High resolution remote sensing recognition of elm sparse forest via deep-learning-based semantic segmentation

Abstract

Elm (Ulmus pumila L.) sparse forest plays an vital role in maintaining local ecological stability and security in the Otingdag Sandy Land area. Prior studies on elm canopy extraction have predominantly relied on manual parameter configuration, resulting in unsatisfactory levels of generalization. To meet the needs of high-precision and rapid recognition of elm sparse forests in large areas, this study proposed a recognition method for elm sparse forest that orients to high spatial resolution remote sensing imageries, using deep-learning-based semantic segmentation techniques. It can automatically learn features that are conducive to segmenting the canopy of elm trees, and retains good generalization ability on the Gaofen-2 imageries obtained in different regions. First, we constructed a dataset specialized for elm canopy semantic segmentation task, and annotated over 130,000 elm canopies based on Gaofen-2 imageries. In addition, we trained 7 deep-learning semantic segmentation model candidates. Among them, MANet showed the best performance, with its F1-score reaching 81.44%. Lastly, we applied edge detection to the elm canopy coverage area, and automatically extract the elm canopy. The proposed method can provide technical support for the investigation and monitoring of elm sparse forests, while facilitates local desertification prevention efforts in the entire Otingdag Sandy Region.