USING HIGH RESOLUTION AIRBORNE LIDAR DATA FOR …€¦ · Additionally LiDAR data allows the detection and outlining of these different geomorphological and anthropogenic features

Nicole KAMP, Bakk. rer. nat.

(0560512)

USING HIGH‐RESOLUTION AIRBORNE LIDAR‐DATA

FOR LANDSLIDE MAPPING IN THE EASTERN ALPS

Master’s Thesis

for the Degree of

Master of Natural Science

Submitted to the

Institute of Geography and Regional Science

University of Graz, Austria

Advised by

Univ.‐Prof. Dr. rer. nat. Oliver Sass

Graz, 2012

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This work is dedicated to my grandparents,

Ferdinand E. Scheiber (1936 – 2009) and Augustine Scheiber (1932 – 2012),

who passed away much too early and who I miss every single day!

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Declaration

I hereby declare that the work in this master’s thesis is the result of my own investigation, except

where otherwise stated. Neither the entirety of this thesis, nor any parts contained within it, has

been previously accepted or is simultaneously being submitted for any other degree.

__________________________

Nicole Kamp

Graz, May 2012

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Preface and Acknowledgments

This master’s thesis was written within the scope of the perennial LiDAR campaign (2008 – 2012)

of the Provincial Government of Styria, Board of Works – Geoinformation Staff Office. The

Geoinformation Staff Office also provided the LiDAR and vector data used in this thesis, as well as

the ArcGIS 10.0 Software Package, including ArcInfo License and Licenses for 3D – Analyst, Spatial

Analyst and LP360 Extensions. Further scientific assistance on the part of the Provincial

Government of Styria, Board of Works – Geoinformation Staff Office was given by DI Rudolf

Huetter.

Since the beginning of my studies at the Alpen‐Adria University of Klagenfurt, Institute of

Geography and Regional Studies, my main concern has been with GIS and Remote Sensing. The

bachelor study at this University provided a good basic knowledge in these two research fields.

During my master’s study at the University of Graz, Institute of Geography and Regional Science

my main focus has been with geomorphology and processes of the earth‐surface. Since March

2010 I have had the chance to be involved in the Styrian LiDAR campaign. This cooperation

delivers a wide basic knowledge of techniques in GIS analysis using ArcGIS software products,

programming with Python scripts, and utilizing different aspects of airborne laser scanning. By

combining this basic knowledge in GIS and Remote Sensing with my interests for LiDAR and

geomorphological processes my master thesis entitled "Using High‐Resolution Airborne LiDAR‐

Data for Landslide Mapping in the Eastern Alps" was born. Internationalisation is the main reason

for choosing English as the language for my thesis.

I would like to thank the staff of the Provincial Government of Styria, Board of Works –

Geoinformation Staff Office, especially DI Rudolf L. Huetter, for his assistance with all my software

and LiDAR problems and DI Babara Piskaty, who helped me with all mathematical and remote

sensing problems, Mag. Bernadette Kreuzer for helping me with cartographic and layout

questions and Mag. Susanne Tiefengraber for allowing me to use her archaeological sites ‐ vector‐

dataset.

I would also like to thank Professor Dr. rer. nat. Oliver Sass for supervising my thesis and for his

patience.

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Special thanks to both DI Joachim Niemeyer, Leibniz University of Hannover, Institute of

Photogrammetry and Geoinformation and Dr. Martin Rutzinger, University of Innsbruck, Institute

of Geography, for helping me with the edge detection tool and for allowing me to implement their

approaches in my tools, to Dr. Jeff Jenness, a US American wildlife biologist and GIS analyst for

allowing me to use and implement his ArcGIS Landfacet Corridor Tool in my ArcGIS Landslide

Mapping Toolbox and Mark Sappington, a US American GIS specialist from

Lake Mead National Recreation Area, Nevada for allowing me to adapt his “Vector Ruggedness

Measure” python script for my research.

I would also like to thank Dr. Christian Bauer, Joanneum Research and Dr. Ingomar Fritz,

Universalmueum Joanneum, Geology and Palaeontology for giving me assistance in the field of

geomorphology and mass‐denudations, Dr. Bernhard Hoefle, University of Heidelberg, Institute of

Geography for sending me information about geomorphology and its application in LiDAR, and

Dustin Hoover, University of Arkansas at Fayetteville, Department of Geosciences and Matthew

Balazs, University of Alaska Fairbanks, Department of Geology and Geophysics for proofreading

my master’s thesis.

Last, but not least very special thanks go to my family, friends and to my boyfriend Michael Haid

for everything they did for me. Without them I would not be where I am now.

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Abstract

Using High‐Resolution Airborne LiDAR‐Data for Landslide Mapping in the Eastern Alps (Province

of Styria, Austria)

Key Words: LiDAR, Geomorphology, Landslides, Raster, DTM, Extraction

Due to the increasing frequency of natural disasters like floods and landslides, the active remote sensing

technique LiDAR (Light Detection and Ranging), has become a topic of great interest to the Provincial

Government of Styria, Federal Republic of Austria. In an on‐going project from 2008 to 2012 high‐resolution 3D

airborne LiDAR data of the Province of Styria, an area about 16,000 km² in south‐eastern Austria, were collected

with a vertical accuracy of ±15 cm and a positional accuracy of ±40 cm. These data were processed to create

Digital Terrain Models (DTM) and Digital Surface Models (DSM) at 1 m resolution.

High resolution DTMs can be used in different geo‐related applications like geomorphological mapping or

natural hazard mapping. Because of their high degree of accuracy, DTMs depict various natural and

anthropogenic terrain features such as erosion scarps, alluvial fans, landslides, old creeks, topographic edges and

karst formations, as well as walking paths and roads. Additionally LiDAR data allows the detection and outlining

of these different geomorphological and anthropogenic features within a GIS environment, geoprocessing and

analysing techniques, mathematical, statistical, and image processing methods, and the open source scripting

language Python. As a result complex workflows and new geoprocessing tools can be implemented in the

standard ArcGIS workspace and are provided as easy‐to‐use toolbox contents.

The landslide phenomena take centre stage of the research work of the author. Therefore the main focus is

targeted on sliding movements out of soils and bedrock with different velocities. Factors like gravity directly

affect slope stability and cause complex mass movements with a downslope‐directed sliding movement of bed‐

and/or loose‐rock and soil material.

On the following pages the author presents the results of her master’s thesis, a semi‐automatic ArcGIS landslide

mapping toolbox using high‐resolution LiDAR data in the rock masses of the Eastern Alps (Province of Styria,

Austria). This toolbox is based on analysing and modeling different land surface parameters such as slope,

variance of slope, curvature or roughness. The ArcGIS Landslide Mapping Tool points out endangered regions in

the Province of Styria and shows the quantity of landslides in a specific area.

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Kurzfassung

Anwendung von hochauflösenden Airborne LiDAR Daten zur Kartierung von Hangrutschungen in

den Ostalpen

Schlüsselwörter: LiDAR, Geomorphologie, Hangrutschungen, Raster, DTM, Extraktion

Die Steiermärkische Landesregierung, Landesbaudirektion ‐ Stabsstelle Geoinformation startete aufgrund der

ständigen Zunahme von Naturkatastrophen wie Hochwasserkatastrophen oder Hangrutschungen eine

mehrjährige Airborne LiDAR (Light Detection and Ranging) Befliegungskampagne im Zeitraum 2008 bis 2012. Im

Zuge dieses Projekts wurden hoch‐auflösende 3D Airborne LiDAR Daten der gesamten Steiermark mit einer

Fläche von über 16.000 km² in Südost‐Österreich und einer vertikalen Genauigkeit von ±15 cm und einer

Lagegenauigkeit von ±40 cm, gesammelt. Diese Daten werden zu Digitalen Gelände Modellen (DGM) und

Digitalen Oberflächen Modellen mit einer Auflösungen von 1 m weiterverarbeitet.

Durch die hohe Auflösung eignen sich diese Daten hervorragend für die Kartierung von geomorphologischen

Formen oder Naturkatastrophen. Die hohe Genauigkeit des DGM lässt verschiedenste natürliche und

anthropogene Formen, wie zum Beispiel Erosionskanten, Schwemmfächer, Hangrutschungen, alte Flussläufe,

topografische Kanten und Karstformen, sowie Wanderwege und Straßen sichtbar werden und ermöglicht eine

Detektion und Abgrenzung dieser unterschiedlichen geomorphologischen und anthropogenen Strukturen mit

Hilfe von ArcGIS Geoprozessierungs‐ und Analyse‐Techniken, mathematischen, statistischen und

Bildverarbeitungs‐Methoden und der open‐source Skriptsprache Python. Komplexe Arbeitsschritte und neue

Geoprozessierungs‐Werkzeuge können somit in eine ArcGIS Arbeitsumgebung implementiert werden und liefern

einfach nutzbare Toolbox‐Inhalte.

Das Hangrutschungs‐Phänomen steht im Mittelpunkt dieser Forschungsarbeit. Dabei richtet sich das

Hauptaugenmerk auf gleitende Erdbewegungen mit unterschiedlichen Geschwindigkeiten aus Bodenmaterial

oder Festgestein. Faktoren, wie Gravitation, beeinflussen direkt die Hangstabilität und verursachen komplexe,

hangabwärts gerichtete, gleitende Massenbewegungen aus Fest‐ und Lockgesteinen, sowie Bodenmaterial.

Auf den nächsten Seiten präsentiert die Autorin die Ergebnisse ihrer Masterarbeit, eine automatische ArcGIS

Landslide Mapping Toolbox für eine semi‐automatische Detektion von Hangrutschungen aus hochauflösenden

LiDAR Daten in den Ostalpen (Steiermark, Österreich). Diese Toolbox basiert auf einer Analyse und Modellierung

unterschiedlicher Parameter der Landoberfläche, wie Hangneigung, Varianz der Neigung, Krümmung oder

Rauigkeit. Mit dieser ArcGIS Landslide Mapping Toolbox werden gefährdete Gebiete in der Steiermark und

räumliche Verteilung von Hangrutschungen in einem speziellen Gebiet aufgezeigt.

Contents

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Contents

1. Introduction........................................................................................................................ 14

1.1 Research Objectives ............................................................................................................ 14

1.2 Goals and Limitations .......................................................................................................... 15

1.3 Methodology ....................................................................................................................... 16

2. Basics .................................................................................................................................. 18

2.1 Airborne LiDAR .................................................................................................................... 18

2.1.1 Definition ................................................................................................................................. 18

2.1.2 Measurement technique ......................................................................................................... 19

2.1.3 ASPRS LAS Format and Classification of LiDAR Point Cloud Data ............................................ 21

2.1.4 Digital Terrain Models (DTMs) and Digital Surface Models (DSMs) ........................................ 24

2.2 Landslides ............................................................................................................................ 25

2.2.1 Definition and Classification .................................................................................................... 25

2.2.2 Landslide Body (see Figure 8) .................................................................................................. 29

2.2.3 Triggers .................................................................................................................................... 30

2.3 Landslides in the field of LiDAR – State of the Art .............................................................. 32

3. Study Areas ........................................................................................................................ 35

3.1 General Settings .................................................................................................................. 35

3.1.1 Geographical Position .............................................................................................................. 36

3.2 Geology and Soils ................................................................................................................ 39

3.3 Vegetation, Land Use and Climate ...................................................................................... 42

Contents

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4. LANDSLIDE MAPPING – Theoretical Part ............................................................................. 44

4.1 Software Description ........................................................................................................... 44

4.1.1 ArcGIS and Extensions (ESRI) ................................................................................................... 44

4.1.2 LP360 for ArcGIS ...................................................................................................................... 45

4.1.3 Python ...................................................................................................................................... 46

4.2 Data ..................................................................................................................................... 46

4.2.1 LiDAR Data ............................................................................................................................... 46

4.2.2 Vector Data .............................................................................................................................. 48

4.3 Land Surface Parameters .................................................................................................... 49

4.3.1 Hillshade .................................................................................................................................. 50

4.3.2 Slope ........................................................................................................................................ 50

4.3.3 Variance of Slope ..................................................................................................................... 51

4.3.4 Aspect ...................................................................................................................................... 51

4.3.5 Variance of Aspect ................................................................................................................... 52

4.3.6 Curvature ................................................................................................................................. 52

4.3.7 Roughness ................................................................................................................................ 53

4.3.8 Contour Lines ........................................................................................................................... 53

4.4 Triangular Irregular Network (TIN) ...................................................................................... 54

4.5 Topographic Position Index ................................................................................................. 55

4.5.1 TPI Elevation ............................................................................................................................ 56

4.5.2 TPI Slope .................................................................................................................................. 57

4.6 SMORPH Landslide Risk Model ........................................................................................... 58

Contents

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5. Landslide Mapping – Practical Part ..................................................................................... 59

5.1 Manual Landslide Mapping ................................................................................................. 60

5.1.1 Manual Landslide Mapping Workflow .................................................................................... 60

5.2 Verification and Results of Manual Landslide Mapping...................................................... 65

5.2.1 Verification of manually mapped landslides ........................................................................... 65

5.2.2 Results of the manual landslide mapping ............................................................................... 73

5.3 Semi‐Automatic Landslide Mapping ................................................................................... 79

5.3.1 Basic Workflow ........................................................................................................................ 80

5.3.2 Semi‐automatic Landslide Mapping Toolbox .......................................................................... 84

5.4 Results ................................................................................................................................. 97

5.4.1 Intermediate Data (Landslide Mapping Toolbox) .................................................................... 97

5.4.2 Potential Landslides ............................................................................................................... 101

5.4.3 Limitations ............................................................................................................................. 106

5.4.4 Adaptability of a semi‐automatic landslide mapping ............................................................ 109

6. Conclusions and Perspectives ........................................................................................... 110

References ............................................................................................................................... 114

Index of Figures

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Index of Figures

Fig. 1. Methodical development of the master's thesis (source: AUTHOR’S ILLUSTRATION, 2012) ........................................ 17

Fig. 2. Helicopter of the Styrian LiDAR campaign and box with the laser scanner (source: AUTHOR’S IMAGE, 2010) ......... 19

Fig. 3. Schematic LiDAR Data Measurement Technique (source: MTC, 2003) .................................................................. 21

Fig. 4. Example of LiDAR Intensity (source: STYRIAN LIDAR CAMPAIGN, 2011) ....................................................................... 22

Fig. 5. Example of LiDAR Return Number (sources: Styrian Lidar Campaign, 2011 and Noaa, 2008) ............................... 22

Fig. 6. Classified LiDAR Point Cloud (source: STYRIAN LIDAR CAMPAIGN, 2011) ..................................................................... 23

Fig. 7. Landslide Classification (source: GEONET, 2011) ..................................................................................................... 27

Fig. 8. Landslide Body (source: Geological Hazards Program, 2011) 29

Fig. 9. Relief image of the Province of Styria (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) ............... 36

Fig. 10. Study Area 1, Spielberg bei Knittelfeld ‐ LiDAR DTM and DSM

(data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION)...................................................................................... 38

Fig. 11. Study Area 2 Wald am Schoberpass ‐ LiDAR DTM and DSM


Fig. 12. Geology and soil maps of Study Area 1 (sources: GEOLOGICAL SURVEY OF AUSTRIA, 2011 AND BMLFUW & BFW, 2009) 40

Fig. 13. Geology and soil maps of Study Area 2 (sources: GEOLOGICAL SURVEY OF AUSTRIA, 2011 AND BMLFUW & BFW, 2009) 41

Fig. 14. Study Area 1 ‐ RGB and CIR orthophotos (source: GIS STYRIA, 2012) .................................................................... 43

Fig. 15. Study Area 2 ‐ RGB and CIR orthophotos (source: GIS STYRIA, 2012) .................................................................... 43

Fig. 16. ArcGIS product line (source: ESRI, 2011) ............................................................................................................... 44

Fig. 17. LP360 surface (source: STYRIAN LIDAR CAMPAIGN, 2011) ......................................................................................... 45

Fig. 18. Classified LiDAR Point Cloud (source: STYRIAN LIDAR CAMPAIGN, 2011) ................................................................... 47

Fig. 19. Hillshade and slope images (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) ............................. 50

Fig. 20. Variance of slope and aspect images (data basis: STYRIAN LIDAR CAMPAIGN, 2011) ................................................ 51

Fig. 21. Variance of aspect and vertical curvature images


Fig. 22. Roughness image and contour lines (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) ............... 53

Fig. 23. TIN and TIN 3D Model (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) ..................................... 54

Fig. 24. Slope Position Classification at different scales (source: JENNESS et al., 2011, p. 45ff.) ........................................ 55

Fig. 25. TPI elevation images (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) ....................................... 56

Fig. 26. TPI slope images (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) ............................................. 57

Fig. 27. Smorph image (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) ................................................. 58

Fig. 28. Schematic landslide sketch and schematic profile view of a landslide

(source of left image: GEOLOGICAL HAZARDS PROGRAM, 2011 and source of right image: AUTHOR’S ADAPTATION) .................. 61

Fig. 29. Visualization of land surface parameters (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) ....... 62

Fig. 30. Visualization of land surface parameters (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) ....... 63

Fig. 31. Landslide example 1 – model and reality (data basis: STYRIAN LIDAR CAMPAIGN, 2011 and AUTHOR’S IMAGE, 2011) 67


Index of Figures

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Fig. 35. Landslide example 5 – model and reality (data basis: Styrian Lidar Campaign, 2011 and AUTHOR’S IMAGE, 2011)71

Fi. 36. Landslide example 6 – model and reality (data basis: STYRIAN LIDAR CAMPAIGN, 2011 and AUTHOR’S IMAGE, 2011) .. 72

