airborne remote sensing data for semi- automated extraction of tree ...

DISS. ETH Nr. 20464

AIRBORNE REMOTE SENSING DATA FOR SEMIAUTOMATED EXTRACTION OF TREE AREA AND CLASSIFICATION OF TREE SPECIES A dissertation submitted to ETH Zurich

for the degree of

Doctor of Sciences presented by

LARS TORSTEN WASER Dipl. geogr., University of Zurich, Switzerland born on April 2, 1972

citizen of Neftenbach (ZH) accepted on the recommendation of Prof. Dr. Lorenz Hurni

Prof. Dr. Barbara Koch

Dr. Emmanuel Baltsavias 2012

LIFE is full of surprises. You never know where the next deflection will take you to. The only way to find out is simply to take it. Be curious and your curiosity will be rewarded. Sometimes leaving your path for a little diversion may provide new insights, a new perspective on everything you thought of as being fixed and numb.

Christopher von Deylen (Schiller)

TABLE OF CONTENTS TABLE OF CONTENTS..................................................................................................................................................... i ACKNOWLEDGEMENTS ................................................................................................................................................ v CURRICULUM VITAE ................................................................................................................................................... vi LIST OF FIGURES ......................................................................................................................................................... vii LIST OF TABLES .............................................................................................................................................................. x ABBREVIATIONS ......................................................................................................................................................... xii ABSTRACT ......................................................................................................................................................................xv ZUSAMMENFASSUNG ............................................................................................................................................... xvii 1

INTRODUCTION..................................................................................................................................................... 1 1.1

1.2

2

Motivation ........................................................................................................................................................................ 1

Goals ................................................................................................................................................................................... 2

1.3

Structure of the thesis ................................................................................................................................................. 4

2.1

Current state of research and technology ........................................................................................................... 6

REMOTE SENSING IN FORESTRY ..................................................................................................................... 6

2.1.1

Sensors ..................................................................................................................................................................... 6

2.1.3

Extraction of tree area .................................................................................................................................... 10

2.1.2

2.1.4

2.2

Classification of tree species ........................................................................................................................ 11

Existing forest information .................................................................................................................................... 12

2.2.1

2.2.2

Forest definitions ............................................................................................................................................. 13

2.2.2.1

Statistical forest information ....................................................................................................................... 13

2.2.2.2

Land use/Land cover statistics............................................................................................... 14

2.2.3

3

Methods for forest parameter extraction .................................................................................................. 9

Swiss National Forest Inventory ............................................................................................ 13

2.2.3.1

Mapped forest information .......................................................................................................................... 14

2.2.3.2

CORINE Switzerland ............................................................................................................... 15

2.2.3.3

Automatically vectorized forest borders ............................................................................... 16

2.2.3.4

Degree of forest mixture ....................................................................................................... 16

2.2.4

The Topographic Landscape Model....................................................................................... 15

Forest Cover and forest type maps ........................................................................................................... 16

INPUT DATA SETS AND PRE-PROCESSING................................................................................................ 18

3.1

Study areas.................................................................................................................................................................... 18

3.1.1

Study area 1 ........................................................................................................................................................ 19 i

3.1.2

3.1.3

Study area 3 ........................................................................................................................................................ 23

3.1.4

Study area 4 ........................................................................................................................................................ 26

3.2.1

3.2.1.1

Optical sensors .................................................................................................................................................. 27

3.2.1.2

Airborne Digital Sensor .......................................................................................................... 30

3.2

Remote sensing data ................................................................................................................................................. 27

3.2.2

3.2.3

3.3

Frame camera RC30............................................................................................................... 29

LiDAR ..................................................................................................................................................................... 32

3.2.3.1

Derived data sets .............................................................................................................................................. 33

3.2.3.2

Automatic DSM generation ................................................................................................... 33

3.2.3.3

Quality control of DSMs ........................................................................................................ 34

Digital surface models ........................................................................................................... 33

Reference data ............................................................................................................................................................. 39

3.3.1

3.3.1.1

Tree area extraction ........................................................................................................................................ 39

3.3.1.2

Stereo-image-interpreted point raster.................................................................................. 40

3.3.1.3

Digitized squares ................................................................................................................... 41

3.3.1.4

AWG-ch03.............................................................................................................................. 41

3.3.2

4

Study area 2 ........................................................................................................................................................ 21

Digitized tree/non-tree polygons .......................................................................................... 39

Tree species classification ............................................................................................................................ 42

3.4

Remarks on the input data sets ............................................................................................................................ 44

4.1

Methods.......................................................................................................................................................................... 46

TREE AREA EXTRACTION ............................................................................................................................... 46

4.1.1

4.1.2

4.1.2.1

Variables derived from remotely sensed data ..................................................................................... 47

4.1.2.2

Spectral variables................................................................................................................... 48

4.1.3

4.1.4

Geometric variables............................................................................................................... 47

Discrete tree cover ........................................................................................................................................... 50

4.1.4.1

Fractional tree cover ....................................................................................................................................... 53

4.1.4.2

Variable selection and validation .......................................................................................... 56

4.1.4.3

Principal component analysis ................................................................................................ 59

4.1.5

4.2

Image segments................................................................................................................................................. 46

Modeling procedure .............................................................................................................. 54

Statistical measures ......................................................................................................................................... 60

Results ............................................................................................................................................................................ 60 ii

4.2.1

4.2.1.1

Quantitative evaluation.................................................................................................................................. 61

4.2.1.2

Comparison to digitized polygons ......................................................................................... 62

4.2.1.3

Stereo image-interpreted point raster .................................................................................. 63

4.2.2

4.2.2.1

Qualitative evaluation..................................................................................................................................... 64

4.2.2.2

Study area 2 ........................................................................................................................... 67

4.2.2.3

Study area 3 ........................................................................................................................... 70

Study area 1 ........................................................................................................................... 65

4.2.3

Summary of results ......................................................................................................................................... 73

4.3.1

Model choice and variable selection......................................................................................................... 74

4.3.3

Context with other studies .......................................................................................................................... 77

4.3

Discussion ..................................................................................................................................................................... 74

4.3.2

5

Cross-validation ..................................................................................................................... 61

Discrete versus fractional tree cover ....................................................................................................... 75

4.4

Conclusions ................................................................................................................................................................... 78

5.1

Methods.......................................................................................................................................................................... 80

TREE SPECIES CLASSIFICATION ................................................................................................................... 80

5.1.1

Variables derived from remotely sensed data ..................................................................................... 80

5.1.3

5.1.3.1

Variable selection and validation............................................................................................................... 85

5.1.3.2

Significant variables ............................................................................................................... 87

5.1.3.3

Step-wise variable selection .................................................................................................. 88

5.1.2

5.1.4

Combining variables to groups .............................................................................................. 86

5.1.4.1

Reduction of variable space dimension .................................................................................................. 89

5.1.4.2

Linear discriminant analysis................................................................................................... 89

5.1.5

5.1.6

5.2

Classification procedure ................................................................................................................................ 83

Principal component analysis ................................................................................................ 89

Alternative classification approach .......................................................................................................... 90 Assignments of field samples to aerial images ..................................................................................... 91

Results ............................................................................................................................................................................ 92

5.2.1

5.2.1.1

Quantitative evaluation.................................................................................................................................. 93

5.2.1.2

Confusion matrices ................................................................................................................ 94

5.2.1.3

Alternative classification approach ....................................................................................... 98

5.2.2

Cross-validation ..................................................................................................................... 93

Qualitative evaluation..................................................................................................................................... 99 iii

5.2.2.1

Study area 1 ......................................................................................................................... 100

5.2.2.2

Study area 2 ......................................................................................................................... 102

5.2.2.3

Study area 3 ......................................................................................................................... 105

5.2.2.4

Study area 4 ......................................................................................................................... 107

5.2.3

Summary of results ........................................................................................................................................ 108

5.3.1

Model choice and variable selection....................................................................................................... 110

5.3.3

Validation........................................................................................................................................................... 113

5.3

Discussion ................................................................................................................................................................... 110

5.3.2

5.3.4

6

8

Context with other studies ......................................................................................................................... 113

5.4

Conclusions ................................................................................................................................................................. 114

6.1

Main contributions and achievements ............................................................................................................ 118

6.3

Recommendation for future research ............................................................................................................. 121

CONCLUSIONS AND FURTHER RESEARCH ............................................................................................. 118

6.2

7

Problem cases .................................................................................................................................................. 111

6.4

Lessons learned ........................................................................................................................................................ 119

Recommendation for operational use in the Swiss NFI ........................................................................... 123

REFERENCES..................................................................................................................................................... 124

APPENDICES ..................................................................................................................................................... 138 8.1

8.2

Automatic DSM generation .................................................................................................................................. 138

Tree area extraction................................................................................................................................................ 139

8.2.1

8.2.2

Significant variables ...................................................................................................................................... 139

Step-wise variable selection ...................................................................................................................... 140

8.2.3

Principal component analysis ................................................................................................................... 142

8.3.1

Tree species appearance in nature ......................................................................................................... 143

8.3

Tree species classification .................................................................................................................................... 143

8.3.2

8.3.3

8.3.4 8.3.5

Significant variables ...................................................................................................................................... 147

Step-wise variable selection ...................................................................................................................... 147

Principal component analysis ................................................................................................................... 151

Alternative classification approach ........................................................................................................ 151

iv

ACKNOWLEDGEMENTS This research was conducted as a PhD thesis under the supervision of Prof. Dr. Lorenz Hurni from the Swiss Federal Institute of Technology (ETH) Zurich. The study was financed by the former unit Land Resources Assessment of the Swiss Federal Research Institute for Forest, Snow and Landscape Research (WSL) and the Swiss National Forest Inventory (NFI). Many people contributed to the completion of the work.

First, I want to express my special gratitude to Prof. Dr. Lorenz Hurni for giving me the opportunity to carry out this research and for supporting me along the way of my PhD work.

I am very grateful to Prof. Dr. Barbara Koch from the Department of Remote Sensing and Landscape Information Systems (FeLis) at the University of Freiburg, for taking the co-referee of the dissertation and for providing valuable comments and criticism on the manuscript. I would like to thank Dr. Emmanuel Baltsavias from the Institute of Geodesy and Photogrammetry, ETH Zurich, for taking the task of another co-referent of this work and for his excellent scientific advice.

There are many other persons I owe to thank for their support and encouragement during my PhD thesis. Special thanks go to my colleague Dr. Meinrad Küchler for the exciting scientific collaboration during the last years (and hopefully in the next years) and for his great support during my PhD work.

My sincere thanks also go to Christian Ginzler for giving me strength and encouragement as well as practical advises, Daniel Übersax for the valuable stereo-interpretations, Patrick Thee for the professional support during my field surveys, Dr. Barbara Schneider for many fruitful discussions, and Dr. Peter Brassel for encouraging me to write a PhD. I am also very grateful to Dr. Niklaus Zimmermann who showed me the joy of doing scientific work. I thank Rossana Gini from the Facoltà di Ingegneria Civile, Ambientale e territoriale del Politecnico di Milano, for her valuable contributions in the framework of her master thesis. I also appreciated that I was always a welcomed visitor at the Institute of Geodesy and Photogrammetry. Therefore I would like to thank Dr. Henri Eisenbeiss and Dr. Martin Sauerbier for fruitful discussions. I give thanks to all my colleagues and friends for sharing “joys and sorrows” during my work.

Last but foremost, grateful thanks go to my parents for supporting me during my whole life. I wish to express my gratitude toward my wife Manuela for her support and for her patience when the working days, field trips and many workshops and conferences tended to become too long. Our children Jana, Mira and Liam reminded me constantly of the richness of life outside the scientific community.

v

CURRICULUM VITAE Name

Lars Torsten Waser

Nationality

Swiss

Date of birth Since October 2007 Since April 1999

October 1993 – Jan. 1999 1987-1992

April 02, 1972

PhD at the Institute of Geodesy and Photogrammetry, ETH Zurich Researcher and project manager at the Swiss Federal Research Institute for Forest, Snow and Landscape WSL

Graduate studies (M.Sc.) in Geography and Environmental Science, University of Zurich

High school diploma, Kantonsschule im Lee, Winterthur, Matura Typus D

vi

LIST OF FIGURES Figure 3.1 Overview of the four study areas. ..................................................................................................................... 19

Figure 3.2 Map extent of study area 1................................................................................................................................... 20

Figure 3.3 Mixed forest in study area 1. ............................................................................................................................... 20

Figure 3.4 Forest borders in study area 1. .......................................................................................................................... 21 Figure 3.5 Dense mixed forest in study area 1. ................................................................................................................. 21 Figure 3.6 Map extent of study area 2................................................................................................................................... 22

Figure 3.7 Center view of study area 2. ................................................................................................................................ 22

Figure 3.8 Mixed forests in study area 2.............................................................................................................................. 23

Figure 3.9 Forest borders in study area 2. .......................................................................................................................... 23 Figure 3.10 Map extent of study area 3. ............................................................................................................................... 24

Figure 3.11 Mixed coniferous forest in study area 3. ..................................................................................................... 24 Figure 3.12 Larch dominated coniferous forest in study area 3. ............................................................................... 25

Figure 3.13 Small coniferous trees in study area 3. ........................................................................................................ 25 Figure 3.14 Forest border with mixed coniferous trees in study area 3. ............................................................... 26

Figure 3.15 Map extent of study area 4. ............................................................................................................................... 26 Figure 3.16 Examples of CIR and RGB images. .................................................................................................................. 29

Figure 3.17 Concepts of image acquisition by the ADS40 and RC30 sensors....................................................... 31

Figure 3.18 Focal plate configuration of ADS40-SH40 and SH52 sensors. ............................................................ 31

Figure 3.19 CIR orthoimages 1-3 of study area 4............................................................................................................. 32

Figure 3.20 Accuracy assessment of DSM profiles. ......................................................................................................... 35

Figure 3.21 Example of ADS40-SH52 images of study area 1 for DSM generation. ADS40-SH52 CIR nadir and backward images. ........................................................................................................................................ 36 Figure 3.22 Colored hillshade of the normalized DSMs using two different strategies in study area 1. .. 36

Figure 3.23 Example of ADS40-SH40 images of study area 2 for DSM generation. ........................................... 37

Figure 3.24 Colored hillshade of the normalized DSMs using two different strategies in study area 2. .. 37 Figure 3.25 Example of ADS40-SH40 images of study area 3 for DSM generation. ........................................... 38

Figure 3.26 Colored hillshade of the normalized DSMs using two different strategies in study area 3. .. 38 Figure 3.27 Examples of the digitized tree and non-tree polygons of study area 2. ......................................... 40

Figure 3.28 Part of photo-interpreted point raster for study area 2 with tree/non-tree decision. ............ 41 vii

Figure 3.29 RGB orthoimage with digitized soil, shadows and polygons of AWG-ch03 for study area 2.. ...................................................................................................................................................................................... 42 Figure 3.30 Examples of the nine collected tree species as they appear in the ADS40 RGB imagery. ....... 43

Figure 3.31 Examples of the digitized tree species polygons of ash, white fir, and maple for study area 1. ...................................................................................................................................................................................... 44 Figure 4.1 Methodological workflow of the discrete tree cover approach. .......................................................... 50

Figure 4.2 Example of RGB orthoimage with the corresponding hillshade of the normalized DSM for study area 1............................................................................................................................................................. 51 Figure 4.3 Step 1: raw canopy cover with extracted potential tree areas. ............................................................ 52

Figure 4.4 Step 2: Segmented buildings and trees and the calculated curvature. .............................................. 52

Figure 4.5 Steps 2 and 3: Separation of non-vegetation areas and shadowed areas. ....................................... 53

Figure 4.6 Corrected discrete tree cover using spectral and geometric information. ...................................... 53 Figure 4.7 Methodological workflow of the fractional tree cover approach. ....................................................... 54

Figure 4.8 The logistic function. .............................................................................................................................................. 55

Figure 4.9 Square 1: Tree over-/underestimation by the discrete and fractional tree covers in study area 1. .................................................................................................................................................................................. 66 Figure 4.10 Square 2: Tree over-/underestimation by the discrete and fractional tree covers in study area 1. ........................................................................................................................................................................ 67 Figure 4.11 Square 1: Comparison between AWG-ch03 layer, discrete and fractional tree covers in study area 2. ........................................................................................................................................................................ 68

Figure 4.12 Square 2: Comparison between AWG-ch03 layer, discrete and fractional tree covers in study area 2.. ....................................................................................................................................................................... 69

