Capabilities of remote sensors to classify coral, algae, and sand as pure and mixed spectra

We investigate the abilities of seven remote sensors to classify coral, algae, and carbonate sand based on 10,632 reflectance spectra measured in situ on reefs around the world. Discriminant and classification analyses demonstrate that full-resolution (1 nm) spectra provide very good spectral separation of the bottom-types. We assess the spectral capabilities of the sensors by applying to the in situ spectra the spectral responses of two airborne hyperspectral sensors (AAHIS and AVIRIS), three satellite broadband multispectral sensors (Ikonos, Landsat-ETM+ and SPOT-HRV), and two hypothetical satellite narrowband multispectral sensors (Proto and CRESPO). Classification analyses of the simulated sensor-specific spectra produce overall classification accuracy rates of 98%, 98%, 93%, 91%, 64%, 58%, and 50% for AAHIS, AVIRIS, Proto, CRESPO, Ikonos, Landsat-ETM+, and SPOT-HRV, respectively. Analyses of linearly mixed sensor-specific spectra reveal that the hyperspectral and narrowband multispectral sensors have the ability to discriminate between coral and algae across many levels of mixing, while the broadband multispectral sensors do not. Applying the results of the general mixing analyses to a specific spatial organization of coral, algae, and sand indicates that the hyperspectral sensors accurately estimate areal cover of the bottom-types regardless of pixel resolution. The narrowband multispectral sensors overestimate coral cover by 11-15%, while the broadband sensors underestimate algae cover by 7-29% and overestimate coral cover by 24-103%. We conclude that currently available satellite sensors are inadequate for assessment of global coral reef status, but that it is both necessary and possible to design a sensor system suited to the task.     

Bidirectional reflectance of coral growth-forms

In situ spectral reflectance measurements of substrates in a coral reef are often obtained by viewing a substrate at nadir. However, it is likely that off-nadir oblique viewing would show different spectral characteristics for most coral reef substrates and provide valuable information on structural properties. To understand the relationship between substrate structure and spectral response, this study examined the bidirectional reflectance distribution function (BRDF) of various growth-forms of hard corals and algae, as well as rock, rubble, and sand. BRDF measurements were collected on Heron Reef, the Great Barrier Reef, Australia, during the spring (October to November) of 2010, using a visible and near-infrared (VNIR) spectroradiometer attached to a goniometer. The measurements were made in the same five view zenith angles as the PROBA-1 Compact High Resolution Imaging Spectrometer (CHRIS) satellite (+55°, +36°, 0°, ?36°, ?55°) in the solar principal plane (SPP). A correction algorithm was used to remove both water column and water surface effects. Uncorrected measurements for sand covered with benthic microalgae appeared to have BRDF effects, but when corrected showed an essentially diffuse spectral response. Corrected measurements for branching corals showed BRDF effects dependent on branch spacing, branch length, and colour. The results indicate that spectral reflectance does vary with view angle for complex substrates such as caespitose corals, macroalgae, and irregular beach-rock and to a lesser extent for digitate corals and rippled sand and that the morphology of the coral and the shadowing between branches determines total spectral response. It is concluded that BRDF information can provide additional discriminating features for some coral reef substrates, particularly in the wavelength range of 550–650 nm.     

Detection limits of coral reef bleaching by satellite remote sensing: Simulation and data analysis

