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3842 KiB

Open AccessArticle

Estimating the Exposure of Coral Reefs and Seagrass Meadows to Land-Sourced Contaminants in River Flood Plumes of the Great Barrier Reef: Validating a Simple Satellite Risk Framework with Environmental Data

by Caroline Petus, Michelle Devlin, Angus Thompson, Len McKenzie, Eduardo Teixeira da Silva, Catherine Collier, Dieter Tracey and Katherine Martin

Remote Sens. 2016, 8(3), 210; https://doi.org/10.3390/rs8030210 - 5 Mar 2016

Cited by 34 | Viewed by 11082

Abstract

River runoff and associated flood plumes (hereafter river plumes) are a major source of land-sourced contaminants to the marine environment, and are a significant threat to coastal and marine ecosystems worldwide. Remote sensing monitoring products have been developed to map the spatial extent, [...] Read more.

River runoff and associated flood plumes (hereafter river plumes) are a major source of land-sourced contaminants to the marine environment, and are a significant threat to coastal and marine ecosystems worldwide. Remote sensing monitoring products have been developed to map the spatial extent, composition and frequency of occurrence of river plumes in the Great Barrier Reef (GBR), Australia. There is, however, a need to incorporate these monitoring products into Risk Assessment Frameworks as management decision tools. A simple Satellite Risk Framework has been recently proposed to generate maps of potential risk to seagrass and coral reef ecosystems in the GBR focusing on the Austral tropical wet season. This framework was based on a “magnitude × likelihood” risk management approach and GBR plume water types mapped from satellite imagery. The GBR plume water types (so called “Primary” for the inshore plume waters, “Secondary” for the midshelf-plume waters and “Tertiary” for the offshore plume waters) represent distinct concentrations and combinations of land-sourced and marine contaminants. The current study aimed to test and refine the methods of the Satellite Risk Framework. It compared predicted pollutant concentrations in plume water types (multi-annual average from 2005–2014) to published ecological thresholds, and combined this information with similarly long-term measures of seagrass and coral ecosystem health. The Satellite Risk Framework and newly-introduced multi-annual risk scores were successful in demonstrating where water conditions were, on average, correlated to adverse biological responses. Seagrass meadow abundance (multi-annual change in % cover) was negatively correlated to the multi-annual risk score at the site level (R² = 0.47, p < 0.05). Relationships between multi-annual risk scores and multi-annual changes in proportional macroalgae cover (as an index for coral reef health) were more complex (R² = 0.04, p > 0.05), though reefs incurring higher risk scores showed relatively higher proportional macroalgae cover. Multi-annual risk score thresholds associated with loss of seagrass cover were defined, with lower risk scores (≤0.2) associated with a gain or little loss in seagrass cover (gain/−12%), medium risk scores (0.2–0.4) associated with moderate loss (−12/−30%) and higher risk scores (>0.4) with the greatest loss in cover (>−30%). These thresholds were used to generate an intermediate river plume risk map specifically for seagrass meadows of the GBR. An intermediate river plume risk map for coral reefs was also developed by considering a multi-annual risk score threshold of 0.2—above which a higher proportion of macroalgae within the algal communities can be expected. These findings contribute to a long-term and adaptive approach to set relevant risk framework and thresholds for adverse biological responses in the GBR. The ecological thresholds and risk scores used in this study will be refined and validated through ongoing monitoring and assessment. As uncertainties are reduced, these risk metrics will provide important information for the development of strategies to manage water quality and ecosystem health. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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2959 KiB

Open AccessArticle

Spectral Reflectance of Palauan Reef-Building Coral with Different Symbionts in Response to Elevated Temperature

by Brandon J. Russell, Heidi M. Dierssen, Todd C. LaJeunesse, Kenneth D. Hoadley, Mark E. Warner, Dustin W. Kemp and Timothy G. Bateman

Remote Sens. 2016, 8(3), 164; https://doi.org/10.3390/rs8030164 - 23 Feb 2016

Cited by 21 | Viewed by 7967

Abstract

Spectral reflectance patterns of corals are driven largely by the pigments of photosynthetic symbionts within the host cnidarian. The warm inshore bays and cooler offshore reefs of Palau share a variety of coral species with differing endosymbiotic dinoflagellates (genus: Symbiodinium), with the [...] Read more.

