Alexander, C, Korstjens, A, Hankinson, E, Usher, G, Harrison, N, Nowak, M, Abdullah, A, Wich, SA and Hill, R (2018) Locating emergent trees in a tropical rainforest using data from an Unmanned Aerial Vehicle (UAV). International Journal of Applied Earth Observation and Geoinformation, 72. pp. 86-90. ISSN 0303-2434

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Abstract

Emergent trees, which are taller than surrounding trees with exposed crowns, provide crucial services to several rainforest species especially to endangered primates such as gibbons and siamangs (Hylobatidae). Hylobatids show a preference for emergent trees as sleeping sites and for vocal displays, however, they are under threat from both habitat modifications and the impacts of climate change. Traditional plot-based ground surveys have limitations in detecting and mapping emergent trees across a landscape, especially in dense tropical forests. In this study, a method is developed to detect emergent trees in a tropical rainforest in Sumatra, Indonesia, using a photogrammetric point cloud derived from RGB images collected using an Unmanned Aerial Vehicle (UAV). If a treetop, identified as a local maximum in a Digital Surface Model generated from the point cloud, was higher than the surrounding treetops (Trees_EM), and its crown was exposed above its neighbours (Trees_SL; assessed using slope and circularity measures), it was identified as an emergent tree, which might therefore be selected preferentially as a sleeping tree by hylobatids. A total of 54 out of 63 trees were classified as emergent by the algorithm and in the field. The algorithm is based on relative height rather than canopy height (due to a lack of terrain data in photogrammetric point clouds in a rainforest environment), which makes it equally applicable to photogrammetric and airborne laser scanning point cloud data.

Item Type:	Article
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QH Natural history Q Science > QK Botany T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions:	Natural Sciences & Psychology (closed 31 Aug 19)
Publisher:	Elsevier
Date Deposited:	01 Jun 2018 08:54
Last Modified:	04 Sep 2021 02:39
URI:	https://researchonline.ljmu.ac.uk/id/eprint/8753

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