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The escalating global threat of forest fires, driven by global warming, requires the development of effective prediction systems to mitigate damages. This research focuses on Madhya Pradesh (MP) and Chhattisgarh (CG) states in central India, where forest fire risk has become particularly pronounced. The primary objectives of the study are to quantify and map the spatial and temporal dynamics of forest fires over the period 2001 to 2020, and to predict future fire risks using satellite derived datasets and machine learning techniques. Through a long-term analysis, the study revealed an alarming increase in the number of forest fire incidents in MP and CG. From an average of 1200 and 1000 during 2001 to 2005, the incidents increased to 2800 and 2100 during 2016 to 2020, in MP and CG respectively. To predict forest fire risk, Random Forest machine learning algorithm was adopted utilizing various satellite derived climatic, topographical, and ecological parameters such as temperature, precipitation, solar radiation, NDVI, soil moisture, litter availability, evapotranspiration and terrain parameters (at monthly scale for 20 years). While forecasting fire probability for 2018–2020, the model achieves high accuracy rate of 86.46 % in MP and 93.78 % in CG. The results highlight significant forest fire likelihood regions in the central MP and the Southern CG, identifying areas requiring enhanced fire management strategies. This study has revealed that NDVI and rainfall have played a positive role in restricting the forest fire, and their negative anomaly amplified the fire risk. The study would help forest planners and administrators to characterise vulnerable areas and prioritise their conservation provisions. [ABSTRACT FROM AUTHOR]

Quantification of forest resilience will help us to manage the sustainability of the forest environment and the safety of biodiversity. Measuring forest resilience is crucial for ensuring long-term health of the forest ecosystem in the face of ongoing environmental changes and disturbances. This study focuses on providing a framework to estimate forest resilience scores to assess the vegetation condition after a disturbance. The resilience calculation framework provided uses number of recovery days, the phenological performance level of vegetation in the year when the disturbance took place, long-term mean phenological performance, and greenness levels in subsequent year to calculate the final resilience score at each pixel. Recovery of forests using Landsat data with the help of Normalized Difference Vegetation Index or Normalized Burn Ratio poses a challenge for continuous monitoring of forested landscapes due to cloud cover and availability of scenes at continuous intervals in Landsat datasets. In this regard, MODIS 16-day EVI products were used in this study (2001 to 2020) for monitoring vegetation health before, during, and after the disaster. Bandhavgarh National Park (BNP) located in Madhya Pradesh, India is considered for this study as it witnessed major forest fire breakouts in the second half of March 2018. The objectives of the study are the following: (1) to estimate post-fire recovery days and (2) to formulate new resilience index. The study revealed that the northern part of BNP is more vulnerable and shows slow recovery. The relationship between occupation of people living inside and in the neighboring area with forest resilience is also investigated in this study. [ABSTRACT FROM AUTHOR]