Spectral reflectance patterns of corals are driven largely by the pigments of photosynthetic symbionts within the host cnidarian. The warm inshore bays and cooler offshore reefs of Palau share a variety of coral species with differing endosymbiotic dinoflagellates (genus: Symbiodinium), with the thermally tolerant Symbiodinium trenchii (S. trenchii) (= type D1a or D1-4) predominating under the elevated temperature regimes inshore, and primarily Clade C types in the cooler reefs offshore. Spectral reflectance of two species of stony coral, Cyphastrea serailia (C. serailia) and Pachyseris rugosa (P. rugosa), from both inshore and offshore locations shared multiple features both between sites and to similar global data from other studies. No clear reflectance features were evident which might serve as markers of thermally tolerant S. trenchii symbionts compared to the same species of coral with different symbionts. Reflectance from C. serailia colonies from inshore had a fluorescence peak at approximately 500 nm which was absent from offshore animals. Integrated reflectance across visible wavelengths had an inverse correlation to symbiont cell density and could be used as a relative indicator of the symbiont abundance for each type of coral. As hypothesized, coral colonies from offshore with Clade C symbionts showed a greater response to experimental heating, manifested as decreased symbiont density and increased reflectance or “bleaching” than their inshore counterparts with S. trenchii. Although no unique spectral features were found to distinguish species of symbiont, spectral differences related to the abundance of symbionts could prove useful in field and remote sensing studies. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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4189 KiB

Open AccessArticle

Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling

by Renata Ferrari, David McKinnon, Hu He, Ryan N. Smith, Peter Corke, Manuel González-Rivero, Peter J. Mumby and Ben Upcroft

Remote Sens. 2016, 8(2), 113; https://doi.org/10.3390/rs8020113 - 4 Feb 2016

Cited by 77 | Viewed by 13461

Abstract

Coral reef habitat structural complexity influences key ecological processes, ecosystem biodiversity, and resilience. Measuring structural complexity underwater is not trivial and researchers have been searching for accurate and cost-effective methods that can be applied across spatial extents for over 50 years. This study [...] Read more.

Coral reef habitat structural complexity influences key ecological processes, ecosystem biodiversity, and resilience. Measuring structural complexity underwater is not trivial and researchers have been searching for accurate and cost-effective methods that can be applied across spatial extents for over 50 years. This study integrated a set of existing multi-view, image-processing algorithms, to accurately compute metrics of structural complexity (e.g., ratio of surface to planar area) underwater solely from images. This framework resulted in accurate, high-speed 3D habitat reconstructions at scales ranging from small corals to reef-scapes (10s km²). Structural complexity was accurately quantified from both contemporary and historical image datasets across three spatial scales: (i) branching coral colony (Acropora spp.); (ii) reef area (400 m²); and (iii) reef transect (2 km). At small scales, our method delivered models with <1 mm error over 90% of the surface area, while the accuracy at transect scale was 85.3% ± 6% (CI). Advantages are: no need for an a priori requirement for image size or resolution, no invasive techniques, cost-effectiveness, and utilization of existing imagery taken from off-the-shelf cameras (both monocular or stereo). This remote sensing method can be integrated to reef monitoring and improve our knowledge of key aspects of coral reef dynamics, from reef accretion to habitat provisioning and productivity, by measuring and up-scaling estimates of structural complexity. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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1261 KiB

Open AccessArticle

Satellite SST-Based Coral Disease Outbreak Predictions for the Hawaiian Archipelago

by Jamie M. Caldwell, Scott F. Heron, C. Mark Eakin and Megan J. Donahue

Remote Sens. 2016, 8(2), 93; https://doi.org/10.3390/rs8020093 - 26 Jan 2016

Cited by 16 | Viewed by 7413

Abstract

Predicting wildlife disease risk is essential for effective monitoring and management, especially for geographically expansive ecosystems such as coral reefs in the Hawaiian archipelago. Warming ocean temperature has increased coral disease outbreaks contributing to declines in coral cover worldwide. In this study we [...] Read more.

