North-Western part of Bangladesh has been experiencing land
degradation as well as changing landuse patterns due to huge population and their
socio-economic activities. To perform this research, Landsat TM and ETM+ of 1989
and 2001 were used to identify land degradation and to assess landuse change over 12
years in these areas. Spectral Angle Mapper (SAM) classifier of supervised
classification technique was used to classify landuse. The overall accuracy of the
landuse classification was 94.23% and 94.72% in TM and ETM+ respectively. Poor
spectral separability was found in the irrigated agriculture-forest, winter agriculture-
forest, and settlement-forest. Consequently, post-classification method of change
detection has been followed to determine the spatial change of landuse pattern in the
study areas. The pixel of the final state image (2001) was compared to the
corresponding pixel of the initial state image (1989). According to the change detection
report from 1989 to 2001, irrigated agriculture increased about 156 Km2 while winter
agriculture is decreased about 49 Km2 over the areas. During this period, forest has
decreased whereas settlements increased by 13 Km2. The overall accuracy and Kappa
coefficient of the change detection were 80 % and 77%, respectively.
The brightness image of a Tasseled Cap Transform (TCT) was used to extract dry soil
in this study. Degraded soils have increased from 1989 (3% of total lands) to 2001 (6%
of total lands) while forest and winter agriculture changed to irrigated agriculture and
settlements. The resulting images show water bodies increased due to new excavation
of ponds, lakes, and canals over 12 years in the study areas.
Keywords: TM, ETM+, changed detection, post classification statistics, spectral angle mapper, tasseled
cap transformation.
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Table of Contents
Acknowledgements i
Abstract iii
Table of Contents v
List of Figures vii
List of Tables viii
Abbreviations ix
1 Introduction 1
1.1 Introduction 1
1.2 Objectives 2
1.3 Structure of Thesis 2
2 Previous Work 5
2.1Previous work 5
3 Study Area and Data-Materials 9
3.1 Study Area 9
3.2 Population 10
3.3 Remote Sensing Data 10
3.4 GIS Data 11
3.5 Field Data 11
4 Methodology 13
4.1 Introduction 13
4.2 Image Resizing 13
4.3 Supervised Image Classification 16
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4.3.1 Spectral Angle Mapper (SAM) 17
4.3.2 Training Data Collection 18
4.4 Image normalization for TCT 19
4.5 Tasseled Cap Transformation 20
4.6 Accuracy Assessment 24
4.7 Change Detection 24
5 Analysis and Results 26
5.1 Spectral Angle Mapper (SAM) 26
5.2 Accuracy assessment of SAM Classification 30
5.3 Change Detection 31
5.4 Land Degradation by TCT 32
5.5 Accuracy assessment of change detection 36
6 Conclusions and Recommendations 38
6.1 Key outcomes of the Research 38
6.2 Research Issues and Recommendations 38
6.3 Conclusions 40
References 41
Appendix A Training data separability of 1989 44
Appendix B Training data separability of 2001 44
Appendix C Training site signature composition of TM data 45
Appendix D Training site signature composition of ETM data 46
Appendix E Accuracy assessment of ETM 46
Appendix F Accuracy assessment of TM 48
Appendix G Change detection report 50
Appendix H TCT basic statistic 50
Appendix I Image normalization in TM 51
Appendix J Error matrix of the Change detection 57
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List of Figures
Figure 1 Study area 9
Figure 2 Population of the study areas 10
Figure 3 Methodology 14
Figure 4 Image Windowing 15
Figure 5 FCC of ETM (2001) 15
Figure 6 FCC of TM (1989) 15
Figure 7 Spectral angle mapper 16
Figure 8 Visualization of SAM 18
Figure 9 Brightness and greenness image of TM and ETM 21
Figure 10 2-D spectral plot of greenness and brightness image of TCT 23
Figure 11 Threshold of ETM and TM of the degraded soils 23
Figure 12 Landuse variations from1989 to 2001 26
Figure 13 Landuse map of 1989 27
Figure 14 Landuse map of 2001 28
Figure 15 Major landuse in 1989 29
Figure 16 Major landuse in 2001 29
Figure 17 Changing trends of landuse in the study areas 30
Figure 18 Trend of land degradation 33
Figure 19 Land degradation in 1989 34
Figure 20 Land degradation in 2001 35
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List of Tables
Table 1 Remote sensing data characteristics 10
Table 2 Pixels and locations of the training data 19
Table 3 TCT coefficient of TM and ETM+ 22
Table 4 Accuracy assessment of the classified map of 1989 30
Table 5 Accuracy assessment of the classified map of 2001 31
Table 6 Change detection statistics report 32
Table 7 Accuracy assessment of change detection 36
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Abbreviations
AVHRR- Advanced Very High Resolution Radiometer
BARC- Bangladesh Agricultural Research council
ENVI- Environment for Visualizing Images
ETM+- Enhanced Thematic Mapper
FCC- False Color Composite
GIS- Geographical Information System
GPS- Global Positioning System
NDVI- Normalized Difference Vegetation Index
NIR- Near Infrared
NOAA- National Oceanic and Atmospheric Administration
SAM- Spectral Angle Mapper
TCT- Tasseled Cap Transformation
TM- Thematic Mapper
UTM- Universal Transverse Mercator
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Chapter 1
Introduction
1.1 Introduction
Satellite imagery is widely used in diverse applications of natural resource management.
Remotely sensed imagery can be used to observe and classify land surface
characteristics (Yonezawa 2007).
Landuse is modification of the natural environment by human activities. Landuse can be
defined as the use of land by humans, usually with emphasis on the functional role of
land in economic activities (Campbell 1996). Information on landuse and its changes
can be used for planning as well as regional development. A land use map is one of the
most important thematic maps because it provides planners with the present status of
land use and pattern of its change (Murai 1991). The Tanor and Godagari thanas of the
North-Western part of Bangladesh are experiencing landuse changes and are the areas
examined in this study.
Land degradation is defined as the loss or the reduction of the potential utility or
productivity of the land (Lal 1994). About 48.35 million square kilometers of the earth
surface are desert while 5.85 million square kilometers are extremely arid, 21.5 million
square kilometers are arid and 21.0 million square kilometers are semi-arid. In Asian
countries, about 2.6 million square kilometer areas are experiencing desertification.
Land degradation and desertification emerged as an issue of global concern over the last
few decades and has been given special importance since the United Nation Conference
on 'Environment and Development' in 1992 (Banglapedia). The topsoil degrades due to
natural processes and human economic activities. In Bangladesh, active land
2
degradation processes are water erosion and loss of fertility due to physical, chemical or
biological degradation of soils (BARC 1997). Land degradation, due mainly to
changing landuse, is one of the most pressing problems in these environment (Rao and
Chen 2008).
High population pressure and poverty, urbanization, improper agricultural practices,
development of rural road network, salinity, improper irrigation, hot climatic condition,
and depletion of ground water are the main reasons for landuse change as well as land
degradation in the North-Western region, particularly the Tanor and Godagari thanas of
Bangladesh. The main objective of this thesis is assessment of landuse and land
degradation of the Tanor and Godagari thana using multi temporal Landsat imageries.
To assess the landuse pattern and identify the land degradation in the study areas, a
supervised image classification technique (SAM) and soil brightness information from
Tasseled Cap Transformation (TCT) was used.
1.2 Objectives
The main objective of the study is to classify landuse and land degradation in the North-
western part of Bangladesh using remote sensing data. The specific objectives are as
follows:
� Assess landuse change using multi-temporal data sets.
