Coral Reef Monitoring Research Articles

From Remote Sensing to Artificial Intelligence in Coral Reef Monitoring

This review comprehensively covers the development of viable unmanned underwater vehicles based on their technical capabilities, in particular those designed to conduct research exploration in underwater ecosystems and address environmental issues through analysis of coral reef vulnerability. The most important elements to be obtained are in situ data samples for analysis and characterization, supported by molecular biomarkers and marine ecology indicators. The following aspects are considered in this study: first, the geographic distribution of coral reefs for the study of marine ecology and molecular biological approaches for the detection of biomarkers to evaluate the vulnerability of coral reefs are detailed; then, the technologies currently available for the study of coral reefs are briefly described, ranging from large-scale capture to local-scale capture directly in the study region, taking advantage of remote sensing systems assisted by aerial technologies, marine vehicles, and artificial intelligence for the mapping, monitoring, and management of coral reefs as well as the characterization of their vulnerability; following this, existing marine vehicle technologies are generally explained, including a categorical description and an updated and highlighted list of innovative and robust marine vehicles that have been used in coral reef applications; the technical capabilities of such vehicle throughout the missions they have been subjected to are presented based on bibliographic references; finally, this review promotes multidisciplinary work to integrate the developments in the associated knowledge areas in order to obtain synergies in the face of challenges related to the massive scale of coral reef degradation worldwide.

A generalized machine learning model for long-term coral reef monitoring in the Red Sea

Coral reefs, despite covering less than 0.2 % of the ocean floor, harbor approximately 35 % of all known marine species, making their conservation critical. However, coral bleaching, exacerbated by climate change and phenomena such as El Niño, poses a significant threat to these ecosystems. This study focuses on the Red Sea, proposing a generalized machine learning approach to detect and monitor changes in coral reef cover over an 18-year period (2000–2018). Using Landsat 7 and 8 data, a Support Vector Machine (SVM) classifier was trained on depth-invariant indices (DII) derived from the Gulf of Aqaba and validated against ground truth data from Umluj. The classifier was then applied to Al Wajh, demonstrating its robustness across different sites and times. Results indicated a significant decline in coral cover: 11.4 % in the Gulf of Aqaba, 3.4 % in Umluj, and 13.6 % in Al Wajh. This study highlights the importance of continuous monitoring using generalized classifiers to mitigate the impacts of environmental changes on coral reefs.

Why keep monitoring coral reefs?

Abstract The high demand for information on how coral reefs are changing often exceeds the capacity of the scientific community to deliver the data necessary to meet this need. However, given the degraded state of coral reefs and the poor prognosis for their recovery, it is reasonable to ask whether coral reef monitoring should continue. Using my experiences from a 37-year study in the US Virgin Islands, I highlight the information that monitoring conveys on the changing state of coral reefs, and underscore how the interpretation of ecological trends matures with increasing longevity of records. Because the past is an imperfect predictor of the future, monitoring coral reefs remains an important endeavor. It offers the only opportunity to understand how reefs will continue to change, to connect patterns of change to the processes causing them to occur, and to create opportunities for management to best ensure their future.

The distribution of coral reef fish biodiversity on Bontosua Island, Spermonde Archipelago, South Sulawesi Province

Background: Bontosua Island, located in the Spermonde Archipelago, is one of the strategic and historical islands in the region. The island plays a vital role in ecosystem conservation efforts by the local community, who are committed to preserving their natural environment. With dynamic ecosystem conditions, it is essential to understand the seabed cover and biodiversity on the island to support more effective conservation efforts. Methods: This study was conducted from March to September 2022 to mark the seabed cover of Bontosua Island and understand the relationships between coral reef fish species and the ecosystem. The method used was a stationary visual census, followed by descriptive analysis and one-way ANOVA to assess the biodiversity and diversity of coral reefs. Findings: The results showed a total of 1,306 coral reef fish from 9 families with 63 species identified, including the families Chaetodontidae, Serranidae, Lutjanidae, Haemulidae, Lethrinidae, Scaridae, Acanthuridae, and Siganidae. The families with the highest citations were Scaridae, Acanthuridae, and Siganidae. The highest coral reef fish were found in the north, west, and southwest of the island, where live coral areas are more dominant. Conclusion: This study shows that Bontosua Island has significant coral reef diversity, and certain areas, such as the north and southwest sides, have higher reported fish. These findings indicate the importance of these areas in conservation efforts. Novelty/Originality: This study provides a comprehensive picture of the seabed cover and coral reef fish diversity in Bontosua Island, which has yet to be widely discussed in previous studies. These findings provide a scientific basis for more targeted conservation strategies and help improve understanding of the dynamics of coral reef ecosystems in the Spermonde Islands.. Novelty/Originality of this article: This study proposes developing a community-based coral reef monitoring system that integrates local knowledge with digital mapping technology, aiming to increase the effectiveness of conservation and management efforts of marine resources on small islands.

