GIBN-Net for automated windthrown tree detection in eucalyptus plantations using UAV imagery

Abstract

Eucalyptus plantations, characterized by uniform stand structure and relatively sparse foliage, are highly susceptible to typhoon damage, which poses substantial economic risks to forestry operations and undermines regional ecosystem stability in southern China. This study focuses on windthrown eucalyptus forests in Shangsi and Bobai Counties, Guangxi Zhuang Autonomous Region, following Typhoons Ma-on and TALIM . We developed a specialized UAV (Unmanned Aerial Vehicle) based deep learning dataset and proposed a novel segmentation model, GIBN-Net, which incorporates an asymmetric global attention module to enhance feature extraction. Evaluation results, including ablation and comparative experiments, show that GIBN-Net outperforms mainstream models, achieving optimal accuracy (OA: 0.9405, Precision: 0.8879, Recall: 0.9105, IoU: 0.8248, F1-Score: 0.8722). The study also revealed that tailored preprocessing strategies significantly improved boundary detection, while simple stacking of the UAV-derived DSM with RGB imagery led to a decrease in model accuracy. This research is the first to develop a dedicated dataset and an optimized deep learning model for typhoon-damaged eucalyptus detection in southern China. The GIBN-Net model is capable of detecting wind damage in eucalyptus forests more accurately and efficiently in complex forest environments. The dedicated dataset, combined with spatial context information, improves boundary accuracy and facilitates faster damage mapping. This study enables rapid, large-scale, and automated assessment of typhoon-induced damage in eucalyptus plantations, thereby providing actionable insights for insurance claim verification, post-disaster resource allocation, and ecological restoration planning. Therefore, it can serve as a timely and effective decision-support tool for forestry management and disaster response.