Integrating Multi-Scale Remote-Sensing Data to Monitor Severe Forest Infestation in Response to Pine Wilt Disease

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 20; p. 5164
• Main Authors: Li, Xiujuan, Liu, Yongxin, Huang, Pingping, Tong, Tong, Li, Linyuan, Chen, Yuejuan, Hou, Ting, Su, Yun, Lv, Xiaoqi, Fu, Wenxue, Huang, Xiaojun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Aerial surveys; Algorithms; Cameras; Color imagery; Data acquisition; Disease; Forest management; Forests; Ground truth; Image resolution; Infestation; Insects; Machine learning; multi-scale remote sensing; Nematodes; Object recognition; Pine; Pine trees; pine wilt disease; Pixels; Prediction models; PWN infestation mapping; Radarsat; random forest; Remote sensing; Sampling; SAR; Satellite imagery; Satellites; Surveys; Training; Trees; Unmanned aerial vehicles; Vegetation; Wilt; Wilting; China

Pine wilt disease (PWD) is one of the most destructive forest diseases that has led to rapid wilting and mortality in susceptible host pine trees. Spatially explicit detection of pine wood nematode (PWN)-induced infestation is important for forest management, policy making, and practices. Previous s...