Red Sea Coral Reef Monitoring Site in Sudan after 39 Years Reveals Stagnant Reef Growth, Continuity and Change

Coral reefs off the coast of the Republic of Sudan are still considered to be among the most pristine reefs in the central Red Sea. The complex coastal fringing reefs, offshore banks, and shoals of Dungonab Bay in the north and Sanganeb atoll situated further to the south, about 23 km off the Sudanese mainland coast, were inscribed on the UNESCO World Heritage List in 2016. Due to their remote location and limited access, monitoring of the status of the reefs has been sporadic. Here, we present the results of a repeated large area photomosaic survey (5 m × 5 m plots) on the Sanganeb atoll, first established and surveyed in 1980, and revisited in 1991 and most recently in 2019. The 2019 survey recovered and reinstated the four original monitoring plots. Evaluation of photographic and video records from one photomosaic plot on the seaward slope of the atoll revealed general continuity of the overall community structure and composition over 39 years. Individual colonies of Echinopora gemmacea and Lobophyllia erythraea were recorded in the exact same positions as in the 1980 and 1991 plots. The genera Acropora and Pocillopora remain dominant, although in altered proportions. Shifts in composition were detected at the species level (e.g., increase in Pocillopora verrucosa, Stylophora pistillata, Acropora hemprichii, Dipsastraea pallida, and Echinopora gemmacea, decrease in Acropora cytherea and A. superba), in addition to changes in the extent of uncolonized substrate (e.g., increase from 43.9% in 1980 to 52.2% in 2019), and other scleractinian, hydrozoan, and soft coral living cover. While the temporal resolution only includes three sampling events over 39 years (1980, 1991, 2019), this study presents one of the longest time series of benthic community surveys available for the entire Red Sea. A semi-quantitative estimate of vertical reef growth in the studied test plot indicates a reduction in net accretion rates of more than 80%, from 2.27 to 2.72 cm/yr between 1980 and 1991 to 0.28–0.42 cm/yr between 1991 and 2019. We carefully conclude that the changes observed in the coral community in the plot in 2019 (Acropora–Pocillopora shift, increase in Montipora and calcareous algae) are representative of impacts at the community level, including rising sea surface temperatures and recent bleaching events.

Beyond texture: unveiling spiny crown-of-thorns starfish with multiresolution analysis

Coral reefs are essential ecosystems in the vast expanses of oceans, nurturing various forms of marine life within their vibrant and expansive structures. However, these underwater paradises suffer considerable threat from the population explosions of crown-of-thorns starfish (COTS), which detrimentally affect scleractinian corals across the Indo-Pacific region. This study addresses the early drawback of solely relying on texture analysis for COTS detection, recognizing the associated insufficiency due to variability in reef substrates. By integrating multiresolution analysis employing wavelet transform, edge information, and texture analysis using gray-level co-occurrence probability, this approach employs crucial Haralick features refined for pattern recognition. This enables a more detailed understanding of COTS traits, including the detection of the numerous sharp spines that cover their upper bodies. This approach considerably enhances classification reliability, making notable progress with an impressive accuracy of 95.00% using the eXtreme Gradient Boosting (XGBoost) Classifier. Moreover, this model streamlines processing requirements by increasing computational and memory efficiencies, making it more resource-efficient than the current models. This advancement enhances detection and opens avenues for early intervention and future research. Furthermore, integrating the model with underwater imagery could enable citizen science initiatives and autonomous underwater vehicle (AUV) surveys. Empowering trained volunteers and equipping AUVs with this technology could considerably expand coral reef monitoring efforts. Early COTS outbreak detection allows for shorter response times, potentially mitigating the damage and facilitating targeted conservation strategies.