Figure 4.13 Square 1: Comparison between AWG-ch03 layer, discrete and fractional tree covers in study area 3. ........................................................................................................................................................................ 71

Figure 4.14 Square 2: Comparison between AWG-ch03 layer, discrete and fractional tree covers in study area 3. ........................................................................................................................................................................ 72

Figure 4.15 Study area 3: Fractional tree cover with two different tree probability thresholds................. 76

Figure 4.16 Study area 3: Fractional tree cover with two different tree probability thresholds................. 77

Figure 5.1 Tree species appearance in spectral variables. ........................................................................................... 81

Figure 5.2 Methodological workflow of the tree species classification approach. ............................................. 83

Figure 5.3 Methodological workflow of the alternative tree species classification approach for study area 1. ........................................................................................................................................................................ 90

Figure 5.4 Digitized polygons representing parts of tree crowns in study area 1. ............................................ 91

Figure 5.5 Digitized polygons representing parts of tree crowns in study area 4. ............................................ 92

Figure 5.6 Part of the ADS40-SH52 RGB orthoimage of study area 1.................................................................... 100 viii

Figure 5.7 Neural network classifications based on PCA for study area 1. ......................................................... 101

Figure 5.8 Neural network classification based on the alternative classification approach in study area 1. .................................................................................................................................................................................... 101

Figure 5.9 Larger part of the neural network classification classification based on PCA with the seven tree species for study area 1. ......................................................................................................................... 102

Figure 5.10 Part of the ADS40-SH40 RGB orthoimage of study area 2. ................................................................ 103

Figure 5.11 Neural network classifications based on LDA for study area 2. ...................................................... 103

Figure 5.12 View from the pastures towards the deciduous trees in study area 2. ........................................ 104

Figure 5.13 Larger part of the neural network classification with the seven tree species based on LDA for study area 2.................................................................................................................................................... 104

Figure 5.14 Part of ADS40-SH52 RGB orthoimage of study area 3......................................................................... 105

Figure 5.15 Neural network classifications based on LDA for study area 3. ...................................................... 105

Figure 5.16 Dominant tree species in open and dense forests in study area 3. ................................................ 106

Figure 5.17 Larger part of the neural network classification with the four tree species based on LDA for study area 3........................................................................................................................................................... 106 Figure 5.18 Typical birch trees in open forest as they appear in study area 3. ................................................. 107

Figure 5.19 ADS40-SH52 CIR orthoimage 3 with different forest characteristics of study area 4. .......... 108

Figure 5.20 Neural network classifications based on LDA for orthoimage 3 of study area 4. .................... 108

Figure 5.21 Problems involved in identifying small and non-dominant deciduous trees in study areas 1 and 2......................................................................................................................................................................... 111

Figure 5.22 Problems involved in identifying Norway spruce in study area 3. ................................................ 112

Figure 8.1 Alder (Alnus glutinosa). ....................................................................................................................................... 143

Figure 8.2 Ash (Fraxinus excelsior)....................................................................................................................................... 143

Figure 8.3 Beech (Fagus sylvatica). ...................................................................................................................................... 144

Figure 8.4 Birch (Betula pubescus).. ..................................................................................................................................... 144

Figure 8.5 Larch (Larix decidua). .......................................................................................................................................... 145

Figure 8.6 Maple (Acer spec.). ................................................................................................................................................. 145

Figure 8.7 Scots pine (Pinus sylvestris). .............................................................................................................................. 146

Figure 8.8 White fir (Abies alba)............................................................................................................................................ 146

Figure 8.9 Norway spruce (Picea abies).. ........................................................................................................................... 147

ix

LIST OF TABLES Table 2.1 Examples of existing digital airborne cameras. .............................................................................................. 7

Table 2.2 Overview of studies utilizing airborne remote sensing data for tree area extraction and individual tree species classification. .............................................................................................................. 12 Table 2.3 Overview of the different forest definitions in Switzerland. ................................................................... 13 Table 2.4 Overview of the existing forest information in Switzerland. .................................................................. 17

Table 3.1 Characteristics of the four study areas. ............................................................................................................ 18

Table 3.2 Characteristics of the image data........................................................................................................................ 28 Table 3.3 Date of flight and re-flights of the DTM-AV data. ......................................................................................... 33 Table 3.4 Statistics of height differences between DSMs and reference points for study area 2................. 34

Table 3.5 Overview of reference data for quantitative and qualitative analysis. ............................................... 39 Table 3.6 Overview of the sampled tree species per study area................................................................................ 43

Table 4.1 Overview of the explanatory variables to model tree area. ..................................................................... 49

Table 4.2 Overview of the variables selected by the different approaches per study area to extract tree area. ............................................................................................................................................................................... 57 Table 4.3 Overview of the variable groups per study area. ......................................................................................... 58

Table 4.4 Overview of 10-fold cross-validation for the three study areas. ........................................................... 61

Table 4.5 Confusion matrix for discrete and fractional tree cover based on digitized polygons for study area 1. ........................................................................................................................................................................... 62



Table 4.8 Confusion matrix for discrete and fractional tree cover based on photo-interpreted point raster for study area 2. .......................................................................................................................................... 64 Table 4.9 Confusion matrix for discrete and fractional tree cover based on photo-interpreted point raster for study area 3. .......................................................................................................................................... 64

Table 4.10 Overview of the different accuracy assessments of the discrete tree cover and the fractional tree cover separated for each study area. ..................................................................................................... 73 Table 5.1 Overview of the generated explanatory variables per study area to classify tree species. ........ 82

Table 5.2 Overview of the selected explanatory variables per study area to classify tree species. ............ 86

Table 5.3 Overview of the variable groups per study area. ......................................................................................... 87

Table 5.4 10-fold cross-validation of the neural network models for the three variable selection approaches and the alternative approach. .................................................................................................... 93 x

Table 5.5 10-fold cross-validation of the neural network models as obtained by PCA and LDA. ................ 94

Table 5.6 Confusion matrix for tree species classification in study area 1 based on the PCA of variable group 7. ........................................................................................................................................................................ 94

Table 5.7 Confusion matrix for tree species classification in study area 2 based on the LDA of variable group 6. ........................................................................................................................................................................ 95

Table 5.8 Confusion matrix for tree species classification in study area 3 based on the LDA of variable group 6. ........................................................................................................................................................................ 96

Table 5.9 Confusion matrix for tree species classification of orthoimage 1 in study area 4 based on the LDA of variable group 6. ....................................................................................................................................... 96



Table 5.12 Confusion matrix for the Mahalanobis distance classification in study area 1 based on the variables selected in the alternative approach. .......................................................................................... 98

Table 5.13 Confusion matrix for the Maximum likelihood classification in study area 1 based on the variables selected in the alternative approach. .......................................................................................... 99 Table 5.14 Summary of the accuracy assessments of the tree species classification. .................................... 109

Table 8.1 Significant contributions of the selected variables per group. ............................................................. 139

Table 8.2. Significant variables of the step-wise variable selection for study area 1...................................... 140

Table 8.3. Significant variables of the step-wise variable selection for study area 2...................................... 141 Table 8.4 Significant variables of the step-wise variable selection for study area 3....................................... 141

Table 8.5 Overview of the 10 PCA components of study area 1. ............................................................................. 142 Table 8.6 Overview of the 10 PCA components of study area 2. ............................................................................. 142

Table 8.7 Overview of the 10 PCA components of study area 3. ............................................................................. 142

Table 8.8 Overview of the selected variables for study area 1. ................................................................................ 148

Table 8.9 Overview of the selected variables for study area 2. ................................................................................ 149

Table 8.10 Overview of the selected variables for study area 3. ............................................................................. 150

Table 8.11 Overview of the 18 PCA components of study area 1. ........................................................................... 151

xi

ABBREVIATIONS ADS40-SH40

Airborne Digital Sensor (sensor head 40, 1st generation)

ADS40-SH52

Airborne Digital Sensor (sensor head 52, 2nd generation)

AIC

Akaike Information Criterion

ADS80-SH82 ALS

APEX

ASTER

AVHRR AVIRIS

Airborne Digital Sensor (sensor head 82) Airborne Laser Scanning

Airborne Prism EXperiment

Advanced Spaceborne Thermal Emission and Reflection Radiometer Advanced Very High Resolution Radiometer

Airborne Visible Infrared Imaging Spectrometer

BAFU

Swiss Federal Office for the Environment (Bundesamt für Umwelt)

BLW

Swiss Federal Office for Agriculture (Bundesamt für Landwirtschaft)

BFS

BRDF CASI CCD

Swiss Federal Statistical Office (Bundesamt für Statistik) Bidirectional Reflectance Distribution Function Compact Airborne Spectrographic Imager Charge-Coupled Device

CCR

Correct Classification Rate

CIR

Colored Infrared

CHM CORINE COST

CP

DGPF dJM dM

DMC

DTM EC

ESA

ETC/SIA FAO

Canopy Height Model

CO-ordination of Information on the Environment

European Cooperation in the field of Science and Technical Research Cumulative Proportion

German Society of Photogrammetry and Remote Sensing Jeffrey-Matusita Distance Mahalanobis Distance

Digital Mapping Camera Digital Terrain Model

European Commission

European Space Agency

European Topic Centre for Spatial information and Analysis Food and Agriculture Organization xii

GSD

Ground Sampling Distance

GMES

Global Monitoring for Environment and Security

GLM GPS IHS

Generalized Linear Model

Global Positioning System Intensity, Hue, Saturation

IAPRS

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

IUFRO

International Union of Forest Research Organizations

ISPRS JRC κ

k-NN LDA

LiDAR

International Society of Photogrammetry and Remote Sensing Joint Research Center Kappa Coefficient

k-Nearest Neighbor

Linear Discriminant Analysis Light Detection And Ranging

LiDAR DOM-AV

Digital surface model of national survey

LV95

Reference frame of Swiss national survey 95

LiDAR DTM-AV LWF

LWN ML

MODIS nDSM NDVI NFI

Digital terrain model of national survey Bavarian State Institute of Forestry Agriculturally productive area Maximum likelihood

MODerate-resolution Imaging Spectroradiometer Normalized Digital Surface Model

Normalized Difference Vegetation Index National Forest Inventory

NGATE

Next Generation Automatic Terrain Extraction (software module of SOCET SET, BAE Systems)

PA

Producer’s Accuracy

NIR

PAN PCA PE

PV

RC30

Near Infrared

Panchromatic

Principal component analysis Parameter estimates

Proportion of variance

Frame film camera (Reihenbildkamera) RC30 xiii

RGB

Red, Green, Blue

SAR

Synthetic Aperture Radar

RMSE SPOT

swisstopo TLM UA

WSL

Root Mean Square Error

Satellite Pour l'Observation de la Terre

Swiss Federal Office of Topography (Bundesamt für Landestopographie) Topographic Landscape Model User’s Accuracy

Swiss Federal Institute for Forest, Snow and Landscape Research

xiv

ABSTRACT Forest is a renewable natural resource and fulfills multiple ecological and economic functions. Temporally frequent and cost-efficient forest information requirements for National Forest Inventories (NFI), monitoring or protection tasks of public and private authorities have grown over time and will continue to do so in the future. Precise and up-to-date forest information is an important basis for assessing resources and understanding functionality of forests. Providing consistent, reproducible and up-to-date information on various forest parameters proves to be the main advantage of using remote sensing data and methods. In Switzerland, growing forest information needs on tree area and species distribution over time will be considered by the Swiss NFI, but will be only partly fulfilled by the existing forest and forest type maps since they are relatively poor regarding spatial accuracy, up-dating, and reproducibility. Thus, the aim of this thesis was to develop new methods to extract tree area and to classify tree species of different representative regions. To guarantee implementation and continuity of this research for the Swiss NFI, an important aspect was the usage of standard remote sensing data sets and the development of semiautomated methods. New possibilities are given by the airborne digital sensor ADS40, which records the entire country every three years. The research of this thesis was subdivided into the following parts: 1. Generation and selection of appropriate geometric and spectral variables for the extraction of tree area and classification of tree species.

2. Extraction of tree area in three study areas by two different approaches: a discrete (deterministic) and a fractional (probabilistic) estimation of tree area using binomial logistic regression models.

3. Classification of the most frequent tree species in Switzerland in four study areas based on multinomial logistic regression models.

4. Quantitative and qualitative accuracy assessments of the results and evaluation whether the used input data and methods are also given for large area applications and the Swiss NFI.

A high degree of automation was achieved with the developed methods. Tree area was extracted for three study areas with a correct classification rate (CCR) of 87-99% by the discrete and 96-99% by the fractional tree cover approach. Visual image inspection revealed that, particularly the discrete tree cover was less accurate in areas with a complex forest structure, i.e. gaps, afforestation and fuzzy borders - especially in steep terrain.

Combining variables from multispectral digital aerial image data with multinomial logistic regression models is shown to be very appropriate for the classification of the five relatively frequent tree species ash, beech, larch, Norway spruce, and white fir in four study areas, providing CCRs of 71-85%. Accuracies below 60% were obtained for small samples of species such as the non-dominant alder, birch, and maple in all study areas, and also for Norway spruce in study area 3 due to reduced vitality. The results of this thesis show that multispectral digital airborne imagery with logistic regression models have a high potential for extraction of tree area and tree species classifications with a xv

reasonable amount of effort regarding data acquisition and pre-processing, derivation of explanatory variables and field work. The accuracies obtained in this thesis are slightly higher than those in similar studies and in line with studies using additionally LiDAR data.

Continuity of the two approaches is guaranteed since the necessary image data is collected every three years (at least every six years during vegetation period) nationwide by the Federal Office of Topography (swisstopo). Thus, tree and tree species maps could be produced more frequently, i.e. by a three or six year update cycle, providing the required up-to date forest information. Due to their higher spatial and temporal resolution, these maps can be additionally and complementarily used to the existing forest and forest type maps or statistical forest information from the Swiss NFI. Besides the Swiss NFI, most probable interest groups that will profit from the up-dated tree area and species maps are public or private authorities with environmental or forest-related issues, private environmental agencies, forest districts, and private forest owners. Constraints of this work were related to the lack of identical input and reference data for all study areas due to time restrictions or inexistence, and the partly needed empirical work for processing and selection of variables from the image data.

xvi

ZUSAMMENFASSUNG Wald als erneuerbare natürliche Ressource erfüllt viele ökologische und ökonomische Funktionen. Die Anforderungen, welche Nationale Forstinventuren (NFI) sowie auch damit verbundene Monitoringund Schutzaufgaben an verfügbare Waldinformationen bezüglich Regelmässigkeit, Genauigkeit und Kosten stellen, steigen permanent. Präzise und aktuelle Waldinformationen bilden dabei eine wichtige Grundlage für die Beurteilung der Waldressourcen und sind erforderlich, um die Funktionalität der Wälder besser zu verstehen.

Der auch in der Schweiz rasant wachsende Bedarf an aktuellen Informationen zur Gehölzfläche und Baumartenverteilung wird durch das Stichprobennetz des schweizerischen Landesforstinventars (LFI) periodisch gedeckt, hingegen nicht oder nur teilweise durch die existierenden Wald- und Waldtypenkarten. Der Hauptvorteil, Daten und Methoden aus der Fernerkundung zu verwenden, liegt darin, dass aktuelle, konsistente und reproduzierbare Waldparameter abgeleitet werden können. Der flugzeuggestützte, digitale Zeilenscanner ADS40 eröffnet hierzu ganz neue Perspektiven, da nun alle drei Jahre landesweit digitale Luftbilddaten mit hoher räumlicher und radiometrischer Auflösung zu Verfügung stehen. Deshalb war das Ziel dieser Arbeit neue Methoden mittels ADS40-Bilddaten zur Ausscheidung von Gehölzen und zur Klassifikation von Baumarten in repräsentativen Regionen zu entwickeln. Um eine Implementierung und Weiterführung zu gewährleisten, ist die Arbeit auf die Verwendung von Standard-Fernerkundungsdaten sowie die Entwicklung von halb-automatischen Methoden ausgelegt. Die Untersuchungen dieser Arbeit wurden in folgende Teilgebiete untergliedert: 1. Ableiten und Selektieren von geeigneten geometrischen und spektralen Variablen zur Extraktion der Gehölzfläche und Klassifikation der Baumarten.

2. Ausscheidung der Gehölzfläche in drei Untersuchungsgebiete anhand zwei unterschiedlichen Vorgehensweisen: eine Trennung in Gehölz bzw. Nichtgehölz (deterministisch) und eine Berechnung der Gehölzwahrscheinlichkeit mittels binomialer logistischer Regressionsmodelle.

