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An optimal model was developed for accounting forest carbon stock from synergistic use of optical data from Landsat TM and synthetic aperture radar (SAR) data from COSMO-Skymed (X-band), Radarsat-2 (C-band) and ALOS PALSAR (L-band) sensors over a tropical deciduous heterogeneous forest of India. The best-fit integrated multiple linear regression model had a model accuracy of 83%, r2 = 0.96, root mean square error = 10.02 Mg/ha and Willmott's index of agreement of 0.98. The model further validated using chi-squared and t-test. Results of models for calculating the aboveground biomass (AGB) were converted to C and CO2 using conversion factors. Average AGB, C and CO2 were 70.5, 35.26 and 130.89 Mg/ha, respectively. The synergistic use of optical and multi-frequency SAR data enhanced the AGB saturation threshold to about 150 Mg/ha for tropical deciduous mixed forests. Hence, the synergistic use of this data is suggested for large-scale AGB and C estimations for tropical forests. Optical remote sensing sensors are extensively used due to greater data availability despite their poor sensitivity toward forest parameters. In contrast, SAR signals are highly sensitive toward forest biophysical and structural parameters, providing a better alternative. This unique integrated approach provides valuable information regarding the spatial distribution and quantification of forest biomass and carbon. [ABSTRACT FROM AUTHOR]

There are various classification techniques available which produce desired results. However, some of the land use/land cover (LU/LC) classes are not discernible in such classifications. The present study attempts for improving LU/LC classification accuracy by applying data fusion techniques. For this, we considered combinations of: (1) Synthetic Aperture Radar (SAR) multi-looked intensity and optical, (2) backscatter with optical and (3) terrain corrected backscatter with optical data. The fusion of terrain corrected backscatter with optical has been considered in this study to negate the effect of topographic undulations on backscatter. The classification accuracy for combinations of cross-polarised terrain corrected backscatter data with Advanced Visible and Near-Infrared Radiometer type 2 (AVNIR-2) (90.33%), co-polarised terrain corrected backscatter data with AVNIR-2 (89.66%), cross-polarised backscatter data with AVNIR-2 (89.0%) and cross-polarised multi-look intensity with AVNIR-2 (87.0%) were found to be better than classified outputs of AVNIR-2 data alone (84.6%), combinations of co-polarised backscatter and AVNIR-2 data (82.7%) and co-polarised multi-look intensity with AVNIR-2 data (80.1%), and combinations of multi-date terrain corrected backscatter (80.66%), multi-date co-polarised backscatter (80.0%) and multi-date co-polarised multi-look intensity (79.0%). The highest accuracy achieved in LU/LC classification is with cross-polarised terrain corrected backscatter with AVNIR-2 (90.33%) data. Data fusion techniques can be an alternative for LU/LC classification. [ABSTRACT FROM AUTHOR]

Topographic corrections of synthetic aperture radar (SAR) images over hilly regions are vital for retrieval of correct backscatter values associated with natural targets. The coarse resolution external digital elevation models (DEM) available for topographic corrections of high resolution SAR images often result into degradation of spatial resolution or improper estimation of backscatter values in SAR images. Also, many a times the external DEMs do not spatially co-register well with the SAR data. The present study showcases the methodology and results of topographic correction of ALOS-PALSAR image using high resolution DEM generated from the same data. High resolution DEMs of Jaipur region, India were generated using multiple pair SAR images acquired from ALOS-PALSAR using interferometric (InSAR) techniques. The DEMs were validated using differential global positioning system measured elevation values as ground control points and were compared with photogrammetric DEM (advanced spaceborne thermal emission and reflection radiometer – ASTER) and SRTM (Shuttle Radar Topography Mission) DEM. It was observed that ALOS-PALSAR images with optimum baseline parameters produced high resolution DEM with better height accuracy. Finally, the validated DEM was used for topographic correction of ALOS-PALSAR images of the same region and were found to produce better result as compared with ASTER and SRTM-DEM. [ABSTRACT FROM PUBLISHER]