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Title: Synthetic Aperture radar data analysis for vegetation classification and biomass estimation of tropical forest area
Researcher: Vyjayanthi, Nizalapur
Guide(s): Jha, Chandra Shekhar
Keywords: Microwave Remote Sensing
Synthetic Aperture radar
Upload Date: 19-Apr-2012
University: Jawaharlal Nehru Technological University
Completed Date: September, 2010
Abstract: The present study extracts the potential of airborne and space-borne Synthetic Aperture Radar (SAR) data for 1) vegetation classification and 2) Biomass estimation over tropical forested areas of Indian region. Three different eco-regions representing distinct vegetation and biomass regimes viz., Rajpipla, Gujarat; Achanakmar-Amarkantak Biosphere Reserve, Bilaspur and Dandeli, Karnataka were selected for the present study. The DLR (German Aerospace Center) - ESAR (Experimental Synthetic Aperture Radar) data and Environmental Satellite (ENVISAT) - Advanced Synthetic Aperture Radar (ASAR) data, Advanced Land Observing Satellite (ALOS) - Phased Array L-band Synthetic Aperture Radar (PALSAR) along with ground based information was used to carry out specific objectives of vegetation type classification and above ground biomass estimation in different ecoregions of India. Different techniques viz., texture measures, multi-sensor fusion and interferometric coherence were used for vegetation classification using space borne ENVISAT-ASAR and ALOS-PALSAR datasets; polarimetric decomposition viz., H A Alpha, Freeman –Durden and Pauli decompositions and polarimetric signatures extracted from airborne polarimetric DLR ESAR data were used to characterize vegetation types. Above ground biomass estimation for the study areas was carried out by regression analysis between SAR backscattering co-efficient and field inventoried above ground biomass. Above ground biomass maps of study areas were generated in C, L and P wavelength bands using airborne DLR-ESAR data and in C and Lbands using spaceborne ASAR and PALSAR data. Salient findings of the present study are given below: The false colour composite of homogeneity, mean and entropy generated from texture measures using Grey level co-occurrence matrices (GLCM) showed the clear discrimination within the vegetation.
Pagination: 225p.
Appears in Departments:Faculty of Spatial Information Technology

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01_title.pdfAttached File22.97 kBAdobe PDFView/Open
02_declaration.pdf77.65 kBAdobe PDFView/Open
03_certificates.pdf75.81 kBAdobe PDFView/Open
04_acknowledgements.pdf64.68 kBAdobe PDFView/Open
05_abstract.pdf67.75 kBAdobe PDFView/Open
06_contents.pdf143.61 kBAdobe PDFView/Open
07_list of figures & tables.pdf81.03 kBAdobe PDFView/Open
08_chapter 1.pdf168.03 kBAdobe PDFView/Open
09_chapter 2.pdf472.72 kBAdobe PDFView/Open
10_chapter 3.pdf1.32 MBAdobe PDFView/Open
11_chapter 4.pdf345.82 kBAdobe PDFView/Open
12_chapter 5.pdf2.13 MBAdobe PDFView/Open
13_chapter 6.pdf2.26 MBAdobe PDFView/Open
14_chapter 7.pdf869.81 kBAdobe PDFView/Open
15_chapter 8.pdf1.72 MBAdobe PDFView/Open
16_chapter 9.pdf142.87 kBAdobe PDFView/Open
17_references.pdf178.88 kBAdobe PDFView/Open

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