3. Klassifikation der häufigsten Baumarten in vier Untersuchungsgebieten mittels multinomialen logistischen Regressionsmodellen.

4. Quantitative und qualitative Beurteilung der Resultate und abschätzen, ob die Inputdaten bzw. die entwickelten Methoden für grosse Gebiete und das LFI ebenfalls gewährleistet sind.

Mit den entwickelten Methoden wurde ein hoher Grad an Automatisierung erzielt. Gehölze wurden für drei unterschiedliche Untersuchungsgebiete mit einer Gesamtgenauigkeit von 87-99% mittels der deterministischen Methode und mit 96-99% mittels der Berechnung der Gehölzwahrscheinlichkeit ausgeschieden. Eine visuelle Analyse der Resultate deckte auf, dass Fehler hauptsächlich bei der deterministischen Methode in Gebieten mit komplexer Waldstruktur, d.h. mit Lücken, Aufforstungen und unscharfen Waldrändern, insbesondere im steilen Gelände, auftreten. Klassifikationsgenauigkeiten von 71-85% für die fünf häufigsten Baumarten Esche, Buche, Fichte Lärche, Weisstanne in den vier Untersuchungsgebieten zeigen, dass sich multinomiale logistische Regressionsmodelle, welche auf Variablen aus multispektralen digitalen Luftbilddaten basieren, zur Baumartenklassifikation bestens eignen. Genauigkeiten unter 60% wurden lediglich für kleine xvii

Stichproben der weniger dominanten Baumarten wie Ahorn, Birke und Erle Untersuchungsgebieten, und auch für nicht vitale Fichten im Untersuchungsgebiet 3 erzielt.

in allen

Die Ergebnisse dieser Arbeit zeigen auf, dass die entwickelten Methoden basierend auf multispektralen digitalen Luftbilddaten zur Ausscheidung von Gehölzen und zur Klassifikation der häufigsten Baumarten durchaus eignen – und zwar mit relativ kleinem Aufwand bezüglich Bildbeschaffung, Datenaufbereitung, Ableitung der erklärenden Variablen und auch Feldarbeit. Die erzielten Genauigkeiten sind dabei etwas höher gegenüber vergleichbaren Studien bzw. in etwa gleichzusetzten mit Studien, in denen zusätzlich LiDAR Daten verwendet wurden.

Eine Weiterführung der entwickelten Ansätze ist insofern gewährleistet, da die benötigten Bilddaten (ADS40/80) alle drei Jahre (mindestens alle sechs Jahre während der Vegetationsperiode) gesamtschweizerisch vom Bundesamt für Landestopographie (swisstopo) zur Verfügung gestellt werden. Wald- und Waldtypenkarten können somit regelmässiger, d.h. in einem Drei- bzw. Sechsjahreszyklus, hergestellt werden. Nebst den existierenden Wald- und Waldtypenkarten, Informationen aus dem Landesforstinventar und der Arealstatistik, stünden wegen ihrer höheren räumlichen und zeitlichen Auflösung somit stets aktuellste Waldinformationen zur Verfügung. Weitere potentielle Nutzer, welche von diesen aktualisierten Daten profitieren sind nebst dem LFI, Bundesamt für Umwelt, sowie forstliche Einrichtungen auf kantonaler oder Bundesebene und Waldbesitzer.

Einschränkungen dieser Arbeit betreffen einerseits das nicht Vorhandensein von identischen Inputund Referenzdaten für alle Untersuchungsgebiete - einerseits aufgrund begrenzter Zeitressourcen oder weil sie inexistent sind, und andererseits empirische Arbeitsschritte, welche zur Aufbereitung und Selektion der Variablen notwendig waren.

xviii

1 INTRODUCTION

1 INTRODUCTION 1.1 Motivation Forest is indispensable for human life. As a renewable natural resource it fulfills multiple functions such as natural habitat and recreation area, providing wood and energy, protection against natural hazards, sustaining biological diversity, and stabilizing the climate on a regional and global level.

In the last decades the social focus on forests has moved from an economic point of view on timber resources for commercial use towards a multi-functional ecosystem. Forest information requirements both on social and political level are manifold and have grown over time and will continue to do so in the future. They include extent and change of forest resources (area and species composition), knowledge of productive functions of forest (growing stock, timber resources and biomass), protective functions (health and vitality), biological diversity (vegetation and wildlife habitats), and carbon storage (according to the Kyoto protocol). Consequently, these requirements lead to an increasing demand on cost-effective methods for monitoring systems, which enable to continuously obtain accurate and reliable forest information with high spatial and temporal resolution.

Precise and up-to-date information on the tree area (area covered by trees as a whole) in particular is an important basis for assessing other forest resources and understanding the functionality of forests. At this point, it should be mentioned, that the term forest is commonly used and often similarly handled as stocked area, tree area or area covered by trees. However, this is not absolutely correct, since forest always implies a definition (usually percentage of area covered by trees, minimum area and tree height). In this thesis a simplified term tree area is used, since forest is an exactly defined part of the tree area. Tree area is a key parameter to derive other forest parameters, i.e. forest structure, biomass, carbon storage etc. and is therefore needed for resource management by public and private authorities. Tree area is also an essential parameter for biotope protection and conservation efforts of monitoring programs. For example, an increasing expansion of shrubs and trees is a considerable danger for sensitive biotopes, i.e. riparian wetlands, mires, and also dry meadows and grasslands, and accelerates their degradation.

Tree area is a key factor for National Forest Inventories (NFIs), which aim at periodically reporting the current state and the changes of forests of a country by providing quantitative statistical information on tree area, species composition, volume, and growing stock. The usage of traditional methods of field survey or aerial image interpretation to gain the required information on the tree area is not feasible with regards to cost, personnel resources and therefore also operability. During the last decade, NFI research has generally more and more focused on utilizing remote sensing data and field surveys, i.e. by estimating tree area and tree species composition of larger areas directly from high-resolution remotely sensed data. The Swiss NFI is being involved in two European programs which aim at harmonizing national forest information on European level. Recently, the first program, the Action 43 finished and was done in the framework of the European Coorporation in Science and Technology (COST) (Tomppo et al., 2010). The follow-up program COST Action FP 1001 Usewood started in 2010 and aims at 1

1 INTRODUCTION improving data and information on the potential supply of wood resources on European level – also using remote sensing data.

Providing consistent, reproducible and up-to-date information on various forest parameters proves to be the main advantage of remote sensing as a tool for both monitoring and ecological analyses. Several efforts of mapping forests using remotely sensed data exist. They all vary in scale (from global level, pan-European to national level), and also in level of detail (sources of information, forest definition and target interest groups).

At global level, information on the current status of forest area and its change over time is given by existing forest maps, e.g. the Global Forest Resources Assessment 2000 (FRA 2000) initiated by the Food and Agriculture Organization (FAO), or e.g. by the Global Forest/Non-forest Map which was generated by the Japan Aerospace Exploration Agency (JAXA).

At European level, the most important forest maps are the CORINE 2006 land cover (CLC2006) data, which was developed by the European Topic Centre for Spatial information and Analysis (ETC/SIA) or the 2006 Forest cover map and Forest type map, which were both developed by the Joint Research Center (JRC) at the Institute for Environment and Sustainability (JRC, 2011a & b). However, all these data sets are to a large extent unsuitable for NFIs, environmental, monitoring or protection tasks on European or national level, due to a varying spatial accuracy, age (often outdated), heterogeneity (based on different data sources), and no fix updating cycle and therefore lack of guaranteed continuity.

At Swiss level, growing forest information needs over time will be considered by the Swiss NFI since it is only partly fulfilled by the existing forest and forest type maps. These were governmental-funded but produced in the framework of other missions than forestry, i.e. the land use/land cover statistics, the Topographic landscape model (TLM), or the updating of agriculturally productive areas. In many cases the available forest information, i.e. tree area and tree species composition was treated at the best as a side product and contains limited information. With the exception of the statistical forest information provided by the Swiss NFI, the lack of detailed mapped forest information is constituted by the deficiencies of the existing data sets (outdated, none or no regular updating cycle, coarse and with a varying spatial resolution) and by the non-existence of national tree species maps. The existing forest type maps are based on a simple distinction between the three classes deciduous, coniferous and mixed. All these restrictions underscore the need of additional accurate, high-resolution and frequently updated forest information, particularly on tree area and species composition for entire Switzerland in the future. This thesis explores the derivation of the two basic forest parameters tree area and tree species for area-wide and operational use in Switzerland in addition to the information obtained by the Swiss NFI.

1.2 Goals The present thesis has two main goals, of which the first is the extraction of tree area, and the second the classification of tree species. With the methods developed in this thesis, the semi-automated 2

1 INTRODUCTION generation of spatial accurate, high-resolution and frequently updated, i.e. in a three year cycle, information on tree area and species in Switzerland should become feasible. In order to be applicable for entire Switzerland, the methods in this thesis have been developed for appropriately selected study areas with different forest conditions, i.e. different types of forest, degree of mixture, crown closure, and density. In the framework of this thesis, the term semi-automated means that the several steps of processing, i.e. image pre-processing, classification and validation, have been tested in a first run, standardized and then are partly done automatically and are therefore not based on manually interpreted images. However, the developed methods still require collection of field data for training and validation, slight adaption to the respective study area, and possible post-editing of results.

In the existing forest maps of Switzerland - which are a binary and generalized representation of tree area - forest borders, gaps or single trees outside the closed forest area are not always represented. Therefore, the first goal includes two different approaches of extracting tree area, i.e. a deterministic and a probabilistic one, and suggests the optimal usage for both methods. In the first approach, tree area is extracted using a straightforward method based on a discrete tree/non-tree decision which enables e.g. direct comparisons to the existing forest maps. In the second approach, tree area is a continuous representation and therefore particularly useful for the extraction of small shrubs and single trees. The second goal is to develop a classification method for the most frequent tree species (at least 5% coverage according to the Swiss NFI) in four study areas. In total, nine tree species have been classified: alder, ash, beech, birch, larch, maple, Norway spruce, Scots pine, white fir. Since area-wide information on tree species distribution in Switzerland are still inexistent and the few existing a simple distinction between deciduous and coniferous trees, the second goal is approached by providing accurate and regularly up-dated information, i.e. maps, on the most frequent tree species.

From a scientific point of view, this thesis aims at developing innovative and new methods for extraction of tree area and classification of tree species and to compare them to the commonly used approaches. Different logistic regression models, are being tested in this thesis since they have been proven as particularly useful for assessing the spatial distribution of plant species in several studies, regarding robustness and performance. To overcome the restrictions of automated step-wise variable selection procedures, also new ways of variable selection are being developed and evaluated.

From a practical point of view, this thesis aims at using standard remote sensing data sets and developing methods, which are easy applicable to any other region, to guarantee continuity by providing tree area and species maps more frequently than in the past, and implementation of this research for the Swiss NFI. The currently existing airborne remote sensing data sets, i.e. ADS40/80 images and digital surface models have already been successfully used by public and private authorities. They are being provided by other national campaigns, e.g. the Swiss orthoimage production and updating national maps, and will be commercially and nationwide available every three years nationwide. Both goals resulted in a series of new developments and methods, which were evaluated by experienced reviewers of the four papers this thesis is based on. The journal publications for the extraction of tree area are those of Waser et al. (2008a & b), and for the tree species classification those 3

1 INTRODUCTION of Waser et al. (2010c & 2011a). Furthermore, the main parts of this work were presented at various international workshops and conferences, either as oral presentations or as invited presentations and have been published in the corresponding proceedings, e.g. Waser et al. (2008c) at the SilviLaser conference in Edinburgh; Waser et al. (2009) at the International Union of Forest Research Organizations (IUFRO) conference in Quebec; Waser et al. (2010a) at the International Society of Photogrammetry and Remote Sensing (ISPRS) conference in Vienna; Waser et al. (2010b) at the German Society of Photogrammetry and Remote Sensing (DGPF) conference in Vienna; Waser et al. (2011b) at the Forestry workshop Operational remote sensing in forest management in Prague, Czech Republic; and Waser et al. (2011c) at the seminar Creation of digital elevation models from aerial images for forest monitoring purposes in Ås, Norway.

1.3 Structure of the thesis The thesis is organized in six main chapters. In the following the contents of each chapter are briefly summarized. After the introduction, Chapter two summarizes the current state of research and technology in remote sensing for the extraction of forest parameters, with a special focus on tree area extraction and tree species classification. A brief introduction to commonly used forest definitions, followed by existing Swiss forest information and datasets regarding this research are presented.

Chapter three describes the four study areas, the remotely sensed data, and the reference data used in this research. It incorporates a detailed introduction of the different airborne remote sensing data and derived data sets, followed by a description of the generation of digital surface models and their quality assessment. Finally, concluding remarks on the input data sets are given.

Chapter four covers the first principal goal of this thesis, tree area which is extracted in three study areas in two different ways, i.e. a deterministic representation of tree area, and a probabilistic representation of tree area. Starting with image segmentation techniques, and computation of variables derived from airborne remote sensing data, the several processing steps for both approaches including variable selection and validation, are then presented separately. The results include both quantitative and qualitative evaluations. The discussion implies a comparison of both approaches and further reveals restrictions and gives suggestions for their optimal usage. Chapter five describes the tree species classification, the second principal goal of this thesis. It covers pre-processing and derivation of candidate variables, variable selection and the entire procedure of tree species classification and validation. The variable selection consists of three different approaches, of which the one producing highest accuracies was used for the final classification model. Additionally, the two alternative classification algorithms were tested for study area 1. In total, up to seven different tree species are classified in four study areas and verified by quantitative and qualitative evaluations. The discussion includes a comparison with other studies, reveals restrictions and gives suggestions of the developed classification method.

4

1 INTRODUCTION Chapter six summarizes this research by drawing general conclusions and underscoring the main contributions and achievements, suggesting improvements, recommending future research, and finally showing the potential of operational use for the Swiss NFI.

5

2 REMOTE SENSING IN FORESTRY

2 REMOTE SENSING IN FORESTRY To structure the great variety of remote sensing for forest applications, this chapter starts with a brief description of the current status of research and technology. Regarding the data sets used in this thesis, the main focus lies on optical airborne and less on spaceborne sensors. Although widely used for forest applications, only a brief overview of relevant contributions is given for LiDAR, SAR, and hyperspectral sensors. Besides a brief summary of applications in forestry on global and European level, the main focus lies on national level.

In Chapter 2.1.1 the recent developments in sensor technology and applications with focus on the forestry sector are given and the most relevant paper contributions are listed. Then, the large variety of forest parameters derived from airborne sensors and different approaches in modeling and classification techniques with main focus on tree area and tree species is given in Chapter 2.1.2. In Chapters 2.1.3 and 2.1.4 the latest developments for the two targeted forest parameters tree area and tree species are presented from both a sensoral and methodological point of view focusing on the latest contributions of airborne optical, LiDAR and multi-sensoral applications. Finally, in Chapter 2.2 the forest definitions as used by Swiss authorities and a brief introduction to the Swiss NFI are given, followed by an overview and description of existing Swiss tree area and tree species data sets.

2.1 Current state of research and technology 2.1.1 Sensors Remote sensing used in the forestry sector covers a wide variety of techniques and applications, while some having been operational for decades, others have only appeared recently and are undergoing fast development (Koch et al., 2008).

Historically, interpretation and mapping of trees based on aerial photography represents the most popular input to remote sensing in forestry on regional and national level (Spurr, 1960; Gillis & Leckie, 1996). Soon, spaceborne optical systems have proven to be advantageous for many forest applications, i.e. mapping and monitoring forests on European or even on global level. According to Koch et al. (2008), the capacity of low spatial resolution satellite systems (0.5–1km), i.e. AVHRR (Kennedy & Bertolo, 2002), MODIS (Hansen et al., 2002) or SPOT4-VEGETATION (Stibig et al., 2004), lies in mapping continents and in the usage for climate change related issues on global level. Medium resolution satellites (10-30m) are frequently used for forest mapping and management over large regions, i.e. on national (Keil et al. 1990; Dees et al., 1998) or European level. Pekkarinen et al. (2009) used Landsat ETM+ imagery for Pan-European forest/non-forest mapping, and Förster et al. (2005) used SPOT5 and ASTER for the determination of forest habitat types in Bavaria. Radar systems with SAR sensors, i.e. ERS-1, ERS-2, ENVISAT and RADARSAT, have increased rapidly over the last few years, and have been frequently used for deriving forest parameters, i.e. forest area (Wagner et al. 2003), tree height (Thiel et 6

2 REMOTE SENSING IN FORESTRY al., 2006), and stem volume (Fransson & Israelsson, 1999). The independency of this data regarding cloud cover and sun illumination enables frequent updates of forest conditions of entire regions (Rosenquist et al., 2007).