Fig. 37. Map of landslide main scarps of Study Area 1 (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) 74

Fig. 38. Map of landslide areas of Study Area 1(data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) ........... 74

Fig. 39. Map of landslide main scarps in combination the local geology of Study Area 1


Fig. 40. Map of landslide main scarps of Study Area 2 (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) 76

Fig. 41. Map of landslide areas of Study Area 2 (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) .......... 77

Fig. 42. Map of landslide main scarps in combination the local geology of Study Area 2


Fig. 43. Continuous Loop (source: AUTHOR’S ADAPTATION, 2012) ........................................................................................ 82

Fig. 44. Basic Workflow of Semi‐Automatic Landslide Mapping (source: AUTHOR’S ADAPTATION, 2012) ............................ 83

Fig. 45. Tool 1 – Land Surface Parameters (source: AUTHOR’S ADAPTATION, 2011) ............................................................. 84

Fig. 46. Workflow of Tool 1 (source: AUTHOR’S ADAPTATION, 2011) .................................................................................... 85

Fig. 47. Tool 2 – Topographic Position Index (source: AUTHOR’S ADAPTATION, 2011) .......................................................... 86


Fig. 49. Tool 3 – Smorph (source: AUTHOR’S ADAPTATION, 2011) ......................................................................................... 87


Fig. 51. Tool 4 – Roughness (source: AUTHOR’S ADAPTATION, 2011) .................................................................................... 89


Fig. 53. Tool 5 – Edge Detection (source: AUTHOR’S ADAPTATION, 2011) ............................................................................. 90


Fig. 55. Tool 6 – Forest Roads (source: AUTHOR’S ADAPTATION, 2011) ................................................................................. 92


Fig. 57. Tool 7 – Streams (source: AUTHOR’S ADAPTATION, 2011) ........................................................................................ 93


Fig. 59. Tool 8 – Potential Landslides (source: AUTHOR’S ADAPTATION, 2011) ..................................................................... 95

Fig. 60.Workflow of Tool 8 (source: AUTHOR’S ADAPTATION, 2011) ..................................................................................... 96

Fig. 61. Aperture of generalised TIN of Study Area 1 (source: AUTHOR’S ADAPTATION, 2011) ............................................. 98

Fig. 62. Aperture of generalised TIN of Study Area 2 (source: AUTHOR’S ADAPTATION, 2011) ............................................. 99

Fig. 63. Aperture of results of Study Area 1 of the “ForestStreets” and “Streams” tools

(source: AUTHOR’S ADAPTATION, 2011) ............................................................................................................................... 100

Fig. 64. Aperture of results of Study Area 2 of the “ForestStreets” tool (source: AUTHOR’S ADAPTATION, 2011) .............. 100

Fig. 65. Results of the semi‐automatic mapping tool in Study Area 1 (source: AUTHOR’S ADAPTATION, 2011) .................. 104

Fig. 66. Results of the semi‐automatic mapping tool in Study Area 2 (source: AUTHOR’S ADAPTATION, 2011) .................. 105

Fig. 67. Diverse terrain surface structures (STYRIAN LIDAR CAMPAIGN, 2011) ................................................................... 112

Index of Tables

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Index of Tables

Table 1. ASPRS Standard LiDAR Point Classes (source: ASPRS, 2010) ................................................................................ 23

Table 2. Landslide Classification according to CRUDEN & VARNES, 1996 ............................................................................. 25

Table 3. Landslide Causes (source: LANDSLIDE HAZARDS PROGRAM, 2011) ............................................................................ 31

Table 4. List of papers and articles (source: AUTHOR’S ADAPTATION, 2011) .................................................................... 33‐34

Table 5. Geographical position of the study areas (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) ...... 35

Table 6. List of important parameters (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) ......................... 37

Table 7. Main equipment of the Styrian LiDAR – campaign (source: STYRIAN LIDAR CAMPAIGN, 2011) ............................... 46

Table 8. In this thesis main used vector data (source: GIS STYRIA, 2012) ........................................................................... 48

Table 9. Visibility of landslide features in different raster images (source: AUTHOR’S ADAPTATION, 2012) ......................... 64

Table 10. Manually mapped landslides (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION) ....................... 73

Table 11. Overview of the 8 contiguous tools of the Landslide Mapping Toolbox (source: AUTHOR’S ADAPTATION, 2011) 81

Table 12. Aperture of the results of Study Area 1 (source: AUTHOR’S ADAPTATION, 2012) ............................................... 101

Table 13. Aperture of the results of Study Area 2 (source: AUTHOR’S ADAPTATION, 2012) ............................................... 102

Table 14. Statistics of the semi‐automatic landslide mapping (source: AUTHOR’S ADAPTATION, 2012) ............................ 103

Table 15. Reasons for poor results in the two study areas (source: AUTHOR’S ADAPTATION, 2012) .......................... 106‐108

1. INTRODUCTION

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1. INTRODUCTION

1.1 Research Objectives

The main research goals of the author’s master thesis with the title “Using High‐Resolution

Airborne LiDAR‐Data for Landslide Mapping in the Eastern Alps” are two‐fold. The first main

objective is to discover the possibilities of the application of airborne LiDAR (Light Detection and

Ranging) data for geomorphological mapping, in particular landslide mapping. The second

objective is to develop a possible technique for a raster‐based semi‐automatic landslide mapping.

To address these objectives, the author has created an ArcGIS toolbox with a package of tools that

extract different landslide and other geomorphological features such as scarps, forest roads, or

streams from the LiDAR DTM. This is done by combining geomorphometric landscape analysis,

ArcGIS geoprocessing and analysing techniques, as well as mathematical, statistical and image

processing operations. The tool was designed in a way that anyone, who is familiar with LiDAR and

its use in geomorphology, can use it, regardless of his or her previous knowledge about a specific

study area.

The high potential of the active remote sensing technique LiDAR, also known as Airborne Laser

scanning (ALS), to study geomorphological features, coupled with the increasing number of

landslides or flood disasters, motivated the Provincial Government of Styria to start an ALS –

campaign. In an on‐going project from 2008 to 2012 high‐resolution 3D ALS data of the Province of

Styria, an area about 16,000 km² in south‐eastern Austria, were collected. Due to of its high

vertical accuracy of ±15 cm and a positional accuracy of ±40 cm, ALS allows the detection and

outlining of different geomorphological structures such as alluvial fans, landslides, creeks,

topographic edges and karst formations as well as anthropogenic geomorphological structures like

forest roads.

As DTMs are a powerful tool for detecting different geomorphological structures, the application

of high resolution models opens up new, unimaginable opportunities.

This thesis will give a short insight into these new chances.

1. INTRODUCTION

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1.2 Goals and Limitations

Since October 2010 the author has worked on her master’s thesis with the main aim to create an

ArcGIS toolbox for semi‐automatic landslide mapping using high‐resolution airborne LiDAR data.

This allowed her to become familiar with LiDAR and its application in geomorphology, the different

software products, and the scripting language Python.

The following research goals were defined:

A short description of LiDAR and its applications in geomorphology

A short introduction in the field of landslides

Calculation of different land surface parameters and Triangular Irregular Networks (TINs)

out of DTMs

Utilisation of the calculated land surface parameters and TINs to map landslides manually

Verifying the manually mapped landslides through field work and by consulting two

experts Dr. Ingomar Fritz, Universalmueum Joanneum, Geology and Palaeontology and

Dr. Christian Bauer, Joanneum Research

Creating a toolbox for semi‐automatic landslide mapping in the ArcGIS 10.0 environment

Semi‐automatic detection of landslide features by analysing and combining

land surface parameters

Comparing the results of the manually mapped and the semi‐automatically mapped

landslides

Improving the toolbox with the knowledge acquired during the manual mapping process

Testing the adaptability and the results of the landslide mapping toolbox

Because of the fact that the author lacked any useable landslide dataset of Styria, she had to map

landslides in two study areas on her own and verify it by conducting field work and consulting

experts. For this purpose it is indispensable to be familiar with handling and reading LiDAR data

and raster datasets. Concerning semi‐automatic landslide mapping, a differentiation between

landslides and landslide‐related features often caused by anthropogenic activities is problematic,

but an integration of other vector datasets (streets, streams, archaeological and mining features,

etc.) improves results. Furthermore noises and artefacts caused by the LiDAR scanner complicate

semi‐automatic landslide mapping and can degrade results.

These problems that represent some of the biggest challenges in this master’s thesis are

addressed at the end.

1. INTRODUCTION

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1.3 Methodology

It must be said that this master’s thesis demands hours of intensive working with the provided

LiDAR data and many hours to acquaint oneself with LiDAR, LiDAR DTMs and the software

packages ArcGIS 10.0 (with its different extensions; 3D Analyst and Spatial Analyst), and the LP360

extension of QCoherent and to learn the open‐source scripting language Python (see Figure 1, No.

2). Since March 2010 the author is a collaborator of the Styrian LiDAR campaign, where she works

on post processing LiDAR point cloud data, calculating DTMs and Digital Surface Models (DSMs),

and investigating different methods for automatic extraction of different geomorphological and

anthropogenic structures out of high‐resolution LiDAR point cloud data, DTMs and DSMs.

Different scientific papers, books and articles (see Figure 1, No. 1) were consulted to get essential

information about landslides, LiDAR and its application in geomorphology, and to investigate a

possible semi‐automatic landslide mapping out of LiDAR DTMs.

The practical part of the master’s thesis is divided into three main parts (Figure 1, green boxes):

1) By using image interpretation techniques the land surface parameters such as hillshade,

slope, curvature, contours, roughness, variance of slope, and variance of aspect were

calculated from LiDAR DTMs (see Figure 1, No. 4). Using this information, actual landslides

were mapped manually as vector datasets (see Figure 1, No. 8). There currently exists no

useable landslide datasets in Styria, hence the reason for manually mapping the landslides

first. Previous datasets in this area are too imprecise to verify semi‐automatically mapped

landslide features. In addition, a majority of the landslides in this area are not digitalized

because they are too old or somewhere in the forest. Fortunately, landslides that are old

and scarred or hidden below dense vegetation, appear in LiDAR DTMs (see Figure 1, No. 3),

because of its high resolution and accuracy.

2) After manually mapping actual landslides the results were verified by going into the field

(see Figure 1, No. 5), and by consulting two experts, the geologist

Dr. Ingomar Fritz (Universalmueum Joanneum, Geology and Palaeontology) and the

geographer Dr. Christian Bauer (Joanneum Research; see Figure 1, No. 6).

1. INTRODUCTION

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3) In the last step the semi‐automatic ArcGIS landslide mapping tool was executed (see Figure

1, No. 7), and potential landslides were detected (see Figure 1, No. 10). The results were

compared and verified with the manually mapped landslide features to determine the

adaptability, and accuracy of the automatic tool (Figure 1, No. 13). The verified manually

mapped landslide features were used to improve the semi‐automatic landslide mapping

tool (Figure 1, No. 12).

Figure 1. Methodical development of the master's thesis (source: AUTHOR’S ILLUSTRATION, 2012)

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2. BASICS

Before describing the main part of the master's thesis, which includes both a manual landslide

mapping component and the creation of an ArcGIS toolbox for semi‐automatic landslide mapping

using airborne LiDAR DTMs, a very short introduction in airborne LiDAR and landslides is given on

the following pages.

2.1 Airborne LiDAR

2.1.1 Definition

Light Detection and Ranging (LiDAR), also known as Airborne Laser scanning (ALS), LADAR or Laser

Altimetry, contains a multiplicity of different applications. To understand a few of the possibilities

of LiDAR some applications are listed below:

Raman LiDAR,

Resonance Scattering LiDAR,

Doppler Wind LiDAR,

temperature measurements,

atmospheric and meteorological measurements,

Terrestrial LiDAR,

Spaceborne LiDAR and

Airborne LiDAR

For landslide mapping terrestrial and airborne LiDAR systems are often used. In this master's

thesis, only airborne LiDAR is considered. Airborne LiDAR is a useful method for studying the

atmosphere, hydrosphere, and lithosphere and determining the density and structure of

vegetation canopy in forests (see BROWELL et al. 2005, p. 723ff.).

In the 1970s and 1980s the U.S. NASA (National Aeronautics and Space Administration) developed

airborne LiDAR, an aircraft‐mounted electro‐optical distance meter that measures the distance

between an aircraft and the ground (see SATO et al. 2007, p. 237ff.). In 1967 the first airborne

LiDAR measurement was conducted by S. Harvey Melfi.

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The active remote sensing technique is similar to radar, but instead of radio waves, discrete light

pulses measure travel times. Unlike radar, LiDAR has to be flown during fair weather, because

LiDAR cannot penetrate clouds, rain, or dense haze (see NOAA 2008, p. 7ff).

The LiDAR technique permits measurement of large regions in a short time and can rapidly

measure the surface at sampling rates greater than 150 kilohertz or 150,000 pulses per second. It

produces a rapid collection of more than 70,000 highly accurate georeferenced elevation points

per second. The major advantages of using LiDAR include a high resolution, its incredibly high

vertical and positional accuracy of measurement, and its ability to penetrate through vegetation in

forested terrain. LiDAR is an active remote sensing technique because the energy for

measurement is generated by the laser sensor. Airborne LiDAR is very useful for surface and

terrain studies or it has been applied to study the density and structure of the forest canopies (see

NOAA 2008, p. 7ff).

2.1.2 Measurement technique

A LiDAR system consists of a transmitter (laser), transmitter optics, receiver optics, a detector, and

an electronic system for the acquisition, evaluation, display and storage of data. Other additional

components differ from type and purpose of the LiDAR (see WEITKAMP 2005, p. 1ff.).

The laser scanner is mounted on either an airplane or a helicopter; as in the case of the Styrian

LiDAR campaign, a helicopter was utilized because of the distinctive topographical relief inherent

in Styria (see Figure 2). With a helicopter the possibility of flying smaller radii is possible, a major

benefit when collecting data in mountainous areas (see BROWELL et al. 2005, p. 724ff.).

Figure 2. Helicopter of the Styrian LiDAR campaign and box with the laser scanner (source: AUTHOR’S IMAGE, 2010)

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The laser scanner has some limitations in size, mass, power and receiver aperture. In order to get

good data, a scanner should have the ability to operate in an environment with high‐ and low‐

frequency vibrations as well as temperature and cabin pressure variations. It should also be

designed and developed to function with limited operator intervention during flight (see BROWELL

et al. 2005, p. 724ff.).

With an integrated Global Positioning System (GPS) the position of the laser scanner is

determined. An Inertial Measurement Unit (IMU) or Inertial Navigation System (INS) provides the

angular altitude of the sensor platform. The ALS emits laser pulses in the surface direction and

measures the time each laser pulse travels to the hit object and return. As a result, LiDAR 3D point

cloud data of the reflection (echo) are computed. In the case of the Styrian LiDAR campaign, a

vertical accuracy of ±15 cm and a positional accuracy of ±40 cm were achieved. The elevation and

location of the reflected surface are derived from the time difference between the laser pulse

being emitted and returned, the angle of the pulse and the location and height of the scanner

within the aircraft. On a rotating or oscillating mirror the emitted beam is deflected, which causes

typical scanning patterns depending on the type of laser scanner. The scan angle determines the

width (swath) of the area captured within one flight strip (see NOAA 2008, p. 7ff.).

Given that such a large quantify of individual points are generated, even if only a small percentage

of laser pulses reach the ground through the trees, there are usually enough to provide adequate

representation of the ground in forested areas. An exception to this can occur in very dense

forests with “leaf‐on” conditions, like rain forests. In this case, the poor ground representation can

cause problems (see NOAA 2008, p. 7ff.). Mainly in the field of geomorphology, the penetration of

vegetation canopy is a big achievement.

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The measurement technique in short and simplified once again (see Figure 3):

The time it takes the laser pulse to hit an object

or surface and return to the aircraft with a

known position and height (GPS x, y and z) is

measured. The distance of the laser pulse (INS z)

is determined by using the travel time (INS x)

and the recorded laser angle (INS y). With this

information the exact location of the reflecting

object or surface is located in three dimensions

(OBJ x, y and z; see NOAA 2008, p. 7ff.).

Figure 3. Schematic LiDAR Data Measurement

Technique (source: MTC, 2003)

2.1.3 ASPRS LAS Format and Classification of LiDAR Point Cloud Data

The ASPRS LAS Format is the most commonly used LiDAR Format, and it is readable by well‐

established software packages like OPALS by Institute of Photogrammetry and Remote Sensing,

Vienna University of Technology, LP360 by QCoherent, E3de by Exelis Visual Information Solution

Products, Pointools by Bentley Systems or the new ArcGIS 10.1. by ESRI, and open‐source software

packages such as SAGA by Institute of Geography, University of Hamburg, or LAStools by Martin

Isenburg.

Created by the American Society of Photogrammetry and Remote Sensing (ASPRS), the intention

of the LAS data format is to provide a compatible file structure that can be commonly accessed

and shared across different LiDAR hardware and software platforms (see ASPRS 2010).

In the ASPRS LAS Format all LiDAR point data records are stored. Each data point has the exact

x, y and z point coordinates with further information about:

the closeness of point to each other is stored, the point spacing,

the strength or intensity of the return, which represents how well the object reflected the

wavelength of light (see Figure 4),

the quantity of returns, known as the return number

and the return combination (first/last returns; see Figure 5). The first, second, third and

ultimately the “last” return from a single laser pulse are captured (see ASPRS 2010).