� Assess land degradation using the soil brightness image of tasseled cap
transformed (TCT) output.
1.3 Structure of the Thesis
The whole thesis is divided into 6 sections.
Chapter 1 outlines research context, problem of the study areas and research objectives.
Chapter 2 describes literature review of the study
Chapter 3 defines a general description of the study areas and data/materials.
3
Chapter 4 describes the main methodological steps to achieve objectives of the study
Chapter 5 explains the analysis and results of the research.
Major findings, conclusions and recommendations are presented in chapter 6.
4
5
Chapter 2
Previous Work
2.1 Previous work
There are numerous examples of traditional and expert based image classification
systems for detection, monitoring and mapping of landuse change and land degradation
using remote sensing data.
Tasseled Cap Transformation (TCT) and Normalized Difference Vegetation Index
(NDVI) are very useful techniques to identify and delineate land degradation from
vegetation coverage. Hu et al (2004) used NDVI and Tasseled Cap Transformation for
negative and positive change of land degradation over time in the northwestern part of
China. They found that the area of vegetation cover and soil wetness increased in this
part from 1987 to 1996.
Breunig et al (2008) have used reflectance and emissivity information from ASTER
imagery to identify exposed soils as well as produce topsoil texture image in an
agricultural area of central Brazil. They used band combination by band 5, 6 and band
10, 14 to discriminate dark red clay soils and bright sandy soils, respectively. The ratio
of the bands followed by correlated with laboratory measured total sand fraction. Form
this study, highest sandy surface at lower elevation and clay surface at higher elevation
were observed. The highest sandy surfaces were coincident with land degradation
process in the area.
An overview of Land degradation using multi-sensor image fusion and post
classification procedure was given by Torrion (2002). He used Landsat TM, ASTER,
ERS-2, SAR, and DEM for the study. Severe soil degraded areas were found in the
South-Western part of the Nakuru district, Kenya.
Vegetation cover, rainfall, surface run-off and soil erosion have an important role in the
prediction of land degradation. Symeonakis and Drake (2004) have used these factors
over Sub-Saharan Africa. They estimated vegetation cover from digital satellite imagery
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using NDVI, surface run-off from Meteosat and soil erosion data from soil conservation
service. Consequently, these factors were combined to highlighting severe susceptibility
of land degradation. They proposed this methodology as for near real-time monitoring
of land degradation as well.
In order to compare land degradation study using different sensors, the spatial and
spectral resolutions of Landsat ETM+ and ASTER were used as well as investigated by
Gao and Liu (2007) in Tongyu County, Western Jilin Province of Northeast China. In
this research degraded soils were found at 462.95 km2 in ASTER image while 400.06
km2 in ETM+ image. The overall accuracy of the study was 72.2% and 79.2% in
ASTER and ETM+ data, respectively.
To identify the characteristics of the arid rangelands of Australia, Graetz (1987) used
the nature of soil and vegetation, spectral modeling and the indices of soil and
vegetation from Landsat and NOAA to assess regular monitoring, mapping and
management of land degradation in Australian rangeland. In this study, the arid and
semi-arid lands were extended from central area of Australia to the Western and
Southern coasts. He suggested that future studies of Australian rangeland will most
likely use high frequency/low resolution spatial data (NOAA AVHRR) with low
frequency/high resolution spatial data ( SPOT, MSS/TM/ETM).
Zurayk et al (2001) assessed land degradation in Aarsal, Lebanon using thematic map of
drainage density, drainage texture, grazing, slope, and land use information. They used
spatial overlay technique to create factorial soil degradation risk map in their study.
From the resulting map they found over 90% of the areas are in low and very low soil
degradation categories. The Nam Cham sub-watershed in Thailand was covered by
dense forest about 35 years before. To neutralize communist activities, the government
has cut down a lot of trees and afterward those cut down forest areas were replaced by
cultivation which has lead to land degradation in the area. Patanakanog and Shrestha
(2004) used Landsat TM and analogue aerial photographs to assess landuse and land
degradation in the sub-watershed area. Aerial photos were used for classify
landuse/landcover while Landsat TM was used for vegetation mapping (NDVI). In this
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study, highest land degradation was found in cropped areas while lowest in forest and
grass areas.
Kiage et al (2007) have used Landsat TM and ETM+ to identify land degradation with
land use change in Lake Baringo catchment in Kenya. In the study, NDVI and post-
classification comparison were depicted the hotspots of land degradation and land use
change. Most of the bare ground was degraded significantly where found lower NDVI
values.
Rao and Chen (2008) used brightness, greenness and wetness from the Kauth-homas
Transforms (KT), NDVI to identify land degradation in the northwest China using
Landsat TM and ETM+ data. Post-classification changed detection was followed to
identify change. Their overall classification accuracy was over 80%. Most of the
degraded grassland was found around the salt-affected soil in the study area.
Mulatu (2006) used Spectral Angle Mapper (SAM) to classify landuse using hyper-
spectral and Landsat data over the Netherlands. Spectral Angle Mapper (SAM)
classifier determines an angle between target spectrum and reference spectrum. The
result of this study suggests that vegetation was accurately mapped using hyperspctral
data sets. In addition, combination of imagery and ancilary data like elevation, aspect
and slope were increased the accuracy of detailed land cover mapping in this study.
Shafri et al (2007) used Spectral Angle Mapper (SAM) with maximum likelihood,
artificial neural network and decision tree classifier for hyperspectral image analysis
over a tropical forest area in Malaysia. Using ground truth information and field
investigation, the highest overall accuracy was 83.61% for the Artificial Neural network
(ANN) whereas it was 50.63% and 48.83% for the decision tree and the spectral angle
mapper classification, respectively. Provoost et al (2005) used Spectral Angle Mapper
(SAM) classifier to identify dynamic dune vegetation along the Belgian coast from
hyperspectral imagery. The overall vegetation classification accuracy was 53% and
64% in Standard Spectral Angle Mapper and Optimized Spectral Angle Mapper,
respectively.
Standard image enhancements and supervised image classification techniques were
used by Ernani and Gabriels (2006) to identify landuse and land degradation in the
8
Yazd-Ardaka basin, Iran. To detect the changes of landuse, they used the post
classification comparison method. From this study it is concluded that the condition of
range land is improving, but an increase in irrigated agriculture lands has lead to a
decrease in ground water levels and an increase in salinity.
Hellden and Stern (1980) carried out research on land degradation using Landsat
imagery and social indicators in Southern Tunisia. They implemented two procedures
during their study: (i) digital image classification by software and (ii) ground truth
sampling of social and physical parameters, e.g., slope, gully erosion, vegetation cover,
population density, dunes, deflation patches and so on. Finally, they combined all
parameters in a weighted overlay table and identified degraded areas. In this research,
population density and gully erosion were identified as the key factors for land
degradation. They found high land degradation in the settlement and agricultural areas.
Combined applications of points-measurements of physical properties, soil spectral
reflectance with Landsat TM and ETM+ data were used to identify physical degradation
of soil by Omuto et al (2007) in the upper Athi river basin in eastern Kenya. In addition,
NDVI and land surface temperature (LST) were used to recognize long term vegetation
as well as thermal condition in the study area. They found 80% of classification
accuracy with respect to ground data.
