As a significant tropical forest region in China, the continuous monitoring of Hainan Island's forest ecosystem is crucial for ecological conservation and sustainable development. The National Forest Inventory (NFI) provides authoritative data support for revealing the dynamic changes in forest ecosystems. In order to clarify the forest changes in Hainan Island in the past three decades. This study used the Landsat 5/7/8 time series images during the growing season of Hainan Island from 1990 to 2023, and conducted a long-term forest change analysis on the Google Earth Engine (GEE) cloud platform, the LandTrendr algorithm was used to study the disturbance monitoring technology suitable for forests in Hainan Island and evaluate the sensitivity of five different spectral indices to different disturbance types. The results show that: (1) The accuracy evaluation based on the ground survey shows that the correlation between the disturbance year monitored by Normalized Burn Ratio (NBR) and the actual disturbance year is the highest, with an R2 of 0.91. (2) The accuracy of the algorithm was evaluated by combining NFI data. The results show that the overall accuracy of NBR is the highest (77.38%), and the overall accuracy of Tasseled Cap Greenness (TCG) is the lowest (61.31%) (3) Based on NFI data, the sensitivity of different spectral indices in LandTrendr algorithm to different disturbance types was further evaluated. The results show that the Enhanced Vegetation Index (EVI) was more sensitive to high intensity disturbance such as fire, wind throw. TCG were more sensitive to low intensity disturbance such as plant diseases and insect pests and other disasters. Among the human factors, EVI has the best recognition effect. In summary, this method can effectively monitor forest disturbance in Hainan Island, and provide data support for the sustainable development and ecological protection of forest ecosystems in Hainan Island. By using NFI data and remote sensing data, the sensitivity of different spectral index disturbance monitoring to different disturbance types is compared and analyzed, which provides data reference for subsequent forest dynamic monitoring research.