Predicting wildlife disease risk is essential for effective monitoring and management, especially for geographically expansive ecosystems such as coral reefs in the Hawaiian archipelago. Warming ocean temperature has increased coral disease outbreaks contributing to declines in coral cover worldwide. In this study we investigated seasonal effects of thermal stress on the prevalence of the three most widespread coral diseases in Hawai’i: Montipora white syndrome, Porites growth anomalies and Porites tissue loss syndrome. To predict outbreak likelihood we compared disease prevalence from surveys conducted between 2004 and 2015 from 18 Hawaiian Islands and atolls with biotic (e.g., coral density) and abiotic (satellite-derived sea surface temperature metrics) variables using boosted regression trees. To date, the only coral disease forecast models available were developed for Acropora white syndrome on the Great Barrier Reef (GBR). Given the complexities of disease etiology, differences in host demography and environmental conditions across reef regions, it is important to refine and adapt such models for different diseases and geographic regions of interest. Similar to the Acropora white syndrome models, anomalously warm conditions were important for predicting Montipora white syndrome, possibly due to a relationship between thermal stress and a compromised host immune system. However, coral density and winter conditions were the most important predictors of all three coral diseases in this study, enabling development of a forecasting system that can predict regions of elevated disease risk up to six months before an expected outbreak. Our research indicates satellite-derived systems for forecasting disease outbreaks can be appropriately adapted from the GBR tools and applied for a variety of diseases in a new region. These models can be used to enhance management capacity to prepare for and respond to emerging coral diseases throughout Hawai’i and can be modified for other diseases and regions around the world. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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2468 KiB

Open AccessArticle

Characterization of Available Light for Seagrass and Patch Reef Productivity in Sugarloaf Key, Lower Florida Keys

by Gerardo Toro-Farmer, Frank E. Muller-Karger, Maria Vega-Rodríguez, Nelson Melo, Kimberly Yates, Sergio Cerdeira-Estrada and Stanley R. Herwitz

Remote Sens. 2016, 8(2), 86; https://doi.org/10.3390/rs8020086 - 23 Jan 2016

Cited by 5 | Viewed by 7681

Abstract

Light availability is an important factor driving primary productivity in benthic ecosystems, but in situ and remote sensing measurements of light quality are limited for coral reefs and seagrass beds. We evaluated the productivity responses of a patch reef and a seagrass site [...] Read more.

Light availability is an important factor driving primary productivity in benthic ecosystems, but in situ and remote sensing measurements of light quality are limited for coral reefs and seagrass beds. We evaluated the productivity responses of a patch reef and a seagrass site in the Lower Florida Keys to ambient light availability and spectral quality. In situ optical properties were characterized utilizing moored and water column bio-optical and hydrographic measurements. Net ecosystem productivity (NEP) was also estimated for these study sites using benthic productivity chambers. Our results show higher spectral light attenuation and absorption, and lower irradiance during low tide in the patch reef, tracking the influx of materials from shallower coastal areas. In contrast, the intrusion of clearer surface Atlantic Ocean water caused lower values of spectral attenuation and absorption, and higher irradiance in the patch reef during high tide. Storms during the studied period, with winds >10 m·s⁻¹, caused higher spectral attenuation values. A spatial gradient of NEP was observed, from high productivity in the shallow seagrass area, to lower productivity in deeper patch reefs. The highest daytime NEP was observed in the seagrass, with values of almost 0.4 g·O₂·m⁻²·h⁻¹. Productivity at the patch reef area was lower in May than during October 2012 (mean = 0.137 and 0.177 g·O₂·m⁻²·h⁻¹, respectively). Higher photosynthetic active radiation (PAR) levels measured above water and lower light attenuation in the red region of the visible spectrum (~666 to ~699 nm) had a positive correlation with NEP. Our results indicate that changes in light availability and quality by suspended or resuspended particles limit benthic productivity in the Florida Keys. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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2142 KiB

Open AccessArticle

Validation of Reef-Scale Thermal Stress Satellite Products for Coral Bleaching Monitoring

by Scott F. Heron, Lyza Johnston, Gang Liu, Erick F. Geiger, Jeffrey A. Maynard, Jacqueline L. De La Cour, Steven Johnson, Ryan Okano, David Benavente, Timothy F. R. Burgess, John Iguel, Denise I. Perez, William J. Skirving, Alan E. Strong, Kyle Tirak and C. Mark Eakin

Remote Sens. 2016, 8(1), 59; https://doi.org/10.3390/rs8010059 - 12 Jan 2016

Cited by 72 | Viewed by 12163

Abstract

Satellite monitoring of thermal stress on coral reefs has become an essential component of reef management practice around the world. A recent development by the U.S. National Oceanic and Atmospheric Administration’s Coral Reef Watch (NOAA CRW) program provides daily global monitoring at 5 [...] Read more.