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Chapter 3
Study Area and Data-Materials
3.1 Study Area
The study areas are located in the north-western part of Bangladesh. Geographically,
Tanor thana lies between 240 40' N latitude to 240 32' N latitude and 880 24' E longitude
to 880 35' E longitude. The other thana, Godagari, is situated between 240 34' N latitude
to 240 22' N latitude and 880 18' E longitude to 880 30' E longitude. The images of both
thana covered 1228 x 1478 rows and columns. The total area of the thanas is about
754.39 km2 (figure 1).
Figure 1. The left image (FCC) shows the study area and the right image shows the whole vector map of Bangladesh.
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The study areas comprises of rivers, small lakes, ponds, settlements, rural road
networks, agricultural fields, forests and so on. There is a lot of agricultural fields;
winter, irrigated etc. The forests in the areas are mainly along road, lake, agricultural
field and around settlement.
3.2 Population
Population has increased by100% from 1989-2001 in the study areas (figure 2). The
population census study was done in 1990 by the Bureau of Census of Bangladesh
(BBS). Increasing population has lead to land degradation and land use change in the
study areas.
Figure 2. Population of the study areas.
3.3 Remote Sensing Data
Landsat TM and ETM+ from 1989 and 2001 were used in the study. Five channels of
both TM and ETM+ imagery were chosen to map for landuse and assess land
degradation. These optical remotely sensed data were collected and downloaded from
the GLCF’s (Global Land Cover Facility) web site. The characteristics of the data are as
follows in (table 1):
6200000
6400000
6600000
6800000
7000000
7200000
7400000
7600000
1990 2001
Population Status of the Study Areas
Population
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Table 1. Remote sensing data characteristics
Sensors Row/Path Bands Date of Acquisition
Projection/Earth Ellipsoid
Resolution (m)
TM 5 138/043 Blue- 0.45-0.52 µm Green- 0.52-0.60 µm RED- 0.63-0.69 µm NIR- 0.76-0.90 µm MIR- 1.55-1.75 µm
1989-11-11 UTM / WGS84 30.00
ETM+ 138/043 Blue- 0.45-0.52 µm Green- 0.52-0.60 µm RED- 0.63-0.69 µm NIR- 0.76-0.90 µm MIR- 1.55-1.75 µm
2001-10-11 UTM / WGS84 30.00
3.4 GIS Data
A vector polygon map of the study areas was collected from Bangladesh agricultural
research council (BARC). Since a scene of Landsat imagery is big and the study area in
between the selected scene, this vector polygon map was used to clip/window study
areas. The projection system of the vector map has been converted to the satellite data
projection system, UTM zone 46N.
3.5 Field Data
In order to develop training data for the image classification and the accuracy
assessment, combination of high resolution of Google map, previously published maps
as well personal field experience on the study areas of the main author were used in this
study.
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Chapter 4
Methodology
4.1 Introduction
The main purpose of this study is to assess land use and land degradation using spectral
bands of Landsat TM and ETM+ sensors. To do this, a spectral angle mapper (SAM)
classifier for landuse and followed by post classification comparison were used to
identify spatial change of the study areas. Post classification comparison change
detection is widely used and easy to understand (Jensen 1996). In addition, a tasseled
cup transformation (TCT) has been used to identify and quantify the land degradation.
To perform a maximum likelihood (MLC) classifier, the training data is needed to
follow a normal distribution. However, remote sensing data are rarely defined by such a
distribution (Sohn and Sanjay Rebello 2002). In contrast, spectral angle mapper (SAM)
is not based on the statistical method. It makes and calculates an angle between
reference spectra and image spectra. As such, the SAM classifier is thought to be more
robust than statistical based classifiers (Rembold and Maselli 2006). The details of the
SAM are discussed in the 4.3 section. In order to accomplish all tasks in the study, PCI
Geomatica and ENVI were used. The overall methodology is shown in figure 3.
4.2 Image Resizing
Image windowing was necessary in the study to extract the exact study areas from the
Landsat scenes. A vector polygon shape file of the study areas was used in order to clip
the study areas (figure 4).
To recognize various features of the study areas and for developing training data, false
color composite (FCC) maps were made from Green, Red and NIR bands (figure 5, 6).
This FCC constitutes the input satellite data for image enhancement (Mishra 1998).
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Figure 3. This flowchart showing the different steps for assessment of landuse and land
degradation.
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Figure 4. Image Windowing from the main Landsat scene.
Study area extraction
Figure 5. A FCC map of ETM (1989). The image is 1228 x 1478 pixels in size.
Figure 6. A FCC map of TM (2001). The image is 1228 x 1478 pixels in size.
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4.3 Supervised Image Classification
A fundamental goal of remote sensing analysis is the classification of an image or scene
(Lusch 1999). Supervised image classification is a widely used technique in satellite
image processing. The success of the method depends on the accuracy of training data
as well as field investigation. Supervised classification is the procedure most often used
for quantitative analysis of remote sensing image data (Richards et al 2006). For the
purposes of the study, Spectral Angle Mapper (SAM) has been selected and used to
classify temporal remotely sensed images. The main advantages of the Spectral Angle
Mapper (SAM) classifier are as follow-
� Automatic method to compare unknown image (target) spectra and known
(reference) spectra.
� This result is not affected by solar illumination factors.
� SAM classification assumes reflectance data (RSI 2007).
� Both spectral profile from a spectrometer and user training data can be added in
the classifier.
� This method compares the angle between image spectra and known spectra. A
smaller angle represents a closer match to the reference spectra, while a large
angle represents a mixed class. In addition, the angle of radiance between
training spectrum vector and the pixel vector might be changed for improvement
of image classification by users (figure 7, 8).
Figure 7. Spectral angle mapper (SAM)
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4.3.1 Spectral Angle Mapper (SAM)
This algorithm compares the angle between an unknown target spectrum to known
reference spectra. This classifier is like a nearest-mean classifier using spectral-angle
distance (Schowengerdt 2007). The formula of the SAM algorithm is as follows,
Where,
= Spectral Angle
nb = the number of bands
ti = image (reflectance) spectrum
r i = known (reference) spectrum
The reflectance image spectrum of each pixel can be denoted as a vector in n-
dimensional space, where n represents the number of spectral channels. Every vector
has a certain length and direction. The direction indicates the spectral feature while
length represents brightness of each pixel. Variation in illumination mainly affects
changes in length of the vector, while spectral variability between different spectra
affects the angle between their corresponding vectors (Kruse et al 1993).
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Figure 8. Visualization of Spectral angle = θ, reference spectrum = r , target spectrum =
t, using 3 channels ß1ß2 ß3.
This classifier (figure 8) calculates the angle (θ) between the target spectrum (t) and the
reference spectrum (r ) where each pixel will be assigned to the class feature of the
lowest spectral angle. The spectral angle can have values between 0 and π/2 (Provoost
et al 2005).
Five different landuse types, i.e. water, forest, settlements, irrigated agriculture and
winter agriculture, were selected as training data from both images for landuse
mapping. Throughout the landuse classification the maximum angle 0.30 radians was
chosen, as this was the acceptable angle between training spectrum vector and pixel
spectrum vector. This angle of 0.30 radians produces a better landuse classification than
other radiance values.
4.3.2 Training Data Collection
Training data collection and development are the main task for any kind of supervised
classification. The purpose of training data is to permit reconstruction, in as much detail
as possible, of ground conditions at the place and time that imagery was acquired
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(Campbell 1996). To perform a supervised image classification, more than 1700 pixels
were extracted for each landuse category (table 2).
