Research Projects

Climate change necessitates a deeper understanding of ecological systems' responses to environmental changes, which is crucial for conserving species and habitats. To address this, a study will use a multidimensional approach involving field inventory data, remote sensing, and machine learning to analyze biomass drivers and map biomass patterns and species distribution in Kashmir Himalayan coniferous forests. The study will establish 15 permanent 1 ha plots and 50-60 0.1 ha plots, recording precise in-situ tree-by-tree data using high precision DGPs and Trupulse 360 R laser range finder. All trees ≥30cm girth at breast height will be mapped and measured, and data-driven models will be tested. High-precision biomass maps will be modelled using multispectral remote sensing datasets from sentinel and Landsat, and compared using different machine learning algorithms. The study aims to contribute integrated, multi-observational, multi-faceted ground reference measurements to facilitate global spatial forest biomass mapping and contain georeferenced data on tree biodiversity. The use of collected data will foster collaboration and knowledge transfer with participating countries and organizations.