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All these attributes are used for further classifications and analysis; for example, the return

number can be used to determine what the reflected pulse is from, such as ground, trees or

buildings. The intensity is used to contrast between different kinds of vegetation, and the point

spacing is used to distinguish between anthropogenic and natural objects (see ASPRS 2010).

Figure 4. Example of LiDAR Intensity

(source: STYRIAN LIDAR CAMPAIGN, 2011)

Figure 5. Example of LiDAR Return Number

(sources: STYRIAN LIDAR CAMPAIGN, 2011

and NOAA, 2008 [left image])

For further analysis of the LiDAR data and for calculation of DTMs and DSMs, the 3D point cloud

data has to be classified in ground points and non‐ground points. Non‐ground points are further

classified into a subset of categories including low, medium and high vegetation , buildings, low

point (noise), model key‐point (mass point), water and overlap points (see ASPRS 2010).

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Table 1 shows the ASPRS Standard LiDAR Point Classes and Figure 6 shows two examples of a

classified point cloud.

Classification Value Meaning

0 Created, never classified

1 Unclassified

2 Ground

3 Low Vegetation

4 Medium Vegetation

5 High Vegetation

6 Building

7 Low Point (noise)

8 Model Key‐point (mass point)

9 Water

10 Reserved for ASPRS Definition

11 Reserved for ASPRS Definition

12 Overlap Points

13 ‐ 31 Reserved for ASPRS Definition

Table 1. ASPRS Standard LiDAR Point Classes (source: ASPRS, 2010)

Figure 6. Classified LiDAR Point Cloud [Red = Buildings, Green = Vegetation and Orange = Ground]

(source: STYRIAN LIDAR CAMPAIGN, 2011)

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2.1.4 Digital Terrain Models (DTMs) and Digital Surface Models (DSMs)

In the case of the Styrian LiDAR campaign, LiDAR data were processed to create Digital Terrain

Models (DTMs) and Digital Surface Models (DSMs). DTMs are surfaces created and interpolated

from ground point data. The ground point data are interpolated with different gridding routines

such as simple nearest neighbour or complex kriging techniques. DSMs include any type of surface

representations from bare‐earth data to the surface of high vegetation (see NOAA 2008, p. 7ff.).

For this study these models are visualised as greyscale raster datasets with 1 m resolution and

projected in UTM33N and BMNM31 and M34. DTMs and DSMs can then be used for further

applications such as flood modeling or object mapping. More information about raster data, DTMs

and DSMs, and their applications especially in the case of Geomorphometry, can be read in

HENGL, T. & REUTER H. (Eds), 2009. Geomorphometry ‐ Concepts, Software, Applications. –

Elsevier, Amsterdam and Oxford, 765 p.

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2.2 Landslides

2.2.1 Definition and Classification

Landslides or gravitational mass movements are characterized by a downslope movement of

slope‐forming materials like soil, rock, and other earth materials. Landslides occur from

mountainous regions to areas of low relief (low relief: mostly influenced by human activities). In

contrast to fluvial, glacial, and aeolian processes, a landslide movement is not influenced by a

transport medium such as wind or water, but by the moving power of gravity. Another

characteristic of mass movements is that proximate material is moved collectively and deposited

unsorted (see ZEPP 2008, p. 103ff.). Landslides can be classified according to CRUDEN & VARNES

(1996), by the kind of the involved material (bedrock, soil), the mode of movement (fall, topple,

slide, lateral spread, flow; see Table 2 and Figure 8) and the velocity of movement from extremely

slow (15 mm/year) to extremely rapid (5 m/sec; see CRUDEN & VARNES 1996, p. 36ff.). Furthermore,

the form and character of the moving body is dependent upon the rate of movement, on bedrock

topology, and the content of water, ice and air in the landslide material. Each kind of landslide can

be a combination of the different modes of movement (see CRUDEN & VARNES 1996, p. 36ff.; ZEPP

2008, p. 104ff.; SASSA 2007, p. 3ff.).

Mode of Movement

Type of Material

Bedrock

Soils

coarse (debris) fine (earth)

Falls Rock fall Debris fall Earth fall

Topples Rock topple Debris topple Earth topple

Slides

Rotational

Rock slide

Debris slide

Earth slide Translational

Block Block slide Block slide

Lateral Spreads Rock spread Debris spread Earth spread

Flows Rock flow Debris flow Earth flow

Complex Forms Combination of two or more modes of movement

Table 2. Landslide Classification according to CRUDEN & VARNES, 1996 [grey boxes = in this master’s thesis considered

landslide form]

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Ad Table 2:

Falls are classified as downward, bouncing, or rolling mass denudations located on steeply sloped

or cliff terrain with little or no shear displacement. Topples are landslides with a forward rotation.

Slides are downslope sliding movement out of soil and bedrock. Spreads are fracturing and lateral

extension of coherent rock or soil material due to liquefaction or plastic flow of subjacent

material. Flows are slow to rapid mass movements in saturated materials that advance by viscous

flow, usually following initial sliding movement. Some flows may be bounded by basal and

marginal shear surface but the domain movement of the displaced mass is flowage. Complex

slides (see Figure 7) involves two or more of the main movement types in combination

(see CRUDEN & VARNES 1996, p.36ff.).

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Figure 7. Landslide Classification (source: GEONET, 2011)

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The term “landslide” is too widespread to consider all the different forms in this master's thesis.

Therefore the main focus of this research work is to identify and map sliding movements of soils

and bedrock with the help of airborne LiDAR data.

Slides: “Landslide” is often used only for a downslope‐sliding movement out of bedrock and soil.

Sliding material is separated from more stable underlying material by a distinct zone of weakness

resulting from shear failure. The occurrence of shear failure is dependent on the local soil material

and geology and on modest to steep slopes (about 10 ° to 50 °). The sliding body can vary in shape,

sliding surface, velocity of movement (extremely slow to extremely rapid) and size from only a few

centimetres to some metres. These large mass movements may have sudden catastrophic effects,

which destroy buildings, pipelines, roads or other constructions. Basically, landslides can be

classified into a rotational (see Figure 7), a translational and a block sliding movement (see Figure

7), but hybrid forms are the norm rather than the exception (see SCHWEIZER EIDGENOSSENSCHAFT

2009b).

Rotational Slide (see Figure 7): The slide movement is rotational about an axis parallel to the

ground surface, the contour of the slope and transverse across the slide with an almost vertically

downward movement. The main scarp is curved upward (spoon‐shaped). Under certain

circumstances the displaced and relatively coherent mass may occur with little internal

deformation. The displaced material tilts backwards toward the scarp. Several parallel curved

planes of movements are called a slump. A rotational slide mostly occurs in homogeneous

material (fill material) and moves with a velocity from extremely slow to extremely rapid. Such

landslides can be triggered by intense rainfall or rapid snowmelt. These triggering mechanisms

lead to a saturation of slopes and can increase groundwater levels within the mass (see LANDSLIDE

HAZARDS PROGRAM 2011; VARNES 1984, p. 18ff.).

Translational Slide (see Figure 7): With translational slides, surface‐parallel beds slide along a

roughly planar surface with little rotation or backward tilting. If the surface of rupture is

sufficiently inclined, the slide may progress over considerable distances in contrast to rotational

slides. The material consists of loose, unconsolidated soils or extensive slabs of rock, or both.

Geologic discontinuities (faults, joints, bedding surface, or the contact between rock and soil or

permafrost and soil) are responsible for a failure of translational slides.

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Figure 8. Landslide Body

(source: GEOLOGICAL HAZARDS PROGRAM, 2011)

Translational slides occur globally in all types of environments. The surface of rupture varies

between failures of only a few square meters to several square kilometres in size. Translational

slides may move with a velocity from extremely slow to extremely rapid, but many are moderate

in velocity (1.5 meters per day). These slides may be triggered by intense rainfall, rise in

groundwater level within the slide, snowmelt, flooding, earthquake, leakage from pipes, or by

human activities such as undercutting of slopes. Rapid movements may disintegrate and develop

into a debris flow (see LANDSLIDE HAZARDS PROGRAM 2011; VARNES 1984, p. 18ff.).

Block Slide: A block slide is a translational slide in which the moving mass consists of a single unit

or a few closely related units that move downslope as a relatively coherent mass (see LANDSLIDE

HAZARDS PROGRAM 2011; VARNES 1984, p. 18ff.).

2.2.2 Landslide Body (see Figure 8)

A landslide begins with a slow movement

along a slip face, at which the sliding material

can stay in coherence. Because of a variable

speed of movement the displaced material is

choked. The downslope movement causes a

steep, mostly arc‐like main scarp (called weak

zone) on the undisturbed ground at the upper

edge of the landslide. This scarp is caused by

movement of the displaced material away

from the undisturbed ground. The highest

part of the main scarp is the landslide crown.

The landslide head is between the displaced

material and the main scarp. Differential

movements within the displaced material cause minor scarps. Transverse ridges and radial and

transverse cracks may form at the bottom (i.e. the toe) of the landslide movement. The surface of

rupture is below the original ground surface (existing before the landslide took place).

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This surface forms or has formed, the lower boundary of the displaced material. The main body of

the sliding movement is like a ductile mass of soil and rock material, which moves downslope, has

a concave curvature and accumulates above the original ground surface in less steep areas at the

toe of the surface of rupture. This overlapping landslide mass is called landslide foot or surface of

separation. The farthest point from the top, the highest part of a landslide, is called the tip. The

volume of the displaced material is called depleted mass and overlies the surface of rupture, but

underlies the original ground surface.

The zone of depletion is below the original surface. The volume limited by the main scarp, the

depleted mass and the original ground surface is called depletion. Adjacent to the sides of a sliding

movement are the flanks. The intersection (usually buried) between the lower part of the surface

of rupture of a landslide and the original ground surface is the toe of surface of rupture (see ZEPP

2008, p. 104ff; AHNERT 2009, p. 89; CRUDEN & VARNES 1996, p. 36ff.).

2.2.3 Triggers

Physical Triggers: As previously mentioned, the moving power of landslides is gravity. Gravity

directly effects slope stability and causes complex mass movements with a downslope‐directed,

sliding movement of bed and/or loose‐rock and soil material. The effect of gravity depends on the

slope gradient, because gravitational mass movements occur parallel to a slope, and soil and other

earth materials press down on a slope bed (see AHNERT 2009, p. 83f.).

Slope stability is further affected by a wide variety of environmental conditions and weathering

such as intense rainfalls, rapid snowmelt, rapid drawdown or filling, groundwater, flooding,

earthquakes, and paraglacial or volcanic processes.

Such conditions trigger spontaneous or continuous displacement of masses with a wide range of

possible sliding speed (see ZEPP 2008, p. 104ff.; CRUDEN & VARNES 1996, p. 36ff.).

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Below is a short list of further geological, morphological and anthropogenic triggers of landslides.

Geological Triggers Morphological Triggers Anthropogenic Triggers

weak or sensitive materials

weathered materials

sheared, jointed, or fissured

materials

adversely oriented

discontinuity (bedding,

shistosity, fault,

unconformity, contact, etc.)

contrast in permeability

and/or stiffness of materials

tectonic or volcanic uplift

glacial rebound

fluvial, wave, or glacial

erosion of slope toe or

lateral margins

subterranean erosion

(solution, piping)

deposition loading slope or

its crest

vegetation removal by fire

or drought

thawing

freeze‐and‐thaw weathering

shrink‐and‐swell weathering

excavation of slope or its

toe

loading of slope or its crest

drawdown (of reservoirs)

deforestation, irrigation,

mining, artificial vibration,

water leakage from utilities

Table 3. Landslide Causes (source: LANDSLIDE HAZARDS PROGRAM, 2011)

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2.3 Landslides in the field of LiDAR – State of the Art

During the last decade LiDAR, and especially its application in geomorphology, gain centre stage

more and more. Several different studies all over the world use LiDAR DTMs, more precisely

raster‐based approaches for landslide analysis and landslide mapping.

HOEFLE & RUTZINGER (2011) give a good overview of applications in geomorphology and related

fields using different LiDAR data products, such as Digital Terrain Models or 3D point cloud data.

High‐resolution airborne LiDAR DTMs are used for geomorphological mapping and to delineate

specific geomorphological landforms, like alluvial fans, debris flows, karst formations, or

landslides.

VAN DEN EECKHAUT et al. (2005; 2007; 2011), BOOTH et al. (2009), KASAI et al. (2009) and AMUNDSEN et

al. (2010) use LiDAR to create or update landslide inventory maps of deep‐seated landslides in

Belgium, USA and Japan. To calculate these maps VAN DEN EECKHAUT et al. (2005; 2007; 2011) utilize

LiDAR‐derived hillshade, slope and contour line maps (old landslides under forest). BOOTH et al.

(2009) present two methods of spectral analysis that utilize LiDAR‐derived digital terrain models of

the Puget Sound lowlands in Washington and the Tualatin Mountains in Oregon to classify and

map automatically deep‐seated landslides. KASAI et al. (2009) use the eigenvalue ratio and slope

filters calculated from a very high‐resolution LiDAR‐derived DTM.

SCHULZ (2004; 2007) also uses LiDAR data to visualise mapped landslides, main scarps and denuded

slopes in Seattle. A good base for the further development and improvement for in this master’s

thesis presented ArcGIS semi‐automatic landslide mapping toolbox, is given by the Topographic

Position Index first presented at the ESRI User Conference in San Diego, CA, by WEISS (2001). An

airborne LiDAR examination of the surface morphology of two canyon‐rim landslides in southern

Idaho is presented by GLENN et al. (2006). In the first step the high‐resolution LiDAR data were

used to calculate surface roughness, slope, semi‐variance and fractal dimension. The second step

is to combine these data with historical movement data (GPS and laser theodolite) and field

observations. The results show that high‐resolution LiDAR data have the potential to differentiate

morphological components within a landslide and provide an insight into the material type and

activity of the sliding movement. MCKEAN & ROERING (2004) use high‐resolution LiDAR DTMs to

characterise a large landslide complex and surrounding terrain near Christchurch, New Zealand by

utilizing the topographic roughness, also known as the variance of aspect.

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MINER et al. (2010) chose an interesting approach to apply airborne LiDAR data to recognise

landslides and erosion with GIS analysis in Australia. MCKENNA et al. (2008) show, as well, that

DTMs derived from LiDAR data express topographic details sufficiently well with the help of slope,

shaded‐relief and contour maps to identify landslides even under densely forested terrain.

Another important paper was. EXTRAKTION GEOLOGISCH RELEVANTER STRUKTUREN AUF RÜGEN IN

LASERSCANNER‐DATEN by NIEMEYER et al. (2010). This approach for an automatic detection of terrain

edges is integrated in the ArcGIS landslide mapping toolbox. Another deep‐seated rockslides,

earth slides, and earth flows mapping technique by using LiDAR‐derived DTMs is presented in

CORSINI et al. (2009). DTMs were used to produce shaded‐relief and roughness maps, which allow

outlining rock‐slide units and sub‐units at the slope scale, a definition of the curvature fingerprint

and a derivation of elevation maps to detect areas of accumulation and depletion.

The following list of papers and articles were used as background for this master’s thesis and as an

input for the creation of the semi‐automatic ArcGIS landslide mapping toolbox.

Journal / Report N° Author(s) Year Title

Earth Surface Processes and Landforms

32 M. Van Den Eeckhaut, J. Poesen, G. Verstraeten, V. Vanacker, J. Nyssen, J. Moeyersons, L. P. H. van Beek and L. Vandekerckhove

2007 Use of LIDAR‐derived images for mapping old landslides under forest

Engineering Geology

89

W.H. Schulz 2007 Landslide susceptibility revealed by LIDAR imagery and historical records, Seattle, Washington

ESRI User Conference, San Diego, CA

‐ A. Weiss 2001 Topographic Position and Landforms Analysis

Geomorphology 109 A.M. Booth, J.J. Roering and J.T. Perron

2009 Automated landslide mapping using spectral analysis and high‐resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon

Geomorphology 113 M. Kasai, M. Ikeda, T. Asahina and K. Fujisawa

2009 LiDAR‐derived DEM evaluation of deep‐seated landslides in a steep and rocky region of Japan

Geomorphology 73 N.F. Glenn, D.R. Streutker, D.J. Chadwick, G.D. Thackray and S.J. Dorsch

2006 Analysis of LiDAR‐derived topographic information for characterizing and differentiating landslide morphology and activity

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Journal / Report N° Author(s) Year Title

Geomorphology 67 M. Van Den Eeckhaut, J. Poesen, G. Verstraeten, V. Vanacker, J. Moeyersons, J. Nyssen and L.P.H. van Beek

2005 The effectiveness of hillshade maps and expert knowledge in mapping old deep‐seated landslides

Geomorphology 57 J. McKean and J. Roering 2004 Objective landslide detection and surface morphology mapping using high‐resolution airborne laser altimetry

Quaternary Research

75 M. Van Den Eeckhaut, J. Poesen, F. Gullentops, L. Vandekerckhofe and J. Hervás

2011 Regional mapping and characterization of old landslides in hilly regions using LiDAR‐based imagery in Southern Flanders

University of Wollongong Research Online

A. S. Miner, P. Flentje, C. Mazengarb, D. J. Windle

2010 Landslide Recognition using LiDAR derived Digital Elevation Models‐Lessons learnt from selected Australian examples

U.S. Geological Survey Open‐File Report

1396 W.H. Schulz 2004 Landslides mapped using LIDAR imagery, Seattle, Washington

U.S. Geological Survey Open‐File Report

1292 J. P. McKenna, D. J. Lidke and J.A. Coe

2008 Landslides Mapped from LIDAR Imagery, Kitsap County, Washington

Zeitschrift für Geomorphologie

55 B. Hoefle and M. Rutzinger 2011 Topographic airborne LiDAR in geomorphology: A technological perspective

‐ ‐ J. Niemeyer, F. Rottensteiner, F. Kühn and U. Soergel

2010 Extraktion geologisch relevanter Strukturen auf Rügen in Laserscanner‐Daten

‐ ‐ A. Corsini , F. Cervi, A. Daehne, F. Ronchetti and L. Borgatti

2009 Coupling geomorphic field observation and LiDAR derivatives to map complex landslide

‐ ‐ J. Amundsena, S. Johnsona, K. Rousea, and H. Wangb

2010 Using LiDAR‐derived DEM’s to delineate and characterize landslides in Northern Kentucky and Hamilton County, Ohio

Table 4. List of papers and articles of a raster based application of LiDAR data in the field of landslide analysis and

mapping (source: AUTHOR’S ADAPTATION, 2011)

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3. STUDY AREAS

3.1 General Settings

Two main study areas in Spielberg bei Knittelfeld and Wald am Schoberpass (Province of Styria,

Republic of Austria) were chosen.

