Mapping Temperate Savanna in Northeastern China Through Integrating UAV and Satellite Imagery
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 × 10<sup>7</sup>m<sup>2</sup>. 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<sup>11</sup> m<sup>2</sup>. 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.
Authors
- Bin Yang (ORCID: https://orcid.org/0000-0001-7948-3823)
- Tao Duan (ORCID: https://orcid.org/0000-0002-0944-6995)
- Qi Lu (ORCID: https://orcid.org/0000-0002-8939-9390)
- Xiaoya Li (ORCID: https://orcid.org/0000-0001-6844-6701)
- Feng Wang (ORCID: https://orcid.org/0000-0003-3595-515X)
- Xin Tian (ORCID: https://orcid.org/0000-0002-6142-2982)
- Chunmei Wang (ORCID: https://orcid.org/0000-0002-4675-0291)
- Kaijie Yang (ORCID: https://orcid.org/0000-0003-2287-1889)
Institutions
- Jiangnan University (CN)
- Inner Mongolia Agricultural University (CN)
- Chinese Academy of Sciences (CN)
- Inner Mongolia Chifeng Forestry Science Research Institute (CN)
- Inner Mongolia Electric Power Survey & Design Institute (China) (CN)
- Institute of Forest Resource Information Techniques (CN)
- Chinese Academy of Forestry (CN)
- Aerospace Information Research Institute (CN)
- Institute of Desertification Studies (CN)
Publication Details
- Journal
- Scientific Data
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1038/s41597-025-05012-w
- Citations
- 2
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 3.43
Funders
- National Natural Science Foundation of China
- Chinese Academy of Forestry
- National Key Research and Development Program of China