Reef Mapping Research Articles

Releasing a dataset of 3D models of artificial reefs from the northern red-sea for 3D printing and virtual reality applications

Artificial reefs are anthropogenic structures that are submerged in purpose to mimic some of the attributes of natural reefs. Here we describe our workflow for 3D mapping of artificial reefs, particularly shipwrecks, and release a dataset containing two 3D models of some of the most epic dive sites in Israel. Our goal is to share our 3D models and protocol with the general public and to enable the scientific and recreational community to document artificial reefs in 3D and use the models in 3D visualization and printing applications. We envision that the models will be used by divers and 3D printing enthusiasts, dive operators, Non-Governmental Organizations, and government agencies dealing with underwater monitoring and marine spatial planning.

Biomass mapping of fjordic cold-water coral reefs reveals distinct functional role of vertical wall habitat

Fjords provide unique habitats for large cold-water coral (CWC) reefs, typically growing on sills and vertical walls. Fjord reefs are among the most thriving CWC reefs in Norway. Yet, these reefs, especially the wall reefs, are notoriously understudied. Here, we mapped the biomass, total carbon (C) stocks and C turnover (as respiration) of the reef-building coral Lophelia pertusa (syn. Desmophyllum pertusum), and dominant, large CWC reef-associated suspension feeders (the sponges Geodia barretti and Mycale lingua, the CWC Madrepora oculata and the bivalve Acesta excavata) within the Hardangerfjord, Norway. Remotely Operated Vehicle (ROV) recorded videos from wall and sill reefs were used to estimate species-specific biomass. Coupled with high resolution terrain data (2 × 2 m), predictive maps of species biomass were produced using a random forest (RF) model. The resulting biomass data were integrated with species-specific C content and C respiration rates from literature to estimate C stocks and C turnover of wall versus sill reefs. Area-specific results from the RF models reveal that wall reefs had a higher habitat suitability for all species except L. pertusa, which was more dominant on the sills. Accordingly, the wall reefs supported an up to 11 times higher biomass, C stock, and turnover for all species, except for L. pertusa, which had two-fold higher values on the sill reef. As a result, the wall reefs showed a 1.5 to 4.8 times higher total mean C turnover by dominant suspension feeders (all studied species) compared to the sill reefs. With their high C turnover and their presumably wide distribution in Norwegian fjords and globally, benthic wall reef megafauna may have a substantial, but overlooked biomass and functional role within CWC reef systems.

Two decades of coral bleaching in selected islands of Pacific Ocean: A holistic impact assessment

Coral reefs are regarded as one of the most diverse, productive as well as sensitive ecosystems existing across the world. Major coral reefs across the Pacific Ocean exhibit a vast variety of species, the majority of which are under threat, largely due to changes in past conducive climatic and oceanic parameters. The present study involves the mapping of coral reefs at select islands of Great Barrier Reefs (Heron Island), Fiji (Namena Island), and Cook Island (Aitutaki Island) using Landsat 7 (ETM+) and Landsat 8 (OLI/ TIRS) satellite data for the years 2002 and 2020. Depth invariant bottom index (DII) is applied for water column correction and identification of bottom type submerged and heterogeneous coral reef classes. Maximum likelihood classifier (supervised classification) is performed to characterize various classes of the coral community. Turbidity values (10.4 – 53 Nephelometric Turbidity unit (NTU)), (4.74–26.1NTU), and (14.56–73.56 NTU) for Heron, Namena, and Aitutaki Islands indicate a pronounced effect of sedimentation due to an increase in Sea level rise and anthropogenic activities. Increasing trend of oceanic parameters viz. SST, PAR, salinity, and sea level rise indicated an adverse impact on coral health and triggered coral bleaching along these Islands especially due to El Niño events in the years 2015–2016. We identified the coral mortality for 18 years of period as 16.1 %, 20.2 % and 11. 2 % in Heron, Namena, and Aitutaki Island respectively. The study provides a holistic impact assessment of various anthropogenic activities and climate change on the health of the coral reef ecosystem.

