Li Siya, Ge Quansheng, Sun Fubao, Ji Qiulei, Liu Wenbin, Liu Ronggao, Xu Duanyang, Tao Zexing
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Sci Data. 2025 Mar 27;12(1):510. doi: 10.1038/s41597-025-04759-6.
Accurate land cover data was fundamental for formulating sound land planning and sustainable development strategies. This study focused on the Tibetan Plateau (TP), a globally sensitive ecological area, and developed a locally tailored annual 30 m resolution land cover dataset from 1990 to 2023 (TPLCD). Leveraging the Google Earth Engine (GEE) platform for Landsat data processing, LandTrendr was employed to generate robust, high-precision training samples. Subsequently, random forest classification and spatiotemporal smoothing strategies were applied to precisely map the land cover dynamics of the TP. Rigorous validation through visual interpretation, authoritative third-party datasets (Geo-Wiki and GLCVSS), and thematic dataset cross-comparisons, revealed an overall accuracy of 84.8%, and a Kappa coefficient of 0.78, fully affirming the dataset's high reliability. This dataset provided invaluable empirical evidence for understanding the vulnerability and adaptability of the TP's ecosystem.
准确的土地覆盖数据是制定合理的土地规划和可持续发展战略的基础。本研究聚焦于青藏高原（TP），这是一个对全球生态敏感的地区，并开发了一个本地化定制的1990年至2023年年度30米分辨率土地覆盖数据集（TPLCD）。利用谷歌地球引擎（GEE）平台处理陆地卫星数据，采用LandTrendr生成强大、高精度的训练样本。随后，应用随机森林分类和时空平滑策略精确绘制青藏高原的土地覆盖动态。通过目视解译、权威第三方数据集（Geo-Wiki和GLCVSS）以及专题数据集交叉比较进行的严格验证显示，总体准确率为84.8%，卡帕系数为0.78，充分肯定了该数据集的高可靠性。该数据集为理解青藏高原生态系统的脆弱性和适应性提供了宝贵的实证依据。
