Integrating a UAV-Derived DEM in Object-Based Image Analysis Increases Habitat Classification Accuracy on Coral Reefs

Abstract

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 into ecologically relevant habitat maps are still relatively underdeveloped. Here, we demonstrate that incorporating geomorphometric variables (derived from the DEM) in addition to spectral information (derived from 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.

UAV-Based Subsurface Data Collection Using a Low-Tech Ground-Truthing Payload System Enhances Shallow-Water Monitoring

“…While the sublabels chosen for this study served as examples, it was shown that the developed payload system could be used to address tasks that go beyond the mapping and monitoring of the most dominant habitats. For example, studies conducted to investigate seagrass health status [53], coverage [54], density [55] or species composition [56] in optically deep waters, as well as the ecological impact of invasive species [57], could benefit from the proposed method by increasing the number of point observations while decreasing the need for diver surveys and other time, labor and cost intensive in-water monitoring methods. The ability to provide reliable information about the depth limit of seagrasses is another feature that has the potential to increase the efficiency of many monitoring campaigns if the proposed method of UAV-based subsurface data collection is applied.…”

“…While the sublabels chosen for this study served as examples, it was shown that th developed payload system could be used to address tasks that go beyond the mappin and monitoring of the most dominant habitats. For example, studies conducted to inves tigate seagrass health status [53], coverage [54], density [55] or species composition [56] i…”

Coastal benthic habitat mapping and monitoring by integrating aerial and water surface low-cost drones

“…In fact, UAVs and SfM processing have been successfully applied for natural resource assessment and environmental monitoring of the coastal areas (Burns et al, 2015;Nikolakopoulos et al, 2017;Ventura et al, 2018;Burns et al, 2019;Taddia et al, 2019;Kabiri, 2020;Ventura et al, 2022). These products, other than playing a key role in assessing 3D habitat complexity and health conditions of specific biotopes such as biogenic reefs in tropical (Raoult et al, 2017;Burns et al, 2019;Fallati et al, 2020;Nieuwenhuis et al, 2022) and temperate (Zapata-Ramıŕez et al, 2013;Marre et al, 2019;Prado et al, 2020;Ventura et al, 2020) environnements, can also provide valuable information for fine-scale assessment and monitoring of seagrass and macroalgal beds limits, level of fragmentation, and restoration activities (Marre et al, 2020;Rende et al, 2020;Ventura et al, 2022). Moreover, UAVs provide a means to map the distribution and behaviour of many organisms in shallow aquatic environments that typically have high contrast against the substrate (Raoult and Gaston, 2018;Williamson et al, 2021).…”

Development of a Google Earth Engine-Based Application for the Management of Shallow Coral Reefs Using Drone Imagery

“…Mat-Zaki et al [34] presented and optimized workflow using Agisoft PhotoScan Pro for coral reef habitat mapping using drone imagery. Nieuwenhuis et al [35] incorporated geomorphometric variables derived from Digital Elevation Models (DEM) and spectral information to increase the accuracy in habitat classification processes, using drone imagery from an RTK-ready (Real-Time Kinematic) aerial vehicle. Alevizos and Alexakis [36] developed a novel approach to describe shallow bathymetry changes using drone multispectral imagery.…”