Mapping coral reefs at reef to reef-system scales, 10s–1000s km2, using object-based image analysis

Coral reef maps at various spatial scales and extents are needed for mapping, monitoring, modelling, and management of these environments. High spatial resolution satellite imagery, pixel <10 m, integrated with field survey data and processed with various mapping approaches, can provide these maps. These approaches have been accurately applied to single reefs (10–100 km²), covering one high spatial resolution scene from which a single thematic layer (e.g. benthic community) is mapped. This article demonstrates how a hierarchical mapping approach can be applied to coral reefs from individual reef to reef-system scales (10–1000 km²) using object-based image classification of high spatial resolution images guided by ecological and geomorphological principles. The approach is demonstrated for three individual reefs (10–35 km²) in Australia, Fiji, and Palau; and for three complex reef systems (300–600 km²) one in the Solomon Islands and two in Fiji. Archived high spatial resolution images were pre-processed and mosaics were created for the reef systems. Georeferenced benthic photo transect surveys were used to acquire cover information. Field and image data were integrated using an object-based image analysis approach that resulted in a hierarchically structured classification. Objects were assigned class labels based on the dominant benthic cover type, or location-relevant ecological and geomorphological principles, or a combination thereof. This generated a hierarchical sequence of reef maps with an increasing complexity in benthic thematic information that included: ‘reef’, ‘reef type’, ‘geomorphic zone’, and ‘benthic community’. The overall accuracy of the ‘geomorphic zone’ classification for each of the six study sites was 76–82% using 6–10 mapping categories. For ‘benthic community’ classification, the overall accuracy was 52–75% with individual reefs having 14–17 categories and reef systems 20–30 categories. We show that an object-based classification of high spatial resolution imagery, guided by field data and ecological and geomorphological principles, can produce consistent, accurate benthic maps at four hierarchical spatial scales for coral reefs of various sizes and complexities.     

Multi-scale,object-based image analysis for mapping geomorphic and ecological zones on coral reefs

Providing accurate maps of coral reefs where the spatial scale and labels of the mapped features correspond to map units appropriate for examining biological and geomorphic structures and processes is a major challenge for remote sensing. The objective of this work is to assess the accuracy and relevance of the process used to derive geomorphic zone and benthic community zone maps for three western Pacific coral reefs produced from multi-scale, object-based image analysis (OBIA) of high-spatial-resolution multi-spectral images, guided by field survey data. Three Quickbird-2 multi-spectral data sets from reefs in Australia, Palau and Fiji and georeferenced field photographs were used in a multi-scale segmentation and object-based image classification to map geomorphic zones and benthic community zones. A per-pixel approach was also tested for mapping benthic community zones. Validation of the maps and comparison to past approaches indicated the multi-scale OBIA process enabled field data, operator field experience and a conceptual hierarchical model of the coral reef environment to be linked to provide output maps at geomorphic zone and benthic community scales on coral reefs. The OBIA mapping accuracies were comparable with previously published work using other methods; however, the classes mapped were matched to a predetermined set of features on the reef.     

The semi-automated classification of acoustic imagery for characterizing coral reef ecosystems

Coral reef habitat maps describe the spatial distribution and abundance of tropical marine resources, making them essential for ecosystem-based approaches to planning and management. Typically, these habitat maps have been created from optical and acoustic remotely sensed imagery using manual, pixel- and object-based classification methods. However, past studies have shown that none of these classification methods alone are optimal for characterizing coral reef habitats for multiple management applications because the maps they produce (1) are not synoptic, (2) are time consuming to develop, (3) have low thematic resolutions (i.e. number of classes), or (4) have low overall thematic accuracies. To address these deficiencies, a novel, semi-automated object- and pixel-based technique was applied to multibeam echo sounder imagery to determine its utility for characterizing coral reef ecosystems. This study is not a direct comparison of these different methods but rather, a first attempt at applying a new classification technique to acoustic imagery. This technique used a combination of principal components analysis, edge-based segmentation, and Quick, Unbiased, and Efficient Statistical Trees (QUEST) to successfully partition the acoustic imagery into 35 distinct combinations of (1) major and (2) detailed geomorphological structure, (3) major and (4) detailed biological cover, and (5) live coral cover types. Thematic accuracies for these classes (corrected for proportional bias) were as follows: (1) 95.7%, (2) 88.7%, (3) 95.0%, (4) 74.0%, and (5) 88.3%, respectively. Approximately half of the habitat polygons were manually edited (hence the name ‘semi-automated’) due to a combination of mis-classifications by QUEST and noise in the acoustic data. While this method did not generate a map that was entirely reproducible, it does show promise for increasing the amount of automation with which thematically accurate benthic habitat maps can be generated from acoustic imagery.     