Nowadays, new perspectives for forest applications are given by new sensor developments (spaceborne and airborne), i.e. highly increased geometric, radiometric, and spectral resolution and as a consequence thereof by the developments in processing and analysis methods. Since the launch of IKONOS end of 1999, a series of Very High Resolution (VHR) satellite imagery from e.g. EROS (1m by super sampling two images over the same area), KOMPSAT-2, ORBVIEW-3, QUICKBIRD-2, RAPIDEYE, or WORLDVIEW-2 have been used for forest applications. With the exception of RAPIDEYE (only multispectral bands with GSD 6.5m), they provide all spatial resolutions for panchromatic images between 0.5 and 1m and for multispectral images between 1.8m and 6.5m. For example Förster & Kleinschmit (2008) used multispectral QUICKBIRD-2 images to detect forest types and NATURA 2000 habitats, and Katoh (2004) used IKONOS images to determine tree composition in northern mixed forests. In the last decade, airborne photogrammetric film cameras were replaced by high resolution digital airborne sensors that provide images with enhanced spatial, spectral, and radiometric resolution (Petrie & Walker, 2007). The data are recorded by frame-based sensors, e.g. Z/I DMC, Ultracam D, X, and Xp, which provide stereo-overlap of up to 90%, or by line-scanning sensors, e.g. ADS40/ADS80, which produce entire image strips. These new image data have facilitated new opportunities for the derivation of forest parameters, by providing higher spectral and radiometric resolution. An overview of commercially available and frequently used optical airborne sensors for the extraction of forest parameters is given in Table 2.1. Waser et al. (2010c) used Z/I DMC, Ultracam X, and ADS40 images to extract tree area and tree species, Hirschmugl et al. (2007) and Hirschmugl (2008) used Ultracam D to detect single trees, Holmgren et al. (2008) or Chubey et al. (2009) to detect and classify individual tree crowns and species, and Waser et al. (2011a) used ADS40 images to classify tree species.

Table 2.1 Examples of existing digital airborne cameras. Source: Stössel (2009). Sensor

ADS40-2nd / ADS80

DMC

D Manufacturer Type

Image size

Leica

Line scanner in flight (mm) -across (mm) 78 in flight (pix) -12000 across (pix)

Spectral resolution (nm) Pixel size (μm) Radiometric resolution

Blue [428-492] Green [533-587] Red [608-662] NIR [833-887] 6.5 12 bit

Z/I imaging frame 92.2 165.9

7680 13824

Blue [429-514] Green [514-600] Red [600-676] NIR [675-850] 12 12 bit

7

frame 67.5 103.5

Ultracam

X Microsoft

XP

frame 68.4 104

frame 68.4 104

Blue [400-580] “ Green [500-650] “ Red [590-675] “ NIR [670-940] “ 9.5 7.2 12 bit 12 bit

“ “ “ “ 6 12 bit

7500 11500

9420 14430

11310 17310

2 REMOTE SENSING IN FORESTRY In the last years, the usage of airborne hyperspectral imagery to forest related applications has also gained increasing interest and several studies underscore their advantages, i.e. a spectrum ranging from 400–2500nm and between 100-300 image bands. Thus, imaging spectroscopy greatly extends the scope of traditional remote sensing and enables to identify surface materials more precisely than is possible with broadband multispectral sensors. Furthermore, the great variety of spectral information also allows to derive biochemical and biophysical vegetation indices. For example, Davison et al. (1999) evaluated the ability of CASI data to determine various forest parameters, i.e. to separate major tree species. Goodenough et al. (1999) investigated with AVIRIS hyperspectral imagery forest stand parameter estimations. Darvishsefat et al. (2002) evaluated the potential of Hyperspectral Mapper (HyMap) data and a spectral mixture model to characterize forest stands in a mixed coniferous and deciduous forest in Switzerland. The Airborne Prism EXperiment (APEX) which uses an airborne imaging spectrometer developed by a Swiss-Belgian consortium on behalf of ESA is intended as a simulator and a calibration and validation device for future spaceborne hyperspectral imagers (APEX, 2011). Great progress is also occurring in 3-D remote sensing including digital stereo-photogrammetry, LiDAR, and SAR interferometry. Digital Surface Models (DSM) can be generated automatically by image matching methods (Baltsavias et al., 2008). The availability of 3-D information leads to a more accurate extraction of tree and canopy heights or forest borders. Canopy Height Models (CHM) can be calculated by subtracting a Digital Terrain Model (DTM) from a DSM. While optical remotely sensed imagery is well-suited for detecting horizontally distributed forest parameters, i.e. tree area, species composition and change, 3-D information as obtained by the penetration of LiDAR is more appropriate for capturing vertically distributed parameters on individual tree level.

In the last fifteen years, the use of LiDAR data has become of increasing interest in the forestry sector and assessing forest conditions has moved from an average forest stand scale to individual tree level. This is clearly encouraged by the fact that improvements in LiDAR technology have led to higher pulse rates and increased LiDAR posting densities. LiDAR proves to be an efficient tool for obtaining information on the vertical forest structure and opens a wide set of new forest applications, i.e. individual tree crowns (Koch et al., 2006), canopy heights (Hollaus et al., 2006), stand delineation (Koch et al., 2009) or even modeling complex forest canopy (Morsdorf et al., 2010), understory (Martinuzzi et al., 2009), and biomass estimations (Popescu, 2007). In the last years, full-waveform LiDAR systems have opened new perspectives not only by penetrating vegetation foliage but by giving a representation of both the canopy profile and the surface topography. By analyzing the waveforms, the derivation of forest parameters, i.e. individual trees (Reitberger et al., 2009), tree species (Hollaus et al., 2009; Heinzel & Koch, 2011), or stem volume (Reitberger et al., 2010) estimations, can be further improved. A review of investigations and methods of full-waveform LiDAR data for forestry applications is given in Pirotti (2011).

Recently, a number of studies reveal the successful use of combining LiDAR with optical data to map the 3-D canopy structure (Lamonaca et al., 2008), as CHM and estimate tree and stand attributes such as tree height (Falkowski et al., 2006), tree growth (Naesset & Gobakken, 2005), crown diameter and basal 8

2 REMOTE SENSING IN FORESTRY area (Hudak et al., 2006), stem volume (Straub & Koch, 2011), biomass (Lefsky et al., 2002; Latifi et al., 2010), and tree species (Heinzel et al., 2008; Holmgren et al., 2008; Reitberger et al., 2008).

According to Hajnsek et al. (2009) or Kugler et al. (2009) there is also an increasing interest of the usage of Polarimetric SAR interferometriy (Pol-InSAR) to obtain forest height and related parameters, i.e. forest structure or area. The applicability of airborne profiling radar technologies to forest inventories has already been shown in Hyyppä and Hallikainen (1996) or in Fransson et al. (2007) to extract forest area and to detect wind-thrown areas obtained from two different SAR systems, and in Mette (2007) to estimate forest biomass from L-band Pol-InSAR data.

2.1.2 Methods for forest parameter extraction A variety of new techniques in image processing, automatic generation of DSMs, have been developed in the last years. Especially new image matching methods and automatic DSM generation (Zhang & Gruen, 2004) have gained much attention. Several studies stress the advantages of combining multi-resolution segmentation (Baatz & Schäpe, 2000) with object-based classification (DeKok & Wezyk, 2006; Wang et al., 2006; Lamonaca et al., 2008) to fully explore the information content of VHR digital images.

The general objective of an image classification is the automatic allocation of all pixels to land cover classes or specific themes (Lillesand et al., 2003). The grey value of each pixel is the numeric base for this allocation. Jensen (2005) groups the classification methods for multispectral images into five categories (see below) but claims that none of these classification methods is principally superior to another. In fact, according to Jensen (2005) and experiences made in this thesis, the most appropriate classification strategy depends on the biophysical characteristics of the research area, i.e. topography or heterogeneity of the land cover, the homogeneity of the remote sensing data, i.e. illumination or date of acquisition, the training data, i.e. representative samples, and the “a priori” knowledge. The categorized classification algorithms are: based on parametric (e.g. discriminance analysis), and non-parametric statistics (e.g. nearest neighbor), supervised (e.g. Maximum Likelihood) or unsupervised (e.g. k-means), hard or soft classifications (fuzzy), pixel- and object-based classification algorithms, and hybrid approaches. In the remote sensing community it is well known, that even a standard algorithm, i.e. the maximum likelihood (Lillesand et al., 2003), could theoretically produce better results than modern algorithms such as artificial neural networks (ANN) (Erbek et al., 2004) or boosting (Bailly et al., 2007). In the last decade, statistical approaches for combining image data with field data to obtain forest parameters have become very popular. According to McRoberts (2006) forest parameters are often modeled using statistical techniques by combining forest parameters measured during the fieldwork campaigns, i.e. NFI field plot data, with remotely sensed data. Spatially explicit maps can be produced by predicting forest parameters, i.e. volume or biomass, using regression techniques. Regression techniques in particular, e.g. Cohen et al. (2003), Holmgren (2004) or Straub et al. (2009) have been frequently used to relate LiDAR data to forest parameters.

Alternatively to these regression techniques, the non-parametric k-Nearest Neighbor (k-NN) methods (Tomppo & Halme, 2004; Mc Roberts et al., 2007) have been frequently used. According to Tomppo (2006), the k-NN method is the prediction of forest variables, i.e. parameters as weighted averages of 9

2 REMOTE SENSING IN FORESTRY observed variable values for the k most similar NFI field plots in a feature space consisting of spectral data, i.e. Landsat TM images, and other ancillary variables. In several countries, the k-NN method has been used extensively with NFI data and Landsat TM imagery to map a variety of forest parameters (McRoberts et al., 2002; Tomppo & Halme, 2004; Magnussen et al., 2009; McRoberts, 2009).

It is widely known that the deterministic representation of land cover (Ju et al., 2003) or forest cover (Mathys et al., 2006) into a limited number of categories results in a loss of information. This loss of information can have a significant impact on subsequent modeling, i.e. biodiversity, as has been demonstrated by Pierce & Running (1995). Furthermore, modern regression techniques such as generalized linear models (GLMs) have been proven as particularly useful for assessing the spatial distribution of tree species (Zimmerman et al., 2007). According to Guisan & Zimmermann (2000), Scott et al. (2002), Guisan et al. (2004) or Guisan & Thuiller (2005) there is an increasing interest of predictive spatial modeling over the past 20 years. Küchler et al. (2004) show that predictive spatial modeling of vegetation using remotely sensed environmental attributes can be used to construct the current vegetation cover of biotopes.

An overview of the above mentioned and frequently used methods for the extraction of tree area and classification of tree species is given in Table 2.2.

2.1.3 Extraction of tree area Forest mapping is either carried out on entire tree areas or on individual tree level (Hyyppä et al., 2000; Leckie et al., 2003). It has been performed in many studies in the framework of assessing change detection and clear-cuts (Eriksson et al. 2003; Thiel et al., 2006), and is often focused in subtropical regions using remote sensing data, including both spaceborne and airborne sensors. Extraction of the area is also done in the framework of NFIs by extrapolating field plot samples, using k-NN methods, or combining forest parameters (Tomppo & Halme, 2004). Laliberte et al. (2004) show that the extraction of trees and occurrence of shrubs can be estimated using high-resolution remotely sensed data combined with object-oriented image analysis. Waser et al. (2008a & 2008b) developed a deterministic and a probabilistic method to extract tree area and shrub encroachment using parameters derived from high-quality DSM data and Color-Infrared (CIR) aerial images. Furthermore, tree and shrub area was extracted by Waser et al. (2010c) using parameters derived from four different digital airborne sensors. In the last years, 3-D LiDAR data has been used operationally to extract tree area by automated delineation of forest/non forest vegetation (Straub et al., 2008) or by assessing forest parameters, i.e. canopy characteristics (Naesset & Gobakken, 2005) or forest stands (Straub et al., 2006; Koch et al., 2009). Wang et al. (2007) used a multispectral and LiDAR data fusion for automated delineation of forest boundaries. An overview of the above mentioned and frequently used data sources and methods for the extraction of tree area is given in Table 2.2.

10


2.1.4 Classification of tree species From a general point of view, remote sensing data which are used for tree species classification are also manifold and include both spaceborne and airborne sensors, i.e. e optical, hyperspectral, LiDAR, SAR and multi-sensoral systems.

In the eighties and nineties, classification of tree species was based on the interpretation and mapping using aerial photographs (i.e. obtained from RC30 sensors) and methods have been developed to identify the individual tree crowns (Wulder, 1998; Bolduc et al., 1999; Erikson, 2004). In recent years, high spatial resolution images (Brandtberg, 2002; Key et al., 2001) and CASI airborne hyperspectral imagery (Leckie et al., 2005) have been used to identify individual tree species. The procedure of identifying tree species on tree-level includes steps of object segmentation, feature computation, and object classification. With the increasing availability of digital airborne imagery, a new round of research on classifying tree species on individual tree level is being initiated. Digital airborne data have facilitated new opportunities for tree species classification since the digital devices are spectrally and radiometrically superior to the analogue cameras (Petrie & Walker, 2007). Several deciduous and coniferous tree species were classified using images from the Z/I DMC sensor (Olofsson et al., 2006; Holmgren et al., 2008; Waser et al. 2010c), Ultracam D (Hirschmugl et al., 2007, Waser et al. 2010c) or ADS40 (Waser et al. 2010c; Waser et al., 2011a).

Complementary, in the last decade, especially high-resolution LiDAR has become an operational tool also to classify different tree species e.g. by analyzing structure and shape of tree crowns (Holmgren & Persson, 2004), by analyzing intensity data of leaf-off and leaf-on conditions (Brandtberg, 2007; Kim et al. 2009), by using intensity and structure features (Ørka et al., 2009), or by exploring full-waveform LiDAR parameters (Reitberger et al., 2006; Heinzel & Koch, 2011). Several studies combine airborne optical data with 3-D information obtained from LiDAR for tree species classification to benefit from additional vertical tree information, i.e. vertical structure and height of trees combined with Z/I DMC images (Persson et al., 2006); shape of individual tree crowns combined with Z/I DMC images (Holmgren et al., 2008), individual tree crowns combined with CIR aerial images (Heinzel et al., 2008), and individual tree crowns combined with ADS40 images (Chubey et al., 2009). An overview of the above mentioned and frequently used data sources and methods for the classification of tree species is given in Table 2.2.

11

2 REMOTE SENSING IN FORESTRY Table 2.2. Overview of studies utilizing airborne remote sensing data for tree area extraction and individual tree species classification. The table presents the reference to the study (study), the location (country), and the classification method used (method). Study Bolduc et al. (1999)

Country Canada

Leckie et al. (2003)

Canada

Key et al. (2001)

Brandtberg (2002)

Holmgren & Persson (2004) Laliberte et al. (2004) Leckie et al. (2005)

Naesset & Gobbaken (2005) Olofsson et al. (2006) Persson et al. (2006)

Reitberger et al. (2006) Straub et al. (2006) Brandtberg (2007)

Hirschmugl et al. (2007) Wang et al. (2007)

Heinzel et al. (2008)

Holmgren et al. (2008) Straub et al. (2008)

Waser et al. (2008a)

Waser et al. (2008b) Chubey et al. (2009) Kim et al. (2009)

Koch et al. (2009) Ørka et al. (2009)

Waser et al. (2010c)

Heinzel & Koch (2011) Waser et al. (2011a)

US-West Virginia

Tree area

Sweden

Aerial images

US-New Mexico

Aerial images

Sweden Canada

Norway Sweden Sweden

Germany Germany

US-Virginia Austria

Aerial images Aerial images LiDAR LiDAR LiDAR

Switzerland

LiDAR, aerial images

Germany

LiDAR

Germany, Poland Sweden

Switzerland Switzerland Canada

US-Washington Germany Norway

Germany Germany

Switzerland

Aerial images Aerial images LiDAR

Aerial images Aerial images

Tree species Aerial images

Method* REG

Aerial images

LM, ML

Aerial images LiDAR

Aerial images

Aerial images

LiDAR, aerial images LiDAR

Aerial images

LiDAR, aerial images LiDAR, aerial images

LiDAR, aerial images LiDAR LiDAR LiDAR

ML FA

LDA, QDA NN

LM / ML REG

LDA ML

KM

REG

LDA LM

RM

LDA

QDA REG REG REG ML

LDA LM

LDA REG

LDA REG

*The classification methods used are: Fuzzy algorithms (FA), k-means clustering (KM), k-nearest neighbor (kNN), Linear discriminance analysis (LDA), Local maxima (LM), Maximum likelihood (ML), Nearest neighbor (NN), Regression techniques (REG), Region merging (RM), and Quadratic discriminance analysis (QDA).

