## 1 Introduction  
Pine trees are important sources of forest products and play other important roles in environmental protection such as preventing wind and sand breaks and landslides, especially in East Asian countries [1]. Parts of the trees are regularly being used as dietary supplements because of their several health and medicinal benefits [2; 3]. However, pine trees are facing a serious and destructive disease, pine wilt disease (PWD), which is caused by the pine wood nematode (PWN, _Bursaphelenchus xylophilus_) and is spread through the insect vector pine sawyer beetle (_Monochamus_ spp.) [4]. PWD first appeared in North America but is now widely found around the world [5; 6; 7]. As most pine trees infected by PWD die within three months, PWD has caused significant economic losses and adverse environmental impacts in China since its first appearance in 1982 in Nanjing [8]. To effectively refrain PWD spread, current control methods usually identify the PWD-infected pine trees through field survey and use fumigation, burning and tree-felling operations when the onset outbreak occurs [9; 10]. However, pine trees usually grow in large mountain areas and communities, which make standard ground identification and sampling methods time- and labor-consuming and sometimes even impractical. To overcome those difficulties, hyperspectral remote sensing (HRS) technology, which can obtain continuous spectral information of objects, has been explored to examine the impact of PWD on the physiological and biochemical changes after infection [11]. According to the data acquisition scale, research on the remote sensing monitoring of PWD is mainly divided into three classes, including ground remote sensing, high-altitude remote sensing and low-altitude remote sensing [12]. Ground remote sensing reveals that the presence of PWD significantly correlates with hyperspectral features. [PERSON] et al. [13] showed that the spectral reflectance of Pinus Massoniana in different stages of PWD appeared to be greatly different. [PERSON] et al. [14] constructed a green-red spectral area index (GRSAI) and found that it detected PWD infection faster than other indices. However, those studies used a field portable spectrometer to collect spectral data and neglected spatial information and therefore could not be applied in a large-scale area [15]. High-altitude remote sensing usually uses satellite imagery from Landsat, Spot, WorldView and BJ-2 to detect PWD [16]. [PERSON] et al. [17] proposed a spatiotemporal change detection method to reduce false detections in tree-scale PWD monitoring in a complex landscape. [PERSON] et al. [18] retrieved PWD-infected areas from medium-resolution satellite images based on the simulations of an extended stochastic radiative transfer model. However, due to the limitations of low spatial-temporal resolutions and weather complications, high-altitude remote sensing methods usually achieve low PWD detection accuracy [19]. Compared with ground remote sensing and high-altitude remote sensing, low-altitude remote sensing with unmanned aerial vehicles (UAVs) is flexible and efficient and has the advantage of yielding low-cost, high-spatial-resolution, and high PWD detection accuracy [20; 21]. The HI generated by a UAV hyperspectral imaging spectrometer has great potential in detecting PWD with its abundant spatial and spectral information [22; 23; 24]. [PERSON] et al. [25] used a fixed-wing UAV equipped with a multispectral digital camera to collect the normalized differential vegetation index (NDVI) of pine trees and recognized the dead pine trees caused by PWD with 80% accuracy. [PERSON] et al. [26] successfully recognized PWD-infected pine trees with 90.4% accuracy using the color characteristics of UAV remote-sensing images. [PERSON] et al. [27] explored SVM and RF classifiers to distinguish pine trees in healthy, Sirex noctilio grey-attacked and lightning-damaged stages with airborne hyperspectral data.