Biomass and Carbon Stock Estimation through Remote Sensing and Field Methods of Subtropical Himalayan Forest under Threat Due to Developmental Activities

Abstract

Mixed subtropical forests possess a high amount of carbon pool owing to their rich species diversity and carbon sequestration potential. The Dhaulasidh forest is located in Himachal Pradesh within the subtropical Himalayan region. This research aimed to identify: (1) Optimal satellite-derived Sentinel-2A indices for predicting biomass, (2) the best-fitting model for biomass estimation, and (3) changes in above-ground carbon stock due to biomass loss, using satellite remote sensing and quadrat-based approaches. Results indicated that Band 3 (Green), Band 5 (Red edge), the vegetation (VEG) index, and the Carotenoid reflectance index (CRI) were suitable for estimating above-ground biomass (AGB). Shannon and Simpson’s diversity indices were calculated as 0.89 and 0.73, respectively. Significant contributors to AGB included Mallotus philippensis, Emblica officinalis, Cassia fistula, Acacia catechu, Ehretia laevis, Kydia calycina, and Lannea coromandelica. The AGB prediction model based on vegetation indices demonstrated a strong correlation between observed and predicted biomass (R²=0.65, p<0.001), with a mean absolute percentage error of 20% and root mean square error of 7.33 tonnes per pixel. The study predicted a total loss of 22,917.15 tonnes of CO2 in mixed subtropical forests, representing a 12.04% reduction in carbon stock within the study area. These findings offer critical baseline data for environmental management and carbon balance in the forest ecosystem, recommending that forest management practices after deforestation should be reviewed for remedial measures for any developmental activities.