Forest disturbances and the attribution derived from yearly Landsat time series over 1990-2020 in the Hengduan Mountains Region of Southwest China

• 作者: Yating Li Zhenzi Wu Xiao Xu Hui Fan Xiaojia Tong Jiang Liu
• 来源期刊: 森林生态系统（英文版）
• 年，卷（期）: 2021,（4）
• 页码范围: 974-990
• 页数: 17页
• 语种: 英文

摘要

Background:Natural forests in the Hengduan Mountains Region(HDMR)have pivotal ecological functions and provide diverse ecosystem services.Capturing long-term forest disturbance and drivers at a regional scale is crucial for sustainable forest management and biodiversity conservation.Methods:We used 30-m resolution Landsat time series images and the LandTrendr algorithm on the Google Earth Engine cloud platform to map forest disturbances at an annual time scale between 1990 and 2020 and attributed causal agents of forest disturbance,including fire,logging,road construction and insects,using disturbance properties and spectral and topographic variables in the random forest model.Results:The conventional and area-adjusted overall accuracies(OAs)of the forest disturbance map were 92.3％and 97.70％±0.06％,respectively,and the OA of mapping disturbance agents was 85.80％.The estimated disturbed forest area totalled 3313.13 km2(approximately 2.31％of the total forest area in 1990)from 1990 to 2020,with considerable interannual fluctuations and significant regional differences.The predominant disturbance agent was fire,which comprised approximately 83.33％of the forest area disturbance,followed by logging(12.2％),insects(2.4％)and road construction(2.0％).Massive forest disturbances occurred mainly before 2000,and the post-2000 annual disturbance area significantly dropped by 55％compared with the pre-2000 value.Conclusions:This study provided spatially explicit and retrospective information on annual forest disturbance and associated agents in the HDMR.The findings suggest that China's logging bans in natural forests combined with other forest sustainability programmes have effectively curbed forest disturbances in the HDMR,which has implications for enhancing future forest management and biodiversity conservation.