Fractal Patterns of Coral Communities: Evidence from Remote Sensing (Arabian Gulf, Dubai, U.A.E.)

In this study, the spatial character of benthic communities is investigated in an Arabian Gulf shallow subtidal carbonate ramp setting, using IKONOS satellite imagery. The patchy distribution of three assemblages of live and dead corals on extensive (but also fragmented) hardground pavements was investigated using a variety of spatial statistics. It was found that the spatial expression of the benthic groups display characteristics that approximate to power-law distributions over several orders of magnitude to an extent that suggests fractal behaviour. Pronounced anisotropy was observed between the spatial patterns in the near-shore and off-shore region which is attributed to different mechanisms of patch formation controlled by the local hydrodynamic regime. The study area is know to be subjected to recurrent and cyclic thermal induced mass mortality events on a decadal time scale, inhibiting reef framework development and likely to be a controlling mechanism in the patchiness of the benthic communities

Coral reef and associated habitat mapping using ALOS satellite imagery

Coral reefs are rich in biodiversity and ecosystem services. However increase in degradation are still occurring at an alarming rate. In management of this ecosystem, determination of its spatial distribution is of importance. Satellite imageries can be used to map distribution extent using spectral characteristics which is a fundamental parameter in mapping. The aims of this study were to determine the spectral characteristics of corals and associated habitats and to map its spatial distribution using 2009 ALOS advanced visible and near infrared radiometer type 2 (AVNIR-2) satellite imagery. Results indicated that coral and habitats surrounding the area display variation in the spectral characteristics magnitude but displays similar spectral curve. Spectral characteristics from the corals and surrounding habitats were determined by presence of benthic microalgae and calcium carbonate. Maximum likelihood classification on the image produced five main classes. Spatial distribution of coral and associated habitats indicated five main zones which are sandy shore zone, sandy intertidal zone, seagrass zone, coral/submerged sandy zone and rocky zone. Distribution of live corals indicated coverage of 0.54 km2, sea grass (0.94 km2), sandy bottom (1.31 km2) and rocky shores (0.19 km2). The results of this study indicated that ALOS satellite data was able to determine variation in spectral characteristics of coral reefs and other habitats thus is capable of mapping the ecosystems spatial distribution

Integrating field data with high spatial resolution multispectral satellite imagery for calibration and validation of coral reef benthic community maps

Our ability to map coral reef environments using remote sensing has increased through improved access to: satellite images and field survey data at suitable spatial scales, and software enabling the integration of data sources. These data sets can be used to provide validated maps to support science and management decisions. The objective of this paper was to compare two methods for calibrating and validating maps of coral reef benthic communities derived from satellite images captured over a variety of Coral Reefs The two methods for collecting georeferenced benthic field data were: 1), georeferenced photo transects and 2), spot checks. Quickbird imagery was acquired for three Fijian coral reef environments in: Suva, Navakavu and Solo. These environments had variable water clarity and spatial complexity of benthic cover composition. The two field data sets at each reef were each split, and half were used for training data sets for supervised classifications, and the other half for accuracy assessment. This resulted in two maps of benthic communities with associated mapping accuracies, production times and costs for each study-site. Analyses of the spatial patterns in benthic community maps and their Overall and Tau accuracies revealed that for spatially complex habitats, the maps produced from photo transect data were twice as accurate as spot check based maps. In the context of the reefs examined, our results showed that the photo- transect method was a robust procedure which could be used in a range of coral reef environments to map the benthic communities accurately. In contrast, the spot check method is a fast and low cost approach, suitable for mapping benthic communities which have lower spatial complexity. Our findings will enable scientists, technicians and managers to select appropriate methods for collecting field data to integrate with high spatial resolution multi-spectral imagery to create validated coral reef benthic community maps. © 2010 Society of Photo-Optical Instrumentation Engineer

The role of integrated information acquisition and management in the analysis of coastal ecosystem change

This book chapter represents a synthesis of the work which started in my PhD and which has been the conceptual basis for all of my research since 1993. The chapter presents a method for scientists and managers to use for selecting the type of remotely sensed data to use to meet their information needs associated with a mapping, monitoring or modelling application. The work draws on results from several of my ARC projects, CRC Rainforest and Coastal projects and theses of P.Scarth , K.Joyce and C.Roelfsema