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%.     

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.     

Mapping shallow nearshore benthic features in a Caribbean marine-protected area: assessing the efficacy of using different data types (hydroacoustic versus satellite images) and classification techniques

Various benthic mapping methods exist but financing and technical capacity limit the choice of technology available to developing states to aid with natural resource management. Therefore, we assessed the efficacy of using a single-beam echosounder (SBES), satellite images (GeoEye-1 and WorldView-2) and different image (pixel-based Maximum Likelihood Classifier (MLC), and an object-based image analysis (OBIA)) and hydroacoustic classification and interpolation techniques, to map nearshore benthic features at the Bluefields Bay marine protected area in western Jamaica (13.82 km² in size). A map with three benthic classes (submerged aquatic vegetation (SAV), bare substrate, and coral reef) produced from a radiometrically corrected, deglinted and water column-corrected WorldView-2 image had a marginally higher accuracy (3%) than that of a map classified from a similarly corrected GeoEye-1 image. However, only one of the two extra WorldView-2 image bands (coastal) was used because the yellow band was completely attenuated at depths ≥3.7 m. The coral reef class was completely misclassified by the MLC and had to be contextually edited. The contextually edited MLC map had a higher overall accuracy (OA) than the OBIA map (86.7% versus 80.4%) and maps that were not contextually edited. But, the OBIA map had a higher OA than a MLC map without edits. Maps produced from the images also had a higher accuracy than the SAV map created from the acoustic data (OAs >80% and kappa >0.67 versus 76.6% and kappa = 0.32). SAV classification was comparable among the classified SBES SAV data points and all the final maps. The total area classified as SAV was marginally larger for satellite maps; however, the total area classified as bare substrate using the images was twice as large. A substrate map with three classes (silt, sand, and coral/hard bottom) produced from the SBES data using a random forest classifier and a Markov chain interpolator had a higher accuracy than a substrate map produced using a fractal dimension classifier and an indicator krig (the default choice) (72.4% versus 53.5%). The coral reef class from the SBES, OBIA, and contextually edited maps had comparable accuracies, but covered a much smaller area in the SBES maps because data points were lost during the interpolation process. The use of images was limited by turbidity levels and cloud cover and it yielded lower benthic detail. Despite these limitations, satellite image classification was the most efficacious method. If greater benthic detail is required, the SBES is more suitable or more effort is required during image classification. Also, the SBES can be operated in areas with turbid waters and greater depths. However, it could not be used in very shallow areas. Also, processing and interpolation of data points can result in a loss of resolution and introduces spatial uncertainty.     

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.     

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.     

Combining optical and acoustic data to enhance the detection of Caribbean forereef habitats

Maps of coral reef habitats are fundamental tools for reef management, and high map accuracy is desirable to support appropriate decisions, such as the stratification of marine reserves by habitat class. While satellite sensors have been used to map different reef communities, the accuracy of these maps tends to be low (overall accuracy < 50%) and optical airborne methods with high spectral resolution have, to date, been the most effective (if expensive) means of achieving higher accuracy. A potential means of compensating for the low spectral and radiometric resolution of optical satellite data, which is a major cause of its poor performance, is to combine satellite data with acoustic remote sensing. This study quantified the benefit of the synergy between optical satellite data (IKONOS) and acoustic (RoxAnn) sensors. The addition of acoustic data provided three new data axes for discriminating habitats: seabed roughness (E1), reef depth (z) and the depth correction of satellite spectral data to uniform depth. Seabed hardness (E2) was not an informative channel in our study. The use of z to conduct the water-column correction of the optical bands to uniform depth is a potential improvement over applying the depth-invariant index approach to optical data in the absence of ancillary information on depth. Habitat maps of the forereef of Glovers Atoll (Belize, Central America) were created using k-means unsupervised classification on eleven different treatment images constructed from various combinations of optical and acoustic data layers. The maximum benefit of data synergy was achieved by depth correcting the optical bands. The accuracy of maps based on the depth-invariant optical index was not enhanced when E1, E2 or z were added as separate layers but was enhanced when these three acoustic measures were added in concert. Data synergy can improve the accuracy of habitat maps and the availability of both data sets allows practitioners to take advantage of each techniques' additional strengths such as providing synoptic continuous imagery for education and general management planning (in the case of optical imagery) and maps of reef rugosity (in the case of acoustic data).     