Optimum image alignment setting selection for structure-from-motion photogrammetry using Remotely Piloted Aircraft Systems (RPAS) to support coral habitat classification

Coral reefs provide a range of ecological services and support highly diverse coastal ecosystem. However, the extent of living corals has been reduced by half globally as a result of anthropogenic natural stressors. Continuous monitoring by deploying accessible remote sensing (RS) methods to map coral habitats is necessary for effective conservation and management strategies. Recently, there has been an increased use of Remotely Piloted Aircraft Systems (RPAS) for coral reef RS. Image misalignment is considered a key problem in the Structure from Motion (SfM) workflow. Combinations of image alignment settings (IAS) and key point limit (KPL) optimizations aim to improve quality of the sparse cloud while lowering SfM reconstruction uncertainty, increasing projection accuracy, and ultimately improving coral habitat classification accuracy. In this study, the orthoimages were produced from a total of 25 IAS (lowest, low, medium, high and highest) and KPM (5k, 10k, 20k, 40k, and 60k) combinations using Agisoft™ Metashape software. Measurements of geometric distortion, efficiency, and completeness were used to evaluate these orthoimages with three visible bands. Results that satisfied the requirements for both geometric quality, spectral accuracy, processing efficiency identified the optimum alignment methods needed for routinely monitoring and mapping of coral reefs of Pulau Bidong. The SfM-image alignment techniques chosen for this study could produce the coral mapping extent, a higher number of tie points and matches, better image alignment success and coral habitat classification accuracy, less processing time and memory usage and no geometric distortions. The development of the methodology for the optimum parametrization of RPAS multispectral imagery would be beneficial to researchers studying coral reefs, marine sciences, and RS data analysts. It would provide them reliable methods for evaluating the quality of orthoimages and faster processing methods to achieve coastal RS objectives.

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.

On the hydrothermal nature of cherts in the Artinskian stage deposits of the Cis-Urals

Relevance. The results of paleontological, geophysical, and mineragraphic studies of flints in Artinskian stage deposits in the Cis-Urals are presented. Purpose of the work. Reef organogenic structures with silicified limestones and flints, silicified sandstones and injections of flints into them have been studied. The goal is to show the hydrothermal nature of chert injections into reefs and sandstones. Research methods. Geophysical electrical prospecting studies of reef limestones with the aim of identifying areas of silicification, geological and paleontological mapping of reefs, biostromes, bioherms, sandstones, areas of accumulation of nodules and flint nodules, study of polished sections of silicified rocks, production of thin sections and their mineragraphic study. Results of the work. Paleontological mapping of the reef structure made it possible to establish the different species composition, size, and preservation of biota in the coastal, summit and outer parts of the reef. Mineragraphic studies have established the high-temperature hydrothermal nature of chert in all types of sediments. Hydrothermal injections of chert into sandstone layers occur by diffusion with the formation of siliceous nodules and Lysigang rings in sandstones. Intensive penetration of silicon hydrotherms into sandstone layers forms areas of silicification in them in the form of laccoliths tens of meters in size. Various degrees of development of flint hydrotherms by marine fauna have been established. Flints may include small specimens of small fauna, be rich in well-preserved fauna, or include layers of biota overlain by layers of flints covered with new colonies of microorganisms. Sometimes cherts contain only fragments of faunal shells caught in the hot gel of cherts. Conclusions. Hydrotherms of siliceous composition in loose marine sediments could be a condition for the development of biota and the basis for reef structures. Increased values of electrical resistivity of flints and silicified limestones allow electrical prospecting to explore the internal structure of reefs. It has been suggested that in the Carboniferous and Early Permian in the Cis-Urals there was a hydrothermal belt extending from Kungur to Sterlitamak.

