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Mapping Coastal Marine Habitats Using UAV and Multispectral Satellite Imagery in the NEOM Region, Northern Red Sea
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Mapping Coastal Marine Habitats Using UAV and Multispectral Satellite Imagery in the NEOM Region, Northern Red Sea
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Journal Article
Mapping Coastal Marine Habitats Using UAV and Multispectral Satellite Imagery in the NEOM Region, Northern Red Sea
2025
Overview
Effective management to conserve marine environments requires up-to-date information on the location, distribution, and extent of major benthic habitats. Remote sensing is a key tool for such assessments, enabling consistent, repeated measurements over large areas. There is particular interest in using freely available satellite images such as from the Copernicus Sentinel-2 series for accessible repeat assessments. In this study, an area of 438 km2 of the northern Red Sea coastline, adjacent to the NEOM development was mapped using Sentinel-2 imagery. A hierarchical Random Forest classification method was used, where the initial level classified pixels into a geomorphological class, followed by a second level of benthic cover classification. Uncrewed Aerial Vehicle (UAV) surveys were carried out in 12 locations in the NEOM area to collect field data on benthic cover for training and validation. The overall accuracy of the geomorphic and benthic classifications was 84.15% and 72.97%, respectively. Approximately 12% (26.26 km2) of the shallow Red Sea study area was classified as coral or dense algae and 16% (36.12 km2) was classified as rubble. These reef environments offer crucial ecosystem services and are believed to be internationally important as a global warming refugium. Seagrass meadows, covering an estimated 29.17 km2 of the study area, play a regionally significant role in carbon sequestration and are estimated to store 200 tonnes of carbon annually, emphasising the importance of their conservation for meeting the environmental goals of the NEOM megaproject. This is the first map of this region generated using Sentinel-2 data and demonstrates the feasibility of using an open source and reproducible methodology for monitoring coastal habitats in the region. The use of training data derived from UAV imagery provides a low-cost and time-efficient alternative to traditional methods of boat or snorkel surveys for covering large areas in remote sites.