Table 2. Number of pixels in each landuse category
Number of Pixels Landuse category
1989 2001
Water 2132 3710
Forest 2984 2060
Settlement 5781 1707
Winter Agriculture 2036 1040
Irrigated Agriculture 9679 8543
The statistical parameters of this training data, including mean and standard deviations
are included in the appendix.
4.4 Image Normalization for Tasseled Cap Transformation (TCT)
Mainly change detection is depend on some natural factors like solar angle, atmospheric
condition (clouds, moisture, particles) with man-made sensor calibration, sensor
physical characteristics and so on. When comparing two image scenes, steps must be
taken to reduce exogenous errors such as atmospheric differences, sensor calibrations,
and illumination angle differences that might cause inaccurate detection of spectral
change (Collins and Woodcock 1994). Therefore, before perform any kind of change
detection calculation, the data has to be normalized. Image regression method has been
used to normalize ETM of 2001 data in this study. Image regression model explains
best fit between two multi-date satellite data of the same study area. It assumes that a
pixel at time-2 (ETM-2001) is linearly related to the same pixel at time-1 (TM-1989).
Here all channels of ETM-2001 were used as independent variable while TM-1989 was
20
input as dependent variable in the regression model (appendix I). The image regression
was achieved using selection of Pseudo Invariant Features (PIFs) like un-changed
water, pond, and settlements within 12 years. The regression calculation is as following-
Y= bX+ c [Where, X represents the each band of ETM in 2001] Normalized Blue= Blue2001*0.78-0.70
Normalized Green= Green2001*0.13+23.18
Normalized RED= RED2001*1.93+37.46
Normalized NIR= NIR2001*0.68+5.90
Normalized MIR= MIR2001*1.04+16.83
Normalized MIR2= MIR22001*1.39+20.50
4.5 Tasseled Cap Transformation (TCT)
The name "tasseled cap" comes from the fact that when the greenness and brightness of
a typical scene are plotted perpendicular to one another on a graph, the resulting plot
usually looks like a cap (Jensen 1996). This is one of the popular techniques for spectral
enhancement of satellite data. The Tasseled Cap Transformations for Landsat images
were derived by looking at a small number of images and determining a new
orthonormal basis for the bands that highlights differences in vegetation and soil (Horne
2003). This spectral transformation was developed by Kauth and Thomas in 1976. They
noted that the digital number (DN) scattergrams of Landsat MSS agricultural scenes
exhibit certain consistent properties (Schowengerdt 2007). Four types of new axes are
derived after TCT operation. TCT 1 (Figure 9) interprets the greenness which conveys
vegetation, TCT 2 (figure 9) refers to soil brightness which is used to identify bare and
degraded soils. TCT 3 depicts yellowness and TCT 4 atmospheric haze which is
associated with atmospheric effects, haze, noise etc. Mainly, TCT 1 and TCT 2 cover
about 95% to 98% of agricultural and soil information.
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Greenness of ETM+ Greenness of TM
Brightness of ETM+ Brightness of TM
Figure 9. Brightness and greenness image derived from TCT operation
The TCT is a guided and scaled principal component analysis, which transforms the 6
Landsat TM bands into 3 bands of known characteristics; soil brightness, vegetation
greenness and soil/vegetation wetness (Lea et al 2003). The Tasseled Cap
Transformation (TCT) is a special case of the equation shown below:
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TC = WTC . DN+B.
Where,
TC= Tasseled cap transformation and axis name
WTC= Specific transformation matrix (coefficients)
DN= Digital Number and, B= Bias
The tasseled cap transformation is a useful tool for compressing spectral data into a few
bands associated with physical scene characteristics (Crist and Cicone 1984). This TCT
matrix is fixed for a given sensor and independent of a scene. Thus, this matrix
transformation can vary from sensor to sensor along with the axis name. The Tasseled
Cap coefficients of TM (Landsat 5) and ETM+ (Landsat 7) which were used in the study
are shown in table 3:
Table 3. TCT coefficients of TM and ETM+ sensor.
In the chart below of 2-D spectral space (brightness on X axis and greenness on Y axis),
high brightness values represented bare soil (red circle, figure 10) while high greenness
revealed agriculture or other vegetation. This 2-D spectral plot has been used to select
the threshold values used to differentiate degraded soil from other classes in the
23
brightness TCT image of TM and ETM+. Certain ranges of these values were assigned
to degraded soils.
Figure 10. 2-D spectral plot of the Greenness and Brightness image of TM and ETM TCT
output.
After visual checking in the 2-D scattergrams, maximum threshold values from 141 to
317 and 180 to 312 of TM and ETM+, respectively were selected to extract dry soil
information in the study (figure 11). In order to extract pure degraded soils, the 2-D
scattergrams and image thresholding were used for pixel by pixel validation.
Figure 11. Threshold of TM and ETM for degraded soils
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4.6 Accuracy Assessment
To evaluate the accuracy of the classified images, an accuracy assessment or confusion/
contingency matrix was implemented. Test data was input as reference data and the
classified map was input as classified data in the accuracy assessment table. The error
matrix compares the relationship between known reference data (ground truth) and the
corresponding results of an automated classification (Lillesand and Kiefer 2000). The
overall accuracy, producer and consumer accuracy are discussed in section 5.2.
4.7 Change Detection
Change detection is the process of identifying differences in the state of an object or
phenomenon by observing it at different times (Singh 1989). Many change detection
techniques have been developed (Moran et al 2004); write function memory insertion,
multi-date image composite, image algebra, post classification comparison, image
regression, image differencing, image rationing, principle component analysis, change
vector analysis. The accuracy of change detection analysis outputs depend on the
following conditions:
� Low RMS error of geometric registration of two-date images,
� High quality of ground truth data and field investigation information,
� The selection of change detection methods or algorithms,
� Proper knowledge of the study area,
� Proper multi-date image classification scheme,
� Analyst effort and experience.
In this study, the post classification comparison method was implemented. To use this
method, rectified and classified remotely sensed image is necessary. The accuracy of
this change detection method depends on the accuracy of the two-date image
classifications. The change detection statistics shows the change of each class feature by
25
pixels, percentages or areas. The main disadvantage of this method is that the accuracy
is dependent on the individual image classification of each state image and its very time
consuming. The change detection results are discussed in the analysis chapter.
26
Chapter 5
Analysis and Results
5.1 Spectral Angle Mapper (SAM)
Figure 12 shows significant landuse changes over 12 years. In this study, winter
agriculture and forest have decreased while irrigated agriculture and settlement
increased significantly from 1989 into 2001. Increasing population and their food
demands are the main causes for this current landuse pattern in the study areas. Indeed,
the green revolution1 after 1960 in Bangladesh is one of the main reasons for the change
from winter agriculture into irrigated agriculture. The green revolution leads to the
proper utilization of chemical fertilizer, hybrid seeds and modern irrigation system for
agricultural purpose. So far, irrigated agriculture has higher yield than any other
farming system in Bangladesh. In addition, figure 12 also shows an increase of water
bodies from 1989 to 2001 due to excavation of new canals, ponds and lakes for drinking
water as well as agricultural purposes.
Landuse Variation from 1989 to 2001
0
5000
10000
15000
20000
25000
30000
35000
Winter Irrigated Settlement Forest Water
Land type
Hectare1989
2001
Figure 12. Landuse variation from 1989 to 2001
1 The Green revolution has emerged in 1960s in order to feed the enormous Bangladeshi population using
high yield variety (HYV) seeds, irrigation, and chemical fertilizer.
