Data from: Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs

Very shallow coral reefs (< 5 m deep) are naturally exposed to
strong sea surface temperature variations, UV radiation and other
stressors exacerbated by climate change, raising great concern over their
future. As such, accurate and ecologically informative coral reef maps are
fundamental for their management and conservation. Since traditional
mapping and monitoring methods fall short in very shallow habitats,
shallow reefs are increasingly mapped with Unmanned Aerial Vehicles
(UAVs). UAV-imagery is commonly processed with Structure-from-Motion (SfM)
to create orthomosaics and Digital Elevation Models (DEMs) spanning
several hundred metres. Techniques to convert these SfM products to
ecologically relevant habitat maps are still relatively underdeveloped.
Here we demonstrate that incorporating geomorphometric variables (the DEM
and its derivatives) in addition to spectral information (the orthomosaic)
can greatly enhance the accuracy of automatic habitat classification.
Therefore, we mapped three very shallow reef areas off KAUST on the Saudi
Arabian Red Sea coast with an RTK-ready UAV. Imagery was processed with
SfM, and classified through Object-Based Image Analysis (OBIA). Within our
OBIA workflow, we observed overall accuracy increases of up to 11% when
training a Random Forest classifier on both spectral and geomorphometric
variables as opposed to traditional methods that only use spectral
information. Our work highlights the potential of incorporating a UAV’s
DEM in OBIA for benthic habitat mapping, a promising but still scarcely
exploited asset.