Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

Abstract

Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.

Multispectral data mining: A focus on remote sensing satellite images

“…Apart from DSF, several ACs processor tools have their mechanisms for adjusting for inland water bodies, but a comparative analysis indicated that six such tools showed high levels of uncertainties in the water-leaving reflectance estimation (Warren et al, 2019). Similar uncertainty has been found for atmospheric correctness computed for oceans when mapping coral reefs (Nguyen et al, 2021).…”

“…Change detection using MSI is particularly important for applications related to environmental monitoring, for example, surface water and flood detection (Albertini et al, 2022), shoreline indicators (McAllister et al, 2022), coral reefs mapping (Nguyen et al, 2021), and biodiversity monitoring (Kuenzer et al, 2014; Schulte to Bühne & Pettorelli, 2018). The latter pertains to specific regions, such as forests (Kacic & Kuenzer, 2022), grasslands (Jackson et al, 2022).…”

Coral Reefs Detecting with Artificial Neural Network Classification and PlanetScope Imagery in Cu Lao Xanh Island, Binh Dinh province

“…The MLC method has been frequently used and regarded as a standard approach in remote sensing image processing [39,40]. This approach assumes that the probability density function follows the normal distribution [41].…”

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

“…In particular, any in situ measurements of coral reefs are challenging for the remote Spratly Islands [11,18], which contain the elements of the present political situation. Currently, in situ measurements for acquiring bathymetric maps in remote coral reefs can be replaced using satellite-derived bathymetry (SDB) [19][20][21][22].…”