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. (c) 2023 COSPAR. Published by Elsevier B.V. All rights reserved.

The COVID-19 lockdown restrictions influenced global atmospheric aerosols. We report aerosol variations over India using multiple remote sensing datasets [Moderate Resolution Imaging Spectroradiometer (MODIS), Ozone Monitoring Instrument (OMI), Cloud-Aerosol Lidar, and Infrared Pathfinder (CALIPSO)], and model reanalysis [Copernicus Atmosphere Monitoring Service (CAMS)] during the lockdown implemented during the COVID-19 pandemic outbreak period from March 25 to April 14, 2020. Our analysis shows that, during this period, MODIS and CALIPSO showed a 30-40% reduction in aerosol optical depth (AOD) over the Indo-Gangetic Plain (IGP) with respect to decadal climatology (2010-2019). The absorbing aerosol index and dust optical depth measurements also showed a notable reduction over the Indian region, highlighting less emission of anthropogenic dust and also a reduced dust transport from West Asia during the lockdown period. On the contrary, central India showed an similar to 12% AOD enhancement. CALIPSO measurements revealed that this increase was due to transported biomass burning aerosols. Analysis of MODIS fire data product and CAMS fire fluxes (black carbon, SO2, organic carbon, and nitrates) showed intense fire activity all over India but densely clustered over central India. Thus, we show that the lockdown restrictions implemented at the government level have significantly improved the air quality over northern India but fires offset its effects over central India. The biomass-burning aerosols formed a layer near 2-4 km (AOD 0.08-0.1) that produced heating at 3-4 K/day and a consequent negative radiative forcing at the surface of similar to-65 W/m(2) (+/- 40 W/m(2)) over the central Indian region.