Seagrass Loss in Belize: Studies of Turtlegrass (Thalassia testudinum) Habitat Using Remote Sensing and Ground-Truth Data

Spatial and temporal change in turtlegrass (Thalassia testudinum) habitat of the South Water Caye Marine Reserve (SWCMR) in Belize were analyzed using satellite images backed up with ground-truth data. We had two pri-mary objectives. First, we wanted to determine areal expanse of seagrass across a large area (~12 km by 3 km) of the SWCMR, and address its change over time. We used paired satellite images taken during 2001 and 2005 to determine coverage by seagrass and measure temporal variables. These analyses recorded an overall seagrass loss of 1.8% (52.3 ha) during the 4 yr period. Secondly, we wanted to determine whether seagrass gains or losses were consistent across the study area. Replicate sampling was used as a statistical basis and confirmed a significant loss of seagrass across the region. It also helped identify two regions of significant seagrass loss; one 600 ha area lost 12.4% of its seagrass; another 240 ha area lost nearly 40%. These components helped us assess seagrass habitat in an area perceived as critical to Belize fisheries, and provided the scale and statistical rigor necessary to adequately assess a broad region of study. The salient results from our study were not the magnitude of seagrass loss per se, but the loss in seagrass habitat from an area that is thought to be relatively pristine. Seagrass-habitat loss in this region of the Caribbean Sea may be evidence that even near-pristine areas can be impacted by anthropogenic factors. Determining the causes of habitat loss may help prevent loss of productivity, habitat, and livelihood for the associated human and nonhuman communities

Posidonia Oceanica habitat mapping in shallow coastal waters along Losinj Island, Croatia using Geoeye-1 multispectral imagery

Seagrasses are important components of marine ecosystem. They are a primary food source for many organisms and provide shelter and nursery areas for many more. Also they stabilize sediments and act as a natural barrier against coastal erosion. But despite their valuable rule, Seagrasses are facing large threats because of human development. The stresses caused by human activities like trawling and anchoring, seabed mining, gas or mineral exploration and production, industrial chemical waste, agricultural run-off and coastal development results in a worldwide decline of seagrass meadows coverage. In this thesis, a study was carried out using Geoeye-1 satellite data acquired on July and August 2011 to extract bottom type features, i.e. seagrass (Posidonia Oceanica), sand and rock in shallow coastal waters of Losinj Island, Croatia. To conduct the study, atmospheric correction, glint removal and water column correct were done to remove the noise from the seabed reflectance but due to some quality problems (sensor calibration) with the imagery dataset prevented us to get satisfactory results from glint removal and water column correction. These techniques are based on empirical models among different band pairs and in the case of a problem in making an accurate reflectance values, their result would be unreliable. So it was decided to perform a principle component analysis to improve the spectral separability of desired classes. Then a hard supervised classification was performed to identify the spectral clusters and label them based on the training phase of the classification algorithm. But before running the classifier to compensate the attenuation effect of water body, it was decided to consider each training sample as a separate class and afterwards reclassify the results into our primary classes. At the end of the classification result were edited using a majority filter to reduce the salt and pepper effect of the classification results and the accuracy of the classification was calculated for each scene. Afterwards a mosaic was produced from the classification results. The overall accuracy of the mosaic and its kappa coefficient was calculated as 80% and 0.7 respectively which proved that the classification was successful and Geoeye-1 imagery can be used reliably to identify the extent of seagrass community in a fast and cost-effective way.Seagrasses are important components of marine ecosystem. They are a primary food source for many organisms and provide shelter and nursery areas for many more. Also they stabilize sediments and act as a natural barrier against coastal erosion. But despite their valuable rule, Seagrasses are facing large threats because of human development. The stresses caused by human activities like trawling and anchoring, seabed mining, gas or mineral exploration and production, industrial chemical waste, agricultural run-off and coastal development results in a worldwide decline of seagrass meadows coverage. In this thesis, a study was carried out using Geoeye-1 satellite data acquired on July and August 2011 to extract bottom type features, i.e. seagrass (Posidonia Oceanica), sand and rock in shallow coastal waters of Losinj Island, Croatia. To conduct the study, atmospheric correction, glint removal and water column correction were done to remove the noise from the seabed reflectance but due to some quality problems with the imagery dataset prevented us to get satisfactory results from the statistical analysis; it was decided to perform a principle component analysis (PCA) to improve the spectral separability of desired classes. Then a hard supervised classification was performed to identify the spectral clusters and label them based on the training phase of the classification algorithm. But before running the classifier to compensate the attenuation effect of water body, it was decided to consider each training sample as a separate class and afterwards reclassify the results into our primary classes. At end the classification result were edited using a majority and the accuracy of the classification was calculated for each scene. Afterwards a mosaic was produced from the classification results. The overall accuracy of the mosaic and its kappa coefficient was calculated as 80% and 0.7 respectively which proved that the classification was successful and Geoeye-1 imagery can be used reliably to identify the extent of seagrass community in a fast and cost-effective way