Integrating Widespread Coral Reef Monitoring Tools for Managing both Area and Point Annotations

Abstract. Large-area image acquisition techniques are essential in underwater investigations: high-resolution 3D image-based reconstructions have improved coral reef monitoring by enabling novel seascape ecological analysis. Artificial intelligence (AI) offers methods for significantly accelerating image data interpretation, such as automatically recognizing, enumerating, and measuring organisms. However, the rapid proliferation of these technological achievements has led to a relative lack of standardization of methods. Remarkably, there are notable differences in procedures for generating human and AI annotations, and there is also a scarcity of publicly available datasets and shared machine-learning models. The lack of standard procedures makes it challenging to compare and reproduce scientific findings. One way to overcome this problem is to make the most used platforms by coral reef scientists interoperable so that the analyses can all be exported into a common format. This paper introduces functionality to promote interoperability between three popular open-source software tools dedicated to the digital study of coral reefs: TagLab, CoralNet, and Viscore. As users of each platform may have different analysis pipelines, we discuss several workflows for managing and processing point and area annotations, improving collaboration among these tools. Our work sets the foundation for a more seamless ecosystem that maintains the established investigation procedures of various laboratories but allows for easier result sharing.

Initial Assessment of the Benthic Profile and Reef Fish Composition of the Damilisan Marine Sanctuary, Miagao, Iloilo, Philippines

Coral reefs are among the world’s most diverse and productive ecosystems. They provide valuable ecosystem services, including support for fisheries production and the livelihood of coastal communities. However, coral reefs are also threatened by anthropogenic and climate stressors. Establishing a marine protected area (MPA) is a typical coastal management tool widely used in the Philippines for conserving coral reefs and managing the fishing effort. This study presents initial information on the benthic and fish community profiles of the Damilisan Marine Sanctuary (DMS) in Miagao, Iloilo, Philippines. A modified point-intercept transect method was used to characterize the benthic profile, while the underwater visual census (UVC) was used to determine the fish community profile (diversity and density). The substrate was mainly composed of hard coral (HC) (39.3±18.3%), and the common HC observed were massive Porites and Goniopora, each contributing 12.8% of the total HC cover. Twenty coral reef fish species belonging to 10 families were identified during the UVC and had an overall density of 99±69 ind./1000 m2. Damselfishes (Family Pomacentridae) was the most abundant family (23±0.4 ind./500 m2), while Lutjanus semicinctus was the most abundant species (8.5±1.4 ind./500 m2). It is recommended that regular coral reef monitoring surveys be conducted in the DMS and in other MPAs in Miagao to monitor trends and changes in the HC cover and reef fish density and diversity in the MPA.

Coral Shoals Detection from Optical Satellite Imagery Using Deep Belief Network Algorithm: A Case Study for the Xisha Islands, South China Sea

Coral islands and reefs are formed by the cementation of the remains of shallow water reef-building coral polyps and other reef dwelling organisms in tropical oceans. They can be divided into coral islands, coral sandbanks, coral reefs, and coral shoals, of which, Coral shoals are located below the depth datum and are not exposed even at low tide, and sometimes are distributed at water depths exceeding 30 m. Satellite images with wide spatial–temporal coverage have played a crucial role in coral island and reef monitoring, and remote sensing data with multiple platforms, sensors, and spatial and spectral resolutions are employed. However, the accurate detection of coral shoals remains challenging mainly due to the depth effect, that is, coral shoals, especially deeper ones, have very similar spectral characteristics to the sea in optical images. Here, an optical remote sensing detection method is proposed to rapidly and accurately detect the coral shoals using a deep belief network (DBN) from optical satellite imagery. The median filter is used to filter the DBN classification results, and the appropriate filtering window is selected according to the spatial resolution of the optical images. The proposed method demonstrated outstanding performance by validating and comparing the detection results of the Yinli Shoal. Moreover, the expected results are obtained by applying this method to other coral shoals in the Xisha Islands, including the Binmei Shoal, Beibianlang, Zhanhan Shoal, Shanhudong Shoal, and Yongnan Shoal. This detection method is expected to provide the coral shoals’ information rapidly once optical satellite images are available and cloud cover and tropical cyclones are satisfactory. The further integration of the detection results of coral shoals with water depth and other information can effectively ensure the safe navigation of ships.

