Journal of Geo-information Science ›› 2023, Vol. 25 ›› Issue (10): 2093-2106.doi: 10.12082/dqxxkx.2023.220691

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Rapid Monitoring of Tropical Forest Disturbance in Hainan Island Based on GEE Platform and LandTrendr Algorithm

YIN Xiong1,2,3(), CHEN Bangqian2,3, GU Xiaowei2,3,6, YUN Ting4, WU Zhixiang2,3, CHEN Yue5, LAI Hongyan2,3,6, KOU Weili1,*()   

  1. 1. College of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming 650224, China
    2. Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
    3. Danzhou National Field Scientific Observation and Research Station for Tropical Agroecosystems, Danzhou 571737, China
    4. College of Forestry, Nanjing Forestry University, Nanjing 210037, China
    5. College of Mechanics and Transportation, Southwest Forestry University, Kunming 650224, China
    6. College of Forestry, Southwest Forestry University, Kunming 650224, China
  • Received:2022-09-14 Revised:2022-11-26 Online:2023-10-25 Published:2023-09-22
  • Contact: * KOU Weili, E-mail:;
  • Supported by:
    National Natural Science Foundation of China(42071418);National Natural Science Foundation of China(32260391);Scientific Research Project of Yunnan Provincial Department of Education(111722043/01117);Natural Science Foundation of Hainan Province(422CXTD527);Earmarked Fund for Agricultural Basic Research System(CARS-33);Natural Science Foundation of Jiangsu Province(BK20221337)


Tropical forests have significant economic and ecological value, and timely and accurate monitoring of forest disturbance is critical to promoting their sustainable development. In this study, all Landsat 5/7/8 and Sentinel-2 optical images since 1987 and the LandTrendr algorithm were used to monitor tropical forest disturbance in Hainan Island. The dense time-series satellite images were quickly processed on the Google Earth Engine platform (GEE), and the spatiotemporal distribution characteristics of annual forest disturbances on Hainan Island over the past 30 years were identified. The main causes of forest disturbance were analyzed with the development of rubber plantations, changes in forestry policies, and several severe natural disasters. The results show that: 1) the total area of forest disturbance from 1990 to 2020 is 2.53×103 km2 (equivalent to 11.74% of the total forest area in 2020), and is mainly concentrated in the central, northern, and northwestern regions. The three regions with the largest forest disturbances are Danzhou City, Qiongzhong County, and Baisha County, respectively; 2) most forest disturbances occur at elevation below 300 m (83.40%) and slope less than 25° (94.86%), and forests at higher elevations are well preserved; 3) forest disturbance occurred more intensely between 2000 and 2010 and decreased significantly after 2010, with the largest affected area in 2005; 4) rapid development of rubber plantations (accounting for 43.48% of total forest disturbance area), changes in forestry policies (e.g., promotion of eucalyptus cultivation), and severe natural disasters (drought and hurricane) around 2005 are the main causes of forest disturbance. The rapid monitoring method of forest disturbance proposed in this study and long-term forest disturbance dataset provide a reference for forest monitoring research and forestry department decision making on Hainan Island.

Key words: Hainan Island, forest disturbance, rubber plantation, LandTrendr, Google Earth Engine, Landsat, Sentinel-2