短轮伐期人工林（Short-Rotation Plantations，SRPs）作为主要的经济型林类，对于生态环境保护以及社会经济发展都有着重要的影响，但精细的SRPs时空分布信息十分缺乏。本研究以中国SRPs种植最为广泛的广西壮族自治区为研究区域，基于Google Earth Engine云平台以及1986-2019年的Landsat影像数据，应用LandTrendr时间序列分割算法对SRPs的时空分布信息进行了提取。结果分析表明：（1）广西SRPs种植面积近30年呈逐年稳定快速增加的态势，1990年种植面积仅1.93×105ha，到2019年达到了4.04×106ha，年均增长速度达到1.33×105ha；（2）从空间分布来看，广西东部、南部SRPs分布较为集中，其主要分布在海拔500m以下的低海拔地区以及地表坡度在20°左右的坡地，且河池市是广西SRPs种植分布面积最大的地级市；（3）SRPs种植面积变化趋势与林业产值存在很强的相关性（r=0.83，p<0.001），表明SRPs是影响林业经济的重要因素。本文提出的基于长时序的SRPs时空变化信息的提取方法，可以为林业管理提供决策支持，并为森林碳循环的研究提供基础数据。

Objective: Short-Rotation Plantations (SRPs) as the main economic forests, have an important impact on ecological environmental protection and social economic development, but the detailed information on the temporal and spatial distribution of SRPs is very lacking. SRPs has nearly half a century of plantation history and extensive distribution in South China. The purpose of this study is to extracting the long-term temporal and spatial distribution information of SRPs, and analyze its changing trends and driving factors. Method: In this study, the Guangxi Zhuang Autonomous Region, where SRPs are most widely grown in China, was used as the research area. Based on the Google Earth Engine cloud platform and Landsat image data from 1986 to 2019, Firstly, the 34-year Normalized Burn Ratio (NBR) long-term series data was reconstructed by Per Pixel Composite method. Then, the LandTrendr time series trajectory segmentation algorithm was used to segment and fit the NBR time series data to extract the spatiotemporal distribution information of SRPs. Finally, Google Earth high-resolution images were used to select samples to verify the accuracy of classification and extraction, and to analyze the spatiotemporal characteristics and related factors of SRPs planting area changes. Result: The results analysis showed that: (1) The accuracy of the SRPs information extraction results was evaluate by the confusion matrix: the overall accuracy of the binary classifications reached 80.52%, the mapping accuracy of SRPs was 79.6%, the user accuracy of SRPs was 81.2%, and the Kappa coefficient over 0.6, indicating that the classification model has a good classification effect. (2) The planting area of SRPs in Guangxi has been increasing steadily and rapidly year by year in the past 30 years. In 1990, the planting area was only 1.93×105ha, and by 2019 it reached 4.04×106ha, with an average annual growth rate of 1.33×105ha. (3) In terms of spatial distribution, SRPs are concentrated in eastern and southern Guangxi. They are mainly distributed in low-altitude areas below 500m and slopes with a surface slope of about 20°. Among them, Hechi City is the prefecture-level city with the largest SRPs planting and distribution area in Guangxi. (4) There is a strong correlation between the change trend of SRPs planting area and forestry output value (r=0.830894, p<0.001), indicating that SRPs are an important factor affecting forestry economy. Conclusion: The method proposed in this paper based on the LandTrendr time series trajectory segmentation algorithm of SRPs spatiotemporal information extraction has proven very effective. The mapping and analysis of the spatiotemporal distribution of SRPs can provide decision support for forestry management and provide basic data for forest carbon cycle research.