Mapping Temperate Savanna in Northeastern China Through Integrating UAV and Satellite Imagery

Abstract

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 ⁷m ². 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 ¹¹ m ². 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.