文献类型：期刊文献

英文题名：Using LandTrendr to analyze forest disturbance, recovery, and attribution in Hunan province from 2001 to 2024

作者：Cao, Panlin[1,2,3] Zang, Zhuo[1,2,3] Zhang, Meng[1,2,3] Wang, Xu[4] Tang, Xian[5] Xiang, Jiahong[1,2,3] Tang, Shu[1,2,3] Wang, Jing[6] Zhang, Yanan[6]

第一作者：Cao, Panlin

通信作者：Zang, Z[1]

机构：[1]Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China;[2]Key Lab State Forestry Adm Forest Resources Manage, Changsha 410004, Peoples R China;[3]Key Lab Forestry Remote Sensing Based Big Data & E, Changsha 410004, Peoples R China;[4]Chinese Acad Forestry, Res Inst Trop Forestry, Guangzhou 510520, Peoples R China;[5]Sanya Acad Forestry, Sanya 572023, Peoples R China;[6]Hunan Zhonglinyao Forestry Remote Sensing Technol, Changsha 410004, Peoples R China

年份：2025

卷号：176

外文期刊名：ECOLOGICAL INDICATORS

收录：;EI(收录号：20252518634501);Scopus(收录号：2-s2.0-105008270645);WOS:【SCI-EXPANDED(收录号:WOS:001509031400006)】；

基金：The study was supported by subject funding from the National Key Research and Development Program of China: "Technology and Demonstration for Enhancing Ecosystem Function and Stability of Damaged Natural Forests (2024YFF1306602) ."

语种：英文

外文关键词：Change detection; Attribution; LandTrendr; Random forest; Hunan

摘要：Forest dynamics monitoring is an important basis for assessing the stability of regional ecosystems and formulating sustainable management strategies, and forest evolution patterns under the interaction of frequent human activities and natural disturbances in subtropical regions have not been fully revealed. This study uses Google Earth Engine and Landsat data (2001-2024) with the LandTrendr algorithm to detect spatiotemporal forest disturbances and recovery in Hunan Province, applying a random forest classifier to identify disturbance types. The results showed that from 2001 to 2024, the forest disturbance area in the study region was 4,723.46 km2, accounting for 3.48 % of the total forest area; the recovery area was 4,380.33 km2, accounting for 3.23 %; and the net loss was 343.13 km2, representing only 0.25 %. Among the disturbance types, weak disturbances accounted for 53.35 % of the total disturbed area, while low-level recovery accounted for 70.4 % of the total recovery area. Fire was the primary disturbance factor, accounting for 40.7 %, followed by land use conversion, which accounted for 27.3 %. This study provides a scientific basis for adaptive management, supporting targeted restoration and policy-making to enhance forest resilience and promote sustainable forest use in subtropical regions.