Seabed mapping in coastal shallow waters using high resolution multispectral and hyperspectral imagery

Coastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of fundamental importance to generate precise seabed maps in coastal shallow water areas. In this work, satellite and airborne multispectral and hyperspectral imagery were used to map benthic habitats in a complex ecosystem. In it, submerged green aquatic vegetation meadows have low density, are located at depths up to 20 m, and the sea surface is regularly affected by persistent local winds. A robust mapping methodology has been identified after a comprehensive analysis of different corrections, feature extraction, and classification approaches. In particular, atmospheric, sunglint, and water column corrections were tested. In addition, to increase the mapping accuracy, we assessed the use of derived information from rotation transforms, texture parameters, and abundance maps produced by linear unmixing algorithms. Finally, maximum likelihood (ML), spectral angle mapper (SAM), and support vector machine (SVM) classification algorithms were considered at the pixel and object levels. In summary, a complete processing methodology was implemented, and results demonstrate the better performance of SVM but the higher robustness of ML to the nature of information and the number of bands considered. Hyperspectral data increases the overall accuracy with respect to the multispectral bands (4.7% for ML and 9.5% for SVM) but the inclusion of additional features, in general, did not significantly improve the seabed map quality.Peer ReviewedPostprint (published version

COMPARISON OF SEAGRASS COVER CLASSIFICATION BASED-ON SVM AND FUZZY ALGORITHMS USING MULTI-SCALE IMAGERY IN KODINGARENG LOMPO ISLAND

Padang lamun mempunyai peranan ekologi bagi lingkungan laut dangkal yaitu sebagai habitat biota, produsen primer, penangkap sedimen serta berperan sebagai pendaur zat-zat hara. Mengingat pentingnya peranan ekosistem padang lamun maka kelestarian sumber daya alam ini perlu dijaga, oleh karena itu pemetaan dan pemantauan yang terus-menerus terhadap keberadaan padang lamun sangat penting dilakukan. Metode penginderaan jauh merupakan metode yang dapat digunakan untuk memetakan dan memantau kondisi padang lamun. Perkembangan teknologi sensor satelit yang pesat saat ini, khususnya resolusi spasial dan spektral sensor meningkatkan kualitas peta sebaran lamun. Penggunaan metode dan skema klasifikasi yang kurang tepat dalam klasifikasi kondisi lamun dari citra satelit juga termasuk hal yang dapat memengaruhi akurasi peta, sehingga dibutuhkan berbagai alternatif kajian algoritma yang digunakan. Pada penelitian ini digunakan algoritma Support Vector Machine dan Logika Fuzzy menggunakan citra satelit WorldView-2 dan Sentinel-2 di Pulau Kodingareng Lompo dengan empat kelas tutupan lamun yaitu jarang (0-25%), sedang (26-50%), padat (51-75%), dan sangat padat (76-100%). Hasil yang diperoleh adalah algoritma Logika Fuzzy menggunakan citra WorldView-2 memiliki akurasi keseluruhan klasifikasi tutupan lamun yang paling baik sebesar 78,60%.Seagrass beds play an ecological role in the shallow marine environment, such as a habitat for biota, primary producers, and sediment traps. They also act as nutrient recyclers. Since they have such an important role, this natural resource needs to be preserved. Therefore, continuous monitoring and mapping of seagrass beds, especially by remote sensing methods, is paramount. The current rapid development of satellite sensor technology, especially its spatial and spectral resolutions, has improved the quality of the seagrass distribution map. The use of proper classification methods and schemes in the classification of seagrass distribution based on satellite imagery can affect the accuracy of the map, which is why various alternative algorithm studies are required. In this study, the Support Vector Machine and Fuzzy Logic algorithms were used to classify the WorldView-2 and Sentinel-2 satellite imageries on Kodingareng Lompo Island with four classes of seagrass cover, sparse (0–25%), moderate (26–50%), dense (51–75%), and very dense (76–100%). The result showed that the Fuzzy Logic algorithm applied to WorldView-2 imagery has the best overall accuracy of 78.60% seagrass cover classification

The value of remote sensing techniques in supporting effective extrapolation across multiple marine spatial scales

