Exploring the Potential of UAV-Based Hyperspectral Imagery on Pine Wilt Disease Detection: Influence of Spatio-Temporal Scales

Abstract

Pine wilt disease (PWD), caused by pine wood nematode (PWN, Bursaphelenchus xylophilus), poses a serious threat to the coniferous forests in China. This study used unmanned aerial vehicle (UAV)-based hyperspectral imaging conducted at different altitudes to investigate the impact of spatio-temporal scales on PWD detection in an monoculture Masson pine plantation. The influence of spatio-temporal scales on hyperspectral responses of pine trees infected with PWD and detection accuracies were evaluated by Jeffries–Matusita (J-M) distances and the random forest (RF) algorithm. The optimal vegetation indices (VIs) and spatial resolutions were identified by comparing feature importance and model accuracy. The main results showed that the VIs and J-M distances were greatly affected by spatio-temporal scales. In the early, mid-, and late infection stages, the RF-based PWD detection model had accuracies ranging between 72.05 and 79.48%, 83.71 and 89.59%, and 96.81 and 99.28%, peaking at the 10 cm, 8 cm, and 4 cm spatial resolutions, respectively. The green normalized difference vegetation index (GNDVI) and red edge position (REP) were the optimal VIs in early and mid-infection stages, respectively. This study can be important to improve the efficiency of PWD detection and reducing the loss of forests resources.