Impact of Climate Change on Coral Reefs Degradation at West Lombok, Indonesia

Coral reefs are one of the ecosystems that provide economic and environmental benefits to coastal communities in Indonesia. However, coral reef ecosystems are also one of the ecosystems threatened by climate change at the local scale. The waters of North Sekotong, West Lombok, Indonesia, are a tropical coastal system with beautiful coral reefs and marine ecosystems. Coral reef damage has been widespread in this area due to increased water temperatures. Increased water temperature results in coral reef degradation. Field surveys were conducted on May 23-28, 2016, in collaboration with the Marine and Coastal Resources Research and Development Center, Ministry of Marine Affairs and Fisheries, and coral reef mapping using Landsat 7 and Landsat 8 during 2002 - 2016 as well as processing monthly sea surface temperature (SST) data from the AquaModis and Oi SST V2 satellites and daily SST data from the NOAA Coral Reef Watch satellite. Changes in coral cover area were compared with temperature changes due to climate change. The increase in temperature creates a hotspot phenomenon in the coral reef ecosystem, resulting in coral reef degradation. The results showed that coral reefs in this area have degraded by 17.55% or 78.21 Ha from 455.68 Ha (2002) to 367.46 Ha (2016), with a degradation rate of 2.8 Ha/year in 2002 - 2014; 8.1 Ha/year (2014 - 2014) and 36 Ha/year (2015 - 2016) caused by an increase in SST which caused a hotspot phenomenon with a high enough intensity that there was an increase in temperature in 2016 which reached 9.77oC.

Atolls of the world: A reappraisal from an optical remote sensing and global mapping perspective

The Millennium Coral Reef Mapping Project (MCRMP) aimed to map coral reefs worldwide at geomorphological thematic scales using Landsat satellite images at 30 m spatial resolution. The 5-level hierarchical classification scheme implemented by MCRMP identified at Level 2 ‘Atolls’ as one of the main types of coral reef complexes. In this review, the qualitative criteria used by MCRMP to identify atolls are presented. Then, we report on the global census of atolls, from which a consistent geomorphologic GIS database is provided. A total of 598 atolls are identified and mapped. The quantitative database provides surface areas for all geomorphologic units at Level 4 and 5 of the MCRMP classification scheme. It allows further work on atoll and coral reef classifications, regionally and globally, in order to identify geomorphic trends and outliers. It is also a convenient database to use in multivariate analyses with ancillary biodiversity, fishery, socio-economic or climate data.

Fine-Grained 3D Modeling and Semantic Mapping of Coral Reefs Using Photogrammetric Computer Vision and Machine Learning.

Corals play a crucial role as the primary habitat-building organisms within reef ecosystems, forming expansive structures that extend over vast distances, akin to the way tall buildings define a city's skyline. However, coral reefs are vulnerable to damage and destruction due to their inherent fragility and exposure to various threats, including the impacts of climate change. Similar to successful city management, the utilization of advanced underwater videography, photogrammetric computer vision, and machine learning can facilitate precise 3D modeling and the semantic mapping of coral reefs, aiding in their careful management and conservation to ensure their survival. This study focuses on generating detailed 3D mesh models, digital surface models, and orthomosaics of coral habitats by utilizing underwater coral images and control points. Furthermore, an innovative multi-modal deep neural network is designed to perform the pixel-wise semantic segmentation of orthomosaics, enabling the projection of resulting semantic maps onto a 3D space. Notably, this study achieves a significant milestone by accomplishing semantic fine-grained 3D modeling and rugosity evaluation of coral reefs with millimeter-level accuracy, providing a potent means to understand coral reef variations under climate change with high spatial and temporal resolution.

The global biogeography of reef morphology

AbstractAimThe Caribbean and Indo‐Pacific are separate biogeographical realms with distinct biogeographical and evolutionary histories, a 10‐fold difference in biodiversity, and highly disparate sea‐level histories. Since reef morphology often reflects interactions between biological activity and biogeographical history, including sea levels, the widths of shallow coral reef habitats are likely to differ markedly between realms, with ramifications for numerous ecosystem functions. Our goal, therefore, was to assess the impact of global‐scale biogeographical and evolutionary histories on coral reef habitats. Specifically, are Indo‐Pacific reefs wider than their Caribbean counterparts?LocationGlobal.Time PeriodModern.Major Taxa StudiedCoral reefs.MethodsWe used the Allen Coral Atlas, a global reef mapping system (3 m pixel resolution), to examine 3765 transects, 3 km long and 1 km apart, on 60 reefs across the two realms, quantifying shallow reef habitat widths (Inner and Outer Reef Flat, and Reef Crest) using ArcGIS.ResultsShallow reef habitat widths were strikingly similar between the Caribbean and Indo‐Pacific. Estimated modal widths diverged by just 37 m; means by just 122 m. Although shallow reef zones appeared to be wider in the Indo‐Pacific, habitat widths on atolls were almost identical across realms (means varying by less than 8 m).Main ConclusionsOur remote sensing approach provides a global description of the biogeography of coral reefs as biogenic structures. Furthermore, we can assess the relative importance of realm‐wide differences in coral diversity and sea‐level history on reef growth. The striking similarity of reef widths across realms suggests that reef growth (net reef accretion) is largely independent of coral diversity, or sea‐level history, and that other factors may have played a major role in constraining shallow reef widths. These factors may include geomorphology (e.g. antecedent topography and historical accommodation space) and, once at sea level, self‐limiting local hydrodynamics.