Table 5 shows the geographical position of these areas in UTM‐33N projection. The reasons for

selecting these areas are based on the availability of high‐resolution, LiDAR derived DTMs with less

noises and artefacts. In these regions brisk landslide activities can be found partly below dense

forest cover, as well as anthropogenic structures. These zones of agriculture and tourism, ancient

mining areas and archaeological sites that affect the terrain surface complicate the semi‐

automatic landslide mapping process.

Geographical Position

Spatial Reference Geographical Extent

Study Area 1 Study Area 2

UTM‐33N

Left

Top

Bottom

Right

481628.36

5231534.76

5230308.35

482853.59

475269.97

5257671.14

5255541.02

476315.10

Table 5. Geographical position of the study areas (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION)

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3.1.1 Geographical Position

Figure 9. Relief image of the Province of Styria, the red polygons show the two study areas in Spielberg bei Knittelfeld

[left image] and Wald am Schoberpass [right image] (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION)

Figure 9 shows the location of the study areas in Spielberg bei Knittelfeld (Study Area 1) and Wald

am Schoberpass (Study Area 2). Study area 1 is located in an inner alpine basin, called Aichfelder

Becken, on the south brink of the Niedere Tauern (Central Alps) and Study Area 2 is situated in a

small tributary valley of the Liesingtal in the Eisenerzer Alpen. Both areas are in the Province of

Styria, Republic of Austria.

Study Area 1 characterizes for lower mountain regions, while Study Area 2 exemplifies high

mountain regions in Styria.

GRAZ

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Study Area 1, Spielberg bei Knittelfeld (see Figure 10) ‐ a short overview: Study Area 1 extends to

an area of 1 km². About 80% of the area is covered by vegetation. The elevation is between 713

and 1087 m a.s.l. and the slope gradient is between 0 and 67° (see Table 6).

Study Area 1 is chosen as an example for a low mountain region in the Province of Styria with a

gentle terrain surface. As part of the Aichfelder Becken, a popular ancient mining area, old mining

signs and archaeological sites affect the terrain surface and complicate the landslide mapping

process.

Study Area 2, Wald am Schoberpass (see Figure 11) ‐ a short overview: Study Area 2 extends to

an area of 2 km². About 70% of the whole area is covered by vegetation. The elevation is between

860 and 1590 m a.s.l. and the slope gradient is between 0 and 86° (see Table 6). Study Area 2 is

chosen as an example for a high mountain region in Styria with a very rough terrain surface and

steep slopes. A giant landslide affects the whole study area of 2 km². In this case, ski‐tourism

affects the terrain surface as well.

Area

[km²]

Elevation [m]

Slope [°]

Forest

Cover [%]

Min Max Mean Min Max Mean

Study Area 1

1.00

713.49

1087.48

846.99

0.01

66.90

20.99

80

Study Area 2

2.00

860.08

1589.59

1157.04

0.00

85.44

25.26

70

Table 6. List of important parameters of the two study areas (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S

ADAPTATION)

3. STUDY AREAS

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Figure 10. Study Area 1, Spielberg bei Knittelfeld ‐ LiDAR DTM [left image] and DSM [right image]

(data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION)

Figure 11. Study Area 2, Wald am Schoberpass ‐ LiDAR DTM [left image] and DSM [right image]

(data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION)

3. STUDY AREAS

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3.2 Geology and Soils

According to the GEOLOGICAL SURVEY OF AUSTRIA, the high situated regions of Study Area 1 belong to

the Sheet Gravel of the Middle East Alpine (Crystalline of the Flatschacher Range; see Figure 12,

upper image). Lower regions near the hillside toe are dominated by younger deposits of the

Quaternary and the Tertiary, but the ratio of materials of the Quaternary is insignificant. The

border zone of the Tertiary is a common area for landslide activities (see GEOLOGICAL SURVEY OF

AUSTRIA 2011). Gley and slope gley soils dominate in the lower parts of Study Area 1. In between

are loose sediments (see Figure 12, lower image).

According to the GEOLOGICAL SURVEY OF AUSTRIA, Sheet Gravel of the Middle and the Upper East

Alpine dominate the Geology of Study Area 2. Old (phyllite) and young Palaeozoic rocks

(sandstones and carbonate) dominate this region. Debris materials of the Quaternary (see Figure

13, left image) can be found in the lower zone of the area (see GEOLOGICAL SURVEY OF AUSTRIA 2011).

Decreased stability as a result of macerated phyllites can cause landslide activities. Soil form

complexes and coloured local soils are the dominant soils between forested areas. In between are

loose sediments, rendzina and slope soils (see Figure 13, right image; see BMLFUW & BFW 2009).

3. STUDY AREAS

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Legend

(G) Gley Soil

(HG) Slope Gley Soil

(LB) Loose Sediment ‐ Brown Calcareous Soil

(PU) Grade Soil (Planieboden)

Forest

Water Bodies

Not Mapped Area

Figure 12. Geology [upper] and soil maps [lower image] of Study Area 1, Spielberg bei Knittelfeld

(sources: GEOLOGICAL SURVEY OF AUSTRIA, 2011 AND BMLFUW & BFW, 2009)

3. STUDY AREAS

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Legend

(FU) Coloured Local Soils

(LB) Loose Sediment ‐ Brown Calcareous Soil

(K) Soil Form Complex

(R) Rendzina Soil + Slope Soil

Forest

Water Bodies

Not Mapped Area

Figure 13. Geology [left] and soil maps [right] of Study Area 2, Wald am Schoberpass

(sources: GEOLOGICAL SURVEY OF AUSTRIA, 2011 AND BMLFUW & BFW, 2009)

3. STUDY AREAS

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3.3 Vegetation, Land Use and Climate

Study Area 1 in Spielberg bei Knittelfeld is part of the climatic region “Knittelfeld‐Judenburger‐

Becken mit Seitentälern im Südosten”. This region is bounded by the Mur basin from Judenburg to

Preg and its tributary valleys. The continental large valley climate is relatively arid in winter with

little snow. The mean annual precipitation is 842 mm\m². High fog in autumn and winter affect

the relative sunshine duration negatively. The low mean temperatures of ‐5.8°C in January, of

16.2°C in July and an annual average temperature of 6.3°C confirm a continental climate. There

exists an increased tendency to temperature inversion because of the large valley exposure. The

coldest areas in this region are the valley floodplains, where about 165 days of the year exhibit

winter conditions while only 34 days per year exhibit typical summer conditions. The majority of

Study Area 1 is coniferous forest (spruces), which is intersected by numerous forest roads. In

between are small mixed forest areas (see Figure 14) with beech and birch trees.

Study Area 2 in Wald am Schoberpass is part of the climate region “Liesingtal”. The Liesingtal

together with its tributary valleys is an inner alpine valley depression bounded by the Schoberpass

in the north to the Seckauer Tauern in the west and the Eisenerzer Alpen in the east. The climate is

cold in winter, moderate warm in summer, with decreasing temperatures in the northward

direction; there is also potential for high fog and temperature inversion, less snow, reduced

sunshine duration because of the high fog and a dominance of down‐valley winds (see LUIS 2011).

Mixed and coniferous forest is the dominant vegetation of Study Area 2. Coniferous forest is in the

upper part of the area as well as small moorland (see Figure 15). Pasture land can be found within

the range of the hillside toe. A settlement area is in the valley (see Figure 15; see LUIS 2011 and GIS

STYRIA 2012).

3. STUDY AREAS

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Figure 14. Study Area 1, Spielberg bei Knittelfeld –RGB [left] and CIR orthophotos [right]

(source: GIS STYRIA, 2012)

Figure 15. Study Area 2, Wald am Schoberpass –RGB [left] and CIR orthophotos [right]

(source: GIS STYRIA, 2012)

4. LANDSLIDE MAPPING – THEORETICAL PART

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4.1 Software Description

The ArcGIS 10.0 Package, the newest GIS software by ESRI, with 3D‐Analyst and Spatial Analyst

Extensions was utilised for the semi‐automatic landslide mapping process. The ArcGIS

environment provides good tools for GIS modeling, for land surface analysis and for the detection

of geomorphological structures like landslides. The LP360 Extensions of QCoherent is used as a

backbone tool for the LiDAR data integration to GIS – Databases as well as for special treatment of

the original point cloud data and for the creation of DTMs and DSMs with 1 m resolution. With the

help of Python, a powerful, simple and open source scripting language of the 1990s, complex

workflows and new geoprocessing tools can be implemented in the GIS‐Software.

4.1.1 ArcGIS and Extensions (ESRI)

The Environmental System Research Institute, Inc., or

ESRI was founded in 1969 in Redlands, California by Jack

Dangermond (Harvard Laboratory for Computer Graphics

and Spatial Analysis) and his wife. In 1982 ARC/INFO, the

first commercial GIS, was released. Vector Data (points,

line and polygons) were combined with a data

management system for assigning attributes to these

features. Raster Data were not considered during this

time. In the late 1990s the GIS platform ArcGIS was

developed (see ESRI 2011).

ArcGIS is the umbrella term for a product line of different, but complementary GIS software

products, a desktop (ArcGIS Desktop, ArcGIS Engine, ArcGIS Explorer), a server (ArcGIS Server,

ArcGIS Server Image), and an online and a mobile GIS (ArcPad) (see Figure 16). The software

package is used to edit, demonstrate, model, analyse and digitise GIS Raster and Vector Data (see

ESRI 2011).

Figure 16. ArcGIS product line (source: ESRI, 2011)


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ArcGIS Spatial Analyst Extension is used to model and analyse spatial relationships of raster data

and to execute complex raster calculations (see ESRI 2011).

ArcGIS 3D Analyst Extension allows the comprehension of the third dimension and is excellent for

analysing and modelling raster data such as 3D LiDAR Digital Terrain and Surface Models

(see ESRI 2011).

4.1.2 LP360 for ArcGIS

LP360 (see Figure 17) is a LiDAR software extension for the ArcGIS environment developed by

QCoherent (Colorado Springs, Colorado), released in August 2006 and is designed for LiDAR point

clouds. The data format utilized is the LAS format by the American Society of Photogrammetry and

Remote Sensing, in short ASPRS, which integrates LiDAR point cloud datasets completely into

ArcGIS and can be combined with different data formats supported by ArcGIS. The LP360

Extension is used to calculate elevation corrections, import and export LiDAR data, generate point

cloud statistics and to employ basic classification filters, height filters, building point classification,

building outlines and macro filter stacks. A further advantage is to display both, a 3D and a profile

view of the point cloud. It is possible to export LAS data into multipoints, which include, for

example, information about the classification, the intensity or the return number (see QCOHERENT

2011).

Figure 17. LP360 surface (source: STYRIAN LIDAR CAMPAIGN, 2011)


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4.1.3 Python

Python (named after the comedian‐group Monty Python), is remarkably powerful and dynamic

open‐source scripting language developed during the 1990’s. Python has a large and

comprehensive standard library and a very clear syntax. The most important component of this

master’s thesis is that this scripting language can be implemented in ArcGIS geoprocessing

workflows, which means new scripts written in Python can be integrated in ArcGIS as new and

powerful tools.

In this research Python was used for creating a semi‐automatic landslide mapping toolbox in the

ArcGIS 10.0 environment (see PYTHON 2012)

For a better insight into Python and its applications go to either www.python.org (January, 5th,

2012) or http://openbook.galileocomputing.de/python/ (January, 5th, 2012).

4.2 Data

The main dataset used in this work is a LiDAR Digital Terrain Model with a 1 m resolution.

Additionally vector data (streets, streams, buildings, etc.) were integrated in the landslide

detection process.

4.2.1 LiDAR Data

LiDAR point cloud data (see Figure 18), adjusted with a First‐/‐Last‐Pulse proceeding and with a

vertical accuracy of ±15 cm and a positional accuracy of ±40 cm were collected of the Province of

Styria, an area of about 16,000 km² with scanners of Riegl GmbH and IMUs of IGImgH carried by a

helicopter (see Table 7; see STYRIAN LIDAR CAMPAIGN 2011).

Equipment Type

Laserscanner LMS‐Q560 / 200kHz

LMS‐Q560 / 240 kHz

LMS‐Q680i / 240 kHz

IMU IMU‐2D / 256 Hz

IMU‐2E / 256 Hz

Table 7. Main equipment of the Styrian LiDAR – campaign (source: STYRIAN LIDAR CAMPAIGN, 2011)


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Raw data were geo‐referenced to UTM‐33N with ellipsoidical heights referred to ETRS89. Output

data were geo‐referenced to BMN‐M31/BMN‐M34 with orthometric heights referred to the geoid.

In this master’s thesis the UTM‐33N dataset was used for executing the semi‐automatic landslide

mapping tool and for mapping landslides manually (see STYRIAN LIDAR CAMPAIGN 2011).

Point cloud data were classified into 7 classes as specified with the ASPRS‐LAS Data Format

Description: multiple echo and outlier, ground, canopy > 0.5 m to 3 m, canopy > 3 m, buildings and

bridges, cables and non‐classifiable points, and water (see STYRIAN LIDAR CAMPAIGN 2011).

The ALS data were processed to create Digital Terrain Models (DTM) out of ALS ground points and

Digital Surface Models (DSM) out of the adjusted first echo ALS data with a 1m resolution. The

mean point‐density above 2000 m a.s.l. is 2 points/m² and the mean point density below 2000 m

a.s.l. is 4 points/m². The existence of snow, ice and water caused artefacts and quality loss. The

quality of the DTM depends on the precision of post processing of LiDAR data and on the quality of

error and artefact elimination, as well as systematic errors and noise or random errors during the

process of generating DTMs. Noise in a DTM causes fuzziness of terrain edges and limits detailed

analyses. Further factors, which influenced the quality of a DTM for later analysis, are the

roughness of the land surface, the sampling density, the grid resolution, the gridding algorithm

and the vertical resolution (see NELSON et al. 2009, p. 65ff.).

Figure 18. Classified LiDAR Point Cloud [Red = Buildings, Green = Vegetation and Orange = Ground] (source: STYRIAN

LIDAR CAMPAIGN, 2011)


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4.2.2 Vector Data

In addition to ALS data, vector datasets maintained by the Geoinformation Office (GIS) of the

Province of Styria and geo‐referenced in UTM‐33N were used (see Table 8).

These data were used for the manual landslide mapping process, for better visualisations of

special topics like geology or soils and they were partly integrated in the semi‐automatic landslide

mapping tool for improving the results.

Shape Name Feature Type Actuality Organisation Content

Archaeological

Sites

Polygon 2011‐12‐19 Mag. Susanne Tiefengraber (Part of her

graduate thesis)

Archaeological Sites of Styria

CORINE_CLC2000 Polygon 2006‐1124 Umweltbundesamt GmbH Corine Landcover of Styria

Gebäude

(Kataster)

Polygon 2010‐02‐03 Bundesamt für Eich‐ und

Vermessungswesen (BEV) Wien

Building mask of Styria

Geologie

1:50.000

Polygon 1986‐07‐01 Provincial Government of Styria ‐ Board

of Works, Geoinformation Staff Office

Geology of Styria (scale 1:50.000)

gew Line 2010‐05‐04 Provincial Government of Styria, FA19B

‐ Schutzwasserwirtschaft und

Bodenwasserhaushalt

Stream Network of Styria

GIP‐ROUTE Line 2011‐01‐17 Provincial Government of Styria, FA18A

‐ Gesamtverkehr und Projektierung

Street Network of Styria

wilbezg Polygon 2009‐10‐07 Provincial Government of Styria, A16

Landes‐ und Gemeindeentwicklung

Torrent Catchment Areas of

Styria

Table 8. In this thesis main used vector data (source: GIS STYRIA, 2012)


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4.3 Land Surface Parameters

The main issue with extraction of any geomorphological structure is Geomorphometry, the science

of quantitative land‐surface analysis, land‐surface modeling and land‐surface parameterisation.

This field is defined as the numerical characterization of topographic forms. The operational focus

is the extraction of different land‐surface parameters and structures out of square‐grid

representations of the surface with mathematical, statistical and image processing methods.

Those representations are DTMs. The used DTMs with a 1 m resolution are a simple raster dataset

showing the height of the surface a.s.l. (see NELSON et al. 2009, p. 65 ff.; SCHMIDT & DIKAU 1999, p.

153 ff.). Land surfaces are divided and classified into different landform elements at a given scale

or spatial resolution. These elements are delineated by topographic discontinuities and should,

ideally, be characterized by mostly uniform morphometry.

