Assessing Forest Fire Susceptibility in the Hindu Kush Himalaya: Implications for Biodiversity and Carbon Stock

Abstract

The Hindu Kush Himalaya (HKH) is a globally significant biodiversity hotspot, with extensive forests, protected areas (PAs) and substantial carbon reserves. However, increasing frequency and intensity of forest fires threaten its ecological integrity. Despite these concerns, there is a lack of comprehensive spatial assessment of forest fire susceptibility, necessitating a data-driven approach to evaluate environmental risks. This study evaluates forest fire susceptibility and its impact on biodiversity and carbon stocks across the HKH region using remote sensing and machine learning models. The models include Analytic Hierarchy Process, Certainty Factor, Maximum Entropy, and Random Forest (RF), based on thirteen ignition factors, representing environmental, meteorological, edaphic, socio-economic, and topographic factors. Active fire data from MODIS and VIIRS was used for training and testing of models. Model performance, evaluated using Area Under Curve (AUC) of Receiver Operating Characteristic curve, showed that RF (AUC = 0.95) outperformed other models. Results indicate that about 13.54–20.47% of HKH forested region is highly susceptible to forest fires, with higher risk in Himalayan belt, Bangladesh, and Myanmar. Key factors influencing fire risk include wind speed, solar radiation, elevation, and precipitation. Forest fires threaten biodiversity, with around 25,878.66 sq. km of PAs identified as highly vulnerable. Additionally, fire-induced carbon emissions from aboveground biomass, estimated at 32.22 million Mg, jeopardize carbon stocks by depleting stored carbon and increase atmospheric CO₂ levels. Forest fire susceptibility maps and risk assessments provide essential spatial insights for policymakers, supporting proactive fire mitigation, biodiversity conservation efforts, and carbon management.