ReScape: transforming coral-reefscape images for quantitative analysis

Ever since the first image of a coral reef was captured in 1885, people worldwide have been accumulating images of coral reefscapes that document the historic conditions of reefs. However, these innumerable reefscape images suffer from perspective distortion, which reduces the apparent size of distant taxa, rendering the images unusable for quantitative analysis of reef conditions. Here we solve this century-long distortion problem by developing a novel computer-vision algorithm, ReScape, which removes the perspective distortion from reefscape images by transforming them into top-down views, making them usable for quantitative analysis of reef conditions. In doing so, we demonstrate the first-ever ecological application and extension of inverse-perspective mapping—a foundational technique used in the autonomous-driving industry. The ReScape algorithm is composed of seven functions that (1) calibrate the camera lens, (2) remove the inherent lens-induced image distortions, (3) detect the scene’s horizon line, (4) remove the camera-roll angle, (5) detect the transformable reef area, (6) detect the scene’s perspective geometry, and (7) apply brute-force inverse-perspective mapping. The performance of the ReScape algorithm was evaluated by transforming the perspective of 125 reefscape images. Eighty-five percent of the images had no processing errors and of those, 95% were successfully transformed into top-down views. ReScape was validated by demonstrating that same-length transects, placed increasingly further from the camera, became the same length after transformation. The mission of the ReScape algorithm is to (i) unlock historical information about coral-reef conditions from previously unquantified periods and localities, (ii) enable citizen scientists and recreational photographers to contribute reefscape images to the scientific process, and (iii) provide a new survey technique that can rigorously assess relatively large areas of coral reefs, and other marine and even terrestrial ecosystems, worldwide. To facilitate this mission, we compiled the ReScape algorithm into a free, user-friendly App that does not require any coding experience. Equipped with the ReScape App, scientists can improve the management and prediction of the future of coral reefs by uncovering historical information from reefscape-image archives and by using reefscape images as a new, rapid survey method, opening a new era of coral-reef monitoring.

Global Chlorophyll Concentration Distribution and Effects on Bottom Reflectance of Coral Reefs

Despite the limited coverage of coral reefs in the world’s oceans, they play a crucial role in global marine biodiversity and providing essential ecosystem services. This study explores the influence of chlorophyll-a (Chl-a) concentration in the water column on the estimation of bottom reflectance (rb) in coral reefs monitored by the Allen Coral Atlas coral reef monitoring system, using satellite imagery from a Sentinel-2 MSI sensor. We conducted a comprehensive analysis, considering Chl-a global distribution and variability, and its combined effect with water column depth over rb calculation. Our results demonstrated that the impact of Chl-a on rb estimation becomes significant when the water column depth exceeds 3 m. While suggesting the optionality of using regional Chl-a values, our study highlights potential overestimations of Chl-a in optically complex environments, such as along the Brazilian coast. This research contributes to refining coral reef monitoring systems and underscores the importance of accurate Chl-a assessments for robust environmental evaluations.