2.2 Existing forest information In the following chapter the existing forest information for Switzerland is divided into statistical and mapped information. A brief overview of the existing forest information and its characteristics is given in Table 2.4. 12


2.2.1 Forest definitions One of the main difficulties in detecting forests is that the definition of forest differs from country to country. A standardized and generally valid forest definition is inexistent since the term forest can be defined according to different criteria. According to Lund (2011) over 950 forest definition exist worldwide while the minimum thresholds for degree of tree cover vary between 5% and 80%. Thus, independently from any forest definition, tree cover is one of the most important forest parameter.

The FAO of the United Nations has defined forest as lands which are more than 0.5 hectares with a tree cover of more than 10% (FAO, 2000). On European level, in each country forest is defined in different ways and depending on different degree of tree cover. According to Tomppo et al. (2010) the tree cover for forest definition in the Swiss NFI is 20%, while the corresponding value raises to 30% in the Austrian NFI, and to 50% in the German NFI. Even on national level, forest can be defined differently, i.e. in Switzerland by the Swiss Federal Statistical Office (BFS) (BFS, 2004b) and by the Swiss Federal Office of Topography (swisstopo, 1993), and also by the Swiss NFI (Brassel & Lischke, 2001). An overview of these three different forest definitions is given in Table 2.3. The two tree covers developed in this thesis can be easily adapted to any of these forest definitions. Table 2.3 Overview of the different forest definitions in Switzerland. Institution / project

Minimum top height

Minimum degree of cover

Minimum width

BFS

3m (for closed forest)

60% ( for closed forest)

50m (closed forest)

swisstopo

3m

50%

--

NFI

3m (for open forest) 3m

20-60% (for open forest) 60% (for shrub forest) 20-60%

25m (shrub forest)

20–50m (depending on the degree of cover)

2.2.2 Statistical forest information Currently, for Switzerland two different statistical data sets of forest exist. 2.2.2.1

Swiss National Forest Inventory

In the framework of this thesis the Swiss national Forest Inventory (NFI) is only briefly introduced, see for further details on the inventory, i.e. project homepage (www.lfi.ch), sample design and methods (Brassel & Lischke 2001), aerial image interpretation (Ginzler et al., 2005), and results (Brändli, 2010).

The Swiss NFI is being carried out by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) in collaboration with the Forest Division at Federal Office for the Environment (BAFU). The WSL is responsible for the planning, survey and analysis, as well as the scientific interpretation and publication of the Swiss NFI. The political interpretation and implementation is done by the Forest 13

2 REMOTE SENSING IN FORESTRY Division. The first survey (NFI1) took place between 1983–1985, the second survey (NFI2) between 1993–95 and the third inventory (NFI3) was carried out between 2004–2006. Since 2009, the fourth (NFI4) is being in progress (2009-2017).

The current state and the changes of the Swiss forests are periodically recorded by the Swiss NFI. The required forest data is collected by terrestrial sample plots and by enquiries at the local forest service. For that purpose a regular 1km-grid was laid over Switzerland in the NFI1. The intersections defined the location of the field sample plots. Since the NFI2, only half of these plots, approx. 6500, have been visited in the field. The grid, which originally had a mesh size of 1km, was extended to 1.4km. To compensate for this reduction, aerial images were interpreted using a regular 0.5km-grid. The same methods have been carried out, since switching from a periodic to a continuous survey in the NFI4, but the sample plots are now visited over a period of nine years. Thereby, a ninth of the sample plots, which are evenly distributed all over Switzerland, are surveyed every year and the corresponding aerial images are stereo-interpreted before the field survey starts.

The usage of aerial images in Swiss NFI tasks reduces the cost of the ground surveys. An important application of aerial images lies in the classification of plot samples from forest and non-forest areas (Ginzler et al., 2005). 2.2.2.2

Land use/Land cover statistics

The land use/land cover statistics (AREA) are based on the interpretation of sample points on a regular 100x100m point grid. The first land use/land cover statistics is dated 1912 and consisted of only four land use categories. Since 1979/85, the BFS has been producing land use/land cover statistics every 12 years which can be compared among each other. The latest land use/land cover statistics (2004/09) uses the standard nomenclature NOAS04 which consists of 72 basic categories (combination of land cover and land use). The nomenclature NOLC04 distinguishes 27 land cover classes, and the nomenclature NOLU04 distinguishes 46 land use categories which can be summarized to the four main category groups: stocked area, agricultural area, urban area, and unproductive areas. Forest information can be obtained from the main category group stocked area, which is subdivided into forest, shrub forest and wood. These are further subdivided into 11 basic categories: normal forest, strips, afforestation, damages, deforestation, coarse forest on agricultural or unproductive areas, shrub forest, linear stands, and groups of trees on agricultural or unproductive areas. The current land use/land cover statistics is based on aerial images recorded between 2004 and 2009. The analogue aerial images from the RC30 sensor were used for 2004 and 2005, and have been replaced by the digital ADS40 imagery since 2006. For further details see (BFS, 2004a & BFS, 2011).

2.2.3 Mapped forest information Currently, for Switzerland six different land use or land cover data sets exist for mapped forest. The Topographic Landscape Model (TLM) and the CORINE Switzerland (CLC90/CLC2000/CLC2006 ) are based on computer-aided image interpretation, the automatically vectorized forest borders (AWG-ch03) 14

2 REMOTE SENSING IN FORESTRY are based on LiDAR data, and Degree of forest mixture (WMG25) and the 2006 forest cover and forest type maps are based on image classifications. 2.2.3.1

The Topographic Landscape Model

2.2.3.2

CORINE Switzerland

The Topographic Landscape Model (TLM) dataset is a digital 3D geo-database, which is still in progress and covers the whole of Switzerland as a comprehensive basic landscape model produced by swisstopo. The map-based model VECTOR25 was migrated into the TLM as a basis. The TLM data is being updated based on an update cycle of 6 years and improvements and additions are made. The natural and artificial features in the landscape are modeled as three-dimensional vector data and grouped into nine categories, whereas forest is part of the category land cover. The geometric accuracy varies between 0.2 and 1.5m for buildings or roads in all three dimensions, and between 1 and 3m for fuzzy landscape features such as forests. The TLM is used as a basis at swisstopo to create the national maps to a scale of 1: 25'000, 1: 50'000 and 1: 100'000, as well as additional 3D data sets. For further details see swisstopo (2007b & 2011a).

The first CORINE Switzerland, CLC90-CH, is part of the Co-ordination of Information on the Environment (CORINE) program. CORINE was initiated in the mid-1980s by the European Commission (EC) and strives to coordinate base information on land cover/land use in Europe. The objective is to create and maintain information about the actual land cover/land use in Europe, based on the same nomenclature and mapping instructions.

The CLC90-CH was produced by the BFS and is based on land use/land cover statistics and therefore not really consistent with the CORINE guidelines which makes it difficult to compare with the other countries. It has a spatial resolution of 250m for the raster version and 25m for the vector version. It contains the categories shrub and tree vegetation and is originally based on the stereo-interpretation of aerial images used in the land use/land cover statistics 1979/85 and 1992/97. The 74 categories of the land use/land cover statistics 1979/85 were aggregated to the 15 level 2 CORINE categories, including forest area, but without distinction between deciduous, coniferous and mixed forest. Due to uncertainties within the context of forested areas regarding the assignment of shrub forests which only exist in alpine areas a fictive CORINE Land Cover type was created. For more details see BFS (1998).

The follow-up product CLC2000-CH dataset represents the year 2000 and follows the CORINE guidelines: The minimum mapping unit (MMU) for the inventory is 25 ha, the minimum mapping width (MMW) for linear features is 100m. CLC2000 is based on the IMAGE2000 data, which is the orthorectified mosaic of Landsat-5 Thematic Mapper (TM). It is difficult to compare to CLC90-CH, since the methodology change is enormous. The latest product CLC2006 is based on change to CLC2000 and based on IMAGE2000 and IMAGE2006 (for details see ESA, 2008), which is a mixture of SPOT and IRS data. The MMU for changes is reduced to 15

2 REMOTE SENSING IN FORESTRY 5ha, but nevertheless the MMU of 25h for the calculated CLC2006 is kept with 25ha and also the MMW of 100m. 2.2.3.3

Automatically vectorized forest borders

2.2.3.4

Degree of forest mixture

The Automatically vectorized forest borders data set (AWG-ch03) consists of automatically vectorized forest borders using the DTM-AV and DOM-AV (acquired between 2000 and 2007) and the orthoimage mosaic product SWISSIMAGE (swisstopo, 2011b). Since the LiDAR DTM-AV and DOM-AV data was only obtained for areas up to 2000m a.s.l., forest borders above were ignored. The accuracy (standard deviation) of the vectorized forest borders is approx. ±2.0m. The extracted forest area has to have a minimum area of 500m2, minimum tree height of 3m and a minimum width of forest area of 10m. The AWG-ch03 data set was developed in the framework of the project Agriculturally productive area (LWN), which aimed at updating the agricultural area to the registered cadastral surveying in Switzerland (AV). It was funded by the Federal Office for Agriculture (BLW) and managed by swisstopo and private companies. For further details see swisstopo (2007a).

Degree of forest mixture was developed by the Swiss Federal Statistical Office (BFS) and consists of two data sets (WMG25 and WMG100) and provides coniferous, deciduous, mixed-coniferous and mixeddeciduous forests. The raster data sets have a spatial resolution of 25m (100m for WMG100), and are based on 11 Landsat-5 TM scenes from 1990-1992 using parallelepiped and Maximum Likelihood classifications (BFS, 2003). The datasets were validated with land use/land cover statistics (1992/97) and partly land use/land cover statistics (1979/85) and NFI1 1983-85 data. The correct classification rate (CCR) is 91.8% validated with the land use/land cover statistics and much lower (60%) with NFI data. These differences are due to a time difference of at least five years between the Landsat TM data and the NFI data and geometrical errors. For further details see BFS (2004b).

2.2.4 Forest Cover and forest type maps The 2000 Forest cover and 2006 Forest cover and forest type maps were developed by the Joint Research Center (JRC) Institute for Environment and Sustainability and cover most European countries including Switzerland.

The 2006 Forest cover map is a follow-up product of the 2000 Forest cover map (see e.g. Pekkarinen et al., 2009) and covers most European countries including Switzerland. It distinguishes between nonforest and forest areas, whereas forest area only includes woodlands with trees greater than 5m height.

The 2006 Forest type map is a classification of the forest areas into broad-leave (synonym for deciduous), coniferous and mixed forest. The mapping and classification approaches were based on a fully automatic image processing method using IMAGE2006 data acquired by the sensors IRS-P6 LISSIII, SPOT4 (HRVIR), and SPOT5 HRG, and as ancillary data CLC2000 data. The spatial resolution of both 16

2 REMOTE SENSING IN FORESTRY maps is 25m with a geometric accuracy of RMS (95%) error less than 25m. A scene by scene approach was applied in order to deal with the phenological differences and equalizing the radiometry of all images (for further details see JRC, 2011a & 2011b). Table 2.4 Overview of the existing forest information in Switzerland. The institution is the creator of the data set, SR is the spatial resolution, GA the geometric accuracy, MMU is the minimum mapping unit, and MMW is the minimum mapping width. Institution

Data basis

Method

SR

GA

WSL-BAFU

Data set name NFI1-4 AREA

aerial images

sample plots, enquiries

0.5, 1.4km

--

BFS

aerial images, field surveys

BAFU

CLC2000, CLC2006

aerial images, LiDAR aerial & satellite images, ancillary data

interpretation

swisstopo

BLW BFS JRC

TLM

AWG-ch03

WMG25/ WMG100 2000/ 2006 Forest cover/ Forest type maps

LiDAR

satellite images satellite images

sample points classification

classification classification

17

100m

--

MMU 25ha, MMW 100m ~2.5m

--

25, 100m 25m

1-3m

0.51.5m --

~25m

Latest Update data cycle 2006 10 years, (NFI4) since 2009 continuously 20042009 20082011 2006

~ 12 years

20002007 19901992 2006

not planned

~ 6 years ~ 6 years

not planned --

3 INPUT DATA SETS AND PRE-PROCESSING

3 INPUT DATA SETS AND PRE-PROCESSING 3.1 Study areas In this Chapter a general overview (Table 3.1 and Fig. 3.1) and more detailed descriptions of the four study areas with geographical (topography, land use, area) and vegetation aspects (main vegetation, forest type, and main tree species) is given. Besides the fact that study areas 2 and 3 were already known from studies in the framework of the Swiss Biotope Monitoring Program (e.g. Küchler et al., 2004), the choice of the four study areas was based on the following criteria (Table 3.1), whereas the last two criteria enable testing the robustness of the developed models. •

• • •

the study area is part of a typical geographic region of Switzerland (central Plateau, Pre-Alps, central Alps) availability of ADS40 images acquired during the vegetation season

the study area consists of different forest characteristics, i.e. structure and density (open and closed forest, age classes), different tree species, and is natural or partly managed

different geographic characteristics, i.e. topography (from flat to hilly to steep), extent (from few square kilometers to 200km2), and elevation (up to 2000m a.s.l.)

Table 3.1 Characteristics of the four study areas. Characteristics

Study area 1

Study area 2

Study area 3

Study area 4

Name (canton)

Uetliberg (ZH)

Breitmoos (AR)

Tarasp (GR)

Appenzell (AI, AR)

Geographic region

Swiss central Plateau

Pre-Alps

Central Alps

Pre-Alps

3.2

47°18’N/9°14’E X:734'980 Y:241'100 X:736'550 Y:239'800 1.9

46°46'N/10°16'E X:815'200 Y:185'400 X:817'650 Y:183'500

hilly-steep

hilly-steep

Commune

Meters a.s.l.

Coordinates (center) Swiss coordinates (Upper left, lower right)

Extent approx. (km2) Extent tree area approx. (km2) Topography

Main tree species

Uitikon, Ringlikon

450-800

47°22’N/8°28’E X:677'380 Y:247'380 X:679'780 Y:245'070 2.7

flat-hilly

ash, beech, larch, maple, Norway spruce, Scots pine, white fir

Urnäsch

900-1300

1.2

alder, ash, beech, birch, maple, Norway spruce, white fir

18

Tarasp

1350-1810

2.9 2.0

birch, larch, Norway spruce, Scots pine

Heiden, Gais, Appenzell, Urnäsch, Herisau 700-2000

47°20’N/9°15’E X:735'000 Y:251'000 X:756’000 Y:240’000 230 90

hilly-steep

ash, beech, maple, larch, Norway spruce, Scots pine, white fir


Figure 3.1 Overview of the four study areas. Left: Shaded relief and Landsat TM images of Switzerland (© 2006 swisstopo JD052552). Top: study area 1 Uetliberg (Swiss central Plateau); middle: study areas 2 Breitmoos (Pre-Alps) and study area 3 Tarasp (Central Alps), bottom: study area 4 Appenzell (PreAlps).

3.1.1 Study area 1 Study area Uetliberg is a typical region of the Swiss central Plateau. It is located in the southwest of Zurich a typical local recreation area. The terrain varies between flat (mainly in the center) and hilly (see Fig. 3.2 for details). The land cover is dominated by forests (tree area covers approx. 2.7km2), followed by pastures and grassland, settlements and the track of the Uetliberg train. The partly managed forest is mainly dense and old-grown (Figs. 3.4 and 3.5), mixed with a dominance of deciduous trees, several homogeneous coniferous stands and afforested young coniferous and deciduous stands (Fig. 3.3).

19


Figure 3.2 Map extent of study area 1. Source Pixelkarte25, scale 1:25’000 © swisstopo with circles where the photos below were taken.

Figure 3.3 Mixed forest in study area 1. Left image: young mixed forest near Liebegg with beech, maple, and Norway spruce and some older white fir in the background (left). The Sequoidendron giganteum in the front is part of an afforestation project. Right image: typical dense mixed forests with beech, maple and Norway spruce in the background. Both pictures were taken a few meters from the rail station of Ringlikon, 690m a.s.l.

20


Figure 3.4 Forest borders in study area 1. Dense mixed forest with various small deciduous trees and shrubs adjacent to the pasture in front, near the rail station Ringlikon.

