Research Projects

The Indian Himalayan Region (IHR) is unique from the rest of the country due to its diverse topography and rich forest cover, which play a critical role in regulating climate, water resources, and the ecological security of India. A wide diversity of forest ecosystems is found in the IHR due to its complex topographic and climatic gradients. These forests serve as a significant carbon sink and biodiversity hotspot, covering about 40% of its geographical area and accounting for about 30% of the country’s total forest cover (FSI, 2023). Climate change and increasing anthropogenic activities are altering the quality and quantity of natural forest cover, potentially leading to carbon emissions from the forest ecosystem. Climatic and anthropogenic drivers, such as agricultural extension, infrastructure development, logging, mining, plantation, drought, fires, landslides, flood, Pests & Diseases etc. are exerting pressure on the natural Himalayan forests (Borthakur and Singh, 2024). Each individual climatic and anthropogenic driver may act differently and exert distinct effects on carbon emissions across spatial and temporal scales, due to the diverse climatic, economic, political, and social contexts within the IHR. Therefore, understanding carbon emissions in relation to climate change and anthropogenic activities is crucial for developing effective management strategies. Analysis of satellite images, such as Landsat and Sentinel, using remote sensing and statistical techniques, will help quantify spatial-temporal forest cover loss and associated carbon emissions in relation to climate change and anthropogenic activities. By using satellite-based indices, such as NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetative Index), NDWI (Normalized Difference Water Index), NDBI (Normalized Difference Built-up Index) along with satellite images, it is possible to identify the clearing process (example: mechanized or non-mechanized), clearing types (example: rotational or semi-permanent), and clearing drivers (example: climatic or anthropogenic) (Bera, et al., 2022). The mean carbon stock density and emission factors associated with different drivers will be used to estimate carbon emissions caused by individual climatic and anthropogenic disturbance drivers. Statistical techniques such as coefficient of variation, correlation and regression analysis, etc. will be applied to examine carbon emissions in relation to changes and variability in climate and anthropogenic activities. A ground survey will be conducted in randomly selected areas using multispectral drones to validate the satellite-based results. Thus, by developing a comprehensive database through the integration of high-resolution satellite data, statistical techniques, and ground-truthing, this study will provide actionable insights for policymakers, conservationists, and climate strategists.