Detection of necrotic foliage in a young Eucalyptus pellita plantation using unmanned aerial vehicle RGB photography – a demonstration of concept. Issue 2 (3rd April 2019)

• Record Type: Journal Article
• Authors: Dell, M.; Stone, C.; Osborn, J.; Glen, M.; McCoull, C.; Rimbawanto, A.; Tjahyono, B.; Mohammed, C.
• Is Part Of: Australian forestry. Volume 82:Issue 2(2019)
• Page Start: 79
• Page End: 88
• Publication Date: 2019-04-03
• Subjects: tree health surveillance -- unmanned aerial vehicle -- digital aerial photography -- structure-from-motion photogrammetry -- RGB point cloud -- RGB vegetation index -- forest inventory; Forests and forestry -- Australia -- Periodicals; Forests and forestry -- Australasia -- Periodicals; 634.90994
• DOI: 10.1080/00049158.2019.1621588
• Languages: English
• ISSNs: 0004-9158

Abstract

ABSTRACT: 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.