Li Xiaoya, Duan Tao, Yang Kaijie, Yang Bin, Wang Chunmei, Tian Xin, Lu Qi, Wang Feng
Institute of Desertification Studies, Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing, 100091, China.
Institute of Great Green Wall, Dengkou County, Bayan Nur, Inner Mongolia, 015200, China.
Sci Data. 2025 Apr 22;12(1):671. doi: 10.1038/s41597-025-05012-w.
Temperate savannas are globally distributed ecosystems that play a crucial role in regulating the global carbon cycle and significantly contribute to human livelihoods. This study aims to develop a novel method for identifying temperate savannas and to map their distribution in Northeastern China. To achieve this objective, Unmanned Aerial Vehicle (UAV) imagery was integrated with Sentinel-2 and Sentinel-1 satellite imagery using Random Forest (RF) regression and Classification and Regression Tree (CART) algorithms. The training and validation datasets were derived from UAV imagery covering a ground area of 5 × 10m. The proposed method achieved an overall accuracy of 0.82 in identifying temperate savanna in Northeastern China, covering a total area of 1.7 × 10 m. The resulting map significantly improves understanding of the spatial distribution and extent of temperate savannas. The developed methodology establishes a framework for assessing regional and global savanna distributions in future studies.
温带稀树草原是全球分布的生态系统，在调节全球碳循环中发挥着关键作用，并对人类生计做出了重大贡献。本研究旨在开发一种识别温带稀树草原的新方法，并绘制其在中国东北的分布地图。为实现这一目标，利用随机森林（RF）回归和分类回归树（CART）算法，将无人机（UAV）图像与哨兵-2和哨兵-1卫星图像相结合。训练和验证数据集来自覆盖5×10米地面区域的无人机图像。所提出的方法在中国东北识别温带稀树草原的总体准确率达到0.82，总面积为1.7×10平方米。生成的地图显著提高了对温带稀树草原空间分布和范围的理解。所开发的方法为未来研究评估区域和全球稀树草原分布建立了一个框架。