The reporting of ecological phenomena and environmental status routinely required point observations, collected with traditional sampling approaches to be extrapolated to larger reporting scales. This process encompasses difficulties that can quickly entrain significant errors. Remote sensing techniques offer insights and exceptional spatial coverage for observing the marine environment. This review provides guidance on (i) the structures and discontinuities inherent within the extrapolative process, (ii) how to extrapolate effectively across multiple spatial scales, and (iii) remote sensing techniques and data sets that can facilitate this process. This evaluation illustrates that remote sensing techniques are a critical component in extrapolation and likely to underpin the production of high-quality assessments of ecological phenomena and the regional reporting of environmental status. Ultimately, is it hoped that this guidance will aid the production of robust and consistent extrapolations that also make full use of the techniques and data sets that expedite this process

Worldview-2 and Landsat 8 Satellite Data for Seaweed Mapping along Karachi Coast

Seaweed is a marine plant or algae which has economic value in many parts of the world. The purpose of this study is to evaluate different satellite sensors such as high-resolution WorldView-2 (WV2) satellite data and Landsat 8 30-meter resolution satellite data for mapping seaweed resources along the coastalwaters of Karachi. The continuous monitoring and mapping of this precious marine plant and their breeding sites may not be very efficient and cost effective using traditional survey techniques. Remote Sensing (RS) and Geographical Information System (GIS) can provide economical and more efficient solutions for mapping and monitoring coastal resources quantitatively as well as qualitatively at both temporal and spatial scales. Normalized Difference Vegetation Indices (NDVI) along with the image enhancement techniques were used to delineate seaweed patches in the study area. The coverage area of seaweed estimated with WV-2 and Landsat 8 are presented as GIS maps. A more precise area estimation wasachieved with WV-2 data that shows 15.5Ha (0.155 Km2)of seaweed cover along Karachi coast that is more representative of the field observed data. A much larger area wasestimated with Landsat 8 image (71.28Ha or 0.7128 Km2) that was mainly due to the mixing of seaweed pixels with water pixels. The WV-2 data, due to its better spatial resolution than Landsat 8, have proven to be more useful than Landsat8 in mapping seaweed patche