Satellite monitoring of thermal stress on coral reefs has become an essential component of reef management practice around the world. A recent development by the U.S. National Oceanic and Atmospheric Administration’s Coral Reef Watch (NOAA CRW) program provides daily global monitoring at 5 km resolution—at or near the scale of most coral reefs. In this paper, we introduce two new monitoring products in the CRW Decision Support System for coral reef management: Regional Virtual Stations, a regional synthesis of thermal stress conditions, and Seven-day Sea Surface Temperature (SST) Trend, describing recent changes in temperature at each location. We describe how these products provided information in support of management activities prior to, during and after the 2014 thermal stress event in the Commonwealth of the Northern Mariana Islands (CNMI). Using in situ survey data from this event, we undertake the first quantitative comparison between 5 km satellite monitoring products and coral bleaching observations. Analysis of coral community characteristics, historical temperature conditions and thermal stress revealed a strong influence of coral biodiversity in the patterns of observed bleaching. This resulted in a model based on thermal stress and generic richness that explained 97% of the variance in observed bleaching. These findings illustrate the importance of using local benthic characteristics to interpret the level of impact from thermal stress exposure. In an era of continuing climate change, accurate monitoring of thermal stress and prediction of coral bleaching are essential for stakeholders to direct resources to the most effective management actions to conserve coral reefs. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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5544 KiB

Open AccessArticle

Comparison of Two Independent Mapping Exercises in the Primeiras and Segundas Archipelago, Mozambique

by Luisa Teixeira, John Hedley, Aurélie Shapiro and Kathryn Barker

Remote Sens. 2016, 8(1), 52; https://doi.org/10.3390/rs8010052 - 12 Jan 2016

Cited by 4 | Viewed by 6876

Abstract

Production of coral reef habitat maps from high spatial resolution multispectral imagery is common practice and benefits from standardized accuracy assessment methods and many informative studies on the merits of different processing algorithms. However, few studies consider the full production workflow, including factors [...] Read more.

Production of coral reef habitat maps from high spatial resolution multispectral imagery is common practice and benefits from standardized accuracy assessment methods and many informative studies on the merits of different processing algorithms. However, few studies consider the full production workflow, including factors such as operator influence, visual interpretation and a-priori knowledge. An end-user might justifiably ask: Given the same imagery and field data, how consistent would two independent production efforts be? This paper is a post-study analysis of a project in which two teams of researchers independently produced maps of six coral reef systems of the archipelago of the Primeiras and Segundas Environmental Protected Area (PSEPA), Mozambique. Both teams used the same imagery and field data, but applied different approaches—pixel based vs. object based image analysis—and used independently developed classification schemes. The results offer a unique perspective on the map production process. Both efforts resulted in similar merged classes accuracies, averaging at 63% and 64%, but the maps were distinct in terms of scale of spatial patterns, classification disparities, and in other aspects where the mapping process is reliant on visual interpretation. Despite the difficulty in aligning the classification schemes clear patterns of correspondence and discrepancy were identified. The maps were consistent with respect to geomorphological level mapping (17 out of 30 paired comparisons at more than 75% agreement), and also agreed in the extent of coral containing areas within a difference of 16% across the archipelago. However, more detailed benthic habitat level classes were inconsistent. Mapping of deep benthic cover was the most subjective result and dependent on operator visual interpretation, yet this was one of the results of highest interest for the PSEPA management since it revealed a continuity of benthos between the islands and the impression of a proto-barrier reef. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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2572 KiB

Open AccessArticle

Development of a Regional Coral Observation Method by a Fluorescence Imaging LIDAR Installed in a Towable Buoy

by Masahiko Sasano, Motonobu Imasato, Hiroya Yamano and Hiroyuki Oguma

Remote Sens. 2016, 8(1), 48; https://doi.org/10.3390/rs8010048 - 8 Jan 2016

Cited by 17 | Viewed by 7527

Abstract

Coral bleaching and mortality is predicted to increase under global climate change. A new observation technique is required to monitor regional coral conditions. To this end, we developed a light detection and ranging (LIDAR) system installed in a towable buoy for boat observations, [...] Read more.

Coral bleaching and mortality is predicted to increase under global climate change. A new observation technique is required to monitor regional coral conditions. To this end, we developed a light detection and ranging (LIDAR) system installed in a towable buoy for boat observations, which acquires continuous fluorescent images of the seabed during day-time. Most corals have innate fluorescent proteins in their tissue, and they emit fluorescence by ultraviolet excitation. This fluorescence distinguishes living coral from dead coral skeleton, crustose coralline algae, and sea algae. This paper provides a proof of concept for using the LIDAR system and fluorescence to map coral distribution within 1 km scale and coral cover within 100 m scale for a single reef in Japan. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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3121 KiB

Open AccessArticle

A Statistical Algorithm for Estimating Chlorophyll Concentration in the New Caledonian Lagoon

by Guillaume Wattelez, Cécile Dupouy, Morgan Mangeas, Jérôme Lefèvre, Touraivane and Robert Frouin

Remote Sens. 2016, 8(1), 45; https://doi.org/10.3390/rs8010045 - 7 Jan 2016

Cited by 13 | Viewed by 7223

Abstract

Spatial and temporal dynamics of phytoplankton biomass and water turbidity can provide crucial information about the function, health and vulnerability of lagoon ecosystems (coral reefs, sea grasses, etc.). A statistical algorithm is proposed to estimate chlorophyll-a concentration ([chl-a]) in [...] Read more.

