A study of the disturbance to mangrove forests in Dongzhaigang, Hainan based on LandTrendr
YU Sen1,2(), JIA Mingming2(), CHEN Gao2, LU Yingying2,3, LI Yi1,2, ZHANG Bochun1,2, LU Chunyan4, LI Huiying5
1. School of Surveying, Mapping and Exploration Engineering, Jilin Jianzhu University, Changchun 130118, China 2. Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchuan 130102, China 3. Changchun New District Beihu Yingcai School, Changchun 130000, China 4. College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China 5. School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China
With the rapid socio-economic development and the increasing demand for natural resources in China, the protection of natural reserves is facing increasing difficulties. The remote sensing-based research on monitoring the disturbance and the restoration of mangrove forests through time series analysis is still in its initial stage. Moreover, time series algorithms are highly complex. Based on the LandTrendr time segmentation algorithm of Google Earth Engine (GEE) and the Landsat image time-series data, this study investigated the disturbance to mangrove forests in the Dongzhaigang Mangrove Nature Reserve during 1990—2020. The results are as follows: ① A total of 42.39 hm2 of mangrove forests were disturbed during 1990—2020, among which the largest disturbance area of 12.78 hm2 occurred in 2014; ② During 1990—2020, minor, moderate, and severe disturbances accounted for 65.39%, 30.78%, and 3.83%, respectively; ③ The overall identification accuracy of the pixels of mangrove forests subject to changes was 89.50%, and the overall detection accuracy of years witnessing disturbance was 88%, with a Kappa coefficient of 0.79. This study analyzed the years and areas of the disturbance to mangrove forests in the Dongzhaigang Mangrove Nature Reserve over 30 years based on LandTrendr. Moreover, this study analyzed the disturbance factors according to the actual situation and concluded that human activities are the main disturbance factor, followed by natural factors, such as diseases, pests, and extreme weather events. This study will provide a scientific basis and a decision reference for the management of the mangrove forest reserve.
于森, 贾明明, 陈高, 鲁莹莹, 李毅, 张博淳, 路春燕, 李慧颖. 基于LandTrendr算法海南东寨港红树林扰动研究[J]. 自然资源遥感, 2023, 35(2): 42-49.
YU Sen, JIA Mingming, CHEN Gao, LU Yingying, LI Yi, ZHANG Bochun, LU Chunyan, LI Huiying. A study of the disturbance to mangrove forests in Dongzhaigang, Hainan based on LandTrendr. Remote Sensing for Natural Resources, 2023, 35(2): 42-49.
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2023, Vol. 35 
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