基于机载多源数据的云南松受小蠹虫危害程度特征提取与诊断研究

高光谱、激光雷达（LiDAR）技术已经成为农业、林业、草原病虫害监测的重要手段。高光谱数据包含丰富的病虫害危害光谱信息，但空间立体结构信息不足；LiDAR数据空间分辨率高、立体结构信息丰富，但病虫害危害特征信息不明显。结合LiDAR和高光谱数据，快速、精准的提取林业病虫害信息实现病虫害的监测、预警，是现阶段急需解决的问题。Hyperspectral and Light Detection and Ranging (LiDAR) technologies have emerged as critical tools for pest and disease monitoring in agriculture, forestry, and grassland ecosystems. Hyperspectral data carries abundant spectral information associated with pest and disease damage, yet it lacks sufficient spatial and stereoscopic structural details. In contrast, LiDAR data features high spatial resolution and rich stereoscopic structural information, but fails to clearly exhibit characteristic signals of pest and disease harm. Combining LiDAR and hyperspectral data to rapidly and accurately extract forestry pest and disease information for monitoring and early warning of such outbreaks is an urgent issue to be addressed at the present stage.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于利用机载高光谱和激光雷达（LiDAR）等多源数据，对云南松受小蠹虫危害程度进行特征提取与诊断研究。它旨在通过结合两种技术的优势，实现林业病虫害信息的快速、精准提取，以应对当前监测预警的迫切需求。