Spatial and temporal dynamics of phytoplankton biomass and water turbidity can provide crucial information about the function, health and vulnerability of lagoon ecosystems (coral reefs, sea grasses, etc.). A statistical algorithm is proposed to estimate chlorophyll-a concentration ([chl-a]) in optically complex waters of the New Caledonian lagoon from MODIS-derived “remote-sensing” reflectance (R_rs). The algorithm is developed via supervised learning on match-ups gathered from 2002 to 2010. The best performance is obtained by combining two models, selected according to the ratio of R_rs in spectral bands centered on 488 and 555 nm: a log-linear model for low [chl-a] (AFLC) and a support vector machine (SVM) model or a classic model (OC3) for high [chl-a]. The log-linear model is developed based on SVM regression analysis. This approach outperforms the classical OC3 approach, especially in shallow waters, with a root mean squared error 30% lower. The proposed algorithm enables more accurate assessments of [chl-a] and its variability in this typical oligo- to meso-trophic tropical lagoon, from shallow coastal waters and nearby reefs to deeper waters and in the open ocean. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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2011 KiB

Open AccessArticle

Scaling up Ecological Measurements of Coral Reefs Using Semi-Automated Field Image Collection and Analysis

by Manuel González-Rivero, Oscar Beijbom, Alberto Rodriguez-Ramirez, Tadzio Holtrop, Yeray González-Marrero, Anjani Ganase, Chris Roelfsema, Stuart Phinn and Ove Hoegh-Guldberg

Remote Sens. 2016, 8(1), 30; https://doi.org/10.3390/rs8010030 - 5 Jan 2016

Cited by 59 | Viewed by 11005

Abstract

Ecological measurements in marine settings are often constrained in space and time, with spatial heterogeneity obscuring broader generalisations. While advances in remote sensing, integrative modelling and meta-analysis enable generalisations from field observations, there is an underlying need for high-resolution, standardised and geo-referenced field [...] Read more.

Ecological measurements in marine settings are often constrained in space and time, with spatial heterogeneity obscuring broader generalisations. While advances in remote sensing, integrative modelling and meta-analysis enable generalisations from field observations, there is an underlying need for high-resolution, standardised and geo-referenced field data. Here, we evaluate a new approach aimed at optimising data collection and analysis to assess broad-scale patterns of coral reef community composition using automatically annotated underwater imagery, captured along 2 km transects. We validate this approach by investigating its ability to detect spatial (e.g., across regions) and temporal (e.g., over years) change, and by comparing automated annotation errors to those of multiple human annotators. Our results indicate that change of coral reef benthos can be captured at high resolution both spatially and temporally, with an average error below 5%, among key benthic groups. Cover estimation errors using automated annotation varied between 2% and 12%, slightly larger than human errors (which varied between 1% and 7%), but small enough to detect significant changes among dominant groups. Overall, this approach allows a rapid collection of in-situ observations at larger spatial scales (km) than previously possible, and provides a pathway to link, calibrate, and validate broader analyses across even larger spatial scales (10–10,000 km²). Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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3212 KiB

Open AccessArticle

Performance Evaluation of CRW Reef-Scale and Broad-Scale SST-Based Coral Monitoring Products in Fringing Reef Systems of Tobago

by Shaazia S. Mohammed, Scott F. Heron, Rajindra Mahabir and Ricardo M. Clarke

Remote Sens. 2016, 8(1), 12; https://doi.org/10.3390/rs8010012 - 24 Dec 2015

Cited by 5 | Viewed by 6193

Abstract

Satellite-derived sea surface temperature (SST) is used to monitor coral bleaching through the National Oceanic and Atmospheric Administration’s Coral Reef Watch (CRW) Decision Support System (DSS). Since 2000, a broad-scale 50 km SST was used to monitor thermal stress for coral reefs globally. [...] Read more.