Figure 3.5 Dense mixed forest in study area 1. Typical forest track with adjacent afforested trees surrounded by a dense mixed forest near the vantage point Hohenstein.

3.1.2 Study area 2 Study area Breitmoos is located in the pre-alpine zone (see Fig. 3.6). The terrain varies between steep and north-exposed slopes in the southern part (see Fig. 3.8) and flat areas in the center (Figs. 3.7 and 3.9). The land cover is characterized by pastures and meadows and few farmhouses. The core area is typical wetlands (protected mire) and characterized by fen surrounded by forests. The forest itself covers approx. 1.2km2 and is mostly characterized by mixed forest with a dominance of deciduous trees along the creeks. The forests are partly managed: clearings and both deforestation and afforestation in several parts of the area.

21


Figure 3.6 Map extent of study area 2. Source: Pixelkarte25, scale 1:25’000 © swisstopo with circles where the photos below were taken.

Figure 3.7 Center view of study area 2. The picture was taken from the farmhouse Tell (980m a.s.l.) from north to the south. Typical mixed forest with deciduous trees are located along the creek Tellbach (ash and maple) and a dominance of coniferous trees (Norway spruce and white fir) in the background.

22


Figure 3.8 Mixed forests in study area 2. Location Fetzeren with beech, very few maple, Norway spruce and white fir.

Figure 3.9 Forest borders in study area 2. Typical are beech, very few maple at forest borders, Norway spruce and white fir.

3.1.3 Study area 3 Study area Tarasp belongs to the Central Alps. It is part of the commune Tarasp (with its famous castle) and located in the lower Engadine with a varying north-oriented terrain (Figs. 3.10 and 3.11). It is characterized by pastures and grassland which are surrounded by open forest or groups of trees and dense forests in the steeper areas (Fig. 3.12). In the left part on top of a hill is the castle, other buildings and touristic infrastructure (e.g. ski-lift) and a small lake are part of the village Tarasp. The center is a typical mire area with another small lake. The relatively flat terrain is characterized by dry meadows and wetlands and a dominance of Norway spruce and Scots pine (see Figs. 3.13 and 3.14). The forest covers approx. 2km2 and is mostly coniferous mixed with birch in the lower parts and coniferous mountain forest with very old stands in the upper parts. 23


Figure 3.10 Map extent of study area 3. Source: Pixelkarte25, scale 1:25’000 © swisstopo, with circles where the photos below were taken.

Figure 3.11 Mixed coniferous forest in study area 3. Mixture of larch, Norway spruce, and Scots pine. View from the small peak Sur Mottas (1802m a.s.l.) to the plane Motta da Sparsels with the small lake Lai Nair (1547m a.s.l.) in the middle of the site and the village of Scuol in the background.

24


Figure 3.12 Larch dominated coniferous forest in study area 3. Left: Dominance of larch in the steep slopes of the southern part Magnüda. Right: The center of study area 3 is dominated by Norway spruce and Scots pine which are partly covered by large and dominant larch trees.

Figure 3.13 Small coniferous trees in study area 3. Groups of small Scots pine with few single larch at the borders of the lake.

25


Figure 3.14 Forest border with mixed coniferous trees in study area 3. East part of the study area with a dominance of Norway spruce and Scots pine.

3.1.4 Study area 4 Study area Appenzell is located in the pre-alpine zone of East Switzerland and is approx. 230km2 in area (see Fig. 3.15). The terrain varies between steep slopes of the Alpsteinmassiv in the southern part and a mixture of few flat areas with hill chains up to 1400m a.s.l. in the rest of the area. The land cover is similar to study area 2. It is a heterogeneous mixture of forest, grasslands, pastures, agricultural and urban areas. The forest itself covers approx. 90km2 and is mostly characterized by mixed forest with a dominance of deciduous trees along rivers and coniferous trees above 1200-1400m a.s.l. The forests are partly managed with clearings and both deforestation and afforestation in several parts of the area.

Figure 3.15 Map extent of study area 4. Source: Pixelkarte 500, scale 1:500’000 © swisstopo.

26


3.2 Remote sensing data This Chapter gives an overview of three different sets of input data which are used for this thesis: Airborne Digital Sensor ADS40 (first and second generation) images, RC30 CIR aerial images, digital surface models derived from the ADS40 images, and digital terrain models derived from LiDAR.

The main criteria for the choice of image data were that it is available for entire Switzerland and will be collected regularly, preferably every three years or at least every six years during the vegetation period. However, due to the fact that not the same image data was available for all four study areas, in this thesis two different types of sensors providing different image data had to be used. Since no ADS40 CIR images were available for study area 2, the predecessor CIR images from the RC30 sensor had to be used instead. Although this is not entirely ideal from a practical or methodological point of view, study area 2 was not omitted since it is representative for typical Pre-alpine forests and therefore very suitable for testing the extraction of tree area and classification of tree species. The technical specifications of each system, its temporal availability, the data acquisition processes, the advantages and disadvantages are described. Chapter 3.2.2.3 focuses on quality aspects and control of the digital surface models and comparison of the image matching-algorithms.

3.2.1 Optical sensors In 2005, swisstopo started a campaign which aims at recording entire Switzerland every three years using ADS40 sensors. In 2008, the 1st generation sensor was replaced by a 2nd generation ADS40 sensor and an additional ADS80 sensor (follow up product) has been used for simultaneous recording. Thus, ADS40 imagery with a spatial resolution of 25–50cm as basic input data for this thesis seem to be most appropriate. End of 2007, when this research started, only 1st generation ADS40 RGB imagery was available. To overcome the missing near infrared data of the ADS40 images from 2005, RC30 imagery had to be used for study area 2. The RC30 images are still available but only for selected areas and on request. All image coordinates are set in the reference frames Landesvermessung LV95 (for details see swisstopo, 2009). Specific details on these two data sets are summarized in Table 3.2. Examples of the two different types of orthoimages are shown in Fig. 3.16. Examples of the different image acquisitions of the RC30 and ADS40 sensors are shown in Fig. 3.17.

27

3 INPUT DATA SETS AND PRE-PROCESSING Table 3.2 Characteristics of the image data. Study area 1 (Uetliberg), 2 (Breitmoos), 3 (Tarasp), and 4 (Appenzell) with R (red), G (green), and B (blue). Study area Sensor type Acquisition date Scale Focal length [mm] Spectral resolution [nm]

Scan pixel size [ µm] GSD [cm] Orthoimage [cm]

Location accuracy of orthoimage (RMSE) Radiometric resolution CCD pixel per line

CCD pixel size[µm] Number of strips Number of orthoimages Overlap Reference frame

1 ADS40 SH52 18/08/2008 ~1:15'000 62.8 B: 428-492 G: 533-587 R: 608-662 NIR: 833-887

2 RC 30 08/08/2005 ~1:5'700 300 G: 500-600 R: 600-700 NIR: 750-1000

~25 25

~8.5 25

--

15

1σ, ± 0.25 m

1σ, ± 0.25 m

11 bit

12'000 pixels / array 6.5 1 1 --

LV95

2 ADS40 SH40 12/08/2005 ~1:15'000 62.8 B: 430-490 G: 535-585 R: 610-660 Pan: 465-680

3 ADS40-SH52 02/09/2008 ~1:20’000 62.8 B: 428-492 G: 533-587 R: 608-662 NIR: 833-887

4 ADS40-SH52 25/7/2008 ~1:15’000 62.8 B: 428-492 G: 533-587 R: 608-662 NIR: 833-887

~25 25

~50 50

~25 25

11 bit

11 bit

11 bit

--

--

Sideward: ~50% LV95

--

8 bit --

1σ, ± 0.25 m

12'000 pixels / array 6.5 1 1

-1 1

Forward: ~75% LV95

LV95

28

--

1σ, ± 0.5 m

12’000 pixels / array 6.5 1 1 LV95

--

1σ, ± 0.25 m

12’000 pixels / array 6.5 6 3


Figure 3.16 Examples of CIR and RGB orthoimages. Top left: RGB ADS40-SH40 (0.25m) orthoimage; top right: CIR RC30 (0.25m) orthoimage of study area 2; bottom RGB + CIR ADS40-SH52 (0.5m) orthoimages of study area 3.

3.2.1.1

Frame camera (RC30)

For study area 2 five consecutive color infrared (CIR) aerial film images were acquired with a RC30 frame camera (for detailed information see Leica Geosystems, 2011a). They were digitized with a Vexcel UltraScan and 15μm pixel size. Image orientation was established with 20 ground control points, previously measured in a differential GPS survey, using bundle adjustment in SOCET SET 5.4.1 (BAE 29

3 INPUT DATA SETS AND PRE-PROCESSING Systems, 2007). The orthoimage was generated in Orthovista 4.5 (Trimble, 2010) using the DSMs as described in Chapter 3.2.2.1. To fit with the ADS40 data the orthoimage was resampled to 0.25m. 3.2.1.2

Airborne Digital Sensor (ADS40)

Swisstopo acquired first generation ADS40 digital images between 2005–2007 for most parts of Switzerland. Since 2008 second generation ADS40 images have been acquired by upgrading the first generation sensor to the second generation sensor. Since 2009 swisstopo has been using additionally the ADS80-SH82 - a follow-up product of ADS40-SH52. Images of both sensors of the ADS40 system were used in this thesis.

First generation ADS40-SH40 RGB images Level 1 preprocessed (swisstopo, 2011c), were used for study area 2 (for further details on the sensor see e.g. Reulke et al., 2006). For technical details and descriptions of earlier applications, see Kellenberger et al. (2007). The main drawback of the first generation ADS40-SH40 is that the NIR line CCD is placed 18° forward from the nadir RGB CCDs which makes it difficult to combine all four lines.

Second generation Airborne Digital Sensor ADS40-SH52 RGB and CIR images Level 1 preprocessed, were used for study area 1 (0.25m), 3 (0.5m), and 4 (0.25m) for details see Kellenberger & Nagy (2008). Unlike sensors of the first generation, the second generation ADS40-SH52 sensors provide the NIR band in the same nadir position as the RGB bands. A comparison of the focal plate configurations of the ADS40-SH40 and SH52 sensors is shown in Figure 3.18. More specific and technical details on the sensors can be found by Leica Geosystems (2011b).

To summarize, ADS40-SH40 data were collected for study area 2, whereas ADS40-SH52 data were collected only for study area 1, 3 and 4 and when this thesis was carried out. The orthoimages for all study areas were generated in Orthovista 4.5 (Trimble, 2010) using the DSMs described in 3.2.2.1 and keeping the original GSD (see Table 3.2). Due to time restrictions, the orthoimage generation of the much larger study area 4 (see Fig. 3.19) which consists of six most-nadir strips (approx. 50% sideoverlapping) was handled differently. From these six strips, three orthoimages were calculated using the calculated DSMs (see Chapter 3.2.2.1).

30


Figure 3.17 Concepts of image acquisition by the ADS40 and RC30 sensors. Source: swisstopo.

Figure 3.18 Focal plate configuration of ADS40-SH40 (left) and SH52 (right) sensors. Source: swisstopo.

31


Figure 3.19 CIR orthoimages 1-3 of study area 4.

3.2.2 LiDAR In this thesis, LiDAR digital terrain (DTM-AV) data was used to generate normalized digital surface models (nDSMs). In the following, an overview and brief description of the used LiDAR data is given. More details on the principles of LiDAR/ALS can be found in e.g. (Naesset, 1997; Lefsky et al. 2002; Hollaus et al. 2006). The also available LiDAR digital surface model (DOM-AV) data was not used in this thesis due to the time gap (over 5 years) between the acquisition of the ADS40 images and the LiDAR data, and the fact that the LiDAR data available for the study areas was mainly acquired in the leaves-off vegetation period. Furthermore, according to Baltsavias et al. (2006) it should be noted that the national LiDAR DOM-AV is only limitedly appropriate for accurate tree detection and vegetation canopy modeling due to the small point density.

Nation-wide LiDAR data up to 2000m a.s.l. was produced by swisstopo and acquired in the framework of the project Landwirtschaftliche Nutzflächen (LWN) on behalf of the Bundesamt für Landwirtschaft (BWL), for details see swisstopo (2007a). The main goal of this project was a nation-wide update of the agricultural areas on the base of the topographic survey (amtliche Vermessung, AV). As outputs a separate DTM-AV and a DOM-AV were generated. LiDAR data was acquired by Swissphoto AG/TerraPoint using a TerraPoint ALTMS 2536 system with an average flying height above ground of 1200m. The raw DTM has an average point density of 0.8 points/m2 and height accuracy (1 sigma) of 0.5m (Artuso et al., 2003). The DTM-AV data used in this thesis is a combination of many flights and was provided by swisstopo as raw DTM-AV point data and was acquired between 2002 and 2003 (see Table 3.3 for details). This data was first interpolated at WSL to a regular grid of 1m and then resampled to the same grid spacing (0.5m for study areas 1, 2 and 4, and 1 m for study area 3) as the DSMs described in Chapter 3.2.2.1. 32

3 INPUT DATA SETS AND PRE-PROCESSING Table 3.3 Date of flight and re-flights of the DTM-AV data. Study area

1

2

3

4

Date of flight

03/2002

03/2002

05/2003

2002-2003

leaves-off

partly leaves-off

leaves-on

partly leaves-off

Re-flight

leaves-off 03/2003

leaves-off 10/2002

partly leaves-off 09/2003 10/2003

partly leaves-off

partly leaves-off 2003

3.2.3 Derived data sets This Chapter gives an overview of the digital surface models which were directly derived from the ADS40 images. 3.2.3.1

Digital surface models

3.2.3.2

Automatic DSM generation

Digital surface models were used in this thesis to support image segmentation and provide geometric information, which plays a key role in the extraction of tree area. Prior to the DSM generation, a Wallis filter (9x9 pixel)was applied to the selected images to enhance contrast, especially in shadowed regions, and to equalize radiometrically the images for matching. For each study area, a digital surface model (DSM) was generated automatically with a spatial resolution of 0.5m (study areas 1 & 2) and 1m (study areas 3 & 4) using the NGATE module of SOCET SET 5.4.1 (BAE Systems, 2007). Due to time restrictions, for study area 4, the DSM was then resampled to 5m (cubic convolution) and smoothed (5x5 pixel window) as it is being used for large area applications in the framework of the Swiss NFI (for details see Ginzler et al. 2011).

According to Baltsavias et al. (2008) automatic DSM generation through image matching has gained much attention in the last years, and many automatic DSM generation packages are meanwhile commercially available on several digital photogrammetric workstations. However, they also encountered some problems when applying these matching strategies, especially to forests and open vegetation land, since they were primarily developed for urban areas. The key to successful and reliable matching is the matching of a dense pattern of features with an appropriate matching strategy, making use of all available and explicit knowledge, concerning sensor model, image content, and geometrical constraints such as the epipolar geometry constraint. They suggest using a new, high-quality multiimage matching method (implemented in the program package SAT-PP © ETH), but this matching method is, at the moment of writing this thesis, still under development for ADS40 image data (Zhang & Gruen, 2004). In Waser et al. (2008a) two fractional tree cover approaches were successfully applied in a mire ecosystem using a high-quality multi-image matching method for RC30 CIR aerial images. 33

3 INPUT DATA SETS AND PRE-PROCESSING In this thesis the commercially available NGATE tool from SOCET SET 5.4.1 was used to generate DSMs. NGATE performs image correlation and edge-matching on each image pixel. Based on a hybrid approach, it uses both area-matching and edge-matching, and enables the area-matching to be used to assist the edge-matching, and vice versa. The final results are the combined results from both areamatching and edge-matching with blunder detection and inconsistency checking. The resulting point cloud is thinned so that smooth areas have low densities (Zhang et al., 2006; BAE Systems, 2007).

