DETECTING FORESTS DAMAGED BY PINE WILT DISEASE AT THE INDIVIDUAL TREE LEVEL USING AIRBORNE LASER DATA AND WORLDVIEW-2/3 IMAGES OVER TWO SEASONS

8 citations as recorded by crossref.

1. Deep learning-based dead pine tree detection from unmanned aerial vehicle images H. Tao et al. 10.1080/01431161.2020.1766145
2. Applicability of UAV-based optical imagery and classification algorithms for detecting pine wilt disease at different infection stages N. Zhang et al. 10.1080/15481603.2023.2170479
3. A Detection Method for Individual Infected Pine Trees with Pine Wilt Disease Based on Deep Learning Y. Zhou et al. 10.3390/f13111880
4. Automatic Detection and Classification of Dead Nematode-Infested Pine Wood in Stages Based on YOLO v4 and GoogLeNet X. Zhu et al. 10.3390/f14030601
5. Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms J. Qin et al. 10.3390/rs13020162
6. Using only the red-edge bands is sufficient to detect tree stress: A case study on the early detection of PWD using hyperspectral drone images N. Li et al. 10.1016/j.compag.2024.108665
7. Satellite Remote Sensing Identification of Discolored Standing Trees for Pine Wilt Disease Based on Semi-Supervised Deep Learning J. Wang et al. 10.3390/rs14235936
8. A Spatiotemporal Change Detection Method for Monitoring Pine Wilt Disease in a Complex Landscape Using High-Resolution Remote Sensing Imagery B. Zhang et al. 10.3390/rs13112083