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Climate change is causing glaciers to retreat across much of the Himalaya, leading to a rapid shift of the vegetation cover to higher altitudes. However, the rate of vegetation shift with respect to glacier retreat, climate change, and topographic parameters is not empirically quantified. Using remote sensing measurements, we estimate (a) the rate of glacier-ice mass loss, (b) the upward vegetation line shift rate, (c) regional greening trends, and (d) a relationship between the factors influencing the greenness of the landscape and vegetation change in the Himalaya. We find that the glacier mass loss rate is 10.9 ± 1.2 Gt/yr and the mean vegetation line shifts upward in altitude by 7-28 ± 1.5 m/yr. Considering the land use/land cover change pattern, the grassland area is found to be expanding the most, particularly in the de-glaciated regions. The vegetation change is found to be controlled by soil moisture and slope of the area.

This study examines the capability assessment of fully polarimetric L-band data for snow and non-snow area classification. The data sets used are the fully polarimetric Advanced Land Observation Satellite – Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) data, optical (ALOS-AVNIR) data close to the radar acquisition, and ENVISAT-ASAR data. Several parameters are used to discriminate snow from non-snow-covered areas in the Indian Himalaya region, including backscattering coefficients, the ratio of cross/co-polarized backscattering power and polarization fraction value. Supervised classification schemes are employed using polarimetric decomposition methods based on the complex Wishart classifier. The accuracy of the classification was found to be 97.95% for the Wishart supervised classification. Among various parameters and methods, it was found that the alternative newly proposed Polarization Fraction (PF) scheme, based on the implementation of fully polarimetric synthetic aperture radar (POL-SAR) data, yielded the best classification result in the absence of training samples. The PF value has been effective for discrimination of snow-covered from non-snow-covered areas, debris covered glacier, and vegetation. The results of this investigation show that L-band fully polarimetric SAR data provide considerable improvement but may not possess the optimal capability to discriminate snow from other inherent natural and man-made scatterers in heavy snow laden mountainous scenarios which may require fully polarimetric S-Band or C-Band POLSAR measurements.

In recent years, there has been increased utilization of fully polarimetric synthetic aperture radar (POLSAR) data to study glaciated terrain features for glaciological and climate change modelling. This article is concerned with more accurate results and appropriate analysis of POLSAR data over a highly rugged glaciated area in Himalayan region. For this purpose, the modified Yamaguchi four-component scattering power decomposition (4-CSPD) method with a rotation concept of 3 × 3 coherency matrix [T] about line of sight is evaluated. It has been found that the modified Yamaguchi 4-CSPD method significantly improved the decomposition results as compared with the original 4-CSPD by minimizing the cross-polarized Horizontal-Vertical (HV) components. This modified 4-CSPD leads to enhancement in the double bounce scattering and surface scattering components and also avoids the overestimation problem in the volume scattering component as compared with the original 4-CSPD from the sloped terrain. The significant reductions of the negative power occurrence in the surface scattering (3.9%) and the double bounce scattering (19.7%) components have also been noticed as compared with the original 4-CSPD method over the glaciated area in this part of the Indian Himalaya.