In Geographic Information Systems, landform elements are characterised by different vector‐

features such as polygons, lines and points. They can be recognised regardless of scale and type of

terrain and are grouped by a finite number of morphologically important points i.e. the following

surface‐specific elements: pits, peaks, ridge lines, course lines, passes and break lines.

Geomorphometric or land surface parameters describe the local morphology. These parameters

can be derived directly from a DTM without further knowledge of an area and they are classified

into local (geometric and statistical) and regional parameters.

In Geomorphometry basic parameters such as slope, aspect and curvature and local statistical

parameters such as roughness, range or variance of slope, skewness and kurtosis coefficient

describe the local morphology. Moreover, hydrological (flow accumulation, flow direction, etc.)

and climatic parameters (e.g. average annual precipitation) are considered in land surface analysis.

The most common parameters of regional relief are catchment area, flow‐path length, slope

length and proximity to local streams and ridges (see NELSON et al. 2009, p. 65ff.; SCHMIDT & DIKAU

1999, p. 153 ff.).

In the following paragraphs the most important land surface parameters for landslide extraction

and mapping are described in brief. All of the images depicted on the following pages resulted

from an analysis of Study Area 1.

Geomorphometric parameter calculation formulas are not quoted in this thesis, they can be found

in HENGL (2009). Both the ArcGIS Spatial Analyst and 3D Analyst extensions can be used to

calculate the following land surface parameters (see ARCGIS DESKTOP 10.0 HELP 2011).


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4.3.1 Hillshade

By determining illumination values for each cell in a raster the hypothetical illumination is shown.

The grey scale hillshade map consists of integer values from 0 to 255. The angular direction of the

sun is called azimuth. It is measured from north in clockwise degrees from 0 to 360 °. The slope or

angle of the illumination source above the horizon is called altitude (see Figure 19). The scale is

from 0 to 90° degrees (see OLAYA 2009, p. 141ff.; ARCGIS DESKTOP 10.0 HELP 2011). The hillshade

map shows a good image of the terrain and movements in the terrain, which support manual

landslide mapping.

Figure 19. Hillshade [left] and slope images [right] of Study Area 1 (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S

ADAPTATION)

4.3.2 Slope

The parameter slope or gradient describes the steepness, grade, alternatively the incline of the

terrain (see Figure 19). The tool calculates the maximum rate of change in value from each cell of a

raster surface with a 3 x 3 cell neighbourhood with an average maximum technique (see OLAYA

2009, p. 141ff.; ARCGIS DESKTOP 10.0 HELP 2011). Rapid changes in terrain are possible signs for

landslides. The slope map supports a manual landslide mapping process. Steep slopes are

visualised by lighter grey colours, plane slope by darker grey colours.

Value of slope [°]

High : 67

Low : 0


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4.3.3 Variance of Slope

Variance of slope (see Figure 20) in a certain search area can point out abrupt changes in

inclination (see OLAYA 2009, p. 141ff.). The variance of slope map supports an automatic location

of abrupt changes in slope. High values with lighter grey colours point out these abrupt changes.

Figure 20. Variance of slope [left] and aspect images ([right] of Study Area 1 (data basis: STYRIAN LIDAR CAMPAIGN, 2011)

4.3.4 Aspect

The slope direction or the downslope direction of the maximum rate of change in value from each

cell to its neighbours is named aspect (see Figure 20). The compass direction, measured clockwise

in degree from 0 ° to 360 °, is indicated by the value of each cell in the output raster. The value of

‐1 is an indicator for a totally flat area. The direction a cell’s slope faces is indicated by the value of

each cell in an aspect dataset (see OLAYA 2009, p. 141ff.; ARCGIS DESKTOP 10.0 HELP 2011). Smooth

areas have similar aspects in a neighbourhood of cells, while rough areas exhibit a strong diversity

of orientations within a neighbourhood of cells.

Value of variance of slope

High : 21

Low : 0


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4.3.5 Variance of Aspect

Variance of aspect demarcates rough areas with a diversity of orientations (see Figure 21). The

variance of aspect map points out anomalies and variability in terrain surface, which are visualised

by high values with lighter grey colours.

Figure 21. Variance of aspect [left] and vertical curvature images [right] of Study Area 1 (data basis: STYRIAN LIDAR

CAMPAIGN, 2011, AUTHOR’S ADAPTATION)

4.3.6 Curvature

Curvature describes the concavity and convexity of the surface (see Figure 21). Vertical and

horizontal curvature can be used to distinguish convex and concave shapes (see OLAYA 2009,

p. 141ff.; ARCGIS DESKTOP 10.0 HELP 2011). 1/R of a plane curve is the inverse radius R of a circle that

is best fitted to this curve at a given point. R > 0 (black) indicates a convex surface, R < 0 (white) a

concave one. Convergence is indicated by concave horizontal curvature, while divergence of flow

lines is indicated by convex horizontal curvature. A convex vertical curvature can be a signal of a

landslide scarp or of an acceleration of flow.

Value of variance of aspect

High : 178

Low : 1

Value of vertical curvature

High : 321

Low : -224


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4.3.7 Roughness

Terrain roughness is a measure of the terrain complexity, which means variation in the three‐

dimensional orientation of grid cells within a defined neighbourhood (see Figure 24). Vector

analysis is used to calculate the dispersion of vectors normal (orthogonal) to grid cells within the

specified neighbourhood. This method effectively captures variability in slope and aspect into a

single measure (see SAPPINGTON et al. 2007, p. 1419ff.). Roughness raster values can range from 0

(no terrain variation) to 1 (complete terrain variation). For instance streets or buildings have a

smooth surface in contrast to vegetation, which typifies a rough surface. A rough surface can be

an indicator for landslide events. The method to calculate the terrain roughness was adapted from

the Vector Ruggedness Measure by SAPPINGTON et al. (2007).

Figure 22. Roughness image [left] and contour lines [left] of Study Area 1 (data basis: STYRIAN LIDAR CAMPAIGN, 2011,

AUTHOR’S ADAPTATION)

4.3.8 Contour Lines

Contour lines or isolines connect cells of equal values in a raster dataset (see Figure 22). Contour

lines are used to represent continuous phenomena such as elevation, temperature (isotherms),

precipitation (isohyets), pollution or atmospheric pressure (isobars; see ARCGIS DESKTOP 10.0 HELP

2011). Closer lines indicate a rapid fall or rise of values, while lines that are spaced farther apart

indicate little changes.

contours (2 meter interval)

Value of roughness

High : 0

Low : 4


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4.4 Triangular Irregular Network (TIN)

Triangular irregular networks (TINs) are used to represent surface morphology as a form of vector‐

based digital geographic data (see Figure 23). TINs are constructed by triangulating a set of

vertices (points) that are connected with a series of edges to form a network of triangles. The

Delaunay triangulation interpolation is supported by the ArcGIS workspace. Contiguous, non‐

overlapping triangular facets are formed by the edges of TINs, which can be used to capture the

position of linear features that play an important role in a surface, such as ridgelines or stream

courses (see ARCGIS DESKTOP 10.0 HELP 2011). Triangular irregular networks support a manual

landslide mapping.

Figure 23. TIN [upper image] and TIN 3D Model of Study Area 1 [lower image] (data basis: STYRIAN LIDAR CAMPAIGN,

2011, AUTHOR’S ADAPTATION)


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4.5 Topographic Position Index

The Topographic Position Index (TPI) Tool is part of the Land Facet Corridor Designer to design

wildlife linkages in the face of impending climate change by JENNESS et al. (2011), and was first

described by WEISS (2001) at the ESRI International User Conference.

The TPI is used to classify a landscape into topographic positions (ridge top, mid‐slope, etc.) and

landform categories (canyons, valleys, etc.) based on the selected scale (see Figure 24). Smaller

neighbourhood sizes capture small and local structures, while large neighbourhoods capture large‐

scale features (see Figure 24). The code for the TPI tool was slightly altered to adapt it to the

demands of this master’s thesis (see JENNESS et al. 2011, p. 45ff.).

Figure 24. Slope Position Classification at different scales (source: JENNESS et al., 2011, p. 45ff.)


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4.5.1 TPI Elevation

The TPI elevation (see Figure 25, left image) is the difference between a cell elevation value and

the average elevation of neighbourhood around that cell. If the cell is higher than its surroundings,

it is visualised by a positive value, while negative values mean that the cell is lower. For example

the value 10 means that the cell is 10 meters higher than the average elevation of the

neighbourhood (see JENNESS et al. 2011, p. 49). The TPI standardized elevation (see Figure 25, right

image) takes the TPI and divides it by the Neighbourhood Standard Deviation, which means that a

value of 1 means that this cell is 1 standard deviation higher than the average elevation of the

neighbourhood (see JENNESS et al. 2011, p. 49). The TPI standardized elevation image below shows

a classified “Terrain Surface Changes” raster dataset (brown = cells lower than the average

indicate a decreasing; green = cells higher than the average indicate an increasing).

For this master’s thesis the TPI Elevation raster is used to extract stream lines out of the high‐

resolution LiDAR Digital Terrain Model and to detect movements in the terrain surface

(landslides).

Figure 25. TPI elevation [left] and TPI standardized elevation images [right] of Study Area 1 (data basis: STYRIAN LIDAR


Value of TPI DTM standardized elevation

High : 3

Low : -3


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4.5.2 TPI Slope

The TPI slope (see Figure 26, left image) is the difference between a cell slope value and the

average slope of neighbourhood around that cell. For example higher values mean that it is likely

near or at the top of a hill or a ridge, lower values suggest the cell is at or near the bottom of a

valley (see JENNESS et al. 2011, p. 49). TPI standardized slope (see Figure 26, right image) takes the

TPI and divides it by the Neighbourhood Standard Deviation, where a value of 1 means that this

cell is 1 standard deviation higher than the average elevation of the neighbourhood (upper image)

(see JENNESS et al. 2011, p. 49). The TPI standardized slope image below shows a classified

“Landform Categories” raster dataset (valleys, lower slopes, upper slopes and ridges).

For this master’s thesis the TPI Slope raster is used to extract forest roads out of the high‐

resolution LiDAR Digital Terrain Model and to detect ridges in the terrain surface (landslide main

scarps).

Figure 26. TPI slope [left] and TPI standardized slope images [right] of Study Area 1 (data basis: STYRIAN LIDAR CAMPAIGN,

2011, AUTHOR’S ADAPTATION)

Value of TPI slope standardized elevation

High : 5

Low : -5


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4.6 SMORPH Landslide Risk Model

The slope morphology matrix (SMORPH) by SHAW & JOHNSON (1995) is formed by the slope gradient

and the horizontal curvature (convex, concave and planar) and classifies the area into low,

medium and high landslide risk areas (see Figure 27).

For this master’s thesis medium landslide risk areas are also an indicator for drainage lines, which

facilitate the detection of landslides as well.

Figure 27. Smorph image of Study Area 1 (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION)

5. LANDSLIDE MAPPING – PRACTICAL PART

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This part is the main and most important part of this master’s thesis. Months of intense fieldwork

contributed to this chapter. It has to be stressed that the bulk of this practical work is developed

on the author’s own, apart from the “Edge Detection Tool” adapted from the paper EXTRAKTION

GEOLOGISCH RELEVANTER STRUKTUREN AUF RÜGEN IN LASERSCANNER‐DATEN by Joachim Niemeyer, Franz

Rottensteiner, Friedrich Kühn and Uwe Sörgel (2010), the “TPI Tool” adapted from the “Landfacet

Corridor Tool” from Jeff Jenness (Jenness Enterprises, Arizona, USA) and the “Roughness Tool”

adapted from the “Vector Ruggedness Measure” python script by Mark Sappington. A written

agreement for using alternatively adapting these tools in the author’s practical work were given by

mail by DI Joachim Niemeyer (University of Hannover), Jeff Jenness (Jenness Enterprises, Arizona,

USA) and Mark Sappington (Lake Mead National Recreation Area, Nevada, USA).

Furthermore it has to be mentioned that the semi‐automatic landslide mapping tool underlies a

continuous improvement process and is a result of a loop system, where verified and manually

mapped landslides are not only used for testing, but also for improving the semi‐automatic

landslide mapping process.

In the next chapters the three work steps (manual landslide mapping, verification, and semi‐

automatic landslide mapping) of a possible landslide mapping workflow are described. The semi‐

automatic landslide mapping chapter will give a short insight in the creation of an ArcGIS landslide

mapping toolbox.

Empiricism and to acquaint oneself with LiDAR, DTMs and land surface parameter images are the

most important instruments used for landslide mapping in this master’s thesis.

Any values and techniques used in chapter 5 underlie empirical research work of the author.

Anybody, who is interested in the toolbox and wants to use or adapt the landslide tools in its own

work, may write a mail to [email protected].


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5.1 Manual Landslide Mapping

Due to the fact that there exists no useable landslide vector dataset for the study areas in

Spielberg bei Knittelfeld and Wald am Schoberpass, the author has identified and mapped

characteristic landslide features like main scarps and landslide areas on her own by using and

analysing land surface parameter images and other maps described in chapters 4.3 to 4.6.

These manually mapped landslide features are verified by doing field work and by consulting

experts, which are described in chapter 5.2. A tool for calculating land surface parameters are

described in chapter 5.3 as well.

The manually mapped landslide vector datasets are used:

to get verification datasets for the landslide features, which are automatically mapped by

the ArcGIS landslide mapping toolbox,

to find out the workability and applicability of semi‐automatic landslide mapping and

to improve the semi‐automatic tool with the knowledge acquired about the study areas,

the land surface parameters and the existing landslides in these areas.

In the following chapter a rough overview of a manual landslide mapping workflow is given.

5.1.1 Manual Landslide Mapping Workflow

It is important to mention at this point that a special knowledge about a region or study area is not

obligatory to identify and map landslide features in LiDAR DTMs and a familiarity with LiDAR and

LiDAR DTMs and further ability to modify them are indispensable. As this thesis describes a

method for mapping actual and not potential landslide areas, other landslide parameters like

anthropogenic factors, geological, hydrological, hydro‐geological and vegetation parameters were

not considered in the manual and semi‐automatic mapping process. That means that further

information about a region can be neglected during a manual mapping process. Such information

negatively affect and influence the analysis than facilitate a manual landslide mapping. For

example the LiDAR DTM with 1 m resolution is too detailed as to compare it with the excessively

generalised geological map 1:50.000 of Styria (BEV), which means that it is often not conformed to

the actual geology.


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But nevertheless further geological, hydrological, hydro‐geological and vegetation information can

be used to verify manually mapped landslides (see chapter 5.2).

The first step for the manual mapping of landslides is to acquaint oneself with a region by studying

the land surface parameter images such as hillshade, slope, variance of slope, variance of aspect,

curvature, roughness and the contour lines, roughness and topographic position index maps and

the TIN (see Figures 29 and Figure 30 and chapter 4.3 to 4.6). These images allow an outlining of

different landslide features like scarps, areas of accumulation, transverse ridges and cracks, radial

cracks, landslide tongues or surface of ruptures (see Figure 28, left image).

In a second step, different land surface parameter images are consulted to draw landslide main

scraps and landslide areas. A comparison of a schematic landslide sketch and a profile view of a

landslide body (see Figure 28, right image) with diverse land surface parameter images

(see chapter 4.3 to 4.6) lists the different adaptability of the parameter maps in regard to landslide

features like main scarps or landslide areas and shows the visibility of landslide features in these

maps (see Table 9).

In a third step the manually mapped landslide features are verified by doing field work and

consulting experts in geology and geomorphology (see chapter 5.2).

Main scarps and sometimes also minor scarps and flanks are the best visible and best distinctive

parts of landslide surfaces. For this reason these features can also be detected automatically out

of a LiDAR DTMs with 1 m resolution (see chapter 5.3).

Figure 28. Schematic landslide sketch [left] and schematic profile view of a landslide [right] (source of left image:

GEOLOGICAL HAZARDS PROGRAM, 2011 and source of right image: AUTHOR’S ADAPTATION)


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Slope, variance of slope, curvature and contour lines indicate rapid variance in terrain like main

and minor scarps, cracks and ridges and concave and convex edges best. Hillshade, slope, variance

of slope and roughness maps allow an outlining of the whole landslide area. Slope and variance of

slope indicate a flattening area best. The profile view helps with verifying the landslide surface.

A big challenge is posed by old, marked landslides – an expert eye can see old landslides in

hillshade, slope and contour line maps, but an automatic outlining of these features is almost

impossible, because of the smooth surface of such landslides. Moreover it is important to know

that it is possible that landslides can be seen and mapped in land surface parameter maps, but not

in the field. The viewing angle from the earth, observation when too close to a landslide object

and dense vegetation often complicate outlining and locating landslides.

Figure 29. Visualization of land surface parameters hillshade [top left], slope [top right], vertical curvature [down

left] and contour lines [down right] with 320 x 200 m (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION)


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Figure 30. Visualization of land surface parameters variance of slope [top left], roughness [top right], variance of

aspect [centre left] and tin [centre right] and of TPI slope [bottom left] and TPI elevation [bottom right] with

320 x 200 m (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION)


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Table 9 shows the used images for mapping different landslide features.