A blueprint for the estimation of seagrass carbon stock using remote sensing-enabled proxies

Seagrass ecosystems sequester carbon at disproportionately high rates compared to terrestrial ecosystems and represent a powerful potential contributor to climate change mitigation and adaptation projects. However, at a local scale, rich heterogeneity in seagrass ecosystems may lead to variability in carbon sequestration. Differences in carbon sequestration rates, both within and between seagrass meadows, are related to a wide range of interrelated biophysical and environmental variables that are difficult to measure holistically using traditional field surveys. Improved methods for producing robust, spatially explicit estimates of seagrass carbon storage across large areas would be highly valuable, but must capture complex biophysical heterogeneity and variability to be accurate and useful. Here, we review the current and emerging literature on biophysical processes which shape carbon storage in seagrass beds, alongside studies that map seagrass characteristics using satellite remote sensing data, to create a blueprint for the development of remote sensing-enabled proxies for seagrass carbon stock and sequestration. Applications of satellite remote sensing included measuring seagrass meadow extent, estimating above-ground biomass, mapping species composition, quantifying patchiness and patch connectivity, determining broader landscape environmental contexts, and characterising seagrass life cycles. All of these characteristics may contribute to variability in seagrass carbon storage. As such, remote sensing methods are uniquely placed to enable proxy-based estimates of seagrass carbon stock by capturing their biophysical characteristics, in addition to the spatiotemporal heterogeneity and variability of these characteristics. Though the outlined approach is complex, it is suitable for accurately and efficiently producing a full picture of seagrass carbon stock. This review has drawn links between the processes of seagrass carbon sequestration and the capabilities of remote sensing to detect and characterise these processes. These links will facilitate the development of remote sensing-enabled proxies and support spatially explicit estimates of carbon stock, ensuring climate change mitigation and adaptation projects involving seagrass are accounted for with increased accuracy and reliability

Optimal scales to observe habitat dynamics: A coral reef example

A new technique to estimate the characteristic length scales (CLSs) of real ecological systems provides an objective means to identify the optimal scale(s) of observation to best detect underlying dynamical trends. Application of the technique to natural systems has focused on identifying appropriate scales to measure the dynamics of species as descriptors of community and ecosystem dynamics. However, ecosystem monitoring is often based not on assessing single species, but on species assemblages, functional groups, or habitat types. We asked whether the concept of CLSs based on dynamic interactions among species could be extended to examine interactions among habitat types and thus to identify optimal scales for observing habitat dynamics. A time series of three spatial maps of benthic habitats on a Caribbean coral reef was constructed from aerial photographs, Compact Airborne Spectrographic Imager (CASI) images, and IKONOS satellite images, providing the short time sequence required for this technique. We estimated the CLS based on the dynamics of three distinct habitat types: dense stands of seagrass, sparse stands of seagrass, and Montastrea patch reefs. Despite notable differences in the areal extent of and relative change in these habitats over the 21-year observation period, analyses based on each habitat type indicated a similar CLS of similar to 300 m. We interpret the consistency of CLSs among habitats to indicate that the dynamics of the three habitat types are linked. The results are encouraging, and they indicate that CLS techniques can be used to identify the appropriate scale at which to monitor ecosystem trends on the basis of the dynamics of only one of a disparate suite of habitat types

Exploring the relationships between biodiversity and benthic habitat in the Primeiras and Segundas Protected Area, Mozambique

