Evaluating Seasonal Wildfire Susceptibility and Wildfire Threats to Local Ecosystems in the Largest Forested Area of China. Issue 5 (19th May 2022) - Tang, Xianzhe; Machimura, Takashi; Li, Jiufeng; Yu, Huafei; Liu, Wei
Abstract: The frequent occurrence of wildfires presents a serious threat to human livelihoods and local ecosystems. The use of machine learning (ML) methods to assess wildfire susceptibility can provide decision support for disaster prevention. However, most current ML‐based wildfire susceptibility assessments overly focus on spatially evaluating the disaster threat, while ignoring the potential threats of wildfires to local ecosystems. This situation makes it difficult to determine seasonal variations in wildfire susceptibility and limits the value of assessment results. We present a framework to assess wildfire susceptibility and wildfire threats seasonally to local ecosystems. The ecosystem service value (ESV) was used as a proxy for the economic value of an ecosystem, the random forest algorithm was used to evaluate wildfire susceptibility, and the Daxinganling region, the largest forested area in China, was selected as the study area, and the dynamic equivalent coefficient factor method was used to calculate the ESV of each cell. Our main findings were as follows: (a) wildfire susceptibility exhibited obvious disparities in terms of spatial distribution across the four seasons; (b) each ecosystem in the study area faced a different magnitude of wildfire disturbance; and (c) the expected ESV loss (USD 10.8 billion) due to wildfires was much higher than the region's total GDP (USD 2 billion) in 2019. This study was repeatable, and all data required were obtained freely. The methodologies used can be applied directly to other regions. Our study will be of particular interest to developing counties where intensive wildfire monitoring is limited. Plain Language Summary: The occurrence of wildfires presents a considerable threat to human livelihoods and ecosystems. Spatially assessing wildfire susceptibility will enable the identification of potential wildfire‐related hot spots, and can provide decision support for wildfire management and ecosystem protection. The use of machine learning (ML) methods to evaluate wildfire susceptibility is an effective way to achieve this. However, most of the current ML‐based wildfire susceptibility assessments merely focus on spatial susceptibility assessments and never consider wildfire consequences, making the seasonal variations of wildfire susceptibility difficult to determine and limiting, therefore, the value of the assessment results. We propose a framework that seasonally evaluates wildfire susceptibility and quantifies the economic loss to local ecosystems due to wildfires. The results demonstrated that wildfire susceptibility varied across the four seasons, and the potential loss due to wildfires in the study region was much higher than the region's GDP in 2019. This study provides a link between wildfire susceptibility assessments and ecosystem service valuation. Key Points: A framework was established to seasonally evaluate the wildfire susceptibility based on machine learning Seasonally economic values of ecosystems were computed Wildfire disturbances to each ecosystems across the four seasons were estimated