Monitoring of coral reef bleaching has hitherto been based on regional-scale, in situ data. Larger-scale trends, however, must be determined using satellite-based observations. Using both a radiative transfer simulation and an analysis of multitemporal Landsat TM images, the ability of satellite remote sensing to detect and monitor coral reef bleaching is examined. The radiative transfer simulation indicates that the blue and green bands of Landsat TM can detect bleaching if at least 23% of the coral surface in a pixel has been bleached, assuming a Landsat TM pixel with a resolution of 30×30 m on shallow (less than 3 m deep) reef flats at Ishigaki Island, Japan. Assuming an area with an initial coral coverage of 100% and in which all corals became completely bleached, the bleaching could be detected at a depth of up to 17 m. The difference in reflectance of shallow sand and corals is compared by examining multitemporal Landsat TM images at Ishigaki Island, after normalizing for variations in atmospheric conditions, incident light, water depth, and the sensor's reaction to the radiance received. After the normalization, a severe bleaching event when 25-55% of coral coverage was bleached was detected, but a slight bleaching event when 15% of coral coverage was bleached was not detected. The simulation and data analysis agreed well with each other, and identified reliable limits for satellite remote sensing for detecting coral reef bleaching. Sensitivity analysis on solar zenith angle, aerosol (visibility) and water quality (Chl a concentration) quantified the effect of these factors on bleaching detection, and thus served as general guidelines for detecting coral reef bleaching. Spatial misregistration resulted in a high degree of uncertainty in the detection of changes at the edges of coral patches mainly because of the low (∼30 m) spatial resolution of Landsat TM, indicating that detection of coral reef bleaching by Landsat TM is limited to extremely severe cases on a large homogeneous coral patch and shallow water depths. Satellite remote sensing of coral reef bleaching should be encouraged, however, because the development and deployment of advanced satellite sensors with high spatial resolution continue to progress.     

Spectral reflectance of coral reef benthos and substrate assemblages on Heron Reef,Australia

Studies investigating the spectral reflectance of coral reef benthos and substrates have focused on the measurement of pure endmembers, where the entire field of view (FOV) of a spectrometer is focused on a single benthos or substrate type. At the spatial scales of the current satellite sensors, the heterogeneity of coral reefs even at a sub-metre scale means that many individual image pixels will be made up of a mixture of benthos and substrate types. If pure endmember spectra are used as training data for image classification, there is a spatial discrepancy, because many pixels will have a mixed endmember spectral reflectance signature. This study investigated the spectral reflectance of coral reef benthos and substrates at a spatial scale directly linked to the pixel size of high spatial resolution imaging systems, by incorporating multiple benthos and substrate types into the spectrometer FOV in situ. A total of 334 spectral reflectance signatures were measured of 19 assemblages of the coral reef benthos and substrate types. The spectra were analysed for separability using first derivative values, and a discrimination decision tree was designed to identify the assemblages. Using the decision tree, it was possible to identify 15 assemblages with a mean overall classification accuracy of 62.6%.     

Spectral discrimination of coral mortality states following a severe bleaching event

Although relatively easy to distinguish the spectra of bleached and living coral, once corals have died their skeletons remained bleached (white) for only a short period. Rapid colonisation by algae can give rise to pigmentation that may be similar to that of living coral. Thus, by the time remotely sensed imagery has been acquired, discrimination of live and dead corals is no longer facile. Field measurements of spectral reflectance of live and algal-colonised dead corals (arising from different mortality events) were made in French Polynesia. Derivative analysis revealed wavelengths and slope characteristics that could be used to discriminate between mortality states with an accuracy of ～ 85%. These results encourage application of hyperspectral remote sensing to quantitatively assess the extent of coral bleaching events.     

Estimating the coverage of coral reef benthic communities from airborne hyperspectral remote sensing data: multiple discriminant function analysis and linear spectral unmixing

A staged approach for the application of linear spectral unmixing techniques to airborne hyperspectral remote sensing data of reef communities of the Al Wajh Barrier, Red Sea, is presented. Quantification of the percentage composition of four different reef components (live coral, dead coral, macroalgae and carbonate sand) contained within the ground sampling distance associated with an individual pixel is demonstrated. In the first stage, multiple discriminant function analysis is applied to spectra collected in situ to define an optimal subset combination of derivative and raw image wavebands for discriminating reef benthos. In the second phase, unmixing is applied to a similarly reduced subset of pre-processed image data to accurately determine the relative abundance of the reef benthos (R ² > 0.7 for all four components). The result of a phased approach is an increased signal-to-noise ratio for solution of the linear functions and reduction of processing burdens associated with image unmixing.     

