LAUSR

Rubber plantation is an important strategic material related to the national economy and people's livelihoods. Up-to-date and accurate rubber plantation maps are critical for monitoring the area and spatial distribution of rubber plantations and assessing their impacts on society, the economy, and the environment. However, existing optical images are greatly limited by frequent cloud cover, which seriously affects the accuracy of rubber plantation area extraction. To overcome this issue, we used dense Landsat time series stacks based on Google Earth Engine, combined phenological features, and applied random forest algorithms to monitor rubber plantations in Xishuangbanna from 1987 to 2020. The results showed that 1) the phenological characteristics of rubber plantations in Xishuangbanna indicated that the leaf-off period lasts from late December to mid-February of the following year, while the leaf-on of rubber plantations occurred in other months; 2) the overall accuracy and kappa coefficient values ranged from 0.82 to 0.96 and 0.76 to 0.95, respectively, showing that the extraction accuracy of rubber plantation information can meet the accuracy requirement; 3) the rubber plantation area in Xishuangbanna increased between 1987 and 2020 from 7.05×10⁴ to 47.78×10⁴ hm². The peak rubber plantation area occurred in 2015 (49.60×10⁴ hm²) followed by a downward trend; 4) spatially, the rubber plantation is mainly distributed in Jinghong City and Mengla County, while less abundant in Menghai County. Overall, this article is expected to contribute to the rapid and accurate mapping of rubber plantations in large-scale applications and analysis.