Monitoring vegetation degradation using remote sensing and machine learning over India – a multi-sensor, multi-temporal and multi-scale approach - Sur, Koyel; Verma, Vipan Kumar; Panwar, Pankaj; Shukla, Gopal; Chakravarty, Sumit; Nath, Arun Jyoti (Frontiers in Forests and Global Change, 2024)
Forest cover degradation is often a complex phenomenon, exhibiting strong correlation with climatic variation and anthropogenic actions. Conservation of biodiversity is important because millions of people are directly and indirectly dependent on the forest and its associated products. United Nations Sustainable Development Goals (SDGs) proposes to quantify proportion of forest as a proportion of total land area, because it can be used to understand discrete changes in forest cover. Satellite images forms as one of the main source of accurate information to capture the fine seasonal changes so that long term forest degradation can be accurately measured. Multi-Sensor, Multi-Temporal and Multi-Scale (MMM) approach was used to estimate vulnerability of forest degradation,in the present study. Open source datasets were systematically analysedon Google Earth Engine (GEE) for monitoring degradation and evaluating the potential of multiple satellite data with variable spatial resolutions for reporting forest degradation. Hotspots were demarked to analyse potential capability of Normalized Differential Vegetation Index (NDVI) of MODIS, Rainfall datasets of Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) during the period 2000-2022. Further, hotspot areas were observed with high resolution datasets in major forest covers to understand and verify the cause of change; whether anthropogenic or climatic in nature. This study is important for several State/Central user departments to lay out managerial plans for protection of forests in India.