A Bayesian Belief Network to assess rate of changes in coral reef ecosystems

It is crucial to identify sources of impacts and degradation to maintain functions and services that the physical structure of coral reef provides. Here, a Bayesian Network approach is used to evaluate effects that anthropogenic and climate change disturbances have on coral reef structure. The network was constructed on knowledge derived from the literature and elicited from experts, and parameterised on independent data.Evaluation of the model was conducted through sensitivity analyses and data integration was fundamental to obtain a balanced dataset. Scenario analyses, conducted to assess the effects of stressors on the reef framework state, suggested that calcifying organisms and carbonate production, rather than bioerosion, had the largest influence on the reef carbonate budgetary state. Despite the overall budget remaining positive, anthropogenic pressures, particularly deterioration of water quality, affected reef carbonate production, representing a warning signal for potential changes in the reef state.     

A new image classification approach for mapping coral density in State of Kuwait using high spatial resolution satellite images

Remote sensing technology can be a valuable tool for mapping coral reef ecosystems. However, the resolution capabilities of remote sensors, the diversity and complexity of coral reef ecosystems, and the low reflectivity of marine environments increase the difficulties in identifying and classifying their features. This research study explores the capability of high spatial resolution (WorldView-2 (WV-2) and Pleiades-1B) and low spatial resolution (Land Remote-Sensing Satellite (Landsat 8)) multispectral (MS) satellite sensors in quantitatively mapping coral density. The Kubbar coral reef ecosystem, located in Kuwait’s southern waters, was selected as the research site. The MS imagery of WV-2, Pleiades-1B and Landsat 8 were, after geometric and radiometric assessment and corrections, subjected to new image classification approach using a Multiple Linear Regression (MLR) analysis. The new approach of MLR coral density analysis used the dependent variable of coral density percentage from ground truth and independent variables of spectral reflectance from selected imagery, depth (as estimated from a surface derived from bathymetric charts) and distance to land or reef unit centre. Accuracy assessment using independent ground truth was performed for the selected approach and satellite sensors to determine the quality of the information derived from image classification processes. The results showed that coral density maps developed using the MLR coral density model proved to have some level of reliability (radiometrically corrected WV-2 image (the coefficient determination denoted as R-squared (R²) = 0.5, Root-Mean-Square Error (RMSE) = 10) and radiometrically corrected Pleiades-1B image (R² = 0.8, RMSE = 10)). This study suggested using high spectral resolution data and including additional factors (variables) (e.g. water turbidity, temperature and salinity) could contribute to improving the accuracy of coral density maps produced by application of the MLR model; however, all of these would add cost and effort to the mapping process. The outcomes of this research study provide coral reef ecosystem researchers, managers, and decision makers a tool to determine and map coral reef density in more detail than in the past. It will help quantify coral density at particular points in time leading to estimates of change, and allow coral reef ecologists to identify the current coral reef habitat health status, distribution and extent.     

Fourth derivative analysis of Red Sea coral reflectance spectra

Reflectance spectra of coral colonies and associated sand and rubble were obtained over the fringing reefs of Eilat, Israel using two GER 1500 radiometers. The overall spectral response curve of Red Sea corals displayed the same three inflection points reported for Pacific corals, with notable differences between in vitro and in situ measurements. Fourth derivative analysis of relatively pure spectra (filling the sensor's field of view) allowed for differentiation of coral and non-coral targets with 95–99% accuracy. The characteristic peaks revealed by the fourth derivative match those obtained on Hawaiian corals.     

Combining Landsat images with historic records to estimate the live coral cover of Luhuitou fringing reef in Northern South China Sea