Developing a population assessment for Caribbean spiny lobster Panulirus argus in the United States Virgin Islands: lessons learned

The Caribbean spiny lobster (Panulirus argus) is one of the most important fisheries in the US Virgin Islands. Low densities and life-history characteristics of lobsters have made reliable surveys in the region notoriously difficult, contributing to sparse and inconsistent fisheries- independent datasets to inform management decisions. Many existing and historical surveys are limited in their temporal and spatial coverage. In 2019, the National Park Service initiated a long-term monitoring effort of spiny lobsters in Buck Island Reef National Monument and Salt River National Historical Park and Ecological Preserve, St. Croix, US Virgin Islands, and Virgin Islands National Park, St. John, US Virgin Islands. Initial surveys show both density (3.06–7.07 lobsters ha –1) and occupancy (0.11–0.12 sites with lobster) are low and not significantly different between the three parks. While survey efforts are currently focused on national parks, the 50 × 50 m gridded sample frame and stratification scheme are adaptable to the wider US Caribbean region, modelled after the multi-agency design to monitor reef fish with the National Coral Reef Monitoring Program. With the same survey footprint as reef fish surveys, the two methods could conceivably be co-located in the future, improving cost-efficiency and overall data quality. If expanded, this methodology could provide regional fishery managers with much needed density indices, though future surveys would benefit from a greater sample size or by supplementing with other methods that increase the number of observed lobsters to allow for robust size-frequency analyses.

Integrating the US Caribbean Reef Fish Visual Census into fishery stock assessments

The United States Caribbean Reef Fish Visual Census (RVC) is a fishery-independent survey of the coral reef fish community, comprised of hundreds of species, designed to provide quantitative information for data-poor fisheries to support regional stock assessments. The Caribbean RVC, implemented in 2014 as part of the National Coral Reef Monitoring Program (NCRMP), uses a probabilistic stratified random sampling design capitalizing on the strong mean-variance relationship of population abundance dependent on hardbottom habitats and depths. Here we highlight RVC data for queen triggerfish in Puerto Rico, where survey precision improved from CVs of 20.6% in 2014 to 12.6% in 2021, which produced accurate and cost-effective estimates of key assessment metrics like size- structured relative population abundance and biomass. We estimated a relatively stable exploited-phase average population abundance for 2014–2021 at 2.3 million queen triggerfish with a biomass of 1246 metric tons. To support stock assessments using "statistical catch at age" models, we generated a 2001–2021 time series of several population metrics by a statistical conversion of spatially-restricted, pre- NCRMP data to the RVC frame. To cross-check assessment modeling, we combined 2014–2021 RVC results with life history demographics in an alternative length-based risk analysis (LBRA) model to evaluate stock sustainability consequences using two lifetime growth scenarios. LBRA models indicated low overfishing risks. We concluded that only a few quality fishery-independent surveys are needed to obtain understanding of stock sustainability status, and that RVC not only provides reliable data for single-species stock assessments, but also for design evaluation of marine protected areas and ecosystem-based fishery management.

Expansion of an established fishery-independent survey into the US Virgin Islands' upper mesophotic zone: feasibility and management implications

A three-year pilot study, the Deep Coral Reef Monitoring Program (DCRMP), expanded the National Coral Reef Monitoring Program's (NCRMP) established fishery-independent, diver-based reef fish visual survey to upper mesophotic reefs (>30 to 50 m) in the United States Caribbean for the first time. The new DCRMP sample domain (>30 to 50 m) encompassed 2.4 times more survey area than NCRMP (0 to ≤30 m) and collected high quality data (CV <20%) on coral reef fishes [three survey years, 29 (5) species; mean (standard deviation)]. For the four representative, fishery-targeted, analysis species selected (i. e., grouper, snapper, triggerfish, and parrotfish), domain-wide density and length comparisons between surveys showed similar or statistically higher abundances and larger lengths for fishes at deeper depths (>30 to 50 m). These results highlight the importance of surveying the entire insular shelf in St. Thomas and St. John, US Virgin Islands for fisheries management applications. Furthermore, the DCRMP survey leveraged NCRMP's methods and resources resulting in a seamless extension to deeper waters. However, if these programs were fully integrated and optimized within a single survey design, approximately half the sites would be needed to achieve the same level of precision, offering substantial time and cost savings. The principles of probabilistic sampling successfully used in the present fishery-independent survey design (0 to 50 m) can be applied more broadly to develop an "ideal" large-scale, multi-gear survey from 0 to about 500 m to encompass the entire depth ranges of managed species in the US Caribbean.