Aplikasi Teknologi Drone Sebagai Pelengkap Data Survei Lapang Untuk Pemetaan Ekosistem Terumbu Karang Menggunakan Citra Worldview-2

Coral reefs are one of the important ecosystems that need data inventory in order to manage marine resources both spatially and temporally. Remote sensing with drone technology is one of the vehicles for collecting data related to coral reef ecosystems in real time. Along with the times, drones are widely used in research both in terrestrial and in coastal areas. This research was conducted on December 4 – 10, 2018 in the Karang Lebar, Seribu Islands, Jakarta. The purpose of this research was to explored the ability of drones as a vehicle to complement field survey data for coral reef mapping. In addition, conducting a pixel-based analysis for coral reef classification based on WorldView-2 satellite imagery using field survey data and additional data from drone data. Based on the results of the study, information was obtained that drone imagery can help visual interpretation in detecting coral reefs. Thus, field survey data that are not covered in the research area can be assisted by the use of drones as a complementary vehicle for field survey data. The coral reef classification process using WorldView-2 imagery can be mapped well. Drone imagery has proven to be an alternative in field data collection as a training area in identifying coral reef objects, such as with massive coral lifeforms that can be clearly seen from drone images. It's just that visual interpretation is limited because of the water column, making it difficult to identify objects.

High-resolution 3D mapping of cold-water coral reefs using machine learning

Structure-from-Motion (SfM) photogrammetry is a time and cost-effective method for high-resolution 3D mapping of cold-water corals (CWC) reefs and deep-water environments. The accurate classification and analysis of marine habitats in 3D provide valuable information for the development of management strategies for large areas at various spatial and temporal scales. Given the amount of data derived from SfM data sources such as Remotely-Operated Vehicles (ROV), there is an increasing need to advance towards automatic and semiautomatic classification approaches. However, the lack of training data, benchmark datasets for CWC environments and processing resources are a bottleneck for the development of classification frameworks. In this study, machine learning (ML) methods and SfM-derived 3D data were combined to develop a novel multiclass classification workflow for CWC reefs in deep-water environments. The Piddington Mound area, southwest of Ireland, was selected for 3D reconstruction from high-definition video data acquired with an ROV. Six ML algorithms, namely: Support Vector Machines, Random Forests, Gradient Boosting Trees, k-Nearest Neighbours, Logistic Regression and Multilayer Perceptron, were trained in two datasets of different sizes (1,000 samples and 10,000 samples) in order to evaluate accuracy variation between approaches in relation to the number of samples. The Piddington Mound was classified into four classes: live coral framework, dead coral framework, coral rubble and sediment and dropstones. Parameter optimisation was performed with grid search and cross-validation. Run times were measured to evaluate the trade-off between processing time and accuracy. In total, eighteen variations of ML algorithms were created and tested. The results show that four algorithms yielded f1-scores >90% and were able to discern between the four classes, especially those with usually similar characteristics, e.g., coral rubble and dead coral. The accuracy variation among them was 3.6% which suggests that they can be used interchangeably depending on the classification task. Furthermore, results on sample size variations show that certain algorithms benefit more from larger datasets whilst others showed discrete accuracy variations (<5%) when trained in datasets of different sizes.