Since SOCET SET 5.4.1 doesn’t offer a DSM strategy for forests, two different DSM strategies were used in this thesis instead: First, the default-strategy, which was originally designed for urban and rural areas, and second, the modified and combined strategy (WSL-strategy), which was designed at WSL especially for vertical vegetation, i.e. single trees and forests. In fact, it is a combination of modified forest and desert strategies, and improves DSM generation within forests and mainly reduces noise and artifacts in open land. The parameters used in the second strategy are described in Chapter 8.1. Finally, a normalized digital surface model (nDSM) was produced for each study area by subtracting the LiDAR DTMs from the DSMs. For study areas 1, 3, and 4 the nadir and backwards NIR bands, for study area 2 the nadir RGB and backward panchromatic (PAN) bands were used since only a single NIR band was provided in 18° forward direction by the older ADS40-SH40 sensor. 3.2.3.3

Quality control of DSMs

Prior to using the DSMs as basis for the derivation of explanatory (geometric) variables for tree area extraction and tree species classification, a two-fold quality control was applied. DSMs derived from both the ADS40-SH40 and ADS40-SH52 images were checked. The first part of the quality control implies analysis of 476 stereo-measured tops of 278 deciduous and 198 coniferous trees with the height values of the corresponding pixels in the two DSMs for study area 2 (see Table 3.4). Table 3.4 Statistics of height differences between DSMs and reference points for study area 2. The differences (m) are compared to 476 manually measured reference points (interpolated surface points in the two DSMs using the WSL-strategy and default-strategy). Difference to reference (m)

reference

DSM (WSL-strategy)

DSM (default-strategy)

Mean

Median

Standard deviation

-6.83

-6.82

0.07

1032.34 -3.98

1027.06 -4.98

36.17 -0.06

Table 3.4 reveals that the adapted WSL-strategy outperforms the default-strategy. Obviously, the DSM heights obtained by the WSL-strategy are less underestimated than those by the default-strategy.

Fig. 3.20 illustrates the corresponding profiles of points 86-140 (for visualization purposes not the entire range of all 476 points are shown). Overall, surface tops are generally underestimated by the two DSMs. Underestimation of the DSMs is mainly due to the coarse spatial resolution of the DSMs, interpolation effects, and artifacts. 34


surface height (m a.s.l.)

Corresponding profiles of points 86-140 1040 1035 1030 1025 1020 1015 1010 1005 1000 995 990

1

6 reference

11

16 21 surface points WSL-strategy

26

31

36

default-strategy

Figure 3.20 Accuracy assessment of DSM profiles. Comparison of the surface points 86-140 obtained by the two DSMs with the reference measurements.

The second part of the quality control focuses on visual inspection of the DSMs and analysis of forest borders, forest clearings, individual trees within forest area, groups of trees, and single trees in open land. The quality control is based on the following criteria: general representation, height of trees, and shape of trees, i.e. crowns. Visual analysis of the digital surface models revealed that for all three study areas the WSL-strategy is superior to the default-strategy. The differences between the two strategies are shown for the colored hillshade in Figs. 3.22, 3.24 and Fig. 3.26. In Figs. 3.21, 3.23, and Fig. 3.25, the ADS40 images providing the bands for DSM generation are shown. In fact, the default-strategy is suitable for the extraction of forested area (tree/non-tree decision), but only partly for forest borders, and clearings or small ground areas. Individual trees (coniferous trees in particular) in forest areas are poorly represented and underestimated in height. Single trees and groups of trees in open land are often not well represented in shape and height. Unlike the WSL-strategy which is very suitable for the extraction of forested areas (tree/non-tree decision) and also for forest borders and clearings. Individual trees within forest, small clearings and single trees in open land are much better extracted than by the standard default-strategy.

35


Figure 3.21 Example of ADS40-SH52 images of study area 1 for DSM generation. ADS40-SH52 CIR nadir (left) and backward images (right).

Figure 3.22 Colored hillshade of the normalized DSMs using two different strategies in study area 1. The 0.5m – DSM was derived from ADS40-SH52 NIR bands using the default-strategy (left) and the WSL-strategy (right). Large underestimation of single trees mainly occur within the afforested area as well as light noise effects (regular patterns) are clearly visible in the left figure.

36


Figure 3.23 Example of ADS40-SH40 images of study area 2 for DSM generation. ADS40-SH40 RGB nadir (left) and PAN backward images (right).

Figure 3.24 Colored hillshade of the normalized DSMs using two different strategies in study area 2. The 0.5m – DSM was derived from ADS40-SH40 RGB/PAN bands using the default-strategy (left) and the WSLstrategy (right). Underestimation of small single trees and along forest borders as well as noise effects are clearly visible in the left figure.

37


Figure 3.25 Example of ADS40-SH40 images of study area 3 for DSM generation. ADS40-SH52 CIR nadir (left) and backward images (right).

Figure 3.26 Colored hillshade of the normalized DSMs using two different strategies in study area 3. The 1m – DSM was derived from ADS40-SH52 NIR bands using the default-strategy (left) and the WSL-strategy (right). Large underestimation of small single trees and along forest borders and within forest as well as light noise effects are clearly visible in the left figure.

To summarize, the adapted WSL-strategy outperforms the standard default-strategy particularly in the forested areas where (small) clearings and groups of individual trees are better extracted. Moreover, forest borders, single trees and groups of trees in open land are mostly extracted correctly in shape but less in height (underestimation). Groups of individual trees within forested areas are generally well represented - when accepting some generalization and a slight underestimation of height (especially for coniferous trees). Noise effects in the form of regular patterns particularly occur in some very homogeneous and structure-less parts of study area 1. 38


3.3 Reference data In this chapter a general overview (Tables 3.5 and 3.6) and more detailed descriptions of the different reference data for the tree area extraction and tree species classification are given. Due to time restrictions or the fact that the data was not available during the time of this thesis, not all the reference data sets were existent for all study areas (for details see below). However, for all study areas, digitized tree-/non-tree samples for the extraction of tree area and digitized tree samples for the tree species classification were always available.

3.3.1 Tree area extraction The different training and reference data used for tree area extraction is given below. Table 3.5 gives an overview of the produced reference data per study area. For study area 1 no photo-interpreted points and no forest border polygons of the AWG-ch03 were available due to limited time resources. Table 3.5 Overview of reference data for quantitative and qualitative analysis. Quantitative analysis Qualitative analysis

3.3.1.1

Study area

1

2

3

No. digitized polygons (tree/non-tree)

197/135

70/71

103/91

Forest borders of the AWG-ch03

not available

available

available

No. photo-interpreted point raster (tree/non-tree) Digitized squares

not available available

5305/4744 available

8229/11878 available

Digitized tree/non-tree polygons

In order to be representative for the three study areas, three types of tree samples were digitized on the corresponding RGB orthoimages using ArcMap from ArcGIS 9.3.1 (ESRI, 2009): 1) trees or tree groups within the forest, 2) at forest borders, and 3) in open land as single trees. Streets, roofs, bare soil, grassland, and shadows were digitized as non-tree samples. Shadows are treated as follows: shadows were digitized on ground behind single trees, along forest borders, and behind buildings. Shadows within or between tree crowns were not sampled to guarantee real shadow samples which do not contain parts of shaded trees. The total number of the digitized polygons which are used for quantitative analysis of the two tree covers is given in Table 3.5. As an example, Fig. 3.27 shows typical tree and non-tree samples for study area 2. The samples for study areas 1 and 3 were digitized similarly.

39


Figure 3.27 Examples of the digitized tree and non-tree polygons of study area 2.

3.3.1.2

Stereo-image-interpreted point raster

Two ADS40 RGB stereo-images based tree/non-tree decisions were performed at each grid point of a regular 10m-grid by a photogrammetric expert from WSL for study areas 2 and 3. Due to limited capacities no stereo-image-interpreted point raster was performed for study area 1. The grids were created using ArcMap from ArcGIS 9.3.1 (ESRI, 2009). For an optimal stereo-interpretation the regular grid was extracted from the DSMs – the same as used for orthoimage generation. A raster point was assigned to non-tree if the cursor was on the ground or within a shadow on the ground. If the cursor was not on the ground, but within a shadow on a tree, it was also assigned to non-tree. Fig. 3.28 shows an example of the photo-interpreted tree/non-tree decision for study area 2.

40


Figure 3.28 Part of photo-interpreted point raster for study area 2 with tree/non-tree decision. The grid has a spacing of 10m and shadows are assigned to non-trees.

3.3.1.3

Digitized squares

3.3.1.4

AWG-ch03

For the qualitative analysis, tree crowns, shadows and soil were digitized using ArcMap (ESRI, 2009) on the ADS40 RGB and CIR orthoimages within two squares of approx. 1ha (100x100m) per study area and visually inspected. Each square represents typical forest conditions of the corresponding study area and includes forest borders, gaps, clearings, and parts with close dense forest (see Fig. 3.29 as an example for study area 2).

Additionally, for qualitative analysis, the forest borders of the AWG-ch03 (automatically derived forest border, see Chapter 2.2.3.3) data (swisstopo, 2007a) were used in study areas 2 and 3 (see Fig. 3.29). For study area 1 no AWG-ch03 data was available. The AWG-ch03 data set is provided by swisstopo and consists of automatically vectorized forest borders based on the DTM-AV and DOM-AV (for both study areas acquired in March 2002 [leaves-off] and re-flown in October 2002 [partly leaves-off]). The forest area in the AWD-ch03 cover is characterized by a minimum of 100m2, a minimum tree height >3m, and a minimum width of 10m. According to swisstopo, the accuracy (standard deviation) of the vectorized forest borders is approx. ±2.0m. Although the data set is relatively old and changes in forest area (especially gaps and forest borders) occurred meanwhile, it is the only existing and available information on forest borders over large areas in Switzerland. 41


Figure 3.29 RGB orthoimage with digitized soil, shadows and polygons of AWG-ch03 for study area 2. Typical example with digitized soil and shadows (left) and the forest border polygons of the AWG-ch03 (right).

3.3.2 Tree species classification This Chapter describes the sampling method for the tree species used to calibrate and validate the logistic regression models of each study area. An overview of the sampled tree species per study area is given in Table 3.6. Appearance of the nine tree species in the ADS40 RGB images is shown in Fig. 3.30. Typical examples of each tree species as seen in nature and on the ADS40 RGB and CIR images are shown in Figs. 8.1 - 8.9. The ground truth data to validate the tree species classifications was collected in the natural environment to be representative for all four study areas. A variety of tree species are present in each study area. Field work was carried out during the main vegetation period one year after the data acquisition (August 2009 for study area 1, July 2006 for study area 2, and August 2009 for study areas 3 and 4). The focus was laid on the most frequent tree species (at least 5% coverage in Switzerland according to the NFI) which were also visible in the ADS40 orthoimages. The question arises, why NFI sample plots were not used as reference data in this thesis. Species information from Swiss NFI terrestrial surveys on sample plot level could not be used because the exact position of the sample centers have planimetric errors of several meters for the four study areas at the time of this thesis. Since summer 2009, the center points of the NFI field sample plots have started to be measured with a differential GPS. The exact positions relative to the plot center of all trees are known: they have been measured using measuring bands and compass. 42

3 INPUT DATA SETS AND PRE-PROCESSING Table 3.6 Overview of the sampled tree species per study area. Species proportion of tree species is based on estimates by an expert during the field surveys for study areas 1 -4. Scientific tree species name

Common tree species name

Number of samples

Species proportion in % per study area

Study area

Acer sp. Alnus sp. Betula sp. Fagus sylvatica Fraxinus excelsior Abies alba Larix decidua Picea abies Pinus sylvestris

maple alder birch beech ash white fir larch Norway spruce Scots pine

38, 40, 123 21 25, 56 56, 82, 183 52, 56, 137 57, 61, 206 45, 107, 72 107, 74, 60, 328 32, 102, 125

5, 95% of the variance (cumulative proportion of all components) are: •

Study areas 1-3: PCA axis 1-5 of variables of the step-wise selected variables

Finally, the predictive power of the models based on the PCA variables was verified by a 10-fold crossvalidation (see Table 4.4).

59

4 TREE AREA EXTRACTION

4.1.5 Statistical measures The statistical measures used to validate the results were: producer's- (PA) and user's accuracy (UA), correct classification rate (CCR), and the kappa coefficient (𝜅). For further details and equations see Cohen (1960) or Monserud & Leemans (1992): •

• •

•

Producer's Accuracy (PA) is calculated by dividing the number of correct pixels for a class by the actual number of ground truth pixels for that class: producer's accuracy [%] = 100% - error of omission [%].

User's Accuracy (UA) is calculated by dividing the number of correct pixels for a class by the total pixels assigned to that class: user's accuracy [%] = 100% - error of commission [%].

Correct classification rate (CCR), also known as overall accuracy, is the percentage of correctly classified pixels and is calculated using the following quotient: The number of pixels assigned to the correct class divided by the number of pixels that actually belong to this class, multiplied by 100. Since it is not biased towards smaller classes it is a good measure of the accuracy of a classification scheme.

Cohen’s Kappa (𝜅) coefficient (Cohen, 1960) is a statistical measure of inter-rater agreement for qualitative (categorical) items. Since 𝜅 takes into account the agreement occurring by chance it is a more robust measure than simple percent agreement calculation. The formula is given in Equation 3: 𝜅=

Pr(𝑎) − Pr(e) 1 − Pr(e)

(3)

where Pr(a) is the relative observed percentage of agreement among raters, and Pr(e) is the expected percentage of chance agreement. The observed percentage of agreement implies the proportion of ratings where the raters agree, and the expected percentage is the proportion of agreements that are expected to occur by chance as a result of the raters scoring in a random manner. If the raters are in complete agreement then 𝜅 = 1. If there is no agreement among the raters (other than what would be expected by chance) then 1> 𝜅 ≥ 0. A commonly used evaluation of the kappa values is given in Fleiss (1971): a value of kappa below 0.40 is considered to represent a poor agreement beyond chance, values between 0.40 and 0.75 indicate fair agreement, and values beyond 0.75 indicate excellent agreement.

4.2 Results In this Chapter, the results of the two different tree cover approaches are presented and discussed. As described in detail (Chapter 3.3.1) the used reference data was not the same in all three study areas, i.e. was differently acquired and therefore not entirely consistent. An accuracy assessment was applied and includes quantitative and qualitative aspects. The assessments focus on typical forest characteristics such as close and open forests, forest borders, and forest clearings, and is based on the ADS40 RGB 60

4 TREE AREA EXTRACTION orthoimages. Besides the accuracy assessment, the results and problem cases are critically discussed and finally compared to other studies.

4.2.1 Quantitative evaluation The quantitative evaluation for both tree cover approaches includes the confusion matrices based on the digitized tree/non-tree polygons (see Chapter 3.3.1.1) and the stereo-interpreted tree/non-tree decision on a 10m point raster (see Chapter 3.3.1.2).

4.2.1.1

Cross-validation

The prediction of tree/non-tree pixels was preliminarily tested for the three variable selection approaches and then for the PCAs of the step-wise selected variables using binomial regression models (see Table 4.4) with a tree probability (P tree) >0.5. The accuracy assessments consist of 10-fold crossvalidations of each model using the digitized tree and non-tree samples. Table 4.4 Overview of 10-fold cross-validation for the three study areas. Cross-validation of the glm.logit models (P tree > 0.5) is based on the variables selected by the three approaches. Best models (PCA of the third approach) are bold-faced. Variable selection approach 1st

2nd 3rd

No.

Variable groups

Study area 1

Study area 2

Study area 3

CCR

κ

CCR

κ

CCR

κ

1

Geometric

0.981

0.961

0.982

0.975

0.916

0.832

4

RGB & CIR

0.844

0.687

0.892

0.785

0.880

0.759

2 3 5

RGB CIR

Geometric & RGB & CIR Significant variables

Step-wise variable selection PCAs of variable selection

0.864 0.791 0.989 0.988 0.989

0.999

0.742 0.561 0.971 0.983 0.984

0.998

0.879 0.779

0.981 0.926 0.985

0.999

0.757 0.558

0.967 0.852 0.976

0.999

0.815 0.774 0.949 0.921 0.951

0.962

0.723 0.532 0.901 0.841 0.902

0.924

Table 4.4 reveals that generally high accuracies are obtained by all three variable selection approaches. Slightly higher accuracies are obtained when using the model based on the PCAs of the step-wise selected variables (proportion of variance ≥0.05, see Tables 8.5 - 8.7). In the first approach, highest accuracies were obtained by variable groups 1 and 5 – however the differences are not significant with the exception of study area 3. Lowest accuracies were obtained when testing the spectral variables (groups 2-4). Moreover, the geometric variables seem to be particularly informative. Similar accuracies were obtained by the second approach, with the exception of study area 2, where accuracies were 5% 61

4 TREE AREA EXTRACTION lower. The accuracies obtained by the third approach were slightly higher but overall in the range with the first approach. 4.2.1.2

Comparison to digitized polygons

The confusion matrices of the fractional tree cover with P tree > 0.5 based on the PCA models producing

highest CCR and κ (Table 4.4) are compared to the discrete tree cover for each study area in Tables 4.5 4.7.