As an efficient indicator of coral reef health, live coral cover (LCC) is regularly surveyed and recorded by many coral reef documents. However, there usually exist some blanks for the historic records, while current in-field surveys are impossible to fill the blanks. To overcome such difficulties, we focus on exploiting the potential of optical satellite images. The purpose is to fill the blanks of the records over the past and further estimate the LCC in future. As historic records were usually lack of accurate geographical locations to match to the satellite images, a spectral index was defined based on the mean of the subsurface remote sensing reflectance. The index was then used to link the LCC with the satellite images by a cubic polynomial function. Thereafter, the LCC and the coefficients of the polynomial function were finally estimated by simultaneously combining the mean subsurface remote sensing reflectance, the historic LCC records, and the constraints among LCC in adjacent years. Experiments on a series of Landsat images of Luhuitou fringing reef (1973 to 2018) demonstrated that the proposed method is effective and feasible, where the introduction of the satellite images can greatly improve the accuracy. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Relative Errors (MRE) of the LCC were able to reach 5.4%, 4.0%, and 15.9% respectively. This is regarded as the first test on LCC estimation by combining such a long-term LCC records with a series of satellite images.     

Quantification of two decades of shallow-water coral reef habitat decline in the Florida Keys National Marine Sanctuary using Landsat data (1984–2002)

The loss of coral reef habitats has been witnessed at a global scale including in the Florida Keys and the Caribbean. In addition to field surveys that can be spatially limited, remote sensing can provide a synoptic view of the changes occurring on coral reef habitats. Here, we utilize an 18-year time series of Landsat 5/TM and 7/ETM+ images to assess changes in eight coral reef sites in the Florida Keys National Marine Sanctuary, namely Carysfort Reef, Grecian Rocks, Molasses Reef, Conch Reef, Sombrero Reef, Looe Key Reef, Western Sambo and Sand Key Reef. Twenty-eight Landsat images (1984–2002) were used, with imagery gathered every 2 years during spring, and every 6 years during fall. The image dataset was georectified, calibrated to remote sensing reflectance and corrected for atmospheric and water-column effects. A Mahalanobis distance classification was trained for four habitat classes (‘coral’, ‘sand’, ‘bare hardbottom’ and ‘covered hardbottom’) using in situ ground-truthing data collected in 2003–2004 and using the spectral statistics from a 2002 image. The red band was considered useful only for benthic habitats in depths less than 6 m. Overall mean coral habitat loss for all sites classified by Landsat was 61% (3.4%/year), from a percentage habitat cover of 19% (1984) down to 7.6% (2002). The classification results for the eight different sites were critically reviewed. A detailed pixel by pixel examination of the spatial patterns across time suggests that the results range from ecologically plausible to unreliable due to spatial inconsistencies and/or improbable ecological successions. In situ monitoring data acquired by the Coral Reef Evaluation and Monitoring Project (CREMP) for the eight reef sites between 1996 and 2002 showed a loss in coral cover of 52% (8.7%/year), whereas the Landsat-derived coral habitat areas decreased by 37% (6.2%/year). A direct trend comparison between the entire CREMP percent coral cover data set (1996–2004) and the entire Landsat-derived coral habitat areas showed no significant difference between the two time series (ANCOVA; F-test, p = 0.303, n = 32), despite the different scales of measurements.     

The effects of ecologically determined spatial complexity on the classification accuracy of simulated coral reef images

Numerous studies have been conducted to compare the classification accuracy of coral reef maps produced from satellite and aerial imagery with different sensor characteristics such as spatial or spectral resolution, or under different environmental conditions. However, in additional to these physical environment and sensor design factors, the ecologically determined spatial complexity of the reef itself presents significant challenges for remote sensing objectives. While previous studies have considered the spatial resolution of the sensors, none have directly drawn the link from sensor spatial resolution to the scale and patterns in the heterogeneity of reef benthos. In this paper, we will study how the accuracy of a commonly used maximum likelihood classification (MLC) algorithm is affected by spatial elements typical of a Caribbean atoll system present in high spectral and spatial resolution imagery.The results indicate that the degree to which ecologically determined spatial factors influence accuracy is dependent on both the amount of coral cover on the reef and the spatial resolution of the images being classified, and may be a contributing factor to the differences in the accuracies obtained for mapping reefs in different geographical locations. Differences in accuracy are also obtained due to the methods of pixel selection for training the maximum likelihood classification algorithm. With respect to estimation of live coral cover, a method which randomly selects training samples from all samples in each class provides better estimates for lower resolution images while a method biased to select the pixels with the highest substrate purity gave better estimations for higher resolution images.     

