Land use and cover classification using airborne MASTER and

106 Pages·2011·3.91 MB·English

Land use and cover classification using airborne MASTER
and spaceborne GeoEye-1 sensors: Focus on coffee-banana
agroforestry systems near Turrialba, Costa Rica.
by
Marina Martignoni
A thesis submitted in partial fulfillment for
the degree of Master of Science in
Sustainable forest and Nature management
at the
Fakultät für Forstwissenschaften und Waldökologie
Chair of Forest inventory and Remote sensing
Georg-August Universität Göettingen, Germany
~ September 2011~
Supervisor: Prof. Dr. Christoph Kleinn ________________________
Co-supervisor: Prof. Dr. Martin Worbes ________________________
Acknowledgments
None of the achievements would have been possible without the constant, supportive
and inspiring guidance of my supervisor, Dr. Hans Fuchs. His attention, hard work,
prompt replies and proactive nature have set an example I hope to match some day. I am
deeply indebted for what I could learn and appreciate during this experience and I shall
never forget his contribution for my development.
For the completion of this thesis special thanks to:
Prof. Dr. Christoph Kleinn, for the opportunity, enlightened expertise and inspiring
advice
Prof. Dr. Martin Worbes, for having being an excellent teacher and correcting this thesis
Dr. Charles Staver, for the granted financial contribution and chance to work for
Bioversity
GIZ, for the funding
Dr. Lutz Fehrmann and Dr. Yang Haijun, for the statistical support and guidance
MSc. Henning Aberle, for kindly translating the abstract and sincere aid
MSc. Paul Magdon, for the inspiring discussion on sampling and classification
the whole AWF department, in particular Basanti Bharad, Beckschäfer Philip,
Buschmann Axel, Dockter Ulrike, Fischer Christoph, Heydecke Hendrik, Kywe Tin Zar,
Malla Rajesh, Schlote Reinhard, Vega-Araya Mauricio, for the amazing availability,
kindness and good advice
Sabine Schreiner, for having being a wonderful mentor and example
Prof. Dr. Niels Stange and the whole SUFONAMA team, for allowing this great
experience
the truly inspiring teachers I had in the last year, especially Dr. Stergios Adamopoulos,
Dr. Gehrard Büttner, Prof. Dr. Hanns Höfle, Dr. Ronald Kühne, Prof. Dr. Bo Larsen,
Prof. Dr. Ralph Mitlöhner, Dr. Carsten Schröder.
Among the Costa Rican partners:
Dr. Miguel Dita, por el apoyo excepcional y disponibilidad
Dr. Pablo Siles and Dr. Oscar Bustamante, para las observaciones siempre constructivas
y la ayuda incondicional en el trabajo de campo
Dr. Ana Tapia, por las ideas
MSc Nancy Chaves, por hacerme sentir como en casa y por la experiencia inolvidable!
Dr. Christian Brennez and MSc. David Brown, por la gentil ayuda y cooperativas
Ligia Quezada and Karol Araya, para la inmensa disponibilidad y asistencia
And last but not least:
mamma e Serena, per avermi insegnato la cura e l'amore
رفظم
اي د ھتاس اريم ےن سج ںوه ىتهاچ اننب ڒتھب ںۑم ےۑڶ ےک سج
all the friends met in Italy, Copenhagen, Göttingen and Costa Rica, for being just so
great.
I am very thankful to you all!
Table of content
List of Figures.....................................................................................................................i
List of Tables......................................................................................................................ii
List of Abbreviations.........................................................................................................iii
Physical constants...............................................................................................................v
File formats........................................................................................................................vi
Abstract.............................................................................................................................vii
Zusammenfassung...........................................................................................................viii
1 Introduction.....................................................................................................................1
2 Study site.........................................................................................................................5
2.1 Location..................................................................................................................5
2.2 Climate...................................................................................................................5
2.3 Geomorphology......................................................................................................5
2.4 Land use..................................................................................................................6
3 Material...........................................................................................................................7
3.1 MASTER images...................................................................................................7
3.1.1 Instrument and data description.....................................................................7
3.1.2 CARTA mission.............................................................................................9
3.2 GeoEye-1 images..................................................................................................11
3.3 OpenStreetMap....................................................................................................12
3.4 Digital elevation model.........................................................................................12
3.5 Software programs................................................................................................12
4 Land use and cover (LUC) classes................................................................................13
5 Methodology.................................................................................................................19
5.1 Sampling design....................................................................................................19
5.1.1 Population.....................................................................................................19
5.1.2 Sampling frame.............................................................................................20
5.1.3 Sampling plot and unit.................................................................................23
5.2 Data collection......................................................................................................24
5.3 Image processing...................................................................................................25
5.3.1 Georeferencing and subsetting the image.....................................................25
5.3.2 Atmospheric correction.................................................................................26
5.3.3 Feature selection............................................................................................27
Orthogonal transformations with PCA and MNF............................................28
Feature selection in R.........................................................................................28
Feature selection with EnMAP Toolbox............................................................29
5.4 Image analysis.......................................................................................................29
5.4.1 Unsupervised classification...........................................................................29
Gaussian Mixture...............................................................................................30
5.4.2 Supervised classification................................................................................30
Maximum Likelihood........................................................................................30
Support Vector Machine....................................................................................31
SVM with EnMAP Toolbox..............................................................................32
ML and SVM by Canty.....................................................................................32
5.4.3 Accuracy assessment.....................................................................................33
5.5 Post-classification processing................................................................................34
6 Results...........................................................................................................................35
6.1 Sampling design....................................................................................................35
6.2 Data collection......................................................................................................35
6.3 Image processing...................................................................................................35
6.3.1 Georeferencing..............................................................................................35
6.3.2 Atmospheric correction.................................................................................36
6.3.3 Feature selection............................................................................................37
6.4 Image analysis.......................................................................................................39
6.4.1 Results of unsupervised classification...........................................................39
6.4.2 Supervised classification................................................................................40
6.5 Post-classification processing................................................................................43
7 Discussion......................................................................................................................47
7.1 Sampling design....................................................................................................47
7.2 Data collection......................................................................................................49
7.3 Image processing...................................................................................................50
7.4 Image analysis.......................................................................................................53
8 Conclusion.....................................................................................................................57
9 Outlooks........................................................................................................................58
10 Appendices.................................................................................................................60
10.1 Appendix A: FRA legend..................................................................................60
10.2 Appendix B: Raunkiaer's life form classification................................................62
10.3 Appendix C: Sampling plot digitization............................................................63
10.4 Appendix D: Field form.....................................................................................66
10.5 Appendix E: Georeferencing MASTER tiles....................................................67
10.6 Appendix F: FLAASH atmospheric correction.................................................71
10.7 Appendix G: Feature selection with R...............................................................74
10.8 Appendix H: Feature selection with EnMAP Toolbox......................................77
10.9 Appendix I: Field manual...................................................................................79
11 References....................................................................................................................82
List of Figures
Fig. 1: MASTER camera..................................................................................................7
Fig. 2: Channels and wavelength regions of MODIS, ASTER and MASTER..............8
Fig. 3: Study site over georectified MASTER tile..........................................................10
Fig. 4: LCCS used by the FAO.......................................................................................14
Fig. 5: Legend used in the IGBP LCCS........................................................................14
Fig. 6: LUC classification scheme applied in this study..................................................16
Fig. 7: Digitized sampling polygons................................................................................23
Fig. 8: Example of confusion matrix...............................................................................33
Fig. 9: MASTER and GeoEye-1 images overlaid with OSM.......................................36
Fig. 10: Radiance and reflectance MASTER images......................................................36
Fig. 11: Feature selection using R randomForest package..............................................37
Fig. 12: Feature selection using EnMAP Toolbox..........................................................38
Fig. 13: ML and SVM classification maps......................................................................40
Fig. 14: LUC class spectra of MASTER radiance-at-sensor image...............................42
Fig. 15: LUC class spectra of MASTER reflectance image...........................................42
Fig. 16: Confusion matrix of sieved & clumped MASTER...........................................44
Fig. 17: Examples of rule images resulting from ML classification................................45
Fig. 18: Confusion matrix of combined class MASTER................................................46
Fig. 19: Sampling grid.....................................................................................................80
i
List of Tables
Table 1: Agroforestry extend estimates.............................................................................2
Table 2: Spectral characteristics of MASTER channels..................................................8
Table 3: MASTER sensor characteristics.........................................................................9
Table 4: Legend of land use and cover (LUC) classes applied in this study...................17
Table 5: Overall accuracies and Kappa coefficients obtained from Gaussian Mixture
classification.....................................................................................................................39
Table 6: Overall accuracies and Kappa coefficients from ML and SVM classification..41
Table 7: Overall accuracies and Kappa coefficients of 3 post-classification processed
images..............................................................................................................................43
ii
List of Abbreviations
6S : Second Simulation of a Satellite Signal in the Solar Spectrum
ACORN : Atmospheric CORrection Now
ASTER : Advanced Spaceborne Thermal Emission and Reflection Radiometer
ATREM : ATmospheric REMoval
CARTA : Costa Rican Airborne Research and Technology Application
CATIE : Centro Agronómico Tropical de Investigación y Enseñanza.

