Ecological Risk Assessment and Management of Forest Fires in Tamil Nadu, India: A MaxEnt Model-Based Approach for Strategic Resource Allocation and Fire Mitigation.

Abstract

Forest fires are integral to forest ecosystems as they influence nutrient cycling, plant regeneration, tree density, and biodiversity. However, human-induced climate change and activities have made forest fires more frequent, more intense, and more widespread, exacerbating their ecological and socioeconomic impact. Forest fires shape Tamil Nadu's diverse forest ecosystems, yet rising anthropogenic pressure and a warmer, drier climate have increased both their frequency and severity. We used a presence-only Maximum Entropy (MaxEnt) model to map the state-wide probability of fire occurrence and to guide the Tamil Nadu Forest Department (TNFD) in proactive suppression planning. Fire-occurrence points for 2020 (around 1900 ignitions) trained the model; independent ignitions from 2021 and 2022 (n = 2,906) validated it. Around nineteen topographic, climatic, and anthropogenic predictors, including Euclidean distance to cropland, rangeland, and roads, were resampled to 1 km resolution. The model showed excellent discrimination (AUC = 0.92) and achieved an overall test-set accuracy of 0.88 (Cohen's κ = 0.71). Distance to cropland (32.8 % permutation importance) and rangelands (25.8%) emerged as the strongest individual drivers, highlighting the combined influence of escaped agricultural burns and fuel condition on ignition risk. Jenks-optimized breaks split the landscape into Low (< 0.30), Medium (0.30-0.60), and High (≥ 0.60) classes, subsequently aggregated to the state's 2109 forest ranges. Although the High-risk zone comprises only 6.4 % of ranges (136/2109), it captured 54% of the 2021-22 ignitions, demonstrating substantial management leverage in the form of pre-season patrol planning and fuel-break maintenance. The resulting fire-probability map can help TNFD to prioritize patrol surges, pre-position water tankers, and refine early-warning bulletins for the 32 ranges exceeding the 0.80 "critical" threshold. Our approach provides a transferable template for data-poor tropical regions seeking to align limited suppression resources with the pockets of greatest ignition pressure. Future work should embed dynamic weather streams and near-real-time fuel-moisture indices to move from seasonal risk zoning toward operational early-warning.