Airborne lidar sensing of massive stony coral colonies on patch reefs in the northern Florida reef tract

In this study we examined the ability of the NASA Experimental Advanced Airborne Research Lidar (EAARL) to discriminate cluster zones of massive stony coral colonies on northern Florida reef tract (NFRT) patch reefs based on their topographic complexity (rugosity). Spatially dense EAARL laser submarine topographic soundings acquired in August 2002 were used to create a 1-m resolution digital rugosity map for adjacent NFRT study areas characterized by patch reefs (Region A) and diverse substratums (Region B). In both regions, sites with lidar-sensed rugosities above 1.2 were imaged by an along-track underwater videography system that incorporated the acquisition of instantaneous GPS positions. Subsequent manual interpretation of videotape segments was performed to identify substratum types that caused elevated lidar-sensed rugosity. Our study determined that massive coral colony formation, modified by subsequent physical and biological processes that breakdown patch reef framework, was the primary source of topographic complexity sensed by the EAARL in the NFRT. Sites recognized by lidar scanning to be topographically complex preferentially occurred around the margins of patch reefs, constituted a minor fraction of the reef system, and usually reflected the presence of massive coral colonies in cluster zones, or their derivatives created by mortality, bioerosion, and physical breakdown.     

Reflections on a decade of autonomous underwater vehicles operations for marine survey at the Australian Centre for Field Robotics

This paper describes insights gained from a decade of autonomous marine systems development at the University of Sydney’s Australian Centre for Marine Robotics. Over the course of this time, we have deployed numerous vehicles and imaging platforms in support of applications in engineering science, marine ecology, archaeology and geoscience. We have operated an Australia-wide benthic observing program designed to deliver precisely navigated, repeat imagery of the seafloor. This initiative makes extensive use of Autonomous Underwater Vehicles (AUVs) to collect high-resolution stereo imagery, multibeam sonar and water column measurements on an annual or semi-annual basis at sites around Australia, spanning the full latitudinal range of the continent from tropical reefs in the north to temperate regions in the south. We have also contributed to expeditions to document coral bleaching, cyclone recovery, submerged neolithic settlement sites, ancient shipwrecks, methane seeps and deepwater hydrothermal vents. We briefly consider how automated tools for working with this imagery have facilitated the resulting science outcomes.     

Accuracy of IKONOS for mapping benthic coral-reef habitats: a case study from the Puerto Morelos Reef National Park,Mexico

Reefs are being threatened by global warming, natural disasters, and the increased pressure of the global population. These habitats are in urgent need of mapping at high resolution so that these threats can be quantified. Remote sensing can potentially provide such quantitative data. In this article, we attempt to map benthic coral-reef habitats at the Puerto Morelos Reef National Park in Yucatan Peninsula (México) and to assess the accuracy of the technique in providing a baseline data for future monitoring of changes and evolution of the reef system. An IKONOS image was used in combination with checkpoint ground sampling and classified using a supervised maximum likelihood classifier (ENVI 4.5). We show that it is possible to map the reef with acceptable accuracy for the lagoon and discriminate the main habitat types, including vegetation, corals, and bare substrate. But, in areas close to the shore and in the front-reef zone, there were significant misclassifications as well as a failure to delineate spatial structures evident on the ground and in aerial imagery. These difficulties and failures occurred either in the areas deeper than 5–8 m where depth limits light transmission (particularly in the red channel) or when the spectral response of habitats were too close to be discriminated. This highlights the need to combine these data with other methods, such as acoustic mapping, in order to provide more accurate representations of the benthic habitats of entire reef systems.     