Simultaneous estimation of benthic fractional cover and shallow water bathymetry in coral reef areas from high-resolution satellite images

This article describes the development of a technique to estimate shallow water benthic cover and depth simultaneously from high-resolution satellite images of reef areas, specifically from the high-resolution sensor onboard IKONOS. The technique to derive the estimates of five bottom benthic cover types (sand, coral, seagrass, macroalgae and pavement) and depth from the four-band images uses a coupling of radiative transfer (RT) theory and spectral unmixing implemented in an iterative manner. To resolve the cover types for the unmixing, the method employed a combinatorial approach to select benthic cover composition. The estimation technique was applied to two reef areas around the coast of the Ishigaki in southern Ryukyus, namely, the Fukido River mouth area and the Shiraho Reef. The IKONOS images of Fukido River mouth area and Shiraho Reef were acquired in 2003 and 2002, respectively. The accuracy of the fractional cover and the depth estimates from the satellite images are then presented and compared with sea truth data and depth measurements. The results indicate good correspondence between estimated and measured depths, while the estimates for the benthic cover were at reasonable levels of accuracy.     

Detecting end-member structural and biological elements of a coral reef using a single-beam acoustic ground discrimination system

A thematic map of benthic habitat was produced for a coral reef in the Republic of Palau, utilizing hydroacoustic data acquired with a BioSonics DT-X echosounder and a single-beam 418 kHz digital transducer. This article describes and assesses a supervised classification scheme that used a series of three discriminant analyses (DAs) to refine training samples into end-member structural and biological elements utilizing E1′ (leading edge of first echo), E1 (trailing edge of first echo), E2 (complete second echo), fractal dimension (first echo shape) and depth as predictor variables. Hydroacoustic training samples were assigned to one of six predefined groups based on the plurality of benthic elements (sand, sparse submerged aquatic vegetation (SAV)) rubble, pavement, rugose hardbottom, branching coral) that were visually estimated from spatially co-located ground-truthing videos. Records that classified incorrectly or failed to exceed a minimum probability of group membership were removed from the training data set until only ‘pure’ end-member records remained. This refinement of ‘mixed’ training samples circumvented the dilemma typically imposed by the benthic heterogeneity of coral reefs, that is either train the acoustic ground discrimination system (AGDS) on homogeneous benthos and leave the heterogeneous benthos unclassified, or attempt to capture the many ‘mixed’ classes and overwhelm the discriminatory capability of the AGDS. It was made possible by a conjunction of narrow beam width (6.4°) and shallow depth (1.2 to 17.5 m), which produced a sonar footprint small enough to resolve the microscale features used to define benthic groups. Survey data classified from the third-pass training DA were found to: (i) conform to visually apparent contours of satellite imagery, (ii) agree with the structural and biological delineations of a benthic habitat map (BHM) created from visual interpretation of IKONOS imagery and (iii) yield values of benthic cover that agreed closely with independent, contemporaneous video transects. The methodology was proven on a coral reef environment for which high-quality satellite imagery existed, as an example of the potential for single-beam systems to thematically map coral reefs in deep or turbid settings where optical methods are not applicable.     

Change detection of coral reef habitat using Landsat-5 TM,Landsat 7 ETM+ and Landsat 8 OLI data in the Red Sea (Hurghada,Egypt)

Owing to continuing touristic developments in Hurghada, Egypt, several coral reef habitats have suffered major deterioration between 1987 and 2013, either by being bleached or totally lost. Such alterations in coral reef habitats have been well observed in their varying distributions using change detection analysis applied to a Landsat 5 image representing 1987, a Landsat 7 image representing 2000, and a Landsat 8 image representing 2013. Different processing techniques were carried out over the three images, including but not limited to rectification, masking, water column correction, classification, and change detection statistics. The supervised classifications performed over the three scenes show five significant marine-related classes, namely coral, sand subtidal, sand intertidal, macro-algae, and seagrass, in different degrees of abundance. The change detection statistics obtained from the classified scenes of 1987 and 2000 reveal a significant increase in the macro-algae and seagrass classes (93 and 47%, respectively). However, major decreases of 41, 40, and 37% are observed in the sand intertidal, coral, and sand subtidal classes, respectively. On the other hand, the change detection statistics obtained from the classified scenes of 2000 and 2013 revealed increases in sand subtidal and macro-algae classes by 14 and 19%, respectively, while major decreases of 49%, 46% and 74% are observed in the sand intertidal, coral, and seagrass classes, respectively.     