Satellite-derived sea surface temperature (SST) is used to monitor coral bleaching through the National Oceanic and Atmospheric Administration’s Coral Reef Watch (CRW) Decision Support System (DSS). Since 2000, a broad-scale 50 km SST was used to monitor thermal stress for coral reefs globally. However, some discrepancies were noted when applied to small-scale fringing coral reefs. To address this, CRW created a new DSS, specifically targeted at or near reef scales. Here, we evaluated the new reef-scale (5 km resolution) products using in situ temperature data and coral bleaching surveys which were also compared with the heritage broad-scale (50 km) for three reefs (Buccoo Reef, Culloden and Speyside) of the southern Caribbean island of Tobago. Seasonal and annual biases indicated the new 5 km SST generally represents the conditions at these reefs more accurately and more consistently than the 50 km SST. Consistency between satellite and in situ temperature data influences the performance of anomaly-based predictions of bleaching: the 5 km DHW product showed better consistency with bleaching observations than the 50 km product. These results are the first to demonstrate the improvement of the 5 km products over the 50 km predecessors and support their use in monitoring thermal stress of reefs in the southern Caribbean. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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2878 KiB

Open AccessArticle

Mapping Submerged Habitats and Mangroves of Lampi Island Marine National Park (Myanmar) from in Situ and Satellite Observations

by Claudia Giardino, Mariano Bresciani, Francesco Fava, Erica Matta, Vittorio E. Brando and Roberto Colombo

Remote Sens. 2016, 8(1), 2; https://doi.org/10.3390/rs8010002 - 22 Dec 2015

Cited by 35 | Viewed by 9282

Abstract

In this study we produced the first thematic maps of submerged and coastal habitats of Lampi Island (Myanmar) from in situ and satellite data. To focus on key elements of bio-diversity typically existing in tropical islands the detection of corals, seagrass, and mangrove [...] Read more.

In this study we produced the first thematic maps of submerged and coastal habitats of Lampi Island (Myanmar) from in situ and satellite data. To focus on key elements of bio-diversity typically existing in tropical islands the detection of corals, seagrass, and mangrove forests was addressed. Satellite data were acquired from Landsat-8; for the purpose of validation Rapid-Eye data were also used. In situ data supporting image processing were collected in a field campaign performed from 28 February to 4 March 2015 at the time of sensors overpasses. A hybrid approach based on bio-optical modeling and supervised classification techniques was applied to atmospherically-corrected Landsat-8 data. Bottom depth estimations, to be used in the classification process of shallow waters, were in good agreement with depth soundings (R² = 0.87). Corals were classified with producer and user accuracies of 58% and 77%, while a lower accuracy (producer and user accuracies of 50%) was found for the seagrass due to the patchy distribution of meadows; accuracies more than 88% were obtained for mangrove forests. The classification indicated the presence of 18 mangroves sites with extension larger than 5 km²; for 15 of those the coexistence of corals and seagrass were also found in the fronting bays, suggesting a significant rate of biodiversity for the study area. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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Open AccessArticle

Accuracy and Precision of Habitat Structural Complexity Metrics Derived from Underwater Photogrammetry

by Will Figueira, Renata Ferrari, Elyse Weatherby, Augustine Porter, Steven Hawes and Maria Byrne

Remote Sens. 2015, 7(12), 16883-16900; https://doi.org/10.3390/rs71215859 - 15 Dec 2015

Cited by 139 | Viewed by 14169

Abstract

In tropical reef ecosystems corals are the key habitat builders providing most ecosystem structure, which influences coral reef biodiversity and resilience. Remote sensing applications have progressed significantly and photogrammetry together with application of structure from motion software is emerging as a leading technique [...] Read more.