27
Figure 13 and 15 show landuse pattern of 1989 where winter agriculture, forest (as
green and yellow color) are predominant features, whereas irrigated agriculture and
settlement are the predominant landuse features in the 2001 image (figure 14, 16).
Figure 13. The result of the landuse map, based on supervised image classification of 1989
data.
28
Figure 14. Landuse map covering the Tanor and Godagai thana. The map is based on supervised
image classification of 2001 image.
29
Major Landuse in 1989
Water
Winter
Irrigated
Settlement
Forest
Figure 15. Major landuse in 1989 (extracted from TM image)
Major Landuse in 2001
Water
Winter
Irrigated
Settlement
Forest
Figure16. Major landuse in 2001 (extracted from ETM image)
The main driving forces of landuse changes in the study areas are mainly increased
population and extensive agricultural practices. Figure 17 shows temporal change of
each landuse class over the study areas where irrigated agriculture (yellow color) and
settlements (cyan color) has upward tendency while forest and winter agriculture has
downward tendency.
30
Changing Pattern of each Landuse of the study areas
0
5000
10000
15000
20000
25000
30000
35000
1988 1990 1992 1994 1996 1998 2000 2002
Years
Hectare
Water
Winter
Irrigated
Settlement
Forest
Figure17. Changing pattern of each landuse of the study areas.
5.2 Accuracy Assessment of SAM classification
Accuracy assessment was performed for both classifications using test data sets and the
classified image. The calculated accuracy is shown in tables 4 and 5. The achieved
classification accuracies like producer accuracy, user accuracy as well as overall
accuracy were acceptable in this study.
Table 4. Accuracy assessment of classified map of 1989
Class Producer accuracy
(using test data)
User accuracy
(using test data)
Water 100% 91%
Forest 81% 88%
Settlement 91% 88%
Winter Agri 99% 98%
Irrigated Agri 98% 94%
Overall Accuracy = (1389/1474) 94.23%
Kappa Coefficient = 0.92
31
Table 5. Accuracy assessment of classified map of 2001
Class Prod. accuracy
User accuracy
Water 100% 100%
Forest 70%, 92%
Settlement 93% 80%
Winter Agri 100% 95%
Irrigated Agri 94% 97%
Overall Accuracy = (1113/1175) 94.72%
Kappa Coefficient = 0.93
After accuracy assessment it was obvious that the lower producer/user accuracy was
found in the forest class of 1989 and 2001 image. The spectral pixels of forest were
mixed with the irrigated agriculture spectral pixels to be possible cause to achieve lower
accuracy in the class. Poor spectral separability was found in the irrigated agriculture-
forest, settlement-forest, and winter agriculture-forest due to mixed pixels (appendix A
and B). The whole report of classification accuracy is given in appendix E and F.
5.3 Change Detection of Image Classification
Post classification comparison was used to produce a detailed tabulation of changes
between the two classified images. The 1989 classified map was input as initial state
and the 2001 classified map was input as final state. The result shows the changes of
landuse from the initial state into the final state.
According to the change detection report (table 6) from 1989 to 2001, irrigated
agriculture increased about 156 Km2 while winter agriculture is decreased about - 49
Km2 over the areas. During this period, forest has decreased whereas settlements
increased by 13 Km2. The main causes of this change are mainly irrigated agriculture
32
and increasing population. Water bodies have increased by 4 Km2 over the period. The
increase and decrease of each landuse category are shown in table 6 also.
Table 6. Change detection report (Km2)
Initial State Image as 1989
1989 2001
Water Winter Agriculture
Irrigated Agriculture
Settlement
Forest Row Total
Class Total
Water
27.88 0.91 1.58 3.69 5.31 39.38 39.92
Winter
0.11 24.98 22.14 23.37 25.15 95.76 95.89
Irrigated
2.28 65.55 75.83 81.97 90.04 315.66 316.88
Forest
4.60 10.99 18.01 13.91 37.24 85.75 85.35
Settlement
0.64 42.02 42.90 70.87 50.02 206.45 207.23
Class Total
35.52
144.44 160.47 193.82 207.77
Class Changes
7.64
119.46 84.64 122.96 170.53
Final Image Image
Difference 4.40 -48.54 156.41 13.40 -122.42
5.4 Land Degradation by Tasseled Cap Transformation
In this study, degraded soils were extracted from the soil brightness image of the TCT.
Maximum threshold value range was used to extract soil information (figure 11).
Figures 18, 19 and 20 show the actual land degradation situation of 1989 and 2001. In
1989, the degraded soil areas were 2777 hectares which increased to 5064 hectares in
2001. Degraded soils have increased by about 2287 Km2 in 2001, which is 6% of the
total land of the study areas. The potential reasons for the land degradation in the study
areas were identified by BARC (1997), which are as followings:
� Population and poverty
� Improper agricultural practices
� Improper irrigation
33
� Development of rural road network
� Land ownership and tenure
� Rural housing
� Urbanization
� Brick making and kiln
Significant degraded soils were found in the south-western part of the study areas
(figure 20). Comparatively, fewer degraded soils were found in the north and middle-
eastern part of the study areas (figure 19).
Trends of Land Degradation
0
1000
2000
3000
4000
5000
6000
1988 1990 1992 1994 1996 1998 2000 2002
Years
HectareTrend line
Figure 18. Trends of land degradation in the study areas
34
Figure 19. Land Degradation in 1989
35
Figure 20. Land Degradation in 2001
36
5.5 Accuracy Assessment of Change Detection
In order to calculate change detection accuracy in this study, the final classified image
of TM and ETM+ was crossed into a single layer which was produced 25 different
classes. As the accuracy assessment required very intensive visual analysis, we
aggregated the sub-change categories of each land cover type into one change class
(Zhou et al 2008). Therefore, the 25 different classes were aggregated into 6 major sub-
categories i,e. changed water, changed winter agriculture, changed irrigated agriculture,
changed settlement, changed forest, and no-changed class. Above 516 pixels for each
changed class and 635 pixels for no-changed class were selected as test pixel in the
error matrix table. The overall accuracy, kappa, producer and user accuracy are in table
7. The whole calculated error matrix is shown in appendix J.
Table 7. Accuracy assessment of change detection
Class Prod. accuracy
User accuracy
Water 77.15% 99.55%
Winter Agriculture 78.24%, 95.19%
Irrigated Agriculture 85.08% 82.14%
Settlement 76.70% 66.39%
C
hang
e cl
ass
Forest 88.54% 89.21%
No
chan
ge c
lass
No-Changed 88.54% 67.20%
Overall Accuracy = (2671/3310) 80.69%
Kappa Coefficient = 0.77
37
38
Chapter 6
Conclusions and Recommendations
6.1 Key Outcomes of the Research
The main outcomes of the study are as follows-
� Water areas (including wetland) have increased in the study areas.
� Forests were reduced to settlement and irrigated agriculture.
� The winter agriculture has been changed into irrigated agriculture.
� Post classification comparison method was used to identify landuse changes
over 12 years in the study.
� The extraction of degraded soils using the brightness image from the tasseled
cap transformation (TCT) output gave good result. Degraded soils have
increased by 50% from 1989 to 2001.
� The process of land degradation is high in the South-Western part of the study
areas.
