College of Earth Sciences, Chengdu University of Technology, Chengdu, China.
School of Architecture and Civil Engineering, Chengdu University, Chengdu, China.
PeerJ. 2023 Feb 6;11:e14557. doi: 10.7717/peerj.14557. eCollection 2023.
Forest fires are one of the significant disturbances in forest ecosystems. It is essential to extract burned areas rapidly and accurately to formulate forest restoration strategies and plan restoration plans. In this work, we constructed decision trees and used a combination of differential normalized burn ratio (dNBR) index and OTSU threshold method to extract the heavily and mildly burned areas. The applicability of this method was evaluated with three fires in Muli County, Sichuan, China, and we concluded that the extraction accuracy of this method could reach 97.69% and 96.37% for small area forest fires, while the extraction accuracy was lower for large area fires, only 89.32%. In addition, the remote sensing environment index (RSEI) was used to evaluate the ecological environment changes. It analyzed the change of the RSEI level through the transition matrix, and all three fires showed that the changes in RSEI were stronger for heavily burned areas than for mildly burned areas, after the forest fire the ecological environment (RSEI) was reduced from good to moderate. These results realized the quantitative evaluation and dynamic evaluation of the ecological environment condition, providing an essential basis for the restoration, decision making and management of the affected forests.
森林火灾是森林生态系统的重要干扰因素之一。快速、准确地提取火烧区域对于制定森林恢复策略和规划恢复计划至关重要。本研究构建了决策树，并结合差分归一化燃烧比（dNBR）指数和 OTSU 阈值法提取重度和轻度火烧区。通过在中国四川木里县的三场火灾对该方法的适用性进行了评估，结果表明，该方法对小面积森林火灾的提取精度可达到 97.69%和 96.37%，而大面积火灾的提取精度较低，仅为 89.32%。此外，还利用遥感环境指数（RSEI）评估了生态环境变化。通过转换矩阵分析了 RSEI 水平的变化，三场火灾均表明，重度火烧区的 RSEI 变化强于轻度火烧区，森林火灾后，生态环境（RSEI）由良好降为中等。这些结果实现了对生态环境状况的定量评价和动态评价，为受灾森林的恢复、决策和管理提供了重要依据。