Features of the

landslide body

Description Raster Images

crown,

main and minor scarps

convex and concave edges

hillshade,

vertical curvature,

TPI

rapid change of slope (steepening in

terrain)

hillshade,

slope,

variance of slope

surface of rupture erosion of earth material (extensive

change in original ground surface)

hillshade,

TPI,

profile view

change of slope (flattening in

terrain)

hillshade,

slope,

profile view

transverse cracks (compression of

earth material)

hillshade,

horizontal curvature

rough surface hillshade,

roughness,

variance of aspect

landslide foot area of accumulation

hillshade,

TPI,

profile view

transverse cracks and ridges hillshade,

horizontal curvature

waved terrain hillshade,

vertical curvature

rough surface hillshade,

roughness,

variance of aspect

Table 9. Visibility of landslide features in different raster images (source: AUTHOR’S ADAPTATION, 2012)


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5.2 Verification and Results of Manual Landslide Mapping

5.2.1 Verification of manually mapped landslides

Field work was done and experts were consulted to verify the manually mapped landslides. The

consulted experts in geomorphology and geology are Dr. Christian Bauer (Joanneum Research)

and Dr. Ingomar Fritz (Universalmueum Joanneum, Geology and Palaeontology). Additionally

landowners, residential farmers, local authorities, and other collaborators were interviewed, and

geological, mining or historical maps were studied to collect information about the two study

areas. The biggest challenge was the dense vegetation and the impassable and steep terrain in

both study areas, shown in Figures 31 – 36, which complicated the verification process and

hindered the differentiation between actual landslide structures and structures with similar

appearance to such mass movements.

An additional advantage during the verification process was the use of the ArcGIS Extensions 3D‐

Analyst and LP360 by QCoherent, which provide profile views and 3D views of a defined aperture

of the terrain surface. These profile views support the identification of landslides (see Figure 28 –

Schematic profile view of a landslide and Figure 31 to 36). The equipment used in the field were

analogue maps (hardcopy of orthophotos and slope and hillshade images with manually mapped

landslide main scarps and areas that facilitate the location in the field) and the GARMIN Dakota 20

GPS device with a GPS accuracy of < 10 m and a DGPS accuracy between 3 to 5 m.

Landslide example 1 (see Figure 31): Example 1 depicts a mostly metamorphic landslide surface in

the midst of a pasture that is roughly 10 years old. Landslide material moved across a road and

spilled over parts of it. Historical maps, like the “Franziszeischer Kataster” (1820 – 1861;

see GIS STYRIA 2012) and the “Josephinischer Kataster” (1787; see GIS STYRIA 2012) indicate that the

course of the road remained unchanged for more than 200 years – an indication that the landslide

was not triggered by road construction activities, but probably by an intense rainfall event. The

surface of rupture measures a length of about 100 m and a width of about 25 m at the widest

point. The main scarp has a length of 55 m and about 1800 m² terrain surface was affected by this

landslide event. The profile view, which shows a surface of rupture and an accumulation of earth

material, also points to a landslide event.


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Landslide example 2 (see Figure 32): A giant rotational landslide along a mountain‐side is

visualised in Figure 32. The landslide is anywhere from a hundred to a thousand years old, and it

altered the whole southern side of the Eggeralpe with a max‐height of 1590 m a.s.l. Unfortunately

the author was not able to find any historical records or map about this landslide event.

A ski region is located at the deeper zone of the mass movement. A large main scarp is visible in

the picture. The surface of rupture measures a length of about 1900 m and a width of about

675 m at the widest point. The main scarp has a length of 2008 m and about 815 000 m² of terrain

surface was affected by this huge landslide event. The larger main scarp can be seen in the profile

view as well. An accumulation of earthen material is shown in the lower part of the event. The

profile view shows a waved terrain surface and a change of the original ground surface.

Landslide example 3 (see Figure 33): Figure 33 shows an accumulation of some smaller landslide

events (between 10 and 30 years old) in a forested area of Study Area 1, probably triggered by

deforestation activities in the early 80s and proximate intense rainfall events. The scarp in the

middle of the slope image is visualised on the photo. An impassable terrain complicates a good

field work in this area significantly, therefore studying LiDAR data before starting with the field

work facilitates a location of such in the forest hidden mass movement. The surface of rupture

measures a length of about 40 m and a width of about 40 m at the widest point. The main scarp

has a length of 60 m and about 1.800 m² terrain surface was affected by this landslide event. The

profile view shows a surface of rupture (erosion of earth material in the upper part of the landslide

event). An accumulation of earth material is shown in the lower part of the event.

Landslide example 4 and 5 (see Figure 34‐35): The impassable and forested terrain complicated

the field work in the landslides shown in Figure 34 and 35 as well. Both landslides were triggered

by human activities. Landslide example 4 was triggered by a forest road construction event and

example 5 by ancient mining activities.

Example 4: The surface of rupture measures a length of about 50 m and a width of about 17 m at

the widest point. The main scarp has a length of 30 m and about 360 m² terrain surface was

affected by this landslide event.

Example 5: The surface of rupture measures a length of about 130 m and a width of about 50 m at

the widest point. The main scarp has a length of about 82 m and about 5500 m² terrain surface

was affected by this landslide event.


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Both profile views show a surface of rupture (erosion of earth material in the upper part of the

landslide event). An accumulation of earth material is shown in the lower part of the event.

Landslide example 6 (see Figure 36): At first glance, this area suggests a landslide event as well,

but in the profile view terrace‐related structures can be seen. Those structures are an allusion to

anthropogenic activities, in this case, ancient mining activities. The profile view shows

anthropogenic formed terraces. This artificial terrain surface is an indicator of ancient mining area

– in this case a landslide event can be excluded.

Figure 31. Landslide example 1 – model and reality: photo, hillshade image and profile view (data basis: STYRIAN LIDAR

CAMPAIGN, 2011 and AUTHOR’S IMAGE, 2011)


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Figure 32. Landslide example 2 – model and reality: photo, hillshade image and profile view (data basis: STYRIAN LIDAR



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Figure 33. Landslide example 3 – model and reality: photo, slope image and profile view (data basis: STYRIAN LIDAR



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5.2.2 Results of the manual landslide mapping

A manual landslide mapping by using LiDAR data in combination with geological and historical

maps, doing field work and interviewing experts provides good and useable results and supports a

semi‐automatic landslide mapping process.

On the following pages the main results of the manual landslide mapping process are presented.

Study Area 1, Spielberg bei Knittelfeld (see Figure 37‐39 and Table 10): Because of its special

geology and soil composition and the ancient mining activities in this area, 50 landslides with a

total landslide area of about 0.2 km² were mapped in the 1 x 1 km study area in Spielberg bei

Knittelfeld. More than 20 % of the whole terrain is influenced by landslides of varying sizes. All

landslide events are in the south of the mountain range and are associated directly with the local

geology (see Figure 39).

Study Area 2, Wald am Schoberpass (see Figure 40‐42 and Table 10): One larger and eleven

smaller landslides with a total landslide area of more than 1 km² were mapped in the 1 x 2 km

study area in Wald am Schoberpass (see Figure 40 and 42). More than 50 % of the whole terrain is

mostly influenced by one massive, ancient landslide event (see Figure 40 and 42). The torrent

debris area, as labeled in the geology map is rather an allusion to a landslide sedimentation area.

Lower‐level rock‐formations (lime) appear within the range of the landslide (see Figure 42).

Landslides

Study Area 1 Study Area 2

Number 50 12

Area km² of whole area 0.27 of 1 1.01 of 2

Area % 27.0 50.5

Mean Scarp Length m 57.97 306.95

Table 10. Manually mapped landslides in the two study areas (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S

ADAPTATION)


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Figure 37. Map of landslide main scarps of Study Area 1 (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION)

Figure 38. Map of landslide areas of Study Area 1 (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION)


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Figure 39. Map of landslide main scarps in combination the local geology of Study Area 1 (data basis: STYRIAN LIDAR


Legend

Tertiary

Miocene

Shell Limestone of Fohnsdorf

Grey‐Blue Clay, partly sandy ‐ gravelly

Base Formation: Base Breccia, Horizontal Sandstone

Quaternary

Holocene

Pleistocene

Floodplain Areas, Colluvial Soils, Torrent Debris

Younger Nappe of Brash (Schweinsbachwaldterrasse, Mindel)

Sheet Gravel of the Middle East Alpine

Crystalline of the Flatschacher Range

Granite Gneiss

Band Amphibolite

Orthogneiss (Gleinalmtypus)


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Figure 40. Map of landslide main scarps of Study Area 2 (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION)


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Figure 41. Map of landslide areas of Study Area 2 (data basis: STYRIAN LIDAR CAMPAIGN, 2011, AUTHOR’S ADAPTATION)


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Legend Sheet Gravel of the Upper East Alpine Laminated Quartzites

Sheet Gravel of the Middle East Alpine Bright, Banded, Marbled Limestones

Sandstone, Phylitte (with graphite) = Graphite‐Coal Formations (Veitscher Decke)

Quarternary Talus materials, Tali (partly glacial stage of Würm)

Alluvial Fans, Alluvial Cones, Debris Cones (partly late postglacial)

Floodplain Zones, “Kolluvien”, Torrent Debris

Figure 42. Map of landslide main scarps in combination the local geology of Study Area 2 (data basis: STYRIAN LIDAR



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5.3 Semi‐Automatic Landslide Mapping

The ArcGIS semi‐automatic landslide mapping tool was developed to facilitate the mapping and

locating of potential landslides in a specific region and then in turn determine hazard prone areas

in the vicinity. A set of different tools supports both field work and a manual mapping landslide

process.

This beta‐version of a possible semi‐automatic landslide mapping tool is a result of the author’s

collaboration with the Provincial Government of Styria, Board of Works – Geoinformation Staff

Office. In addition to the main tool for potential landslide detection, several other tools were

designed. For example, no useful vector dataset of forest roads exists at the Provincial

Government of Styria, however since forest roads interfere with the semi‐automatic landslide

mapping process, they have to be erased in the LiDAR Digital Terrain Model. For that reason a

forest roads detection tool was developed. This and some other tools together with the main

potential landslide detection tool are described from chapter 5.3.1 to chapter 5.3.2.

By analyzing land surface parameters like slope, curvature, variance of slope, variance of aspect

and roughness in connection with other external vector‐data objects such as streets, stream lines

or building shapes, important landslide indicators can be extracted in the form of polygon‐shapes

with the help of ArcGIS neighbourhood analysing techniques. Analysing and combining those

different land surface parameters, as derived from raster‐based LiDAR DTM data, allows for the

identification of the characteristic landslide features, but the main problems in this context are to

distinguish main scarps from embankments and other artificial objects and to outline the actual

landslide area. Therefore the assessment of the landscape using mathematical workflows and

computer techniques is important.

The main reason for working with the LiDAR Digital Terrain Model with 1 m resolution instead of

the high‐resolution original LiDAR point cloud was an easier and practicable use of the DTM. The

tool was designed in that way that people can use it without buying expensive LiDAR software

before. The LiDAR software LP360 by QCoherent was only used to process the Digital Terrain

Model.


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Another reason was that the 1 m ‐ resolution of the DTM is enough to extract specific landslide

features out of it. A DTM with 20 cm resolution for example is too detailed and complicates the

semi‐automatic detection of landslides.

5.3.1 Basic Workflow

Before explaining the basic workflow of the ArcGIS semi‐automatic landslide mapping toolbox it

has to be stressed that no two landslides resemble one another and usually do not look like

sketches presented in a schoolbook, which turns out to be the biggest challenge of semi‐automatic

landslide mapping.

But even though landslides have irregular surface texture and boundaries, characteristic features

can be detected in LiDAR imagery through GIS analysis (see Table 9, p. 64). Therefore, the

classification of the landscape using ArcGIS geoprocessing workflows and python modules is

important (see Figure 44).

At this point it has to be mentioned that the developed ArcGIS toolbox applies only as assistance

in mapping landslides, and does not replace field work as well as a post‐processing of the results

of the semi‐automatic landslide mapping tool. That is why a fully‐automatic landslide mapping

process is impossible. LiDAR is a big achievement in geosciences and it is very important and useful

for any kind of geomorphic studies, whether using manual or automatic mapping and analysing

processes.

As written before, with the help of Python, an open‐source scripting language, entire workflows

can be implemented in ArcGIS as new tools. Therefore a special knowledge in writing scripts is

essential.

This chapter will give a good insight into how this ArcGIS semi‐automatic landslide mapping tool

was written, but will not explain the Python Code in detail.


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As seen in Figure 47, Python was used to create a new Landslide Mapping Toolbox including

8 contiguous tools (see Table 11):

Tool Short Description

land surface parameter tool calculates different land surface parameter maps

topographic position index tool calculates the elevation and slope TPI of a study area

smorph tool classifies a study area into 3 classes – low, medium and high

landslide risk areas

roughness tool calculates the roughness of a special area

edge detection tool detects concave and convex edges of a terrain and creates and

Triangulated Irregular Network (TIN),

forest roads tool extracts forest roads out of the Digital Terrain Model and

creates a forest roads polygon shape

streams tool extracts streams out of the DTM and creates a streams polygon

shape

potential landslide tool detects potential landslide features

Table 11. Overview of the 8 contiguous tools of the Landslide Mapping Toolbox (source: AUTHOR’S ADAPTATION, 2011)

The output datasets are a result of a simple ArcGIS neighbourhood analysis with inclusion of other

external objects like

streets

streams

buildings

and archaeological sites.

In principle, the more additional vector information can be added to the semi‐automatic landslide

mapping process the merrier are the results.


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The source of these external objects are described in chapter 4.2

An exact explanation of the separate tools is given in chapter 5.3.2

This Landslide Mapping Toolbox is a result of a continuous loop between the toolbox, the

manually mapped landslides, and the semi‐automatically mapped landslides (see Figure 43).

Figure 43. Continuous Loop (source: AUTHOR’S ADAPTATION, 2012)

Manually mapped landslides help with the upgrading and developing of the ArcGIS Landslide

Mapping Toolbox. The results, described in chapter 4, of the first 5 tools – the Land Surface

Parameter‐, the Topographic Position Index‐, the Smorph‐, the Roughness‐ and the Edge

Detection‐Tools are also considered as support for the manual landslide mapping.

The knowledge acquired about the manually mapped and verified landslides in a specific study

area was on the one hand used to upgrade the ArcGIS semi‐automatic landslide mapping toolbox

and on the other hand to compare it with the semi‐automatically mapped landslides, both of

which help check the adaptability of the toolbox. The knowledge gained with the semi‐

automatically mapped landslides supports the manual mapping of landslides in a specific area and

helps to upgrade ArcGIS toolbox as well.

Manually Mapped Landslides

Compare

Support Semi‐Automatically Mapped Landslides

ArcGIS Landslide Mapping Toolbox


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Figure 44. Basic Workflow of Semi‐Automatic Landslide Mapping (source: AUTHOR’S ADAPTATION, 2012)


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5.3.2 Semi‐automatic Landslide Mapping Toolbox

The Landslide Mapping Toolbox created within the ArcGIS 10.0 environment, consists of a

collection of different tools, also explained in chapter 5.3.1. In this chapter a detailed explanation

of the separate tools is given.

Anybody, who is interested in the toolbox and wants to use or adapt the landslide tools in its own

work, may write a mail to [email protected].

Tool 1 – Land Surface Parameters: This tool calculates all land surface parameters mentioned in

chapter 4.3, namely hillshade, slope, variance of slope, aspect, variance of aspect, variance of

elevation, curvature and contour lines by using the LiDAR Digital Terrain Model with 1 m

resolution (input data). The usual 3D Analyst – Raster Surface Tools together with Spatial Analyst

Tools (Focal Statistics) were combined in one tool to facilitate and speed up the land surface

parameters calculation (geoprocessing) (see Figure 45 and 46). A polygon shape of the study area

determines the calculation area (input data). The images (output data) of the different land

surface parameters can be found in chapter 4.3.

Figure 45. Tool 1 – Land Surface Parameters (source: AUTHOR’S ADAPTATION, 2011)


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Figure 46. Workflow of Tool 1 (source: AUTHOR’S ADAPTATION, 2011)

Tool 2 – Topographic Position Index: The Topographic Position Index Tool is part of the Land Facet

Corridor Designer to design wildlife linkages in the face of impending climate change by Jenness

Enterprise, Land Facet Corridor Designer and was first described by WEISS (2001) at the ESRI

International User Conference. The DTM, together with the slope image (input data) is processed

with a special neighbourhood analysis and a topographic position classification (geoprocessing) to

“Terrain Surface Changes” and “Landform Categories” raster data (output data; see Figure 47 and

48). Like in Tool 1 and all other following tools a polygon shape determines the study area.


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Figure 47. Tool 2 – Topographic Position Index (source: AUTHOR’S ADAPTATION, 2011)



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This tool classifies a landscape into a “Terrain Surface Changes” raster dataset that depicts the

increasing and decreasing areas in the terrain surface and also into a “Landform Category” raster

dataset imaging the four landforms (valley, surface drawdown, terrain increase and ridge) based

on the two raster datasets slope and elevation. (see JENNESS et al. 2011, pp. 45; see Figure 47 and

48). With the help of ArcGIS Focal Statistic (Spatial Analyst) – Neighbourhood Analysis and other

mathematical raster tools of Spatial Analyst, differences in a specified neighbourhood were

calculated and in a further step the terrain is classified into different classes (valley, surface

drawdown, terrain increase and ridge).

A polygon shape of the study area determines the calculation area and in addition to that an

output workspace and the name of the study area are indicated as inputs for this tool. The images

of the two TPI raster datasets can be found in chapter 4.5.

Tool 3 – Smorph: The slope morphology matrix (SMORPH) by SHAW & JOHNSON (1995), which was

integrated in the python script, is formed by the slope gradient and the horizontal curvature

(convex, concave and planar; input data) and classifies the area into low, medium and high

landslide risk areas (output data; see Figure 49 and 50).