6.2 Research Issues and Recommendations
Landsat TM and ETM+ were used to detect changes of landuse and identify land
degradation in the study. To improve landuse classification and detection of land
degradation, use of a soil erosion model, and a digital elevation model (DEM) along
with high resolution satellite data is suggested. Spatial correlation between landuse
pattern, ground water level, population density and soil moisture could be used to
identify and quantify land degradation. The application of image fusion method needs
to be tested and verified. Ground truthing, which has not been done in the study, is
recommended in future research as well. From our analysis it could be observed that a
large amount of soil was degraded.
39
Deforestation is one of the main reasons for soil degradation; thus, proper management
of forest resources and soils would be an effective way to prevent land degradation.
Promoting extensive forestation activity in Bangladesh would be an effective
management strategy. Forestation activities should be applied in the study areas by
government and non-governmental (NGO) policy. In addition, wetlands have an
important role in the environment. Their natural ability to improve ground water storage
increases the production of green vegetation. Therefore, more wetland areas adjacent to
lakes, ponds and canals could serve as protection against land degradation in the study
areas. Top soil erosion is a main problem in the land degradation process. Proper crop
rotation and management of overgrazing can reduce erosion of top soil.
To maximize productivity of irrigated agriculture, local farmers are using chemical
fertilizers, pesticides, and deep tubewells (for sub-surface water pumping) in the study
areas. These types of activities are also causes for land degradation in the study areas.
The use of bio-fertilizer is recommended.
Land degradation is a common problem in the study areas. Irrational landuse and
inappropriate land management are direct causes of this situation. To prevent land use
changes that lead to land degradation, the following actions can be utilized by
government as well as NGOs in the study areas:
• Refined landuse policy,
• Planned land and settlement,
• Planed land and agriculture policy,
• Planed land and forest,
• Planed wetland,
• Planed land and industry.
40
6.3 Conclusions
Remote sensing technology is a powerful tool for measuring, mapping, monitoring and
modeling of natural resources. In this study, changes landuse and land degradation were
determined and assessed using Landsat satellite imagery. The results of the study
indicate that landuse changes led to the degradation of large areas of soil from 1989 to
2001. These changes were brought about by over-population and socio-economic
activities. As a conclusion, it should be stated-
“IF Population increases THEN
Settlement will be increased
Irrigated agriculture will be increased
AND
Forest will be reduced
Winter agriculture will be reduced
ANDIF all Process continue for a long period of time THEN
Landuse will be changed
Land degradation will be increased”
41
References BARC - Bangladesh Agricultural Research council, (1997). Land Degradation Situation in Bangladesh, Soil Division. Dhaka. Banglapedia - the National Encyclopedia of Bangladesh. http://banglapedia.net/ (last visited on 02 December, 2008) Breunig, F.M., Galvao,L.S. and Formaggio, A.R (2008). Detection of sandy soil surfaces using ASTER-derived reflectance, emissivity and elevation data: potential for the indentification of land degradation. Int: Journal of Remote Sensing, 29:6, 1833-1840 Collins, J. B. and C. E. Woodcock., (1994). Change detection using the Gramm-Schmidt transformation applied to mapping forest mortality. Remote Sensing of Environment 50:267–79. Campbell, J, B., (1996). Introduction to Remote Sensing. Taylor and Francis, London. Crist, E. P and Cicone, R. C., (1984). A physically-based transformation of Thematic Mapper data—the Tm Tasseled Cap, IEEE Trans. On Geosciences and Remote Sensing, GE-22: 256-263. Ernani, Zare. Mohammad and Gabriels, Donald., (2006). Detection of land cover changes using MSS, TM, ETM+ sensors in Yazd-Ardaka basin, Iran. Proceeding of Agro Environ 2006. pp 513-519 Gao, Jay and Liu, Yansui (2008) Mapping of land degradation from space: a comparative study of Landsat ETM+ and ASTER data. Int: Journal of Remote Sensing, 29:14, 4029-4043 Graetz, D, R., (1987). Satellite Remote Sensing of Australian Rangelands. Remote Sensing of Environment, Vol 23 issue 2, pp 313-331 Helldén, Ulf and Stern, Mikael., (1980) Monitoring land degradation in Southern Tunisia. A test of Landsat Imagery and Digital Data. Lunds Universitets Naturgeografiska Institution: Rappoter och Notiser: 48 Horne, H. James., (2003). A Tasseled Cap Transformation for IKONOS Images. ASPRS 2003 Annual Conference Proceeding, May 2003, Anchorage, Alaska.
42
Hu, Dao. Gaung., Chen, Guo. Jian and Al-Quarishi, Fahad Mohammed. Ayad., (2004). Land degradation detection, mapping, and monitoring in the Northwestern part of Hebei province, China, Using RS and GIS Technologies. Map Asia Conference 2004. Jensen, J, R., (1996). Introductory Digital Image Processing- A remote sensing perspective. Prentice-Hall, 2nd Edition, New Jersey. Kauth, R. J. and G. S. Thomas. (1976). The Tasseled Cap - A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by Landsat. In LARS: Proceedings of the Symposium on Machine Processing of Remotely Sensed Data. West Lafayette, IN: Purdue University, pp. 4B-41-4B-51 Kruse, F.A., Boardman, J.W., Lefkoff, A.B., Heidebrecht, K.B., Shapiro, A.T., Barloon, P.J., and Goetz, A.F.H. (1993). The Spectral Image Processing System (SIPS) – Interactive Visualization and Analysis of Imaging Spectrometer Data. Remote Sensing of Environment, Vol. 44, p.145-163. Kiage, L.M., Liu, K.B., Walker, N.D., Lam, N. and Huh, O.K. (2007). Recent land-cover/use change associated with land degradation in the Lake Baringo catchement, Kenya, East Africa: evidence from Landsat TM and ETM+.Int: Journal of Remote Sensing, 28:19, 4285-4309 Lal, R., (1994). Soil Erosion Research Method. Soil and Water Conservation Society. St. Lucie Press, Delray Beach, FL. Lillesand , T., and Kiefer, R., (2000). Remote Sensing and Image Interpretation. John Wiley & Sons, Inc. Lea, Rnnie., Blodgett, Clayton., Diamond, David and Schanta, Mike. Using The Tasseled cap Transformation to identify change in the Missouri Ozark Forests. http://137.227.231.90/morap/projects/forest_change/change_det.pdf (last visited on 20 August, 2008) Lusch, P. Davis., (1999). Introduction to Environmental Remote Sensing. Center for Remote Sensing and GIS, Michigan State University. Mishra, K.J., (1998). Enhanced visual displays of clouds over Indian monsoon region using INSAT-1D data. Int: Journal of Remote Sensing, Vol 19, issues 17, pp 3261-3269 Moran, E., Brondizio, E., Mausel, P., and Lu, D., (2004) Change detection techniques. Int: Journal of Remote Sensing, Vol 25, issues 12, pp 2365-2401
43
Mulatu, Habtamu., (2006). Landcover change detection using Expert System and Hyperspectral imagery in the Island of Schiermonnikoog, The Netherlands. M.Sc. thesis ITC, The Netherlands. Murai, Shunji., (1991). Application of remote sensing in Asia and Oceania- Environmental change monitoring. Asian association on remote sensing, 1991. Omuto, C. T. and Shrestha, D.P. (2007). Remote sensing techniques for rapid detection of soil physical degradation Int: Journal of Remote Sensing, Vol 28, issues 21, pp 4785-4805. Provoost, Sam., Debruyn., Kempeneers, Pieter., Deronde, Bart., and Luc, Bertels., (2005). Optimized Spectral Angle Mapper Classification of spatially heterogeneous dynamic dune vegetation, a case study along the Belgian coastline. The 9th Int: Sumposium on Physical Measurements and Signatures in Remote Sensing (ISPMSRS). Beijing, October 17-19, 2005. Rao, P., and Chen, S., (2008). Land degradation monitoring using multi-temporal Landsat TM/ETM data in a transition zone between grassland and cropland of northeast China. Int. Journal of Remote Sensing, First published on, 03 Jan, 2008. Rembold, F., Maselli, F., (2006). Estimation of Inter-annual Crop Area Variation by the Application of Spectral Angle Mapping to Low Resolution Multitemporal NDVI Images, Photogrammetric Engineering & Remote Sensing, 72(1), pp. 55-62. Richards, A. John., and Jia, Xiuping., (2006). Remote Sensing Digital Image Processing-An Introduction. Springer-Verlag Berlin Heidelberg 2006. RSI, 2006. ENVI Tutorials. Research System Inc, USA. Schowengerdt, A. Robert., (2007). Remote Sensing- model and methods for image processing. USA, ELSEVER. Shafri, Helmi Zulhaidi Mohd; Affendi, Suhaili and Mansor, Shattri, (2007). The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis. Journal of Computer Science. Vol 3 (6). pp 419-423 Singh, A., (1989). Digital change detection techniques using remotely sensed data, International Journal of Remote Sensing, Vol.10, pp 989-1003. Sohn, Y., Sanjay Rebello, N., (2002). Supervised and unsupervised Spectral Angle Classifiers, Photogrammetric Engineering & Remote Sensing, 72(1), pp.1271-1280.