CENAT : Centro Nacional de Alta Tecnología
DEM : Digital Elevation Model
DLR : Deutsches Zentrum für Luft- und Raumfahrt
DN : Digital Number
ERSDAC : Earth Remote Sensing Data Analysis Center
FAO : Food and Agriculture Organization
FLAASH : Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes

FOC : Fusarium oxysporum formae specialis (f.sp) cubense
GDEM : Global Digital Elevation Model
GIS : Geographic Information System
GIZ : Gesellschaft für Internationale Zusammenarbeit
GSD : Ground Sample Distance
ICRAF : International Centre for Research in Agroforestry
IDL : Interface Description Language
IGM : Input GeoMetry
LCCS : Land Cover Classification Scheme
LP DAAC : Land Processes Distributed Active Archive Center
LUC : Land use / cover
MASTER : MODIS / ASTER airborne simulator
MDA : Mean Decrease Accuracy
MDG : Mean Decrease Gini
iii
ML : Maximum Likelihood
MNF : Minimum Noise Fraction
MODIS : Moderate Resolution Imaging Spectroradiometer
MODTRAN : MODerate resolution atmospheric TRANsmission
NASA : National Aeronautics and Space Administration
OSM : OpenSteetMap
PCA : Principal Component Analysis
RGB : Red Green Blue
ROI : Region Of Interest
SGL : Super Geometry Lookup
SVM : Support Vector Machine
SWIR : Short Wave InfraRed
TIR : Thermal InfraRed
ToA : Top Of Atmosphere
VNIR : Visible Near InfraRed
iv