摄影测量学
正射影像
点云
遥感
RGB颜色模型
航空摄影
树(集合论)
环境科学
全球定位系统
计算机科学
数码相机
人工智能
植被(病理学)
计算机视觉
森林资源清查
森林健康
由运动产生的结构
地理
数学
农林复合经营
森林经营
运动估计
医学
数学分析
病理
电信
作者
M. Dell,Christine Stone,JE Osborn,Morag Glen,Colin J. McCoull,A. Rimbawanto,B. Tjahyono,CL Mohammed
标识
DOI:10.1080/00049158.2019.1621588
摘要
Recent advances and commercialisation of unmanned aerial vehicle/red blue green (RGB) camera systems and digital photogrammetric techniques now provide a cheap and flexible alternative to higher-cost airborne platforms for routine monitoring of canopy health in timber plantations. Structure-from-Motion photogrammetry produces very dense three-dimensional (3D) point clouds which can be used to derive metrics for inventory estimation. Unmanned aerial vehicle RGB photography also captures data that can relate to tree health. In contrast to the more common use of orthorectified RGB photography to extract this spectral information, we used the software package Agisoft Photoscan to assign a simple Vegetation Index value directly to each point in the 3D point cloud. Using data acquired by a DJI Phantom 4 Pro, we present a simple processing and photogrammetric workflow solution for detecting dead and dying trees in a young Eucalyptus pellita plantation located in the provenance of Riau, Sumatra. Trees affected by the bacterial wilt Ralstonia sp. present symptoms of necrotic foliage on individual branches or the whole crown. Assigning the Visible Atmospheric Resistant Index Vegetation Index colour-coded values to individual points in the 3D point cloud significantly enhanced visualisation of necrotic foliage on individual trees in both the point cloud and the associated orthophoto compared to the RGB equivalent images. This approach could easily be operationally deployed for the rapid detection and mapping of unhealthy trees with symptoms of necrotic foliage.
科研通智能强力驱动
Strongly Powered by AbleSci AI