Study area 1

Table 4.5 Confusion matrix for discrete and fractional tree cover based on digitized polygons for study area 1. Pixels where the tree covers and the digitized polygons are in agreement (diagonal) are bold-faced. The total number of pixels is 81275. Discrete tree cover

digitized Non-tree tree UA

CCR

κ

classified

PA

Non-tree

tree

0.99

0.99

42847 668

0.99 0.98

Fractional tree cover

5

37755

digitized

0.99 0.99

Non-tree tree UA

CCR

κ

classified

PA

Non-tree

tree

0.99

0.99

42841 11

0.99 0.99

81

38342

0.99 0.99

Table 4.5 shows that for study area 1, CCR and 𝜅 of both approaches exceed 98%, and UA, PA ~99% were obtained. Best agreements are obtained by the fractional tree cover, which shows very little misclassified tree and non-tree pixels. In contrast, the tree area of the discrete tree cover is slightly underestimated.

Study area 2 Table 4.6 Confusion matrix for discrete and fractional tree cover based on digitized polygons for study area 2. Pixels where the tree covers and the digitized polygons are in agreement (diagonal) are bold-faced. The total number of pixels is 67102. Discrete tree cover


CCR

κ

classified

PA

Non-tree

tree

0.86

0.99

33282 5499 0.92 0.84


14

28307

digitized

0.99 0.84

Non-tree tree UA

CCR

κ

62

classified

PA

Non-tree

tree

0.99

0.99

33284 36

0.99 0.99

13

33769

0.99 0.99

4 TREE AREA EXTRACTION Table 4.6 shows that for study area 2, best agreements are obtained by the fractional tree cover producing very few misclassified pixels. In contrast, the tree area of the discrete tree cover is substantially (~15%) underestimated. Since study area 2 is characterized by fuzzy forest borders in relatively steep terrain conditions the findings made in Waser et al. (2008a & b) confirm that the fractional tree cover clearly outperforms the discrete tree cover in such areas.

Study area 3 Table 4.7 Confusion matrix for discrete and fractional tree cover based on digitized polygons for study area 3. Pixels where the tree covers and the digitized samples are in agreement (diagonal) are bold-faced. The total number of pixels is 56666. Discrete tree cover


CCR

κ

classified

PA

Non-tree

tree

0.79

0.99

27428 7328 0.87 0.74


6

21904

digitized

0.99 0.75

Non-tree tree UA

CCR

κ

classified

PA

Non-tree

tree

0.97

0.96

26506 928

0.96 0.92

1222

28010

0.97 0.97

Table 4.7 reveals that for study area 3, best agreements are obtained by the fractional tree cover, and that over- and underestimation of trees is averaged out and in the range of 3-4%. In contrast, the discrete tree cover is solely characterized by a large tree underestimation of ~25% which is even higher than it is in study area 2. However, compared to study areas 1 and 2, substantial lower accuracies are obtained by both approaches. A reason for this might be the coarse image resolution of 0.5m. Waser et al. (2008a) have shown that also the fractional tree cover is less accurate in mountainous areas with lower spatial resolution and therefore less dense and accurate DSMs. 4.2.1.3

Stereo image-interpreted point raster

In this Chapter, the predictions of the fractional tree covers are validated with the tree/non-tree decision obtained by a stereo image-interpreted 10m tree/non-tree point raster (see Chapter 3.3.1.2). For study area 1 no such reference data was available. The confusion matrices of the fractional tree cover with P tree > 0.5 based on the PCA model producing highest CCR and κ (Table 4.4) are compared to the discrete tree cover for study areas 2 and 3 in Tables 4.8 and 4.9. Overall, the discrete tree cover produced similar accuracies in study areas 2 and 3, whereas the fractional tree cover was less accurate in study area 3 which resulted in a larger underestimation.

Study area 2

63

4 TREE AREA EXTRACTION Table 4.8 reveals that also in this second accuracy assessment the fractional tree cover in study area 2 clearly outperforms (12%) the discrete tree cover. Trees are substantially underestimated in the discrete tree cover, whereas only slight over- and underestimation occur in the fractional tree cover. Table 4.8 Confusion matrix for discrete and fractional tree cover based on photo-interpreted point raster for study area 2. Pixels where the tree covers and 10m tree-/non-tree raster are in agreement (diagonal) are bold-faced. The total of photo-interpreted points is 10049. Discrete tree cover

interpreted Non-tree tree UA

CCR

κ

classified

PA

Non-tree

tree

0.67

0.99

3399 1648 0.83 0.66


67

4935

interpreted

0.98 0.75

Non-tree tree UA

CCR

κ

classified

PA

Non-tree

tree

0.94

0.94

4806 320

0.95 0.88

287

4636

0.94 0.94

Study area 3 Table 4.9 Confusion matrix for discrete and fractional tree cover based on photo-interpreted point raster for study area 3. Pixels where the tree covers and 10m tree-/non-tree raster are in agreement (diagonal) are bold-faced. The total of photo-interpreted points is 20107. Discrete tree cover

interpreted Non-tree tree UA

CCR

κ

classified

PA

Non-tree

tree

0.62

0.97

5027 3029 0.83 0.63


356

11695

interpreted

0.93 0.79

Non-tree tree UA

CCR

κ

classified

PA

Non-tree

tree

0.85

0.92

7328 1263 0.89 0.78

930

10586

0.89 0.89

Table 4.9 illustrates that also for study area 3 the fractional tree cover outperforms (approx. 8%) the discrete tree cover. Again, trees are substantially underestimated by the discrete tree cover, whereas both over- and underestimation occur in the fractional tree cover.

4.2.2 Qualitative evaluation Visual inspections of the discrete and fractional tree covers are based on the ADS40 RGB orthoimages and were established within two digitized squares (for details see Chapter 3.3.1.3) with an extent of approx. 1ha for each study area. For each square, the representation of typical forest properties, i.e. closed and open tree areas, forest borders, gaps, and single trees, was critically assessed, and over- and 64

4 TREE AREA EXTRACTION underestimation was depicted. For study areas 2 and 3, the forest borders of the AWG-ch03 (see Chapter 3.3.1.4) layer were depicted for additional visual comparisons.

In general, visual inspection revealed that the fractional tree cover outperforms the discrete tree cover not only by less over-and underestimation but also regarding its representation within different forest characteristics. Tree area in general, gaps, and forest borders are more accurately represented by the fractional tree cover than by the discrete tree cover, and both over- and underestimation is generally much greater in the latter. 4.2.2.1

Study area 1

An overview of the two squares of study area 1 is given in Figs. 4.9 and 4.10. Visual inspection of both squares reveals that tree area, forest borders and gaps are well represented in the fractional tree cover. Square 1 (approx. center coordinates X:678’714, Y:246’052) is characterized by a general tree underestimation of both tree covers, whereas tree underestimation in square 2 (approx. center coordinates X:678’025, Y:246'7879) mainly occurs in the discrete tree cover. Tree underestimation is greater in the discrete tree cover and mainly occurs in gaps and afforestation and less along forest borders. This is a bit in contrast to Table 4.5, where the comparison with the digitized polygons only reveals small tree underestimation in the discrete tree cover. In both squares, some regular patterns (matching artifacts of the DSMs) are visible in the fractional tree cover between extracted trees/shrubs.

65


Figure 4.9 Square 1: Tree over-/underestimation by the discrete and fractional tree covers in study area 1. RGB orthoimages with digitized soil and shadows (top), and over- and underestimation of trees by the discrete tree cover (left) and fractional tree cover (right).

Fig. 4.9 reveals a substantial underestimation of small trees and shrubs in the afforested area of square 1 by the discrete tree cover (red areas), and less underestimation by the fractional tree cover.

66


Figure 4.10 Square 2: Tree over-/underestimation by the discrete and fractional tree covers in study area 1. RGB orthoimage with digitized soil and shadows (top), and over- and underestimation of trees by the discrete tree cover (left), and fractional tree cover (right).

In Fig. 4.10 underestimation can be found in the discrete tree cover (left) for small trees and shrubs in the afforested area and along the forest borders, whereas slight overestimation mainly occurs along forest islands. Less underestimation can be found in the fractional tree cover. 4.2.2.2

Study area 2

An overview of the two squares of the pre-alpine study area 2 is given in Figs. 4.11 and 4.12. In general, tree area, forest borders, and large gaps are well represented in both cases. Again, the tree/non-tree areas in study area 2 were extracted more precisely by the fractional tree cover. Tree underestimation which mainly occurs in gaps (square 1 with approx. center coordinates X:735’702, Y:240’840) and groups of small trees (square 2 with approx. center coordinates X:735’852, Y:240’619) of the discrete tree cover is equal to the findings of the accuracy assessments (Tables 4.6 and 4.8). Whereas underestimation of trees along forest borders and in afforested areas is large in the discrete tree cover it remains small in the fractional tree cover. Furthermore, overestimation mainly occurs in the discrete tree cover. This is in contrast to Table 4.8, where the comparison with the photo-interpreted point raster reveals almost no overestimation in the discrete tree cover and slight tree overestimation in the fractional tree cover.

67


Figure 4.11 Square 1: Comparison between AWG-ch03 layer, discrete and fractional tree covers in study area 2. (clockwise) Left: RGB orthoimage with digitized soil, and shadows. Right: Forest border delineation according to the AWG-ch03 layer (no forest borders along gaps were extracted). Extraction of trees/non-trees by the fractional tree cover (right) and by the discrete tree cover (left).

Fig. 4.11 shows that in both cases small tree overestimation mainly occurs along forest borders and shadows, and is more distinctive in the discrete tree cover. Tree underestimation mainly occurs in the discrete tree cover. The AWG-ch03 layer as additional reference is not appropriate since the gaps in the middle of square 1 are not extracted. 68


Figure 4.12 Square 2: Comparison between AWG-ch03 layer, discrete and fractional tree covers in study area 2. (clockwise) Left: RGB orthoimage with digitized soil and shadows. Right: Forest border delineation according to the AWG-ch03 layer. Extraction of trees/non-trees by and the fractional tree cover (right), and by the discrete tree cover (left).

Figure 4.12 reveals that in both cases tree overestimation mainly occurs along forest borders and shadows and is much more distinctive in the discrete tree cover. Underestimation of trees in the discrete tree cover is concentrated to small groups of trees in the afforested part in the middle of the square. 69

4 TREE AREA EXTRACTION 4.2.2.3

Study area 3

An overview of square 1 (approx. center coordinates X:816'420, Y:184’587) and square 2 (approx. center coordinates X:816’960, Y:184’873) of the alpine study area 3 is given in Figs. 4.13 and 4.14. Again, tree area and forest borders are relatively well represented in both cases. Overall, in the fractional tree cover, the predicted tree/non-tree areas are more precisely extracted than in the discrete tree cover. Furthermore, tree underestimation can be detected in both squares and in both tree covers. This is in contrast to Tables 4.7 and 4.9, where large tree underestimation only appears in the discrete tree cover and under- and overestimation is smaller and more averaged out in the fractional tree cover. Moreover, Fig. 4.13 illustrates that the discrete tree cover seems especially weak for the extraction of single trees and along gaps in relatively steep terrain (many trees are not extracted) which confirms the large underestimation of Tables 4.7 and 4.9.

70


Figure 4.13 Square 1: Comparison between AWG-ch03 layer, discrete and fractional tree covers in study area 3. (clockwise) Left: RGB orthoimage with digitized soil, shadows and coniferous trees. Right: Forest border delineation according to the AWG-ch03 layer. Extraction of trees/non-trees by the fractional tree cover (right) and the discrete tree cover (left).

Fig. 4.13 illustrates under- and overestimation along the forest borders. Especially the discrete tree cover shows a large underestimation along the shadows and between small gaps. As already observed in study area 1, some regular patterns in the fractional tree cover (matching artifacts of the DSMs) are also visible in these over- and underestimated areas (see black narrows in Fig. 4.13).

71


Figure 4.14 Square 2: Comparison between AWG-ch03 layer, discrete and fractional tree covers in study area 3. (clockwise) Left: RGB orthoimage with digitized soil and shadows. Right: Forest border delineation according to the AWG-ch03 layer. Extraction of trees/non-trees by the fractional tree cover (right) and the discrete tree cover (left).

Fig. 4.14 shows that for both cases, the predicted tree areas are underestimated along forest borders and gaps with shadows. Underestimation is substantially lower when using the fractional tree cover. Again, some regular patterns in the fractional tree cover (matching artifacts of the DSMs) are also visible in the underestimated tree areas. 72


4.2.3 Summary of results The accuracy assessment is summarized in Table 4.10 and considers different forms of forest such as close and open tree areas, borders, clearings, and afforestation and includes quantitative and qualitative aspects. Table 4.10 Overview of the different accuracy assessments of the discrete tree cover and the fractional tree cover separated for each study area. For study area 2, no photo-interpreted point raster was available. The results for the fractional tree cover are 10-fold cross-validated and based on the PCAs of the step-wise selected variables. Evaluation method Digitized polygons

Photointerpreted points

Visual image inspection

Tree cover

Study area 1

Study area 2

CCR

κ

CCR

κ

CCR

κ

discrete

0.99

0.98

0.92

0.84

0.87

0.74

fractional

--

--

0.95

0.88

0.89

0.78

fractional discrete discrete fractional

0.99 --

0.99 --

large underestimation along borders, gaps, single trees

small underestimation, regular patterns

0.99 0.83

Study area 3

0.99 0.66

large over- and underestimation along borders and gaps small overestimation along borders

0.96 0.83

0.92 0.63

small overestimation, large underestimation along borders, gaps, single trees small under- and overestimation along borders, regular patterns

10-fold cross-validation reveals that generally high overall accuracies are obtained by all three variable selection approaches and only slightly higher accuracies are obtained when using the model based on the PCAs of the step-wise selected variables.

Analysis of the confusion matrices based on the digitized tree/non-tree polygons show that the fractional tree cover outperforms the discrete tree cover by 5-10% with the exception of study area 1 where almost similar accuracies are obtained. The reason for this might be that the discrete tree cover generally performs better in areas with flat topography and in dense old-grown (and therefore more homogenous) forests as it is typical for study area 1. It is also shown that tree underestimation mainly occurs in the discrete tree cover and in particular in study areas 2 and 3. Analysis of the confusion matrices for study areas 2 and 3 based on the stereo image-interpreted points show that the fractional tree cover outperforms the discrete tree cover again by 5-10%, but the obtained accuracies are approx. 5% lower. Visual image inspection reveals that the discrete tree cover can be characterized by large underestimation, and for study area 2 also overestimation, which both increase along forest borders and within gaps. Unlike the fractional tree cover, where both over- and underestimation are smaller, more averaged out, and where especially single trees are better extracted. 73


4.3 Discussion 4.3.1 Model choice and variable selection In contrast to the discrete tree cover approach (which is based solely on a discrete tree/non-tree decision), for the fractional tree cover approach, the choice of an appropriate model and variable selection were necessary. Combining remote sensing data with regression analysis as it is performed in many studies for land cover mapping (e.g. Guisan & Zimmermann, 2000; Ju et al., 2003; Mathys et al., 2006) is also shown to be very appropriate for tree cover mapping. Since parametric models enable easy and experienced variable selection procedures, the usage of logistic regression models was considered for the fractional tree cover.

Latifi et al. (2010) claim that for practical data analysis, one usually doesn’t know what function should be used to fit the observed data and that many studies tend to use familiar functions without knowing if they are better than other possible choices. According to Hosmer & Lemeshow (2000), the more variables included in a model, the greater the estimated standard errors become, and the more dependent the model becomes on the observed data.

Besides the commonly used step-wise variable selection techniques as suggested by e.g. Hosmer & Lemeshow (2000) or Guisan et al. (2002), the AIC criterion has become a standard tool in model fitting. However, since it is originally adapted to linear models, Guisan et al. (2002) and Guisan & Zimmermann (2000) claim that it should be handled with reservation when modeling in a transformed data space, i.e. generalized linear models (GLMs).

Besides the step-wise selection method, in this thesis additional and more empirical effort was taken to assess the explanatory power of the variables and to determine a small set of powerful variables for the final classification. To find the best models, the selected variables of three different approaches were tested using 10-fold cross-validations. Pros and cons of these three approaches are discussed below.

In the first approach, variables were assigned to groups according to their sensor affiliation (geometric, RGB, CIR bands and combinations). Since no variable selection was performed, some of the variables are correlated. However, this simple and straightforward method gives an idea on the power of the variables derived from a specific sensor without performing a variable selection and Table 4.4 confirms that with a minimum effort generally high overall accuracies can be obtained.

In the second approach, it was tested which variables of the five variable groups were significant with an error probability of

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