Time-lapse side-scan sonar imaging of bleached coral reefs: A case study from the Seychelles

High-resolution (675 kHz) side-scan sonar surveys were collected six and thirty months after a major coral bleaching event in the Seychelles. The surveyed areas contain four different reef morphologies, the distribution of which depends on water depth and distance from the shoreline. These four reef types also have different coral densities and colony morphologies (branching or mixed massive/branching). Textural analysis, based on first order statistics, unsupervised cluster analysis and Mann-Whitney U-tests, showed a correlation between recorded backscatter response and reef type that is attributed to the link between community composition and rugosity on a millimetre to tens of metre scale. Branching coral colonies are found to be relatively good scatterers of acoustic energy, and result in a broad intermediate to high intensity response. Massive coral colonies and reefs with a hard carbonate pavement are found to be principally reflectors of the acoustic energy, resulting in a narrow low intensity response. Comparison of the two surveys separated in time by two years, showed seabed texture to change most significantly over the reef areas that contained the highest coral abundances and mortality rates. In particular, the disintegration of dense branching colonies that suffered almost 100% mortality during the bleaching event resulted in a characteristic loss of intermediate to high backscatter intensity. The work demonstrates the contribution that side-scan sonar could make in the assessment of loss of rugosity following a bleaching episode, which has important implications to both the recovery of the reef itself and the abundance and distribution of associated reef organisms.     

Change detection in coral reef communities using Ikonos satellite sensor imagery and historic aerial photographs

For decades, aerial photographs have been the only source of very high spatial resolution data for coral reef researchers. With the launch of the Ikonos satellite in 1999, imagery with a 4 m spatial resolution in multispectral mode can now be combined with historical aerial photographs for change detection. We demonstrate this potential by combining two aerial photographs (1981 and 1992) and an Ikonos image (2000) to detect change in the coral reef communities for Carysfort Reef, Florida, USA. The results show a loss of 'coral-dominated' bottom from 52% (1981) to 16% (1992) to finally 6% (2000), a trend similar to in situ observations.     

Feasibility of hyperspectral remote sensing for mapping benthic macroalgal cover in turbid coastal waters—a Baltic Sea case study

Quantitative analysis of coastal marine benthic communities enables to adequately estimate the state of coastal marine environment, provide better evidence for environmental changes and describe processes that are conditioned by anthropogenic forces. Remote sensing could provide a tool for mapping bottom vegetation if the substrates are spectrally resolvable. We measured reflectance spectra of green (Cladophora glomerata), red (Furcellaria lumbricalis), and brown (Fucus vesiculosus) macroalgae and used a bio-optical model in estimating whether these algae distinguish optically from each other, from sandy bottom or deep water in turbid water conditions of the Baltic Sea. The simulation was carried out for three different water types: (1) CDOM-rich coastal water, (2) coastal waters not directly impacted by high CDOM discharge from rivers but with high concentration of cyanobacteria, (3) open Baltic waters. Our modelling results indicate that the reflectance spectra of C. glomerata, F. lumbricalis, F. vesiculosus differ from each other and also from sand and deep water reflectance spectra. The differences are detectable by remote sensing instruments at spectral resolution of 10 nm and SNR better than 1000:1. Thus, the lowest depth limits where the studied macroalgae grow do not exceed the depth where such remote sensing instruments could potentially detect the spectral differences between the studied species.     

A novel advanced local binary pattern for image-based coral reef classification

Multimedia Tools and Applications - High computational burden and low accuracy in non-uniform textures are the two main challenges of coral reef classification frameworks. To overcome these...