A5038056463- Cryospheric studies and observations
- Climate change and permafrost
- Arctic and Antarctic ice dynamics
- Remote Sensing in Agriculture
- Winter Sports Injuries and Performance
- Hydrology and Watershed Management Studies
- Landslides and related hazards
- Hydrology and Drought Analysis
- Adsorption and biosorption for pollutant removal
- Food Waste Reduction and Sustainability
- Land Use and Ecosystem Services
- Municipal Solid Waste Management
- Healthcare and Environmental Waste Management
- Nanomaterials for catalytic reactions
- Environmental Changes in China
- Climate variability and models
- Flood Risk Assessment and Management
- Environmental remediation with nanomaterials
- Urban Heat Island Mitigation
Indian Institute of Science Bangalore
2023
University of Jammu
2020-2022
Birla Institute of Technology, Mesra
2015-2016
Terrain variables are the main factors affecting spatial distribution of snow cover. This paper aims to find a relationship between snow-cover area (SCA) and topographic (elevation, slope aspect), using MODIS Terra data (MOD09A1) in parts Chenab basin, western Himalayas. The inter-annual variability SCA% for each month has been analysed years 2000 2011. analysis reveals that mean annual SCA value was maximum (37.89%) 2005 minimum (32.07%) 2001. classes with 5°–10° 30°–35°, respectively....
Mountainous terrains severely affect the sun-target-sensor geometry due to surface dispersion of solar radiation, resulting variation in observed radiance. The effect topographic shadow on raw, atmospherically and topographically processed Normalized Difference Snow Index (NDSI) Vegetation (NDVI) values was investigated for Jhelum basin, Kashmir Himalaya. results indicate that NDSI NDVI derived from raw images show less area snow vegetation, underestimation corresponding pixels regions,...
Abstract Land use/land cover has become a prime concern that urgently needs to be addressed in the study of global environmental change. In present study, supported by land maps retrospective time periods 2000, 2010 and 2020, derived using Landsat TM OLI datasets, respectively; we used land-use transition matrix, Markov-CA chain model derive detailed information spatio-temporal variation Additionally, highlight decrease forest (19 km 2 37.7 , i.e., 0.88% 1.75% total area), rangeland (0.2 1.9...