A comprehensive dataset of above-ground forest biomass from field observations, machine learning and topographically augmented allometric models over the Kashmir HimalayaZenodo

Abstract

Accurate estimates of forest dynamics and above-ground forest biomass for the topographically challenging Himalaya are crucial for understanding carbon storage potential, assessing ecosystem services, and guiding conservation efforts in response to climate change. This dataset provides a manually delineated multi-temporal forest inventory and a comprehensive record of above-ground biomass (AGB) across the Kashmir Himalaya, generated from field observations, advanced remote sensing and machine learning. Data were collected and generated through remote sensing techniques and extensive in-situ measurements of 6220 trees (n=275 plots), including tree diameter at breast height, species composition, and tree density to map forest area and model AGB across varied terrain. The dataset captures major forest types and species-specific AGB variation influenced by elevation, slope, and aspect. Additionally, newly developed species-specific allometric models, improved through the integration of normalized difference vegetation index (NDVI) and topographical augmentation are provided to improve AGB estimation accuracy. This dataset serves as a crucial resource for forest management, carbon monitoring, and ecological modeling, with broad applications in regional conservation strategies, biodiversity planning, and climate policy development in mountainous ecosystems.

Keywords

• Forest carbon stock
• Forest inventory
• Remote sensing
• Model hyperparameter optimization