A comprehensive evaluation of classification algorithms for coral reef habitat mapping: challenges related to quantity,quality, and impurity of training samples

The preparation of control data is a primary concern in many supervised classification schemes. In coral reef mapping, this issue becomes more severe for three reasons: (1) control samples, located beneath the water, are quite difficult and costly to access; (2) because of the high spatial variability of coral reef habitats, it is very difficult to obtain high-quality samples; and (3) pure training samples are also hardly achievable. These issues, namely quantity, quality, and impurity challenges, are the main focus of this study. Three classification algorithms, including Maximum Likelihood Classifier (MLC), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs), are comprehensively evaluated, and their requirements for control data are determined. To accomplish this, rich field data, collected from diving off of Lizard Island in eastern Australia, and Landsat-8 images are used as the input data. With respect to accuracy, ANN is best, as it can deal with the complexity of coral reef environments; however, it requires a higher number of training samples (i.e. ANN cannot manage the quantity challenge). On the other hand, SVM shows the best resistance against the quantity and impurity challenges. Being aware of these points, a coral reef map is produced, for the first time, of the northern Persian Gulf, a coral habitat with very special environmental conditions. In this region, SVM achieved 68.42% overall accuracy, even though a very limited field work campaign was conducted to provide the control data.     

Mapping of coral reefs using hyperspectral CASI data; a case study: Fordata,Tanimbar, Indonesia

Airborne remote sensing with a CASI‐550 sensor has been used to map the benthic coverage and the bottom topography of the Pulau Nukaha coral reef located in the Tanimbar Archipelago (Southeast Moluccas, Eastern Indonesia). The image classification method adopted was performed in three steps. Firstly, five geomorphological reef components were identified using a supervised spectral angle mapping algorithm in combination with data collected during the field survey, i.e. benthic cover type, percentage cover and depth. Secondly, benthic cover mapping was performed for each of the five geomorphological components separately using an unsupervised hierarchical clustering algorithm followed by class aggregation using both spectral and spatial information. Finally, 16 benthic cover classes could be labelled using the benthic cover data collected during the field survey. The overall classification accuracy, calculated on the biological diverse fore reef, was 73% with a kappa coefficient of 0.63. A reliable bathymetric model (up to a depth of 15 m) of the Pulau Nukaha reef was also obtained using a semi‐analytical radiative transfer model. When compared with independent in‐situ depth measurements, the result proved relatively accurate (mean residual error: ?0.9 m) and was consistent with the seabed topography (Pearson correlation coefficient: 86%).     

Change detection using remote sensing in a reef environment of the UAE during the extreme event of El Niño 2015–2016

ABSTRACT

Coral reefs of the United Arab Emirates (UAE) are living in the world’s hottest sea. Recently, corals harbouring Symbiodinium thermophilum, a thermotolerant microalgae, were found to be prevalent among UAE reefs and were reported to endure extreme sea-surface temperatures. Late 2015–early 2016 was marked with the strongest El Niño on record worldwide, which caused massive coral bleaching (loss of symbiotic microalgae from reef-building corals). In September 2015, the waters flanking UAE coasts were identified to be among the areas facing a thermal stress reaching its highest level liable to cause massive coral bleaching. However, the effect of this thermal stress on UAE corals remained largely unknown. Here, multi-temporal DubaiSat-2 satellite images were used to show that changes in the reef environment of Dalma Island, UAE, between 2014 and 2016, occurred in macroalgae-dominant habitats, whereas live corals remained unaltered. Furthermore, extending the study to a larger area helped in discovering a continuum of live and pristine corals, which was not reported or studied before. While sea-surface temperature anomalies of 1°C were reported to significantly damage coral reefs around the world, the live coral habitat was observed to exhibit no-change despite four consecutive months of +2°C to 3°C anomalies reported during the study period. These findings point to the tolerance of UAE live corals faced with extreme climate conditions.     