Figure 49. Tool 3 – Smorph (source: AUTHOR’S ADAPTATION, 2011)


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Tool 4 – Roughness: By processing the slope and aspect images (input data) the variation

(roughness) in three‐dimensional orientation of grid cells within a defined neighborhood is

calculated (output data; see Figure 51 and 52). Vector analysis is used to calculate the dispersion

of vectors normal (orthogonal) to grid cells within the specified neighbourhood. This method

effectively captures variability in slope and aspect into a single measure. Roughness raster values

can range from 0 (no terrain variation) to 1 (complete terrain variation). For instance, streets or

buildings have a smooth surface, while vegetation has a rough surface (see OLAYA 2009). A rough

surface can be an indicator for landslide events. The method to calculate the terrain roughness

was adapted from the Vector Ruggedness Measure by SAPPINGTON et al. (2007).


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Figure 51. Tool 4 – Roughness (source: AUTHOR’S ADAPTATION, 2011)



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Tool 5 – Edge Detection: The Edge Detection tool (see NIEMEYER et al. 2010) was adapted to

calculate convex and concave edge lines and to create a generalized TIN image (output data). In

this case the slope image (input data) is used as well as an orientation image and a vertical

curvature image (input data) to outline the locations of convex and concave edges in the defined

study area, thus the decision between low‐ and high‐mountain areas is important for the best

possible edge detection. A Non‐Maxima‐Suppression has to be executed to convert convex and

concave raster data into convex and concave line shapes. With this process pixels that are not at

the edge‐maxima are suppressed. Additionally, holes in the dataset are closed with a

morphological closing operation, and in a further step they are thinned with a morphological

skeleton operation to get edge lines (see Figure 53 and 54).

Figure 53. Tool 5 – Edge Detection (source: AUTHOR’S ADAPTATION, 2011)


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Tool 6 – Forest Roads: The TPI “Landform Categories” raster dataset (input data) is utilized to

extract forest roads as a polygon shape (output data) out of high‐resolution LiDAR data. Because

of the high diversity of the terrain surface a post‐processing of the output polygon shape after

execution of tool 6 is essential. In this working step features that have similar parameters to forest

roads (slope gradient, roughness, etc.) have to be deleted (see Figure 55 and 56).

Figure 55. Tool 6 – Forest Roads (source: AUTHOR’S ADAPTATION, 2011)



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Tool 7 – Streams: The TPI “Landform Categories” raster dataset (input data) is also used to detect

streams as a polygon shape (output data) out of high‐resolution LiDAR data. To reduce the

processing area an old and highly generalised streams vector dataset is used. It is important to

decide between small‐ and large‐scale streams before starting the process. Because of the high

diversity of the terrain surface a post‐processing of the output polygon shape after execution of

tool 7 is essential. In this working step features that have similar parameters to streams (slope

gradient, roughness, etc.) have to be deleted (see Figure 57 and 58).

Figure 57. Tool 7 – Streams (source: AUTHOR’S ADAPTATION, 2011)


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Figure 58. Workflow of Tool (source: AUTHOR’S ADAPTATION, 2011)


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Tool 8 – Potential Landslides: The Potential Landslide tool was created to outline potential

landslide features and areas in a specified area (study area) of a DTM with 1 m resolution. It is the

main and most important tool of the Landslide Mapping Toolbox. By combining the TPI “Landform

Categories”and TPI “Terrain Surface Changes” raster datasets with the land surface parameter

images ‐‐ aspect, slope and vertical curvature (input data) ‐‐ the terrain is classified into the

following different features: convex and concave edges, steep slope and rough areas, areas with

terrain increasing and decreasing and ridges (geoprocessing). These special terrain features are

possible indicators for landslide activities.

With special neighbourhood analysis of the different terrain features and by integration of

additional vector data like streets, streams, forest roads, buildings and archaeological sites the

study area is classified into potential and non‐potential landslide areas. The tool can be expanded

with other additional vector datasets, which are integrated in the neighbourhood analysis at any

time. These landslide areas are extracted as a new polygon shape, where elements that are too

small are deleted to improve the results (geoprocessing).

The output dataset is a polygon shape, which shows potential landslide areas in a specified region.

With the help of the 1:50 000 geology map of Styria (BEV), shapes of torrent catchments, corine

landcover datasets and the manually mapped and verified landslides, the potential landslide shape

is post‐processed to get a good and useable landslide dataset (final results). This dataset should

support the decision‐making of experts and facilitate field work and the locating of landslides.

Figure 59. Tool 8 – Potential Landslides (source: AUTHOR’S ADAPTATION, 2011)


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5.4 Results

A polygon vector dataset depicting potential landslide areas (see Figure 61) and two additional,

intermediate polygon shapes showing streams and forest roads are the primary results of this

research work. It is important to mention that only the possible landslide areas are extracted

during this semi‐automatic process, which means that this tool identifies regions that are most

likely influenced by landslide activities; not necessarily actual landslide events Additionally other

surfaces similar to landslides, like ridges, embankments of streets or forest roads or erosion lines

are outlined as landslide areas as well. The quality and accuracy of the results depend more or less

on the quality of the input data, of the existence of noises and artefacts in LiDAR data and on the

accuracy and availability of additional vector information like streets, buildings, mining areas or

archaeological sites.

5.4.1 Intermediate Data (Landslide Mapping Toolbox)

Edge Lines and TINs: The convex and concave edge lines were calculated to find distinctive edges

in a terrain. In a further step a generalised Triangulated Irregular Network (TIN) is generated out of

these edge lines, with the goal to highlight these from landslides influenced areas and to erase

smaller and insignificant edges (see Figure 61 and 62). In a TIN, terrain edges like main scarps and

irregular surfaces are delineated and this process facilitates manual landslide mapping. The

surfaces of rupture are indicated by irregular surface textures.


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Figure 61. Aperture of generalised TIN of Study Area 1 calculated with the “Edge Detection” tool (source: AUTHOR’S

ADAPTATION, 2011)


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Figure 62. Aperture of generalised TIN of Study Area 2 calculated with the “EdgeDetection” tool (source: AUTHOR’S

ADAPTATION, 2011)

Forest Roads and Streams: Practical landslide detection is only possible if forest roads and

streams are erased out of a high‐resolution LiDAR‐DTM. A post‐processing of this data is essential

and must be done manually. In the following image (see Figure 63 and 64), forest roads are

visualised as red polygons, streams as blue ones.


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Figure 63. Aperture of results of Study Area 1 of the “ForestStreets” and “Streams” tools (source: AUTHOR’S ADAPTATION,

2011)

Figure 64. Aperture of results of Study Area 2 of the “ForestStreets” tool (source: AUTHOR’S ADAPTATION, 2011)


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5.4.2 Potential Landslides

The results of the semi‐automatic landslide mapping show potential, not necessarily actual,

landslide areas. With the semi‐automatic process landslide‐related features can be outlined and

regions likely influenced by landslide activities can be found. Changes in the terrain surfaces,

convex and concave edges, rough and wavy areas or ridges can all indicate a possible landslide

event, but they can indicate features such as forest roads, mining areas, erosion lines or

anthropogenic‐formed structures as well.

The quality of results depends on the quality of the DTM; noises and artefacts in the original LiDAR

data can cause fuzziness or uncertainty in the results. The quality and the availability of additional

vector data, such as streets, buildings or archaeological sites, also improve the usability of the

results.

The following three tables show apertures of the results of the two study areas in Spielberg bei

Knittelfeld and Wald am Schoberpass. The green highlighted rows indicate the potential landslides

derived from the semi‐automatic mapping process.

OBJECTID photo date SHAPE_Leng semi‐automatic

1 08_pics_fieldwork\spielberg\IMG_0152 20111002 29,37487012000 Y


3 08_pics_fieldwork\spielberg\DSCN0732 20111106 47,56245820120 N

6 08_pics_fieldwork\spielberg\DSCN0674 20111106 60,04371042410 Y

















46 08_pics_fieldwork\spielberg\DSCN0736 20111106 55,22384273330 Y Table 12. Aperture of the results of Study Area 1, Spielberg bei Knittelfeld (source: AUTHOR’S ADAPTATION, 2012)


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OBJECTID photo date SHAPE_Leng semi_automatic

1 08_pics_fieldwork\wald\IMG_0359 20111811 2007,93535551000 Y








9 08_pics_fieldwork\wald\IMG_0359 20111811 144,88936331800 N



12 08_pics_fieldwork\wald\IMG_0359 20111811 64,49493709180 Y Table 13. Aperture of the results of Study Area 2, Wald am Schoberpass (source: AUTHOR’S ADAPTATION, 2012)

Fifty landslides were mapped manually for Study Area 1, Spielberg bei Knittelfeld. Eighteen of

those fifty landslides were successfully mapped using the semi‐automatic process, a success rate

of 36%. Of the fifty manually mapped landslides, twenty were located and hidden under forest

canopy, while the other thirty were found in agricultural areas. In forested areas, there was a

success rate of 85%, and in the agricultural areas a success rate of 3.33% was found. This indicates

the strength of the tool in delineating landslides in forested areas as opposed to agricultural areas.

In total, the error rate of the semi‐automatic landslide mapping process for Study Area 1 was 54%.

About 50% of the semi‐automatically mapped landslide area was erroneously detected with this

tool.

Twelve landslides were mapped manually in Study Area 2, Wald am Schoberpass. Eleven out of the

twelve were correctly mapped with the semi‐automatic process with a 92% success rate. All

twelve of the manually mapped landslides were found in forested areas; the low error rate of 8%

in this type of land cover again supports the strength of the tool in identifying landslides in

forested areas. This time only 30% of the semi‐automatically mapped landslide areas were

erroneously detected, most likely due to the lack of agricultural areas in this study area.

“Erroneously” does not mean that there cannot be any earth movements in these areas, but that

there are not those kinds of landslides movements treated in this master’s thesis and described in

chapter 2.2.


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Landslides Study Area 1 Study Area 2

total 50 12

semi‐automatic mapped 18 11

percentage of semi‐automatic mapped [%] 36 92

in forested areas 20 12

semi‐automatic mapped

in forested areas ‐

17

11

in agriculturally marked areas 30 0

semi‐automatic mapped

in agriculturally marked areas

1

0

Error rates

not semi‐automatic mapped 32 1

percentage of not semi‐automatic mapped [%] 54 8

wrongly detected landslide areas [%] 50 30

not semi‐automatic mapped

in forested areas

3

1

percentage of not semi‐automatic mapped

in forested areas [%]

15

8.33

not semi‐automatic mapped

in agriculturally marked areas

29

0

percentage of not semi‐automatic mapped

in agriculturally marked areas [%]

96.67

0

Table 14. Statistics of the semi‐automatic landslide mapping (source: AUTHOR’S ADAPTATION, 2012)

The following figures (65 and 66) show the results of the semi‐automatic mapping process.


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Figure 65. Results of the semi‐automatic mapping tool in Study Area 1, Spielberg bei Knittelfeld (source: AUTHOR’S

ADAPTATION, 2011)


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Figure 66. Results of the semi‐automatic mapping tool in Study Area 2, Wald am Schoberpass (source: AUTHOR’S

ADAPTATION, 2011)


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5.4.3 Limitations

Due to the high degree of accuracy, a LIDAR DTM not only depicts mass movements, but also a

multiplicity of various anthropogenic structures and terrain surfaces such as excavations, ancient

mining areas, collapse shafts, archaeological sites, old footpaths or agricultural lands as well as

various naturally formed structures and terrain surfaces such as ridges, steep and rugged terrain

or erosions. These man‐made and natural structures and other special terrain surfaces like alluvial

fans, karst formations or old creeks cause poor results in the semi‐automatic landslide mapping

process. Due to the high diversity of the terrain surface a fully‐automatic landslide mapping

process is not possible and a manual post‐processing is indispensable.

The following table (see Table 15) shows some reasons for poor results in the two study areas.

Ancient mining areas are the main problems in Study Area 1 in Spielberg bei Knittelfeld, a steep

and rugged terrain in Study Area 2 in Wald am Schoberpass.

Reasons for poor results in the two study areas

Anthropogenic formed structures and terrain surfaces like

‐ excavations

‐ ancient mining areas


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‐ collapse shafts

‐ archaeological sites

‐ old footpaths

‐ agriculturally marked lands


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Natural formed structures and terrain surfaces, like

‐ ridges

‐ steep and rugged terrain

‐ erosions

Table 15. Reasons for poor results in the two study areas (source: AUTHOR’S ADAPTATION, 2012)


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5.4.4 Adaptability of a semi‐automatic landslide mapping

The semi‐automatic landslide mapping tool was designed in such a way that it can be used

world‐wide, at least anywhere there are already high‐resolution LiDAR DTMs available.

Although the tool is dependent on DTMs with high accuracy, high resolution, less noises, and

artefacts, it is universally applicable in each mountainous region of the world. These DTMs are not

necessarily derived by LiDAR, but can also be acquired through techniques such as

photogrammetry or InSAR (Interferometric synthetic aperture radar). However, a special

knowledge of LiDAR, DTMs and Geomorphometry is essential.

Because of the high diversity of the terrain surface a fully‐automatic landslide mapping tool lacking

manual post‐processing does not work. So, in this master’s thesis the semi‐automatic landslide

mapping tool is considered an aid to the decision making process, and it never actually replaces

manual labour or field work.

6. CONCLUSIONS AND PERSPECTIVES

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The goals of this master’s thesis (see chapter 1.2.) were accomplished. In this last chapter a short

review will be given as well as future perspectives regarding the author’s research work in the field

of LiDAR and its application in geomorphology.

The description of the basics in airborne LiDAR and landslides is given in chapter 2. The author has

shown that the application of LiDAR DTMs is an enormous achievement and facilitation in the field

of geomorphology and other geosciences. It not only facilitates the outlining of landslide features,

but also the mapping and detection of other mass movements, glacial or fluvial forms, karst

formations or geomorphological structures caused by archaeological sites.

Two special regions in Spielberg bei Knittelfeld and Wald am Schoberpass were introduced shortly

in chapter 3. The reasons for selecting these areas are based on the availability of high‐resolution,

LiDAR derived DTMs with less noises and artefacts. Study Area 1 (Spielberg bei Knittelfeld) is

located in an inner alpine basin, called Aichfelder Becken, on the south brink of the Niedere

Tauern (Central Alps), and Study Area 2 (Wald am Schoberpass) is situated in a small tributary

valley of the Liesingtal in the Eisenerzer Alpen. Brisk landslide activities can be found in the two

areas partly below dense forest cover, within agriculturally used zones, ancient mining areas,

tourism areas and archaeological sites, which affect the terrain surface and complicate the

mapping process.

Study Area 1 stands for lower mountain regions, while Study Area 2 for high mountain regions in

Styria (Republic of Austria).

Chapter 4 gives a short introduction of the software environments and data used for this thesis,

the field of Geomorphometry, and its different land surface parameter images. This chapter lists

the basics for manual and semi‐automatic landslide mapping. For a semi‐automatic landslide

mapping by means of LiDAR derived DTMs, it has to be stressed that it is neither impossible nor

too complex. Good results can be achieved with standard GIS environments and models without

extensive mathematical algorithms.


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This master’s thesis has presented possible workflows of manual and semi‐automatic landslide

mapping (see chapter 5) in two specified regions. While in its present form, automatic and semi‐

automatic landslide mapping is not successful 100% of the time and an expert with special

knowledge in the field of LiDAR and geomorphology is still better able to outline different landslide

features in the various land surface parameter images than any kind of computer software can

detect during a semi‐ or fully‐automatic mapping process. The tool never replaces field work and

on‐site measurements, but it is a useful tool to aid in the process of decision making and provides

an indication of geographical position and extension of mass movements.

In conducting the work of this master’s thesis the author created a semi‐automatic landslide

mapping tool within the ArcGIS environment. The tool is able to detect landslide structures in a

DTM with 1 m resolution by combining and analysing different land surface parameters and TPI

images. By comparing the results of the manually mapped and verified landslides with the semi‐

automatically mapped landslide features, and in familiarising with the different images,

raster‐based methods, and the knowledge won during the manual mapping process, the tool was

improved by and by and the adaptability of the results was checked.

There does not exist any published approach for mapping landslides semi‐automatically with the

help of ArcGIS geoprocessing techniques, which means that the author demonstrates something

new in this master’s thesis and highlights LiDAR as an up to now unimaginable opportunity in the

field of geo science. It simplifies field work and the determination of diverse structures of the

terrain surfaces like block glacier (see Figure 67, top left), karst formations (see Figure 67, top

right), alluvial fans (see Figure 67, centre left), old creeks (see Figure 67, centre right), terraced

landscapes (see Figure 67, bottom left) or burial mounds (see Figure 67, top left).

It is common to use LiDAR point cloud data, DTMs or DSMs to map and outline diverse terrain

surfaces structures manually, but the use of automatic and semi‐automatic processes is something

extraordinary. It simplifies a mapping of landslide activities in a larger area, like the area of Styria is

where an automation of different workflows is indispensable.


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The potential landslides tool as well as the forest roads and streams tool are a big achievement for

the author and the Provincial Government of Styria, Board of Works – Geoinformation Staff Office

and are already used by different governmental departments to reestablish a new stream network

dataset with a high accuracy for the whole Province of Styria or to map forest roads under thick

forests.

Figure 67. Diverse terrain surface structures (source: STYRIAN LIDAR CAMPAIGN, 2011)


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The biggest contribution to the author’s master thesis was the book GEOMORPHOMETRY by HENGL &

REUTER (2009) and in addition to that the paper EXTRAKTION GEOLOGISCH RELEVANTER STRUKTUREN AUF RÜGEN

IN LASERSCANNER‐DATEN by NIEMEYER et al. (2010). These two references gave the best compulsion for

the utilisation and further processing of LiDAR DTMs.

