Multi-temporal UAV data and bject-based image analysis (OBIA) for estimation of substrate changes in a post-bleaching scenario on a Maldivian reef
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Red Sea Research Center (RSRC)
Permanent link to this recordhttp://hdl.handle.net/10754/668207
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AbstractCoral reefs are declining worldwide as a result of the effects of multiple natural and anthropogenic stressors, including regional-scale temperature-induced coral bleaching. Such events have caused significant coral mortality, leading to an evident structural collapse of reefs and shifts in associated benthic communities. In this scenario, reasonable mapping techniques and best practices are critical to improving data collection to describe spatial and temporal patterns of coral reefs after a significant bleaching impact. Our study employed the potential of a consumer-grade drone, coupled with structure from motion and object-based image analysis to investigate for the first time a tool to monitor changes in substrate composition and the associated deterioration in reef environments in a Maldivian shallow-water coral reef. Three key substrate types (hard coral, coral rubble and sand) were detected with high accuracy on high-resolution orthomosaics collected from four sub-areas. Multi-temporal acquisition of UAV data allowed us to compare the classified maps over time (February 2017, November 2018) and obtain evidence of the relevant deterioration in structural complexity of flat reef environments that occurred after the 2016 mass bleaching event. We believe that our proposed methodology offers a cost-effective procedure that is well suited to generate maps for the long-term monitoring of changes in substrate type and reef complexity in shallow water
CitationFallati, L., Saponari, L., Savini, A., Marchese, F., Corselli, C., & Galli, P. (2020). Multi-Temporal UAV Data and Object-Based Image Analysis (OBIA) for Estimation of Substrate Changes in a Post-Bleaching Scenario on a Maldivian Reef. Remote Sensing, 12(13), 2093. doi:10.3390/rs12132093
SponsorsThis research was funded by MaRHE Center (https://marhe.unimib.it/) which supported in situ data collection and travel grants for L.F. and L.S., and by ULR CoNISMa of Milano-Bicocca (https://marinegeolab. disat.unimib.it/) which provided lab facilities and software licenses for data processing. L.F. and L.S. are funded respectively through a PhD fellowship funded by the University of Milano-Bicocca. F.M. is funded through a post-doctoral fellowship in Earth Sciences by the University of Milano-Bicocca. This article is also an outcome of Project MIUR-Dipartimenti di Eccellenza 2018-2022. The authors are grateful to Magoodhoo island local communities, and Diamonds Thudufushi Resort (PlanHotel Hospitality Group) for the logistic support provided during the fieldwork.
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