In tropical reef ecosystems corals are the key habitat builders providing most ecosystem structure, which influences coral reef biodiversity and resilience. Remote sensing applications have progressed significantly and photogrammetry together with application of structure from motion software is emerging as a leading technique to create three-dimensional (3D) models of corals and reefs from which biophysical properties of structural complexity can be quantified. This enables the addressing of a range of important marine research questions, such as what the role of habitat complexity is in driving key ecological processes (i.e., foraging). Yet, it is essential to assess the accuracy and precision of photogrammetric measurements to support their application in mapping, monitoring and quantifying coral reef form and structure. This study evaluated the precision (by repeated modeling) and accuracy (by comparison with laser reference models) of geometry and structural complexity metrics derived from photogrammetric 3D models of marine benthic habitat at two ecologically relevant spatial extents; individual coral colonies of a range of common morphologies and patches of reef area of 100s of square metres. Surface rugosity measurements were generally precise across all morphologies and spatial extents with average differences in the geometry of replicate models of 1–6 mm for coral colonies and 25 mm for the reef area. Precision decreased with complexity of the coral morphology, with metrics for small massive corals being the most precise (1% coefficient of variation (CV) in surface rugosity) and metrics for bottlebrush corals being the least precise (10% CV in surface rugosity). There was no indication however that precision was related to complexity for the patch-scale modelling. The 3D geometry of coral models differed by only 1–3 mm from laser reference models. However, high spatial variation in these differences around the model led to a consistent underestimation of surface rugosity values for all morphs of between 8% and 37%. This study highlights the utility of several off-the-shelf photogrammetry tools for the measurement of structural complexity across a range of scales relevant to ecologist and managers. It also provides important information on the accuracy and precision of these systems which should allow for their targeted use by non-experts in computer vision within these contexts. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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4737 KiB

Open AccessArticle

Designing Climate-Resilient Marine Protected Area Networks by Combining Remotely Sensed Coral Reef Habitat with Coastal Multi-Use Maps

by Joseph M. Maina, Kendall R. Jones, Christina C. Hicks, Tim R. McClanahan, James E. M. Watson, Arthur O. Tuda and Serge Andréfouët

Remote Sens. 2015, 7(12), 16571-16587; https://doi.org/10.3390/rs71215849 - 8 Dec 2015

Cited by 31 | Viewed by 9997

Abstract

Decision making for the conservation and management of coral reef biodiversity requires an understanding of spatial variability and distribution of reef habitat types. Despite the existence of very high-resolution remote sensing technology for nearly two decades, comprehensive assessment of coral reef habitats at [...] Read more.

Decision making for the conservation and management of coral reef biodiversity requires an understanding of spatial variability and distribution of reef habitat types. Despite the existence of very high-resolution remote sensing technology for nearly two decades, comprehensive assessment of coral reef habitats at national to regional spatial scales and at very high spatial resolution is still scarce. Here, we develop benthic habitat maps at a sub-national scale by analyzing large multispectral QuickBird imagery dataset covering ~686 km² of the main shallow coral fringing reef along the southern border with Tanzania (4.68°S, 39.18°E) to the reef end at Malindi, Kenya (3.2°S, 40.1°E). Mapping was conducted with a user approach constrained by ground-truth data, with detailed transect lines from the shore to the fore reef. First, maps were used to evaluate the present management system’s effectiveness at representing habitat diversity. Then, we developed three spatial prioritization scenarios based on differing objectives: (i) minimize lost fishing opportunity; (ii) redistribute fisheries away from currently overfished reefs; and (iii) minimize resource use conflicts. We further constrained the priority area in each prioritization selection scenario based on optionally protecting the least or the most climate exposed locations using a model of exposure to climate stress. We discovered that spatial priorities were very different based on the different objectives and on whether the aim was to protect the least or most climate-exposed habitats. Our analyses provide a spatially explicit foundation for large-scale conservation and management strategies that can account for ecosystem service benefits. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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3584 KiB

Open AccessArticle

Derivation of High-Resolution Bathymetry from Multispectral Satellite Imagery: A Comparison of Empirical and Optimisation Methods through Geographical Error Analysis

by Sarah M. Hamylton, John D. Hedley and Robin J. Beaman

Remote Sens. 2015, 7(12), 16257-16273; https://doi.org/10.3390/rs71215829 - 3 Dec 2015

Cited by 98 | Viewed by 10219

Abstract

The high importance of bathymetric character for many processes on reefs means that high-resolution bathymetric models are commonly needed by marine scientists and coastal managers. Empirical and optimisation methods provide two approaches for deriving bathymetry from multispectral satellite imagery, which have been refined [...] Read more.

The high importance of bathymetric character for many processes on reefs means that high-resolution bathymetric models are commonly needed by marine scientists and coastal managers. Empirical and optimisation methods provide two approaches for deriving bathymetry from multispectral satellite imagery, which have been refined and widely applied to coral reefs over the last decade. This paper compares these two approaches by means of a geographical error analysis for two sites on the Great Barrier Reef: Lizard Island (a continental island fringing reef) and Sykes Reef (a planar platform reef). The geographical distributions of model residuals (i.e., the difference between modelled and measured water depths) are mapped, and their spatial autocorrelation is calculated as a basis for comparing the performance of the bathymetric models. Comparisons reveal consistent geographical properties of errors arising from both models, including the tendency for positive residuals (i.e., an under-prediction of depth) in shallower areas and negative residuals in deeper areas (i.e., an over-prediction of depth) and the presence of spatial autocorrelation in model errors. A spatial error model is used to generate more reliable estimates of bathymetry by quantifying the spatial structure (autocorrelation) of model error and incorporating this into an improved regression model. Spatial error models improve bathymetric estimates derived from both methods. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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Open AccessArticle

