Coral Reef Mapping with Remote Sensing and Machine Learning: A Nurture and Nature Analysis in Marine Protected Areas

Abstract

Mapping habitats is essential to assist strategic decisions regarding the use and protection of coral reefs. Coupled with machine learning (ML) algorithms, remote sensing has allowed detailed mapping of reefs at meaningful scales. Here we integrated WorldView-3 and Landsat-8 imagery and ML techniques to produce a map of suitable habitats for the occurrence of a model species, the hydrocoral Millepora alcicornis, in coral reefs located inside marine protected areas in Northeast Brazil. Conservation and management efforts in the region were also analyzed, integrating human use layers to the ecological seascape. Three ML techniques were applied: two to derive base layers, namely geographically weighted regressions for bathymetry and support vector machine classifier (SVM) for habitat mapping, and one to build the species distribution model (MaxEnt) for Millepora alcicornis, a conspicuous and important reef-building species in the area. Additionally, human use was mapped based on the presence of tourists and fishers. SVM yielded 15 benthic classes (e.g., seagrass, sand, coral), with an overall accuracy of 79%. Bathymetry and its derivative layers depicted the topographical complexity of the area. The Millepora alcicornis distribution model identified distance from the shore and depth as topographical factors limiting the settling and growth of coral colonies. The most important variables were ecological, showing the importance of maintaining high biodiversity in the ecosystem. The comparison of the habitat suitability model with species absence and human use maps indicated the impact of direct human activities as potential inhibitors of coral development. Results reinforce the importance of the establishment of no-take zones and other protective measures for maintaining local biodiversity.

Current Trends and Issues in Applications of Remote Sensing in Coastal and Marine Conservation

“…From the onset, RS can provide essential information on environmental parameters and habitats to improve the designation process of MPAs [9]. MPAs should be designated and delimited based on representativeness of the habitat types in the area and prior knowledge of biologically important species, habitats, and ecosystems; however, this information is seldom available, and the process of MPA designation often relies on socioeconomic factors [13]. In this way, RS can significantly contribute to retarding the loss of marine biodiversity and meeting international agreements, such as the SDG 14.5.1 MPA 10% designation target.…”

Seafloor Habitat Mapping by Combining Multiple Features From Optic and Acoustic Data: A Case Study From Ganquan Island, South China Sea

“…The optical remote sensing techniques for seafloor habitat mapping mainly include active airborne light detection and ranging (LiDAR) and passive satellite sensors [5], [6], [7]. Through waveform processing, airborne LiDAR can differentiate several types of seafloor from bottom returns and measure the depth of shallow-water regions.…”

A Combined Color and Wave-Based Approach to Satellite Derived Bathymetry Using Deep Learning

“…In recent years, remote sensing tools such as shore-based video cameras and space-born satellite constellations have emerged as important tools for the collection of large amounts of earth observation data. These data products have been used to study a wide array of natural processes in the coastal zone (Brando and Dekker, 2003, Yuan et al, 2020, Wei et al, 2021, Schratz et al, 2021, da Silveira et al, 2021. In the domain of bathymetry inversion from satellite imagery, two main methodologies have been studied and can be identified in the literature (Al Najar et al, 2022).…”