A review of active and passive optical methods in hydrography

The history of using optical methods for hydrographic applications dates back to the mid of the last century. Since these early days, both passive and active techniques have evolved considerably and with ever new sensors and platforms available, there is unbroken scientific progress in this field capable of capturing the submerged topography of shallow water bodies. This review article gives an overview of the employed optical methods, which can be categorized into passive and active techniques. Passive methods comprise spectrally derived bathymetry as well as multimedia stereo photogrammetry, also referred to as photo bathymetry, and the active method is laser bathymetry. Another approach for categorizing optical methods is based on the used carrier platform, where satellites, crewed aircraft, uncrewed aerial systems and lately also remotely piloted underwater vehicles are used for mapping water bodies at different scales, resolution and accuracy. Another attempt to classify the approaches is whether the sensor is above or below the water table, referred to as through-water or underwater, respectively. Another focus of the paper is on data processing techniques including refraction correction, which is crucial for most optical hydrography approaches, signal processing of the radiometric content in spectrally derived bathymetry, and analysis of the backscattered waveform signal in laser bathymetry. In the recent past, machine learning techniques play an increasingly important role not only for object classification but also for regression-based depth estimation. And finally, the paper discusses the huge variety of applications ranging from mapping navigable channels for save ship navigation, disaster documentation, flood simulation, coastal protection, benthic habitat modeling, river restoration, monitoring of hydromorphological changes, maintenance of hydroelectric power plants, mapping of off-shore infrastructure, coral reef mapping to underwater archaeology. The paper presents seminal work in the field but also focuses on the multitude of recent scientific work demonstrating the unbroken relevance of optical hydrography.

Classification of underwater photogrammetry data for temperate benthic rocky reef mapping

The fine characterization of the substrate is a baseline to thoroughly investigate the relations between organisms and their biotopes. Cutting edge spatial technologies now provide access to accurate information on biotopes and biocenoses both in terrestrial and in marine environments. Photogrammetry is one of them and has recently been applied in submarine environments especially in shallow clear water. In this study, we investigated the potential of photogrammetry to characterise benthic habitats in turbid environments. Although more challenging, turbid environments are more frequent in temperate marine coastal areas. We selected two rocky sites in the bay of Saint-Malo (Brittany, France), differentiated by their level of turbidity, one being a marine site exposed to natural tides (Buharats), while the other (Bizeux) is subjected to both natural tides and artificial currents created by the functioning of a hydroelectric dam. The different substrates observed were classified into eight classes at a centimetre resolution using photogrammetry-based spatial and multispectral predictors. The spatial benthic terrain predictors were derived from a digital surface model (DSM) at various spatial scales, and the multispectral predictors were retrieved from the red-green-blue (RGB, natural colours) orthomosaic imagery. An overall classification was computed for Buharats and Bizeux, with accuracies of 84.76% and 79.54% respectively, revealing a good quality of the substrate classification. The combination of RGB, DSM, and several spatial benthic terrain variables, with a pixel resolution of 5 and 10 mm, and a kernel size of 30, 60 and 90 pixels leads to the best benthic substrate classification (highest overall accuracy). At the class scale, producer's (PA) and user's (UA) accuracy showed that big boulders and field material were correctly distinguished. Small boulders and cobbles, having similar sizes, showed the lowest classification performances. This classification methodology provides new perspectives for mesoscale (100 m2 to 1 km2) semi-automatic mapping of the fine resolution (1 cm) relationship between benthic organisms and their substrate.

Assessing optimal UAV-data pre-processing workflows for quality ortho-image generation to support coral reef mapping

Since ortho-image constructed from unmanned aerial vehicles (UAV) acquired images has significant misalignment effect, an optimized and precise workflow (WF) is proposed in this study. Based on the parameters of photo alignment, sparse point cloud model, blending and seamline refinement, the 24 combinations of ortho-image producing methods were tested. The optimal WF is evaluated from the aspects of coral mapping accuracy, geometric fidelity, completeness, and efficiency. Statistical error analysis shows that blending and seamline refinement are the most relevant WF components that influence accuracy of orthorectification and consequently coral reef classification. The optimal WF found to be when ‘highest’ photo alignment, with ‘high’ tie points in the sparse cloud model are applied, in presence of blending and seamline refinements and can achieve 87.9% overall mapping accuracy. With the available photogrammetric software packages, the proposed WF can be used in mosaicking and mapping large scale macroalgae, seagrass and seaweed.

Mapping coral reef using photogrammetry technique: A preliminary study at Pulau Udar Besar, Sabah, Malaysia.