44
Symeonakis, E. and Drake, N., (2004). Monitoring Desertification and Land Degradation over sub-Saharan Africa. Journal of Remote Sensing, Vol 25, issues 3, pp 573-592 Patanakanog, Boonruck and Shrestha, Pikha. Dhruba., (2004). Land use change and land degradation: A case study in Nam Chun Subwatershed in Thailand. 25th ACRS Chiang Mai, Thailand. Torrion, Jassica. A., (2002). Land degradation detection, mapping and monitoring in the lake Naivasha Basin, Kenya. M.Sc. thesis ITC, The Netherlands. Yonezawa, C., (2007). Maximum likelihood classification combined with spectral angle mapper algorithm for high resolution satellite imagery. Int: Journal of Remote Sensing.Vol.28 N0. 16 pp 3729-3737 Zhou, Weiqi., Troy, Austin., and Grove, Morgan., (2008). Object-based land cover classification and change analysis in the Baltimore Metropolitan area using multitemporl high resolution remote sensing data. Sensors 2008,8,1613-1636 Zurayk, Rami., Christine Sayegh. Awar et Faraj., Hamadeh, Shady., and Ghehab, Ghani. Abdel., (2001). A GIS based methodology for soil degradation Evaluation. Sustaining the Global Farm. Selected papers from the 10th Int. Soil Conservation Organization meeting held May 24-29, 1999 at Purdue University and the USDA-ARS National Soil Erosion Research Laboratory. Appendix A Pair Separation ; TM of 1989 Input File: tm_89 ROI Name: (Jeffries-Matusita, Transformed Divergence) Pair Separation (least to most); Irrigated Agriculture [Red] 9679 points and Forest [Yellow] 2984 points - 1.98474678 Winter Agriculture [Green] 2036 points and Settlement [Cyan] 578 points - 2.00000000 Winter Agriculture [Green] 2036 points and Forest [Yellow] 2984 points - 2.00000000 Water [Blue] 2132 points and Irrigated Agriculture [Red] 9679 points - 2.00000000 Winter Agriculture [Green] 2036 points and Irrigated Agriculture [Red] 9679 points - 2.00000000 Irrigated Agriculture [Red] 9679 points and Settlement [Cyan] 578 points - 2.00000000
45
Water [Blue] 2132 points and Settlement [Cyan] 578 points - 2.00000000 Water [Blue] 2132 points and Winter Agriculture [Green] 2036 points - 2.00000000 Settlement [Cyan] 578 points and Forest [Yellow] 2984 points - 2.00000000 Water [Blue] 2132 points and Forest [Yellow] 2984 points - 2.00000000 Appendix B Pair Separation ; ETM of 2001 Input File: etm_01 ROI Name: (Jeffries-Matusita, Transformed Divergence) Pair Separation (least to most); Irrigated [Red] 8543 points and Forest [Yellow] 327 points - 1.98649053 Winter [Green] 104 points and Forest [Yellow] 327 points - 1.98801401 Set [Cyan] 1707 points and Forest [Yellow] 327 points - 1.99999979 Irrigated [Red] 8543 points and Set [Cyan] 1707 points - 2.00000000 Water [Blue] 3710 points and Irrigated [Red] 8543 points - 2.00000000 Water [Blue] 3710 points and Set [Cyan] 1707 points - 2.00000000 Winter [Green] 104 points and Set [Cyan] 1707 points - 2.00000000 Winter [Green] 104 points and Irrigated [Red] 8543 points - 2.00000000 Water [Blue] 3710 points and Winter [Green] 104 points - 2.00000000 Water [Blue] 3710 points and Forest [Yellow] 327 points - 2.00000000 Appendix C Training site signature composition of TM-1989 data Filename: G:\Thesis\Image\All_Masked\Study_final\TM\tm_89 ROI: Water [Blue] 2132 points Basic Stats Min Max Mean Stdev Band 1 1 6 2.817542 1.031194 Band 2 22 29 24.720450 0.956439 Band 3 5 8 6.943246 0.899484 Band 4 15 22 19.317542 2.343759 Band 5 27 52 47.026266 1.815845 Band 6 30 50 44.202627 1.854955 Band 7 75 104 95.337711 2.588721
46
Appendix D Training site signature composition of ETM data Filename: G:\Thesis\Image\All_Masked\Study_final\ETM+\etm_01 ROI: Water [Blue] 3710 points Basic Stats Min Max Mean Stdev Band 1 1 24 12.194340 1.787504 Band 2 1 25 14.914286 1.557104 Band 3 32 34 33.000539 0.789524 Band 4 64 80 72.563342 1.963582 Band 5 75 77 75.592183 0.605086 Band 6 84 97 90.124528 1.744418 Appendix E Detailed accuracy assessments report of ETM, 2001 Confusion Matrix: G:\Thesis\Image\All_Masked\Study_final\ETM+\SAM_New Overall Accuracy = (1113/1175) 94.7234% Kappa Coefficient = 0.9308 Ground Truth (Pixels) Class Test-Water Test-Winter Test-Irrigate Test-Settle Test-Forest Unclassified 0 0 0 0 0 Water [Blue] 400 0 0 0 0 Winter [Green 0 194 8 2 0 Irrigated [Re 0 0 280 2 6 Set [Cyan] 17 0 0 7 154 30 Forest [Yello 0 0 1 6 85 Total 400 194 296 164 121 Ground Truth (Pixels) Class Total Unclassified 0 Water [Blue] 400
47
Winter [Green 204 Irrigated [Re 288 Set [Cyan] 17 191 Forest [Yello 92 Total 1175 Ground Truth (Percent) Class Test-Water Test-Winte r Test-Irrigate Test-Settle Test-Forest Unclassified 0.00 0.00 0.00 0.00 0.00 Water [Blue] 100.00 0.00 0.00 0.00 0.00 Winter [Green 0.00 100.00 2.70 1.22 0.00 Irrigated [Re 0.00 0.00 94.59 1.22 4.96 Set [Cyan] 17 0.00 0.00 2.36 93.90 24.79 Forest [Yello 0.00 0.00 0.34 3.66 70.25 Total 100.00 100.00 100.00 100.00 100.00 Ground Truth (Percent) Class Total Unclassified 0.00 Water [Blue] 34.04 Winter [Green 17.36 Irrigated [Re 24.51 Set [Cyan] 17 16.26 Forest [Yello 7.83 Total 100.00 Class Commission Omission Commission Omission (Percent) (Percent) (Pixels) (Pixels) Water [Blue] 0.00 0.00 0/400 0/400 Winter [Green 4.90 0.00 10/204 0/194 Irrigated [Re 2.78 5.41 8/288 16/296 Set [Cyan] 17 19.37 6.10 37/191 10/164 Forest [Yello 7.61 29.75 7/92 36/121 Class Prod. Acc. User Acc. Prod. Acc. User Acc. (Percent) (Percent) (Pixels) (Pixels) Water [Blue] 100.00 100.00 400/400 400/400 Winter [Green 100.00 95.10 194/194 194/204