Mapping the distribution of coral reefs and associated sublittoral habitats in Pacific Panama: a comparison of optical satellite sensors and classification methodologies

This research compared the ability of Landsat ETM+, Quickbird and three image classification methods for discriminating amongst coral reefs and associated habitats in Pacific Panama. Landsat ETM+ and Quickbird were able to discriminate coarse and intermediate habitat classes, but this was sensitive to classification method. Quickbird was significantly more accurate than Landsat (14% to 17%). Contextual editing was found to improve the user's accuracy of important habitats. The integration of object‐oriented classification with non‐spectral information in eCognition produced the most accurate results. This method allowed sufficiently accurate maps to be produced from Landsat, which was not possible using the maximum likelihood classifier. Object‐oriented classification was up to 24% more accurate than the maximum likelihood classifier for Landsat and up to 17% more accurate for Quickbird. The research indicates that classification methodology should be an important consideration in coral reef remote sensing. An object‐oriented approach to image classification shows potential for improving coral reef resource inventory.     

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.     

The use of multi‐temporal Landsat images for the change detection of the coastal zone near Hurghada,Egypt

A Landsat 5 Thematic Mapper (TM) image of 1987 and a Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image of 2000 were used to examine changes in land use/land cover (LULC) around Hurghada, Egypt, and changes in the composition of coral reefs offshore. Prior to coral reef bottom‐type classification, the radiance values were transformed to depth‐invariant bottom indices to reduce the effect of the water column. Subsequently, a multi‐component change detection procedure was applied to these indices to define changes. Preliminary results showed significant changes in LULC during the period 1987–2000 as well as changes in coral reef composition. Direct impacts along the coastline were clearly shown, but it was more difficult to link offshore changes in coral reef composition to indirect impacts of the changing LULC. Further research is needed to explore the effects of the different image‐processing steps, and to discover possible links between indirect impacts of LULC changes and changes in the coral reef composition.     

Using bathymetric lidar to define nearshore benthic habitat complexity: Implications for management of reef fish assemblages in Hawaii

Habitat complexity plays a major role in determining the distribution and structure of fish assemblages in the aquatic environment. These locations are critical for ecosystem function and have significant implications for conservation and management. In this study, we evaluated the utility of remotely sensed lidar (light detection and ranging) data for deriving substrate rugosity (a measure of habitat complexity) on a coral reef in Hawaii. We also assessed the potential application of lidar data for examining the relationship between habitat complexity and Hawaiian reef fish assemblage characteristics. Lidar-derived rugosity (4 m grid size) was found to be highly correlated with in-situ rugosity and was concluded to be a viable method for measuring rugosity in analogous coral reef environments. We established that lidar-derived rugosity was a good predictor of fish biomass and demonstrated a strong relationship with several fish assemblage metrics in hard bottom habitat at multiple spatial resolutions. This research demonstrates (i) the efficacy of lidar data to provide substrate rugosity measures at scales commensurate with the resources and their environment (ii) the applicability of lidar-derived rugosity for examining fish–habitat relationships on a coral reef in Hawaii and (iii) the potential of lidar to provide information about the seascape structure that can ultimately be used to prioritize areas for conservation and management.     

Coral Point Count with Excel extensions (CPCe): A Visual Basic program for the determination of coral and substrate coverage using random point count methodology

Photographic and video methods are frequently used to increase the efficiency of coral reef monitoring efforts. The random point count method is commonly used on still images or frame-grabbed video to estimate the community statistics of benthos. A matrix of randomly distributed points is overlaid on an image, and the species or substrate-type lying beneath each point is visually identified. Coral Point Count with Excel extensions (CPCe) is a standalone Visual Basic program which automates, facilitates, and speeds the random point count analysis process. CPCe includes automatic frame-image sequencing, single-click species/substrate labeling, auto-advancement of data point focus, zoom in/out, zoom hold, and specification of random point number, distribution type, and frame border location. Customization options include user-specified coral/substrate codes and data point shape, size, and color. CPCe can also perform image calibration and planar area and length calculation of benthic features. The ability to automatically generate analysis spreadsheets in Microsoft Excel based upon the supplied species/substrate codes is a significant feature. Data from individual frames can be combined to produce both inter- and intra-site comparisons. Spreadsheet contents include header information, statistical parameters of each species/substrate type (relative abundance, mean, standard deviation, standard error) and the calculation of the Shannon–Weaver diversity index for each species. Additional information can be found at http://www.nova.edu/ocean/cpce/.