# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
## 1 Introduction  
Forest pests, such as _Tomicus_ spp., have seriously affected the economic benefits and ecological environment ofiferous forests [1, 2]. Moreover, forest pests also cause the issues of forest degradation and disturbed ecosystem services and have an impact on forest carbon sequestration and sustainable forest management [3, 4]. The appearance and spread of pests and diseases can be reflected quickly and objectively using data acquired from high-precision satellite technology [5]. Therefore, it is becoming increasingly necessary to use spatial data at various scales to rapidly locate and assess affected areas and the degrees of pest stress in forests [6]. Optical remote sensing, which uses spectral characteristics tomonitor forest pests, is limited to red and gray attacks (late stage) [7; 8; 9]. Accordingly, some researchers use the water content sensitivity of thermal infrared (TIR) remote sensing to monitor forest pests during the early stage of an attack [10; 11]. The canopy surface temperature (CST) reflects the transfer and exchange of energy within a soil-plant-atmosphere continuum (SPAC). The changes in CST monitored by TIR imaging exhibit responses to environment stress, such as drought [12; 13], disease [14], and pest infestation [15]. With improvements in the method and accuracy of the technique, the TIR satellite images can provide surface temperatures to determine CSTs at the regional scale [1; 16]. However, CSTs obtained from satellite images are unsuitable for the precise analysis of small areas because of their coarse resolution compared with optical images. The resolution of thermal data was limited to 1-10 m for manned airborne sensors or to centimeter-scale resolutions for infrared thermal images via a settled flux tower. The advent of unmanned aerial vehicles (UAVs) supports the application of high-resolution thermal images with low-weight thermal cameras at centimeter-scale resolutions [17; 18; 19]. These images can describe in detail the transpiration from canopies according to the basic relationship between canopy temperatures and evaporation in plant ecophysiological research [20; 21]. High-resolution thermal images can help to quantify the link between CST and canopy health and clarify the response mechanism of canopy physiological parameters to pest stress [14]. Forest pests can disrupt water equilibrium in plants and decrease the water content of the canopy. Whether by defoliating needles or boring into shoots, bark, or wood, pests may cause temporary or permanent damage to physiological and biochemical parameters of forests [22]. The main species of pests responsible for destroying 1.5 million hectares of Yunnan pine forest in Southwest China over the past 20 years is _Tomicus_ spp. [23; 24]. The beetles of these species attack the shoots of the Yunnan pine from May and commence wood boring in November. Over several weeks, the affected needle shoots gradually change from green to yellow and, typically, the needles turn red within several months (red attack). During December, the host trees bored by beetles lose their needles (gray attack) (this occurs within 3 years of the initial attack) [25]. The spectral changes that occur during the late stage are easily identified by optical remote sensing [26]. However, it is difficult to recognize the spectral characteristics of a green attack (early stage) [27]. Despite the proposal of various remote sensing methods for evaluating forest damage, the forecasting of beetle attacks remains a challenge, particularly during the green attack stage [22; 28]. Thermal infrared imaging techniques can be used to monitor abnormal canopy temperatures due to the disruption to the water cycle of canopy during the green attack stage [29]. However, the use of thermal remote sensing for monitoring forest pests is uncommon. One reason is the difficulty of interpreting the CST, which is heavily influenced by environmental factors and physiological vegetation parameters [13].