48
Irrigated [Re 94.59 97.22 280/296 280/288 Set [Cyan] 17 93.90 80.63 154/164 154/191 Forest [Yello 70.25 92.39 85/121 85/92 Appendix F Detailed accuracy assessments report of TM, 1989 Confusion Matrix: G:\Thesis\Image\All_Masked\Study_final\TM\SAM_TM Overall Accuracy = (1389/1474) 94.2334% Kappa Coefficient = 0.9240 Ground Truth (Pixels) Class Test-Water Test-Winter Test-Irrigate Test-Settle Test-Forest Unclassified 0 0 0 0 0 Water [Blue] 505 0 0 0 0 Winter Agricu 0 147 0 1 1 Irrigated Agr 0 0 170 4 5 Settlement [C 0 1 0 347 44 Forest [Yello 0 0 2 27 220 Total 505 148 172 379 270 Ground Truth (Pixels) Class Total Unclassified 0 Water [Blue] 505 Winter Agricu 149 Irrigated Agr 179 Settlement [C 392 Forest [Yello 249 Total 1474 Ground Truth (Percent) Class Test-Water Test-Winter Test-Irrigate Test-Settle Test-Forest
49
Unclassified 0.00 0.00 0.00 0.00 0.00 Water [Blue] 100.00 0.00 0.00 0.00 0.00 Winter Agricu 0.00 99.32 0.00 0.26 0.37 Irrigated Agr 0.00 0.00 98.84 1.06 1.85 Settlement [C 0.00 0.68 0.00 91.56 16.30 Forest [Yello 0.00 0.00 1.16 7.12 81.48 Total 100.00 100.00 100.00 100.00 100.00 Ground Truth (Percent) Class Total Unclassified 0.00 Water [Blue] 34.26 Winter Agricu 10.11 Irrigated Agr 12.14 Settlement [C 26.59 Forest [Yello 16.89 Total 100.00 Class Commission Omission Commission Omission (Percent) (Percent) (Pixels) (Pixels) Water [Blue] 0.00 0.00 0/505 0/505 Winter Agricu 1.34 0.68 2/149 1/148 Irrigated Agr 5.03 1.16 9/179 2/172 Settlement [C 11.48 8.44 45/392 32/379 Forest [Yello 11.65 18.52 29/249 50/270 Class Prod. Acc. User Acc. Prod. Acc. User Acc. (Percent) (Percent) (Pixels) (Pixels) Water [Blue] 100.00 100.00 505/505 505/505 Winter Agricu 99.32 98.66 147/148 147/149 Irrigated Agr 98.84 94.97 170/172 170/179 Settlement [C 91.56 88.52 347/379 347/392 Forest [Yello 81.48 88.35 220/270 220/249
50
Appendix G Change detection report from 1989 to 2001
Appendix H TCT basic statistics of ETM and TM data
ETM Data Min Max Mean Stdev
Band 1 0.000000 312.570679 77.846225 78.777735
Band 2 -114.223999 32.888000 -29.530885 31.269670
Band 3 -127.077301 0.000000 -24.656711 25.561440
Band 4 -36.266594 0.000000 -9.274698 9.471597
Band 5 -60.996700 67.695892 1.953724 8.152022
Band 6 -44.664707 37.268898 -5.469848 8.285749
TM Data Min Max Mean Stdev
Band 1 0.000000 318.829437 57.459002 58.665302
51
Band 2 -76.331940 197.056625 8.041407 12.060491
Band 3 -175.968689 90.832397 4.343508 10.257398
Appendix I Image normalization in TM data
Blue-2001 vs. Blue-1989
52
Green-2001 vs. Green-1989
53
RED-2001 vs.RED-89
54
NIR-2001 vs. NIR-1989
55
MIR-2001 vs. MIR-1989
56
MIR2-2001 vs. MIR2-1989
57
Appendix J Error matrix of the Change detection
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09-005 Erik Olsson. Exporting 3D Geoinformation from Baggis Database to CityGML. Supervisors: Peter
Axelsson and Yifang Ban. April 2009.
09-006 Henrik Nilsson. Referenssystemsbyte i Oskarshamns kommun – en fallstudie (Change of reference
systems in Oskarshamn – a case study). Master’s of Science thesis in geodesy No.3112. Supervisor:
Huaan Fan. May 2009.
09-007 Chi-Hao Poon. Interaktiv Multikriteria-Analys (Interactive Multi-Criteria Evaluation). Supervisor: Mats Dunkars and Yifang Ban. May 2009.
09-008 Emma Lundberg. Fastighetsdokumentation – en jämförelse mellan två geodetiska tekniker.
Master’s of Science thesis in geodesy No.3113. Supervisor: Milan Horemuz, Karin Klasén and Ivar
Andersson. May 2009.
09-009 Andenet Ashagrie Gedamu. Testing the Accuracy of Handheld GPS Receivers and Satellite Image
for Land Registration. Master’s of Science thesis in geodesy No.3114. Supervisor: Milan Horemuz and Lars Palm. May 2009.
09-010 Abubeker Worake Ahmed and Workaferahu Abebe Mergia. Determination of transformation
parameters between WGS 84 and ADINDAN. Master’s of Science thesis in geodesy No.3115.
Supervisor: Huaan Fan. May 2009.
09-011 Andreas Jungner. Ground-Based Synthetic Aperture Radar Data Processing for Deformation
Measurement. Master’s of Science thesis in geodesy No.3116. Supervisors: Milan Horemuz and
Michele Crosetto. May 2009.
09-012 Anna Miskas and Andrea Molnar. Establishing a Reference Network in Parts of Amhara Region,
Ethiopia Using Geodetic GPS Equipment. Master’s of Science thesis in geodesy No.3117.
Supervisors: Milan Horemuz and Lars Palm. June 2009.
09-013 Shareful Hassan. Assessment of Landuse and Land Degradation in the North-Western Part of
Bangladesh Using Landsat Imagery. Supervisors: Hans Hauska. June 2009.
TRITA-GIT EX 09-013
ISSN 1653-5227
ISRN KTH/GIT/EX--09/013-SE