Nevertheless, the research work and the tool are still in the starting phase, and there are still

plenty of ideas to explore and improve. The author intends to continue working on this special and

interesting topic after graduation.

With the help of the tools and methods described in this master’s thesis, landslide‐prone regions

may be outlined to determine hazardous areas. The DTM visualises structures that are indicators

for different downslope movements and determines where mass movements could take place,

but it does not show changes in terrain surfaces or movement velocities. An additional integration

and comparison of the LiDAR DTMs with Terrestrial Laser Scanning (TLS), Global Positioning

System (GPS) and ALS data from different periods would improve the results and simplify the

detection of landslides and the work in natural disaster control. The first Styrian ALS campaign has

opened the doors to explore the physical features and to get a good insight into the high diversity

of the terrain surface of the Province of Styria. Subsequent campaigns will strengthen our

understanding for geomorphic processes. A temporal comparison utilising ALS data acquired over

different time periods would help to identify changes in terrain surfaces and would improve a

semi‐automatic mapping process of delineating regions endangered by mass denudations

considerably.

Even if this tool is still in under construction and there are still lots of things to improve, it shows a

good example of the application of high‐resolution LiDAR data especially in the field of

geomorphology and landslide research and gives a good insight in the new possibilities and

advantages based on LiDAR for geomorphologists or alternatively any kind of researcher in the

field of geosciences.

The most important research goal for the author was to get a good expert knowledge in the field

of LiDAR and its application in geomorphology and to acquaint herself with different software

products, tools and techniques. This acquired knowledge will serve the author as a basis for future

research work!

REFERENCES

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REFERENCES

References from Books

AHNERT, F., 1996: Einführung in die Geomorphologie. – Ulmer Verlag, Stuttgart, 393 p.

BROWELL, E. V. et al., 2005: Airborne LiDAR Systems.– in: FUJI, T. & FUKUCHI, T. (Eds), 2005:

Laser Remote Sensing. – Taylor & Francis Group, Boca Raton, London, New York and Singapore,

PP 723‐780.

CRUDEN, D.M. & VARNES, D.J., 1996: Landslide types and processes. – in: TURNER A.K. & SHUSTER R.L.

(Eds), 1996: Landslides: Investigation and Mitigation. Transp Res Board, Spec Rep 247,

Washington, PP 36‐75.

FUJI, T. and FUKUCHI, T. (Eds), 2005: Laser Remote Sensing. – Taylor & Francis Group, Boca Raton,

London, New York and Singapore, 888p.

HENGL, T. and REUTER H. (Eds), 2009: Geomorphometry ‐ Concepts, Software, Applications.

– Elsevier, Amsterdam and Oxford, 765p.

MCCORMICK, M. P., 2005: Airborne and Spaceborne Lidar. – in: WEITKAMP, C. (Eds), 2005:

Lidar‐ Range‐Resolved Optical Remote Sensing of the Atmosphere. – Springer Verlag, Singapore,

PP 355‐397.

NELSON, A. et al., 2009: Dem Production Methods and Sources. – in: HENGL, T. & REUTER, H. (Eds),

2009: Geomorphometry ‐ Concepts, Software, Applications. – Elsevier, Amsterdam and Oxford,

PP 65‐139.

OLAYA, V., 2009: Basic Land‐Surface Parameters. – in: HENGL, T. & REUTER H. (Eds), 2009:

Geomorphometry ‐ Concepts, Software, Applications. – Elsevier, Amsterdam and Oxford,

PP 141‐170.

REFERENCES

115 / 122

PENCK, W., 1924: Die morphologische Analyse. – Verlag von J. Engelshorns, Stuttgart, PP 59‐85.

SASSA, K., 2007: Landslide Science as a New Scientific Discipline. – in: SASSA, K., et al. (Eds), 2007:

Progress in Landslide Science. – Springer Verlag, Berlin, Heidelberg and New York. PP 3‐11.

SATO, H.P. et al., 2007: Airborne LiDAR Data Measurement and Landform Classification ‐ Mapping

in Tomari‐no‐tai Landslide Area, Shirakami Mountains, Japan. – in: SASSA, K., et al. (Eds), 2007:

Progress in Landslide Science. – Springer Verlag, Berlin, Heidelberg and New York. PP 237‐249.

SCHMIDT, J & DIKAU, R., 1999: Extracting geomorphometric attributes and objects from digital

elevation models – semantics, methods, future needs. – in: DIKAU, R. & SAURER, H., 1999: GIS for

Earth Surface Systems ‐ Analysis and Modelling of the Natural Environment. – Gebrüder

Borntraeger, Berlin and Stuttgart, PP 153‐173.

VARNES, D.J., 1978: Slope movement types and processes. – in: Special Report 176: Landslides:

Analysis and Control (Eds: SCHUSTER, R.L. & KRIZEK, R.J.). – Transportation and Road Research Board;

National Academy of Science, Washington D.C., PP 11‐33.

VEDER, C., 1979: Rutschungen und ihre Sanierung, – Springer Verlag, Wien and New York, 241 p.

VÖGTLE, Th. & STEINLE, E., 2005: Flugzeuggetragenes Laserscanning. – in: BÄHR, H.‐P. & VÖGTLE, Th.

(Eds), 2005: Digitale Bildverarbeitung ‐ Anwendung in Photogrammetrie, Fernerkundung und GIS.

– Herbert Wichmannverlag, Heidelberg, PP 29–45.

WEITKAMP, C., 2005: LiDAR ‐ Introduction. – in: FUJI, T. & FUKUCHI, T. (Eds), 2005: Laser Remote

Sensing. – Taylor & Francis Group, Boca Raton, London, New York and Singapore, PP 1‐36.

WANDINGER, U., 2005: Introduction to LiDAR. – in: WEITKAMP, C. (Eds), 2005: Lidar ‐ Range‐Resolved

Optical Remote Sensing of the Atmosphere. – Springer Verlag, Singapore, PP 1‐18.

ZEPP, H., 2008: Geomorphologie. – Schöningh UTB, Padeborn, München, Wien and Zürich,

PP 103‐115.

REFERENCES

116 / 122

References from Journals and Posters

BOOTH, A.M. et al., 2009: Automated landslide mapping using spectral analysis and high‐resolution

topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon. – in:

Geomorphology 109 (3‐4), PP 132‐147.

(source: http://www.sciencedirect.com/science/article/pii/S0169555X09000877, last access:

March 2012)

DIKAU, R., 1996: Geomorphologische Reliefklassifikation und –analyse. – in: Heidelberger

Geographische Arbeiten, Vol. 104, PP 15‐36.

(source: http://www.geomorphology.uni‐bonn.de/persons/dikau/publications_view, last access:

October 2011)

GLENN, N.F. et al., 2006: Analysis of LiDAR‐derived topographic information for characterizing and

differentiating landslide morphology and activity. – in: Geomorphology 73 (1‐2), PP 131‐148.

(source: http://bcal.geology.isu.edu/manuscripts/Glenn_2006_Geomorph.pdf, last access:

October 2011)

HOEFLE, B. & RUTZINGER, M., 2011: Topographic airborne LiDAR in geomorphology: A technological

perspective. –in: Zeitschrift für Geomorphologie Vol. 55, Suppl. 2, PP 1‐29.

(source: http://koenigstuhl.geog.uni‐heidelberg.de/publications/2011/Hoefle/Hoefle_Rutzinger_

2011_ZfG.pdf, last access: January 2012)

KASAI, M. et al., 2009: LiDAR‐derived DEM evaluation of deep‐seated landslides in a steep and

rocky region of Japan. – in: Geomorphology 113 (1‐2), PP 57‐69.


March 2012)

REFERENCES

117 / 122

MCKENNA, J.P. et al., 2008: Landslides Mapped from LIDAR Imagery, Kitsap County, Washington. –

in: U.S. Geological Survey Open‐File Report 1292, 81 p.

(source: http://pubs.usgs.gov/of/2008/1292/downloads/OF08‐1292.pdf, last access: March 2012)

MCKEAN, J. & ROERING, J., 2004: Objective landslide detection and surface morphology mapping

using high‐resolution airborne laser altimetry. – in: Geomorphology 57 (3‐4), PP 331‐351.


March 2012)

MINER, A.S. et al., 2010: Landslide Recognition using LiDAR derived Digital Elevation Models‐

Lessons learnt from selected Australian examples. – in: University of Wollongong Research Online,

10 p.

(source: http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1590&context=engpapers, last access:

March 2012)

VAN DEN EECKHAUT, M. et al., 2011: Regional mapping and characterization of old landslides in hilly

regions using LiDAR‐based imagery in Southern Flanders. – in: Quaternary Research 75, 13 p.

(source: http://www.blug‐ublg.be/FR/down/VanDenEeckhaut_et_al_Regional_map_landslides

_QR2011.pdf, last access: March 2012)

VAN DEN EECKHAUT, M. et al., 2007: Use of LIDAR‐derived images for mapping old landslides under

forest. – in: Earth Surface Processes and Landforms 32, PP 754‐769.

(source: http://www.wou.edu/las/physci/taylor/g473/eekhaut_etal_2007_Lidar_landslides.pdf,

last access: March 2012).

VAN DEN EECKHAUT, M. et al., 2005: The effectiveness of hillshade maps and expert knowledge in

mapping old deep‐seated landslides. – in: Geomorphology 67 (3‐4), PP 351‐363.


March 2012).

REFERENCES

118 / 122

SCHULZ, W.H., 2007: Landslide susceptibility revealed by LIDAR imagery and historical records,

Seattle, Washington. – in: Engineering Geology 89, PP 67‐87.

(source: http://landslides.usgs.gov/docs/schulz/lidar_enggeo.pdf, last access: March 2012)

SCHULZ, W.H., 2004: Landslides mapped using LIDAR imagery, Seattle, Washington. – in: U.S.

Geological Survey Open‐File Report 1396, 11 p.

(source: http://pubs.usgs.gov/of/2004/1396/OF2004‐1396_508.pdf, last access: March 2012)

SHAW, S.C. & JOHNSON, D.A., 1995: Slope Morphology Model Derived from Digital Elevation Data.

– in: Proceedings, 1995: Northwest Arc/Info Users Conference, Coeur d'Alene, Idaho, Oct. 23‐25,

12 p.

(source: http://www.krisweb.com/biblio/gen_xxxx_shawetal_1995_slopemorph.pdf, last access:

March 2012)

MONTGOMERY, D.R. & DIETRICH, W.E., 1994: A Physically Based Model for the Topographic Control on

Shallow Landsliding. – in: Water Resources Research, Vol. 30, No. 4, PP 1153‐1171.

(source: http://www.geo.oregonstate.edu/classes/geo582/week_3_1_eros_thresh_channel_net

/montg_dietr_wrr_94.pdf, last access: March 2012)

SAPPINGTON, J.M. et al., 2007: Quantifying Landscape Ruggedness for Animal Habitat Analysis ‐ A

case Study Using Bighorn Sheep in the Mojave Desert. – Journal of Wildlife Management 71(5), PP

1419‐1426.

(source: http://arcscripts.esri.com/details.asp?dbid=15423, last access: March 2012)

REFERENCES

119 / 122

References from Papers

AMUNDSENA, J. et al, 2010: Using LiDAR‐derived DEM’s to delineate and characterize landslides in

Northern Kentucky and Hamilton County, Ohio, 26 p.

(source: http://www.trishock.com/academic/pdf/lidar_landslides.pdf, last access: March 2012)

ASPRS, American Society for Photogrammetry & Remote Sensing, 2010: LAS Specification ‐ Version

1.3 – R11. Maryland, 18 p.

(source: http://liblas.org/_static/files/specifications/asprs_las_format_v13.pdf, last access:

March, 2012)

CORSINI, A. et al., 2009: Coupling geomorphic field observation and LiDAR derivatives to map

complex landslide. 18 p.

(source: http://eost.u‐strasbg.fr/omiv/Landslide_Processes_Conference/Corsini_et_al.pdf,

last access: March 2012)

CRUDEN, D.M., 1993: The Multilingual Landslide Glossary. – Bitech Publishers, Richmond., British

Columbia, for the UNESCO Working Party on World Landslide Inventory in 1993, 32 p.

(source: http://www.cgs.ca/pdf/heritage/Landslide%20Glossary.pdf, last access: March 2012)

JENNESS, J. et al., 2011: Land Facet Corridor Designer. – USDA Forest Service Rocky Mountain

Research Station, McIntire‐Stennis Cooperative Forestry Program and Arizona Board of Forest

Research, 102 p.

(source: http://www.jennessent.com/downloads/Land_Facet_Tools_A4.pdf , last access:

March, 2012).

NIEMEYER, J. et al., 2010: Extraktion geologisch relevanter Strukturen auf Rügen in Laserscanner‐

Daten. 10 p.

(source: http://www.ipi.uni‐hannover.de/uploads/tx_tkpublikationen/2010_Niemeyer_

Ruegen_01.pdf, last access: March 2012)

REFERENCES

120 / 122

NOAA, National Oceanic and Atmospheric Administration Coastal Services Center, 2008: Lidar 101 ‐

An Introduction Lidar Technology, Data, and Applications. – Charleston, SC: NOAA Coastal Service

Center, 68 p.

(source: http://www.csc.noaa.gov/digitalcoast/data/coastallidar/_pdf/What_is_Lidar.pdf, last

access: October, 2011).

VARNES D.J. & IAEG, 1984: Commission on Landslides and other Mass Movements ‐ Landslide

hazard zonation ‐ a review of principles and practice. – in: The UNESCO Press, Paris, 60 p.

(source: http://www.bib.ub.edu/fileadmin/fdocs/landslidehazard.pdf, last access: March, 2012)

WEISS, A., 2001: Topographic Position and Landforms Analysis. – Poster presentation, ESRI User

Conference, San Diego, CA, 1 p.

(source: http://www.jennessent.com/arcview/TPI_Weiss_poster.htm, last access: March, 2012)

SCHWEIZER EIDGENOSSENSCHAFT – Bundesamt für Umwelt – Abteilung Gefahrenprävention, 2009a:

Rutschungen – Dynamik. – Schweiz, 2 p.

(source: http://www.bafu.admin.ch/naturgefahren/01917/index.html?lang=de, last access:

October 2010).

SCHWEIZER EIDGENOSSENSCHAFT – Bundesamt für Umwelt – Abteilung Gefahrenprävention, 2009b:

Rutschungen – Prozess. – Schweiz, 3 p.

(source: http://www.bafu.admin.ch/naturgefahren/01917/index.html?lang=de, last access:

October 2010).

REFERENCES

121 / 122

References from Websites

ARCGIS DESKTOP 10 HELP, 2011.

(source: http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html, last access:

October, 2011)

ArcGIS, 2011. SynergisInformationssysteme GmbH. Official Distributor of ESRI.

(source: http://www.esri‐austria.at/products/arcgis/index.html, last access: October 2011)

ESRI, 2011.

(source: http://www.esri.com/ , last access October, 2011)

GEOLOGICAL SURVEY OF AUSTRIA, 2011.

(source: http://www.geologie.ac.at/, last access April, 2011)

GEOLOGICAL HAZARDS PROGRAM, 2011: Landslides. – Geological Survey of Alabama.

(source: http://www.gsa.state.al.us/gsa/geologichazards/landslides/, last access: May, 2011)

GEONET, 2011: LANDSLIDE GLOSSARY.

(source: http://www.geonet.org.nz/landslide/glossary.html, last access: October, 2011)

LANDSLIDE HAZARDS PROGRAM, 2011. – U.S. Geological Survey

(source: http://landslides.usgs.gov/, last access: October, 2011)

LUIS, 2011: Climate. – Environmental Information of Styria, Provincial Government of Austria.

(source: http://www.umwelt.steiermark.at/cms/ziel/25199/DE/, last access: March, 2012)

MTC, Midwest Transportation Consortium, 2003: Use of LIDAR‐based Elevation Data for Highway

Drainage Analysis ‐ A Qualitative Assessment.

(source: http://www.ctre.iastate.edu/mtc/reports/lidar_hydro.htm, last access: October, 2011)

REFERENCES

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PYTHON, 2012.

(source: http://www.python.org/about/success/, last access: October, 2011)

QCoherent – a GeoCue company – LP360, 2011.

(source: http://www.qcoherent.com/index.html, last access: October 2011)

Data References

BEV – Bundesamt für Eich‐ und Vermessungswesen, 2011.

(source: http://www.bev.gv.at/portal/page?_pageid=713,1604790&_dad=portal&_

schema=PORTAL, last access: March 2012)

STYRIAN LIDAR CAMPAIGN, 2011.

All in this thesis used LiDAR products (point clouds, DTMs, DSMs) data are property of the

Provincial Government of Styria, Board of Works – Geoinformation Staff Office.

GIS STYRIA, 2012.

Most in this thesis used GIS data are property of the Provincial Government of Styria, Board of

Works – Geoinformation Staff Office.

BMLFUW (Bundesministerium für Land‐ und Forstwirtschaft, Umwelt und Wasserwirtschaft or

Lebensministerium) & BFW (Federal Research and Training Centre for Forests, Natural Hazards and

Landscape), 2009:

(source: http://gis.lebensministerium.at/eBOD/frames/index.php?PHPSESSID=f0945e4330d621

76192f0674797be30d&145=true&gui_id=eBOD, last access: March 2012)

Documents

USING HIGH RESOLUTION AIRBORNE LIDAR DATA FOR …€¦ · Additionally LiDAR data allows the detection and outlining of these different geomorphological and anthropogenic features