Benthic mapping of the Bluefields Bay fish sanctuary, Jamaica

Small island states, such as those in the Caribbean, are dependent on the nearshore marine ecosystem complex and its resources; the goods and services provided by seagrass and coral reef for example, are particularly indispensable to the tourism and fishing industries. In recognition of their valuable contributions and in an effort to promote sustainable use of marine resources, some nearshore areas have been designated as fish sanctuaries, as well as marine parks and protected areas. In order to effectively manage these coastal zones, a spatial basis is vital to understanding the ecological dynamics and ultimately inform management practices. However, the current extent of habitats within designated sanctuaries across Jamaica are currently unknown and owing to this, the Government of Jamaica is desirous of mapping the benthic features in these areas. Given the several habitat mapping methodologies that exist, it was deemed necessary to test the practicality of applying two remote sensing methods - optical and acoustic - at a pilot site in western Jamaica, the Bluefields Bay fish sanctuary. The optical remote sensing method involved a pixel-based supervised classification of two available multispectral images (WorldView-2 and GeoEye-1), whilst the acoustic method comprised a sonar survey using a BioSonics DT-X Portable Echosounder and subsequent indicator kriging interpolation in order to create continuous benthic surfaces. Image classification resulted in the mapping of three benthic classes, namely submerged vegetation, bare substrate and coral reef, with an overall map accuracy of 89.9% for WorldView-2 and 86.8% for GeoEye-1 imagery. These accuracies surpassed those of the acoustic classification method, which attained 76.6% accuracy for vegetation presence, and 53.5% for bottom substrate (silt, sand and coral reef/ hard bottom). Both approaches confirmed that the Bluefields Bay is dominated by submerged aquatic vegetation, with contrastingly smaller areas of bare sediment and coral reef patches. Additionally, the sonar revealed that silty substrate exists along the shoreline, whilst sand is found further offshore. Ultimately, the methods employed in this study were compared and although it was found that satellite image classification was perhaps the most cost-effective and well-suited for Jamaica given current available equipment and expertise, it is acknowledged that acoustic technology offers greater thematic detail required by a number of stakeholders and is capable of operating in turbid waters and cloud covered environments ill-suited for image classification. On the contrary, a major consideration for the acoustic classification process is the interpolation of processed data; this step gives rise to a number of potential limitations, such as those associated with the choice of interpolation algorithm, available software and expertise. The choice in mapping approach, as well as the survey design and processing steps is not an easy task; however the results of this study highlight the various benefits and shortcomings of implementing optical and acoustic classification approaches in Jamaica.Persons automatically associate tropical waters with spectacular views of coral reefs and colourful fish; however many are perhaps not aware that these coral reefs, as well as other living organisms inhabiting the seabed are in fact extremely valuable to our existence. Healthy coral reefs and seagrass assist in maintaining the sand on our beaches and fish populations and are thereby crucial to the tourism and fishing industries in the Caribbean. For this reason, a number of areas are protected by law and have been designated fish sanctuaries or marine protected areas. In order to understand the functioning of theses areas and effectively inform management strategy, the configuration of what exists on the seafloor is crucial. In the same vein that a motorist needs a road map to navigate unknown areas, coastal stakeholders require maps of the seafloor in order to understand what is happening beneath the water’s surface. The location of seafloor habitats within fish sanctuaries in Jamaica are currently unknown and the Government is interested in mapping them. However a myriad of methods exist that could be employed to achieve this goal. Remote sensing is a broad grouping of methods that involve collecting information about an object without being in direct physical contact with it. Many researchers have successfully mapped marine areas using these techniques and it was believed crucial to test the practicality of two such methods, specifically optical and acoustic remote sensing. The main question to be answered from this study was therefore: Which mapping approach is better for benthic habitat mapping in Jamaica and possibly the wider Caribbean? Optical remote sensing relates to the interaction of energy with the Earth’s surface. A digital photograph is taken from a satellite and subsequently interpreted. Acoustic/ sonar technology involves the recording of waveforms reflected from the seabed. Both methods were employed at a pilot site, the Bluefields Bay fish sanctuary, situated in western Jamaica. The optical remote sensing method involved the classification of two satellite images (named WorldView-2 and GeoEye-1) and this process was informed using known positions of seafloor features, this being known as supervised image classification. With regard to the acoustic method, a field survey utilising sonar equipment (BioSonics DT-X Portable Echosounder) was undertaken in order to collect the necessary sonar data. The processed field data was modelled in order to convert lines of field point data to one continuous map of the sanctuary, a process known as interpolation. The accuracy of each method was then tested using field knowledge of what exists in the sanctuary. The map resulting from the image classification revealed three seafloor types, namely submerged vegetation, coral reef and bare seafloor. The overall map accuracy was 89.9% for the WorldView-2 image and 86.8% for GeoEye-1 imagery. These accuracies surpassed those attained from the acoustic classification method (76.6% for vegetation presence and 53.5% for bottom type - silt, sand and coral reef/ hard bottom). Similar to previous studies undertaken, it was shown that the seabed of Bluefields Bay is primarily inhabited by submerged aquatic vegetation (including seagrass and algae), with contrastingly smaller areas of bare sediment and coral reef. Ultimately, the methods employed in this study were compared and the pros and cons of each were weighed in order to deem one method more suitable in Jamaica. Often, the presence of cloud and suspended matter in the water block the view of the seafloor making image classification difficult. On the contrary, acoustic surveys are capable of operating throughout cloudy conditions and attaining more detailed information of the ocean floor, otherwise not possible with optical remote sensing. A major step in the acoustic classification process however, was the interpolation of processed data, which may introduce additional limitations if careful consideration is not given to the intricacies of the process. Lastly, the acoustic survey certainly required greater financial resources than satellite image classification. In answer to the main question of this study, the most cost effective and feasible mapping method for Jamaica is satellite image classification (based on the results attained). It must be stressed however that the effective implementation of any method will depend on a number of factors, such as available software, equipment, expertise and user needs, that must be weighed in order to select the most feasible mapping method for a particular site

Three-Dimensional Mapping of Habitats Using Remote-Sensing Data and Machine-Learning Algorithms

