Title
Using multiscale lidar to determine variation in canopy structure from African forest elephant trails
Author(s)
Keany, Jenna M.;Burns, Patrick;Abraham, Andrew J.;Jantz, Patrick;Makaga, Loic;Saatchi, Sassan;Maisels, Fiona;Abernethy, Katharine;Doughty, Christopher E.
Published
Early View
Publisher
Remote Sensing in Ecology and Conservation
Abstract
Recently classified as a unique species by the IUCN, African forest elephants (Loxodonta cyclotis) are critically endangered due to severe poaching. With limited knowledge about their ecological role due to the dense tropical forests they inhabit in central Africa, it is unclear how the Afrotropics are influenced by elephants. Although their role as seed dispersers is well known, they may also drive large-scale processes that determine forest structure through the creation of elephant trails and browsing the understory, allowing larger, carbon-dense trees to succeed. Multiple scales of lidar were collected by NASA in Lopé National Park, Gabon from 2015 to 2022. Utilizing two airborne lidar datasets in an African forest elephant stronghold, detailed canopy structural information was used in conjunction with elephant trail data to determine how forest structure varies on and off trails. Forest along elephant trails displayed different structural characteristics than forested areas off trails, with lower canopy height, canopy cover, and different vertical distribution of plant density. Less plant area density was found on trails at 1 m in height, while more vegetation was found at 12 m, compared to off trail locations. Trails in forest areas with previous logging history had lower plant area in the top of the canopy. Forest elephants can be considered as “logging light” ecosystem engineers, affecting canopy structure through browsing and movement. Both airborne lidar scales were able to capture elephant impact along trails, with the high-resolution discrete return lidar performing higher than waveform lidar.
Keywords
Canopy structure; conservation; lidar; megaherbivore; remote sensing; trails
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PUB36322