Evaluating Seasonal Wildfire Susceptibility and Wildfire Threats to Local Ecosystems in the Largest Forested Area of China

The frequent occurrence of wildfires presents a serious threat to human livelihoods and local ecosystems. use machine learning (ML) methods assess wildfire susceptibility can provide decision support for disaster prevention. However, most current ML-based assessments overly focus on spatially evaluating the threat, while ignoring potential threats This situation makes it difficult determine seasonal variations in limits value assessment results. We present framework seasonally ecosystem service (ESV) was used as proxy economic an ecosystem, random forest algorithm evaluate susceptibility, Daxinganling region, largest forested area China, selected study area, dynamic equivalent coefficient factor method calculate ESV each cell. Our main findings were follows: (a) exhibited obvious disparities terms spatial distribution across four seasons; (b) faced different magnitude disturbance; (c) expected loss (USD 10.8 billion) due much higher than region’s total GDP 2 2019. repeatable, all data required obtained freely. methodologies be applied directly other regions. will particular interest developing counties where intensive monitoring is limited.