A Reserva dos Arquipélagos Primeiras e Segundas, localizada no norte de Moçambique, é a maior zona marítima protegida de África, estendendo-se por mais de 200 km de costa. Apesar da sua importância para a economia local, informações sobre os seus ecossistemas marinhos, e particularmente habitats bênticos, são escassas. Doze atóis foram mapeados na região usando object-based image classification de imagens de satélite de muito alta resolução (IKONOS, Quickbird, and WorldView-2). Dados georreferenciados sobre a superficie bêntica e profundidade foram recolhidos em três campanhas de campo, abrangendo um total de quarto atóis e dois baixos. Os mapas produzidos permitem a estimativa de três tipos distintos de superfície coralina (campo, retalhos e falésias), a diferenciação de areia, cascalho e rocha e a detecção de ervas marinhas e macroalgas castanhas, identificando-se até 24 habitats bênticos, com precisão média superior a 50%. Novas informações recolhidas indicam a presença de superfícies bênticas profundas a prolongarem-se dos atóis, o que sugere a necessidade de pesquisa adicional, e está de acordo com o conhecimento actual da existência de um recife de coral quase contínuo desde o Quénia até Moçambique. A análise da biodiversidade das comunidades coralinas e ictiológicas apoia a percepção local de que os ecossistemas estão em declínio. Não foi, no entanto, possível confirmar a sua ligação a práticas de pesca, nem o pressuposto de que a biodiversidade de peixes é maior nas ilhas mais a sul, i.e. longe do principal porto de pesca. Este trabalho contribui para uma descrição detalhada dos habitats marinhos, adequada a usos de gestão e planeamento típicos, nomeadamente a definição de zonas de pesca e monitorização da superfície coralina, contribuindo simultaneamente para o desenvolvimento da aplicação de detecção remota aos campos da biodiversidade e conservação.The Primeiras and Segundas Archipelago Reserve, located in the waters of northern Mozambique, is the largest marine protected area in Africa, extending over 200 km of coastline. Despite the region’s importance for the local economic, information on the marine ecosystem, notably benthic habitat, is very scarce. Twelve atolls were mapped in the region using object-based image classification of very-high resolution satellite imagery (IKONOS, Quickbird, and WorldView-2). Geographically referenced data on benthic cover and depth were gathered in the course of three fieldwork expeditions covering a total of four atolls and two shallow reef structures. The resulting maps allow the estimation of three distinct types of coral cover (field, patches, spurs and grooves); the differentiation of sand, rubble and rock substrate; and the detection of seagrass and brown macroalgae, identifying up to 24 benthic habitats with overall accuracy above 50%. New information indicates the presence of deep benthic cover extending from the atolls, suggesting the need for further research, and supporting current knowledge of the existence of an almost continuous coral reef from Kenya to Mozambique. The results of the analysis of coralline and ichthyological data support the local perception that ecosystems are in decline. It was not possible to verify its connection with fishing practices and the assumption of greater fish biodiversity farther away from the main fishing harbour, i.e. in the southern islands. This work provides a detailed depiction of marine habitats adequate for standard management and planning purposes, namely in the definition of fishing zones and coral cover monitoring, while contributing to the advance of the application of remote sensing to the biodiversity and conservation fields.The Primeiras and Segundas Environmental Protected Area, located in the waters of northern Mozambique, is the largest marine protected area in Africa, extending over 200 km of coastline. Despite the region’s importance for the local economic, information on the marine ecosystem, notably benthic habitat is very scarce. Twelve islands surrounded by coral reefs were mapped in the region using very high resolution satellite images and descriptions of the sea bottom gathered in the field. The resulting maps allow the differentiation of sand, rubble and rock on the sea bottom; the detection of different types of maritime vegetation; and of three distinct types of coral cover. Three types of maps were produced, with different detail levels. The most detailed map has a maximum of 24 classes with an overall accuracy above 50%. The analysis of coral and fish biodiversity data indicate the local ecosystems decline – both quantity and diversity of coral and fish have registered a decrease when compared to 2006 values. It was not possible to verify that fish stocks are decreasing because of current fishing practices, nor that the southern islands, further away from the main fishing harbour, support larger and healthier fish communities. Unidentified structures extending from mapped coral were observed, suggesting the existence of a deeper benthic cover. Further research is recommended, as this additional extension of the coral reef systems could prove of great importance for local and regional ecosystems. With this work it was possible to provide a detailed description of local marine habitats and its coral and fish biodiversity, essential to the Protected Area management and planning, while contributing to the advance of the application of remote sensing to nature conservation