Scalable semantic 3D mapping of coral reefs with deep learning

Abstract Coral reefs are among the most diverse ecosystems on our planet, and essential to the livelihood of hundreds of millions of people who depend on them for food security, income from tourism and coastal protection. Unfortunately, most coral reefs are existentially threatened by global climate change and local anthropogenic pressures. To better understand the dynamics underlying deterioration of reefs, monitoring at high spatial and temporal resolution is key. However, conventional monitoring methods for quantifying coral cover and species abundance are limited in scale due to the extensive manual labor required. Although computer vision tools have been employed to aid in this process, in particular structure‐from‐motion (SfM) photogrammetry for 3D mapping and deep neural networks for image segmentation, analysis of the data products creates a bottleneck, effectively limiting their scalability. This paper presents a new paradigm for mapping underwater environments from ego‐motion video, unifying 3D mapping systems that use machine learning to adapt to challenging conditions under water, combined with a modern approach for semantic segmentation of images. The method is exemplified on coral reefs in the northern Gulf of Aqaba, Red Sea, demonstrating high‐precision 3D semantic mapping at unprecedented scale with significantly reduced required labor costs: given a trained model, a 100 m video transect acquired within 5 min of diving with a cheap consumer‐grade camera can be fully automatically transformed into a semantic point cloud within 5 min. We demonstrate the spatial accuracy of our method and the semantic segmentation performance (of at least 80% total accuracy), and publish a large dataset of ego‐motion videos from the northern Gulf of Aqaba, along with a dataset of video frames annotated for dense semantic segmentation of benthic classes. Our approach significantly scales up coral reef monitoring by taking a leap towards fully automatic analysis of video transects. The method advances coral reef transects by reducing the labor, equipment, logistics, and computing cost. This can help to inform conservation policies more efficiently. The underlying computational method of learning‐based Structure‐from‐Motion has broad implications for fast low‐cost mapping of underwater environments other than coral reefs.

Unidentified fish sounds as indicators of coral reef health and comparison to other acoustic methods

As biodiversity hotspots, coral reefs are rich with sound cues. Monitoring these soundscapes for changes is essential as coral reefs decline around the world rapidly. Despite this, acoustic metrics that reliably represent reef health are still debated, and ground-truthing of methods are limited. We sought to investigate the occurrence and prevalence of fish sounds in relation to reef health, providing a foundation to compare assessment methods. We first quantified fish call rates for three U.S. Virgin Islands reefs exhibiting different community assemblages, by manually annotating fish calls across 8 days per site. Call rates were then compared with traditional visual surveys, and several acoustic methods commonly used in underwater soundscape research. Manually detected fish call rates successfully differentiated between the three reefs, capturing variation in crepuscular activity levels and predicting hard coral cover, fish abundance, and fish species richness. Meanwhile, most acoustic indices failed to parse out fine distinctions among the three sites, although sound pressure level showed the greatest correlation to call rates. Development of methods to improve the utilization of unknown fish sounds, such as automatic detection and classification tools, supports global sound library efforts and is essential to implement large-scale passive acoustic monitoring of coral reefs.

Epizoic yellow sponge (Poecilosclerida, Demospongiae) expansion on living Scleractinian corals in Bintan, Riau Archipelago, Indonesia

AbstractCoral reef ecosystems in Indonesia are under threat due to changes in the environment driven by global climate change, along with local disturbances such as sedimentation and eutrophication. Consequently, comprehensive coral reef monitoring activities have been initiated at numerous locations across Indonesia. In this study, the findings from coral reef health surveys across 14 reef sites (within 40 hectares) in the Bintan area (Riau Archipelago, Indonesia; 100 km southeast of Singapore) revealed a potentially novel epizoic yellow sponge species (Phorbas sp.) that overgrows coral colonies. This species, tentatively classified as a new Phorbas sp. (order Poecilosclerida: family Hymedesmiidae), was identified through a combined approach employing classical taxonomic methods along with DNA barcoding using the cytochrome c oxidase I (COI) gene. At every site, three permanent 20‐m transects were established to annually monitor live coral coverage and species composition between 2014 and 2017. The survey indicated a notable change in the overall coral cover during this period. The abundance of coral diseases was investigated in 2014 and 2017. Additionally, the progress of Phorbas sp., was closely monitored (i.e., every second day for one week) at Bintan Island (site 11) during the dry season in August 2017. This approach aimed to approximate the relative impact of each incident on the coral's condition. The results indicated that the most comprehensive change occurred due to the overgrowth of Phorbas sp., which affected 12 scleractinian coral species across eight genera in almost all sites except one. The abundance of this epizoic sponge infestation was highest at Pulau Beralas Pasir (site 10), constituting 22.9% of all recorded life forms, and lowest at Pulau Pangkil‐Besar (site 13), with only 0.7%. The expansion of the thin yellow sponge tissue was estimated to increase by up to 0.51 ± 0.48 cm2 per day on Porites coral.

