松材线虫为外来入侵物种，其引起的病害传播快速且防治难度大，被称为松树的“癌症”。近年来，在我国不断有新增疫区出现且疫区范围逐步扩大，造成了极大的经济和生态损失。及时准确的病害监测成为亟待解决的问题。针对当下存在的病害信息获取不及时，星载遥感数据空间分辨率低且受天气影响大，难以实现受害木的准确定位及识别等问题，本项目利用无人机搭载成像高光谱及高清数码相机，依据松材线虫以及媒介天牛的生命周期，获取病害主要疫区时间序列无人机遥感数据，研究“时-空-谱”数据融合及相关分析算法，实现病害发生各阶段指示性特征图谱的构建和病害早期识别时间点的确定，并在此基础上从空谱特征提取和分类框架构建两个方向进行早期受害木以及病害枯死木的识别及精确定位研究。为建立有效的定数据、定时间、定位置的森林病虫害无人机监测技术提供支撑，同时为松材线虫病的实际防控工作提供依据。

Bursaphelenchus xylophilus is an invasive alien species, pine wilt disease (PWD) is caused by it and always be called the ‘cancer’ of pine trees because of its rapid spread and difficult to control. In recent years, there are more and more new PWD epidemic areas in China, and the scope of the epidemic areas is gradually expanding, resulting in great economic and ecological losses. Thus, timely and accurate disease identification and monitoring must be solved imperatively. Focus on the problems in forest diseases and pests monitoring using remote sensing technology: the disease information cannot be acquired timely, and it is difficult to realize the precise positioning and identification of damaged trees because satellite remote sensing is greatly affected by weather and has low spatial resolution. Based on the life cycle of Bursaphelenchus xylophilus and its vector insects, the time-series UAV-based hyperspectral and digital images are utilized as the main data sources in this project. Combined with field survey data and developed ‘temporal-spatial-spectral’ fusion and correlation algorithm, the main work of this project are as follows: (1) establishing the indicative spatial-spectral features for each stage of disease occurrence; (2) determining the time point of early disease identification; and (3) realizing the individual tree precise positioning and identification of early damaged trees and dead trees from two directions finally: spatial-spectral features extraction and classification framework construction. The successfully implementation of this project will provide the technology support for establishing an effective, timing and positioning UAV-based identification system of forest diseases and pests, and provide the basis for timely and effective prevention and control of PWD.