文献类型：期刊文献

英文题名：Mapping Temperate Savanna in Northeastern China Through Integrating UAV and Satellite Imagery

作者：Li, Xiaoya[1,2] Duan, Tao[3] Yang, Kaijie[1,2] Yang, Bin[4] Wang, Chunmei[5] Tian, Xin[6] Lu, Qi[1,2] Wang, Feng[1,2]

第一作者：Li, Xiaoya

通信作者：Wang, F[1];Wang, F[2]

机构：[1]Chinese Acad Forestry, Inst Desertificat Studies, Inst Ecol Conservat & Restorat, Beijing 100091, Peoples R China;[2]Inst Great Green Wall, Bayan Nur 015200, Inner Mongolia, Peoples R China;[3]Inner Mongolia Agr Univ, Coll Resources & Environm Sci, Hohhot 010010, Inner Mongolia, Peoples R China;[4]Wuxi Univ, Sch Elect & Informat Engn, Wuxi 214105, Jiangsu, Peoples R China;[5]Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China;[6]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

年份：2025

卷号：12

期号：1

外文期刊名：SCIENTIFIC DATA

收录：;Scopus(收录号：2-s2.0-105003802184);WOS:【SCI-EXPANDED(收录号:WOS:001473006700001)】；

基金：This research was supported by National Key Research and Development Program of China (Grant No. 2023YFF1304103), National Natural Science Foundation of China (Grant No. 32171875), and Chinese Academy of Forestry Science Funds (Grant No. CAFYBB2020QD002, CAFYBB2021MC002 & CAFYBB2023ZA009). Furthermore, we would like to acknowledge Weiwei Cong, Rina Wu, Yifei Cai, Yu Jiang and Yuxin Zhang for their support during the field work.

语种：英文

摘要：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 x 107m2. The proposed method achieved an overall accuracy of 0.82 in identifying temperate savanna in Northeastern China, covering a total area of 1.7 x 1011 m2. 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.