Semi-Automated Object-Based Classification of Coral Reef Habitat using Discrete Choice Models

by Steven Saul and Sam Purkis

Remote Sens. 2015, 7(12), 15894-15916; https://doi.org/10.3390/rs71215810 - 27 Nov 2015

Cited by 33 | Viewed by 7255

Abstract

As for terrestrial remote sensing, pixel-based classifiers have traditionally been used to map coral reef habitats. For pixel-based classifiers, habitat assignment is based on the spectral or textural properties of each individual pixel in the scene. More recently, however, object-based classifications, those based [...] Read more.

As for terrestrial remote sensing, pixel-based classifiers have traditionally been used to map coral reef habitats. For pixel-based classifiers, habitat assignment is based on the spectral or textural properties of each individual pixel in the scene. More recently, however, object-based classifications, those based on information from a set of contiguous pixels with similar properties, have found favor with the reef mapping community and are starting to be extensively deployed. Object-based classifiers have an advantage over pixel-based in that they are less compromised by the inevitable inhomogeneity in per-pixel spectral response caused, primarily, by variations in water depth. One aspect of the object-based classification workflow is the assignment of each image object to a habitat class on the basis of its spectral, textural, or geometric properties. While a skilled image interpreter can achieve this task accurately through manual editing, full or partial automation is desirable for large-scale reef mapping projects of the magnitude which are useful for marine spatial planning. To this end, this paper trials the use of multinomial logistic discrete choice models to classify coral reef habitats identified through object-based segmentation of satellite imagery. Our results suggest that these models can attain assignment accuracies of about 85%, while also reducing the time needed to produce the map, as compared to manual methods. Limitations of this approach include misclassification of image objects at the interface between some habitat types due to the soft gradation in nature between habitats, the robustness of the segmentation algorithm used, and the selection of a strong training dataset. Finally, due to the probabilistic nature of multinomial logistic models, the analyst can estimate a map of uncertainty associated with the habitat classifications. Quantifying uncertainty is important to the end-user when developing marine spatial planning scenarios and populating spatial models from reef habitat maps. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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Review

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Open AccessReview

Remote Sensing of Coral Reefs for Monitoring and Management: A Review

by John D. Hedley, Chris M. Roelfsema, Iliana Chollett, Alastair R. Harborne, Scott F. Heron, Scarla Weeks, William J. Skirving, Alan E. Strong, C. Mark Eakin, Tyler R. L. Christensen, Victor Ticzon, Sonia Bejarano and Peter J. Mumby

Remote Sens. 2016, 8(2), 118; https://doi.org/10.3390/rs8020118 - 6 Feb 2016

Cited by 297 | Viewed by 48442

Abstract

Coral reefs are in decline worldwide and monitoring activities are important for assessing the impact of disturbance on reefs and tracking subsequent recovery or decline. Monitoring by field surveys provides accurate data but at highly localised scales and so is not cost-effective for [...] Read more.

Coral reefs are in decline worldwide and monitoring activities are important for assessing the impact of disturbance on reefs and tracking subsequent recovery or decline. Monitoring by field surveys provides accurate data but at highly localised scales and so is not cost-effective for reef scale monitoring at frequent time points. Remote sensing from satellites is an alternative and complementary approach. While remote sensing cannot provide the level of detail and accuracy at a single point than a field survey, the statistical power for inferring large scale patterns benefits in having complete areal coverage. This review considers the state of the art of coral reef remote sensing for the diverse range of objectives relevant for management, ranging from the composition of the reef: physical extent, benthic cover, bathymetry, rugosity; to environmental parameters: sea surface temperature, exposure, light, carbonate chemistry. In addition to updating previous reviews, here we also consider the capability to go beyond basic maps of habitats or environmental variables, to discuss concepts highly relevant to stakeholders, policy makers and public communication: such as biodiversity, environmental threat and ecosystem services. A clear conclusion of the review is that advances in both sensor technology and processing algorithms continue to drive forward remote sensing capability for coral reef mapping, particularly with respect to spatial resolution of maps, and synthesis across multiple data products. Both trends can be expected to continue. Full article

(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)

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