## 1 Introduction  
Pine wilt disease (PWD) is a lethal wilting disease caused by the pine wood nematode (Bursaphelenchus xylophilus; PWN). After becoming infected with the disease, pine trees show certain symptoms, in which the needles of the tree gradually change color; the pine resin stops flowing; and the tree wilts, withers, and eventually dies, with the whole process occurring over a few months [1, 2, 3]. In 2020, PWD has been reported in 18 provinces, autonomous regions, and municipalities that are directly under the Central Government of China, with an infestation area of 1.8 million ha and a death toll of 19.5 million trees [4]. Studies have shown that, with the changes in climate in recent years, the potentially suitable areas for pine wood nematodes have expanded toward China's northern and western regions [5; 6; 7]. These results indicate that pine wilt disease has already become one of the most dangerous forest biological disasters in China, not only causing serious damage to pine forest resources and endangering the ecological security of scenic spots and other areas but also seriously affecting China's import and export trade. Currently, it is believed that PWD is the interactive outcome of the pathogen, the host pine tree, the insect vector, and climatic conditions [8]. Moreover, there is currently no effective method to completely eradicate the disease, and the timely removal of the diseased trees is the most effective way to control the disease once the infected trees are identified [9]. In recent years, the development of high-resolution commercial satellites and unmanned aerial vehicle (UAV) technology has also provided new data support for natural resources monitoring. Compared with other survey methods, remote sensing technology has many advantages when making surveys of forest pests and diseases, including (1) fast information acquisition with a short revisit cycle, (2) less restriction by ground conditions and low labor costs, and (3) the capability for large-scale monitoring [10]. Therefore, using remote sensing technology to accurately locate trees that are infected with pine wilt disease can enable timely monitoring of the health status of the forest and cleanup of diseased trees while helping to elucidate the pattern of prevalence of the disease at multiple scales and its interactions with humans and the natural environment. The Joint Research Center (JRC) conducted a pilot study in 2014 and 2015 to confirm the feasibility of identifying dead pine trees through high-resolution satellite remote sensing images and UAV images and to establish a basic methodological system of using remote sensing to monitor PWD-infected trees [11]. Currently, in monitoring discolored pine trees caused by forest pests and diseases, optical satellite remote sensing is the most widely used method. Previous studies based on hyperspectral data showed that the canopy color change of pine trees include higher red band reflectance than green band reflectance, a decreased slope, and a blue shift of the red edge [12; 13; 14]. Unlike the stand or landscape-scale forest mortality [15], PWD usually occurs at the tree-scale, which needs high-resolution imagery to provide distinct information on the infestation patterns [11]. The commonly used high-resolution satellite remote sensing data for PWD detection include IKONOS [16; 17], QuickBird [18; 19], WorldView [20], and GeoEye-1 [21]. Methodologically, image enhancement is achieved through a variety of methods, such as constructing the spectral index, principal component analysis, and tasseled cap transformation, on which the wilted pine trees are further extracted through the threshold segmentation method or machine learning [22; 23; 24; 25]. The studies mentioned above are mostly based on single-date image classification, which have been well applied in studies for pure pine forests.