Assessing the consistency and reliability of the line intercept transect method in coral cover estimation using structure from motion photogrammetry techniques

Appropriate coral reef monitoring methods and descriptors determine the effectiveness of ecosystem status assessment. We combined line intercept transect (LIT) and SfM technologies for image acquisition, and the POS information of images is defined and assigned in the relative coordinate system we build. Then, constructed high-resolution orthomosaics of coral reefs along measuring tapes in Metashape. We compared the estimates of coral cover by three methodologies (LIT, LIT on orthomosaic (LITO), surface analyses on orthomosaic (SAO)) and used virtual transects on orthomosaics and their estimates of coral cover to assess the consistency and reliability of LIT. The results show that the estimation of coral cover by LITO or LIT is more consistent and reliable when coral cover is higher, and the probability of overestimation is lower. SfM technology is increasingly used in coral reef survey and assessment, and has the potential to become the new standard for coral reef survey.

Monitoring climate change effects on coral reefs using edge-based image segmentation

Coral reefs are valuable ecosystems that face vulnerability to climate change impacts. Underwater images often encounter noise from various factors, such as water turbidity, lighting conditions, attenuation, and scattering, which can complicate edge detection and segmentation processes, leading to inaccuracies. However, image processing techniques offer a viable solution to this issue. In this study, an edge-based segmentation approach is proposed that uses multiple contrast techniques to detect and quantify changes in coral reef imagery. The proposed approach effectively identifies changes in coral reef imagery, making it a valuable tool for monitoring climate change's effects on these ecosystems. Furthermore, high-resolution images at different time points and locations were collected, and then an edge-based segmentation approach was utilized to enhance the accuracy of edge detection and segmentation. Comparing the proposed method with traditional segmentation techniques showed a significant improvement in terms of segmentation precision. Subsequently, alterations in the structure and composition of coral reefs are observed, indicating the influence of climate change on these ecosystems. This research highlights the capabilities of image processing techniques using edge-based segmentation in monitoring coral reefs. It offers an effective and precise approach to detecting changes in coral reef images, thereby contributing to conservation endeavors.

Remotely sensed habitat diversity predicts species diversity on coral reefs

The ability to map coral reef biodiversity from orbit would greatly advance marine spatial planning. Presently, reef biodiversity is measured in situ by divers, but this method is inefficient and expensive. Remote sensing techniques have been developed in the terrestrial realm to lever the “habitat heterogeneity hypothesis,” which contends that the biodiversity of organisms is directly linked to the diversity of habitats which they occupy. Here, we evaluated this hypothesis using satellite-derived reef habitat maps across 880 shallow-water field stations distributed on a global transect through the Atlantic, Pacific, and Indian Oceans where we measured the taxonomic and functional diversity of reef fish and corals. Benthic habitat maps produced from WorldView-2 and PlanetScope were compared as proxies for coral reef species diversity. We found satellite-mapped habitat heterogeneity to be positively correlated with both fish (R ≤ 0.64) and coral diversity (R ≤ 0.56), at an optimum spatial scale between 110 and 320 ha, even when the data were partitioned across global biodiversity gradients. Best results were realized when using the WorldView-derived habitat maps, indicating that the enhanced spectral and radiometric resolution of this sensor is better poised for retrieving species biodiversity than PlanetScope. Our study offers an efficient method for monitoring coral reef ecosystems by leveraging satellite maps as a surrogate for species biodiversity.