Above Ground Biomass Estimation for Alpine Grasslands of Kashmir Himalayas Using Remote Sensing and Field-Data

This study presents a comprehensive analysis of Alpine pastures in the Kashmir Himalayas through a multidisciplinary approach, combining remote sensing and field-based assessments for biomass estimation and time series analysis of the (NDVI) Index for the growing season from May to October 2022. The Alpine and Subalpine region of Kashmir was delineated using ALOS PALSAR Digital Elevation Model, and Landsat 8 imagery was classified using a maximum likelihood algorithm, revealing a total grassland area of 160,974 hectares. After grassland delineation Biomass estimation was carried out based on data collected from 18 pastures, each of which was subjected to a stratified sampling approach to establish four 1 m² quadrats, with two designated for grazed areas and two for ungrazed areas, this yielded average biomass yields of 20.87 t/ha and an average dry weight biomass of 5.16 t/ha. Pastures like Daksum (28.36 t/ha), Tragbal (28.22 t/ha), Krush (27.83 t/ha), Lung Marg (27.03 t/ha), observed high biomass availability, while moderate levels were found in locations like Gangbal (22.75 t/ha), Hangel Marg (22.68 t/ha), Dagwan (21.76 t/ha), Gumri (20.82 t/ha), Bangus (20.66 t/ha), Pir Galli (18.52t/ha), Maalish (18.21 t/ha), In contrast, lower biomass values were recorded in Mohand Marg (11.47 t/ha), and Thajwas (9.81 t/ha). These findings were complemented by (NDVI) metrics, which varied across sites. For example, high NDVI values were observed for sites such as Pir Gilli, Bangus, and Kud Marg, indicating a healthier vegetative profile with less impact of grazing during the grazing season. In contrast, pastures like Mohand Marg, Thajwas, Razdan, and Tragbal recorded moderate NDVI values, suggesting a moderate level of grazing impact. Pasture sites with lower NDVI values and high standard deviation, such as Hangel Marg and Gumari, witnessed high seasonal variability, suggesting a high grazing impact, besides other natural factors responsible, like early snowfall. The study emphasizes the need for ongoing, multifaceted ecological assessments for the sustainable management and conservation of these critical Alpine ecosystems.