Progress toward habitat protection goals can effectively be performed using satellite imagery and machine-learning (ML) models at various spatial and temporal scales. In this regard, habitat types and landscape structures can be discriminated against using remote-sensing (RS) datasets. However, most existing research in three-dimensional (3D) habitat mapping primarily relies on same/cross-sensor features like features derived from multibeam Light Detection And Ranging (LiDAR), hydrographic LiDAR, and aerial images, often overlooking the potential benefits of considering multi-sensor data integration. To address this gap, this study introduced a novel approach to creating 3D habitat maps by using high-resolution multispectral images and a LiDAR-derived Digital Surface Model (DSM) coupled with an object-based Random Forest (RF) algorithm. LiDAR-derived products were also used to improve the accuracy of the habitat classification, especially for the habitat classes with similar spectral characteristics but different heights. Two study areas in the United Kingdom (UK) were chosen to explore the accuracy of the developed models. The overall accuracies for the two mentioned study areas were high (91% and 82%), which is indicative of the high potential of the developed RS method for 3D habitat mapping. Overall, it was observed that a combination of high-resolution multispectral imagery and LiDAR data could help the separation of different habitat types and provide reliable 3D information

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

High-Resolution Habitat and Bathymetry Maps for 65,000 sq. km of Earth’s Remotest Coral Reefs

With compelling evidence that half the world’s coral reefs have been lost over the last four decades, there is urgent motivation to understand where reefs are located and their health. Without such basic baseline information, it is challenging to mount a response to the reef crisis on the global scale at which it is occurring. To combat this lack of baseline data, the Khaled bin Sultan Living Oceans Foundation embarked on a 10-yr survey of a broad selection of Earth’s remotest reef sites—the Global Reef Expedition. This paper focuses on one output of this expedition, which is meter-resolution seafloor habitat and bathymetry maps developed from DigitalGlobe satellite imagery and calibrated by field observations. Distributed on an equatorial transect across 11 countries, these maps cover 65,000 sq. km of shallow-water reef-dominated habitat. The study represents an order of magnitude greater area than has been mapped previously at high resolution. We present a workflow demonstrating that DigitalGlobe imagery can be processed to useful products for reef conservation at regional to global scale. We further emphasize that the performance of our mapping workflow does not deteriorate with increasing size of the site mapped. Whereas our workflow can produce regional-scale benthic habitat maps for the morphologically diverse reefs of the Pacific and Indian oceans, as well as the more depauperate reefs of the Atlantic, accuracies are substantially higher for the former than the latter. It is our hope that the map products delivered to the community by the Living Oceans Foundation will be utilized for conservation and act to catalyze new initiatives to chart the status of coral reefs globally

Coastal benthic habitat mapping and monitoring by integrating aerial and water surface low-cost drones

Accurate data on community structure is a priority issue in studying coastal habitats facing human pressures. The recent development of remote sensing tools has offered a ground-breaking way to collect ecological information at a very fine scale, especially using low-cost aerial photogrammetry. Although coastal mapping is carried out using Unmanned Aerial Vehicles (UAVs or drones), they can provide limited information regarding underwater benthic habitats. To achieve a precise characterisation of underwater habitat types and species assemblages, new imagery acquisition instruments become necessary to support accurate mapping programmes. Therefore, this study aims to evaluate an integrated approach based on Structure from Motion (SfM) photogrammetric acquisition using low-cost Unmanned Aerial (UAV) and Surface (USV) Vehicles to finely map shallow benthic communities, which determine the high complexity of coastal environments. The photogrammetric outputs, including both UAV-based high (sub-meter) and USV-based ultra-high (sub-centimetre) raster products such as orthophoto mosaics and Digital Surface Models (DSMs), were classified using Object-Based Image Analysis (OBIA) approach. The application of a supervised learning method based on Support Vector Machines (SVM) classification resulted in good overall classification accuracies > 70%, proving to be a practical and feasible tool for analysing both aerial and underwater ultra-high spatial resolution imagery. The detected seabed cover classes included above and below-water key coastal features of ecological interest such as seagrass beds, “banquettes” deposits and hard bottoms. Using USV-based imagery can considerably improve the identification of specific organisms with a critical role in benthic communities, such as photophilous macroalgal beds. We conclude that the integrated use of low-cost unmanned aerial and surface vehicles and GIS processing is an effective strategy for allowing fully remote detailed data on shallow water benthic communities