1 Analysis of Polarimetric SAR data for Land cover mapping in Mountainous Landscape John Richard Otukei Prof. Dr. Thomas Blaschke (Uni. Salzburg) Prof. Dr. Michael Collins (Uni. Calgary) Center for GeoInformatics University of Salzburg

P RESENTATION OUTLINE Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations 2

3 COSPAR (BR) MSC (GIS), UKPhD, AT PGD (GIS& RS), NGMSC (GEOMATICS, SA BSC(SUR), UG Uganda

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations Land cover is a fundamental variable that impacts on and links with many parts of human and physical Environment (Foody, 2002) and is strongly associated with climatic change (Skole, 1994) Despite the significant role land cover plays, our knowledge of land cover especially in Sub-Saharan Africa is lacking (Otukei& Blaschke, 2009) The limited knowledge can be attributed to a number of factors:  Weak Government support to Mapping agencies and research institutions  Expensive hardware and software  Resistance to changes from traditionalists in the field of mapping  Brain drain  Lack of experts in Mapping sciences 4 Why bother with Land Cover?

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations Because of aforementioned factors, “most developing countries lack up-to-date information, a key component for environmental monitoring and understanding” The traditional approaches for collecting environmental information, notably, aerial photography, although accurate are:  Laborious  Expensive  Done infrequently In the case of Uganda, the existing reference maps are based on the 1954 aerial photography carried by the ordnance survey of GB. These maps were re-printed in 1978 and The problem statement

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations Since up-to-date information is critical for increased awareness of environmental issues and sustainable use of natural resources, alternative and suitable approaches especially those based on earth observation are desired As a result this study is motivated by the desire to explore advancements in the field remote sensing for land cover mapping i.e. Analysis of Polarimetric SAR data for Land cover mapping in Mountainous Landscape 6 The motivation for the study The BiG Point: What information can be obtained from analysis of SAR data in such environments???

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations The overall goal of this study is to exploit the inherent high spatial, high textured and multi-polarised SAR data for tropical mountain forest cover mapping in the BINP and its immediate surroundings 7 Overall goal

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations UNESCO’S world heritage site Diversity of fauna and flora Social benefits Ecological benefits Economic benefits 8 WHY BWINDI????

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations To carry out an in-depth study of the use of SAR data for land cover mapping and in particular examining the effect of different polarisations as well as derived spatial features such as image texture for improved land cover mapping To critically examine the potential of combining high spatial SAR data and high spectral optical data for improved land cover mapping 9 Research Objectives

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations Hypothesis 1: A multi polarised SAR data with high spatial resolution provides better land cover identification and mapping compared with single SAR channels. Hypothesis 2: Inclusion of derived features such as image texture provides improved land cover information extraction compared to processing of original multi-polarised bands or Landsat TM data only. Hypothesis 3: The fusion of the high textured and high resolution SAR data with high spectral but medium spatial resolution Landsat TM provides better results compared to those obtained using either SAR or Landsat TM alone. 10 Research hypothesis

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations What land cover types can be identified and mapped using SAR data. To what extent does the inclusion of image texture improve the identification and mapping of land cover types? And Which image textures are suitable for the land cover identification and mapping? Does the fusion of SAR and Landsat TM data improve the identification and mapping of the land cover classes? If so, what extent does it improve the classification results 11 Research questions

12 Location of Study Area

13 Data Swamps Processed data Target data Transformed data Knowledge patterns Data selection Data Pre-processing Data Transformation Data mining Interpretation Cleaning, geo-referencing, Sub-setting, mosaicking... Looking for: A research problem(s) Objectives questions e.t.c Textures Classification DTs, OBIA, Maps Tables Statistics Research Framework

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations SAR DATA: Dual Polarised TerraSAR-X data, StripMap Mode.  HH & VV Polarised channels Quad polarised ALOS-PALSAR Category-1 proposal number Data Selection Courtesy of UNESCO-DLR agreement Project ID: LAN0599

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations Optical Data: 4 Band IKONOS data ( 2005, 2006, 2007) Landsat ETM+ (2008) 15 Data Selection Courtesy of GeoEye Foundation Project ID: Other Data sets:  ASTER DEM  SRTM  Aerial photos/other  Shapefiles

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusions Recommendations Ortho-rectification ( IKONOS data) Landsat (ETM+) gap filling Mosaicking, sub-setting, layer stacking 16 Pre-processing

17 December 15 HH/VV image Dec 04 HH/VV image Mosaicking

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations Texture derivations. 3 different classes of textures were computed:  SAR specific textures (4)  Textures based on Grey level Co-occurance Matrices (GLCM)(9)  Textures based on image histogram (12) Big points:  How to determine the appropriate window size (W)?  How to determine the grey level value (R)to the computation of GLCM 18 Data transformation

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations Pixel based approaches (DTs and WMLC) OBIA/GEOBIA 19 Polararimtric data analysis BUT…. BUT …..BUT: There was need for some ground truth here…

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendation 20

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusions Recommendations A classification scheme was established using 21 Got ground truth data, what next? AFRICOVER LULC classification system

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations 22 Polarimetry analysis (DTs)

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations 23 Polarimetry analysis (Wishart)

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations 24 Image fusion (Pixel)

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations 25 OBIA

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations Qual polarimetric ALOS PALSAR performs better than Dual Polarimetric TerraSAR-X The Cross polarised SAR channel has the highest contribution to overall classification accuracy OBIA analysis shows more realistic results than pixel based method. 26 Polarimetric Analysis

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations Improves the classification accuracy of the SAR data SAR specific textures have high potential for classification especially when only single texture band is included 27 Texture analysis

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations Improved classification accuracy only possible with OBIA Terrain effects (layover, shadows and foreshortening affects image fusion using both techniques but the effect is more noticeable when using pixel based methods 28 Image Fusion

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations Data... Data... Data... Data, not easy to get Incompatible data, Most software packages have limited support for TerraSAR-X data Some software packages that have support may not support all levels of processing of TerraSAR-X data. Case of Geomatica, PolSARPro, Photomod e.t.c How to process large data sets ( case of high spatial resolution TerraSAR-X 29 Some Challenges..

Introduction Problem statement Motivation Overall goal Research objectives Research hypothesis Research questions Study area Research framework Results Conclusion Recommendations Full quad polarised TerraSAR-X is desirable BUT cross-polarised channel is essential. OBIA and analysis based on Wishart probability are recommended for analysis of ALOS-PALSAR and TerraSAR-X data For mountainous areas, image fusion in the context of OBIA is recommended. High demands on data processing can be minimised using batch processing 30 Recommendations

31 Ackowledgements

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