Mapping of coral reefs is useful for understanding, monitoring and tracking their development. However, studies on reef mapping in the Southeast Asian region are limited. The main objectives of this study were to 1) map the coral reefs using photogrammetry in order to create a 3D map of the reef, and 2) determine the coral growth forms and identify the corals to the lowest possible taxa from the images. Data collection was conducted on 19 and 24 January 2018. All images were captured by applying photogrammetry technique using a digital camera with waterproof housing. Image reconstruction process was performed in Agisoft Photoscan and Blender, using the structure-from-motion (SfM) algorithms to reconstruct the 2-Dimensional images into a 3D model. The reef map generated from Agisoft Photoscan is of medium quality. Coral growth forms were determined and identified to the lowest possible taxa through the images. From the images, free-living corals of the family Fungiidae were the most common coral observed in the study site. In conclusion, the output of this study shows that reef mapping is possible using a photogrammetry approach and images can be used to identify coral growth forms.

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

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.

Using Remote Sensing and in situ Measurements for Efficient Mapping and Optimal Sampling of Coral Reefs

Coral reefs are of undeniable importance to the environment, yet little is known of them on a global scale. Assessments rely on laborious, local in-water surveys. In recent years remote sensing has been useful on larger scales for certain aspects of reef science such as benthic functional type discrimination. However, remote sensing only gives indirect information about reef condition. Only through combination of remote sensing andin situdata can we achieve coverage to understand reef condition and monitor worldwide condition. This work presents an approach to global mapping of coral reef condition that intelligently selects local,in situmeasurements that refine the accuracy and resolution of global remote sensing. To this end, we apply new techniques in remote sensing analysis, probabilistic modeling for coral reef mapping, and decision theory for sample selection. Our strategy represents a fundamental change in how we study coral reefs and assess their condition on a global scale. We demonstrate feasibility and performance of our approach in a proof of concept using spaceborne remote sensing together with high-quality airborne data from the NASA Earth Venture Suborbital-2 (EVS-2) Coral Reef Airborne Laboratory (CORAL) mission as a proxy forin situsamples. Results indicate that our method is capable of extrapolatingin situfeatures and refining information from remote sensing with increasing accuracy. Furthermore, the results confirm that decision theory is a powerful tool for sample selection.

Coral Reef Mapping Using Remote Sensing Techniques and a Supervised Classification Algorithm

The vitality of the Great Barrier Reef (GBR) is threatened by many human-made impacts. Monitoring this ecosystem makes it possible to study the general condition and the health of the GBR. However, due to the large extent of the GBR and limited accessibility in the ocean environment, mapping and monitoring this ecosystem has been always challenging task and connived. In this regard, Remote Sensing (RS) is an effective technique that provides valuable information for mapping and monitoring this ecosystem. In an attempt to monitor the GBR, this article applied a supervised machine learning algorithm to classify the Landsat 8 imagery collected over the GBR. To this end, the spectral responses of coral reefs, shallow water, deep ocean, rocks and sands, and green alga were initially determined from the satellite images. This information was then ingested to the Maximum Likelihood supervised classifier to map coral reefs in the GBR. Additionally, this study discusses how the GBR has been affected by anthropogenic disturbance. The results provide confirmatory evidence that RS techniques present great promise as a means of mapping coral reefs and monitoring their general conditions. We used the ambiguity matrix and validation data to estimate the accuracy of the proposed method. Overall, the proposed method was able to identify 5 different classes considered in this article with an average accuracy of 90%.

Reef Cover, a coral reef classification for global habitat mapping from remote sensing

Coral reef management and conservation stand to benefit from improved high-resolution global mapping. Yet classifications underpinning large-scale reef mapping to date are typically poorly defined, not shared or region-specific, limiting end-users’ ability to interpret outputs. Here we present Reef Cover, a coral reef geomorphic zone classification, developed to support both producers and end-users of global-scale coral reef habitat maps, in a transparent and version-based framework. Scalable classes were created by focusing on attributes that can be observed remotely, but whose membership rules also reflect deep knowledge of reef form and functioning. Bridging the divide between earth observation data and geo-ecological knowledge of reefs, Reef Cover maximises the trade-off between applicability at global scales, and relevance and accuracy at local scales. Two case studies demonstrate application of the Reef Cover classification scheme and its scientific and conservation benefits: 1) detailed mapping of the Cairns Management Region of the Great Barrier Reef to support management and 2) mapping of the Caroline and Mariana Island chains in the Pacific for conservation purposes.