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Doukari, M., and K. Topouzelis. "UAS DATA ACQUISITION PROTOCOL FOR MARINE HABITAT MAPPING: AN ACCURACY ASSESSMENT STUDY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1321–26. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1321-2020.
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Abstract. Marine habitat mapping is essential for updating existing information, preserving, and protecting the marine environment. Unmanned Aerial Systems (UAS) are an important tool for monitoring and mapping coastal and marine environment because of their ability to provide very high-resolution aerial imagery.Environmental conditions have a critical role in marine mapping using UAS. This is due to the limitations of UAS surveys in coastal areas, i.e. the environmental conditions prevailing in the area. The limitations of weather and oceanographic conditions affecting the quality of marine data led to the creation of a UAS protocol for the acquisition of reliable marine information. The produced UAS Data Acquisition Protocol consists of three main categories: (i) Morphology of the study area, (ii) Environmental conditions, (iii) Flight parameters. These categories include the parameters that must be considered for marine habitat mapping.The aim of the present study is the accuracy assessment of the UAS protocol for marine habitat mapping through experimental flights. For the accuracy assessment of the UAS protocol, flights on different dates and environmental conditions were conducted, over a study area. The flight altitude was the same for all the missions, so the results were comparable. The high-resolution orthophoto maps derived from each date of the experiment were classified. The classification maps show several differences in the shape and size of the marine habitats which are directly dependent on the conditions that the habitats were mapped. A change detection comparison was conducted in pairs to examine the exact changes between the classified maps.The results emphasize the importance of the environmental conditions prevailing in an area during the mapping of marine habitats. The present study proves that the optimal flight conditions that are proposed of the UAS Data Acquisition protocol, respond to the real-world conditions and are important to be considered for an accurate and reliable mapping of the marine environment.
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Amani, Meisam, Candace Macdonald, Abbas Salehi, Sahel Mahdavi, and Mardi Gullage. "Marine Habitat Mapping Using Bathymetric LiDAR Data: A Case Study from Bonne Bay, Newfoundland." Water 14, no. 23 (2022): 3809. http://dx.doi.org/10.3390/w14233809.
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Marine habitats provide various benefits to the environment and humans. In this regard, an accurate marine habitat map is an important component of effective marine management. Newfoundland’s coastal area is covered by different marine habitats, which should be correctly mapped using advanced technologies, such as remote sensing methods. In this study, bathymetric Light Detection and Ranging (LiDAR) data were applied to accurately discriminate different habitat types in Bonne Bay, Newfoundland. To this end, the LiDAR intensity image was employed along with an object-based Random Forest (RF) algorithm. Two types of habitat classifications were produced: a two-class map (i.e., Vegetation and Non-Vegetation) and a five-class map (i.e., Eelgrass, Macroalgae, Rockweed, Fine Sediment, and Gravel/Cobble). It was observed that the accuracies of the produced habitat maps were reasonable considering the existing challenges, such as the error of the LiDAR data and lacking enough in situ samples for some of the classes such as macroalgae. The overall classification accuracies for the two-class and five-class maps were 87% and 80%, respectively, indicating the high capability of the developed machine learning model for future marine habitat mapping studies. The results also showed that Eelgrass, Fine Sediment, Gravel/Cobble, Macroalgae, and Rockweed cover 22.4% (3.66 km2), 51.4% (8.39 km2), 13.5% (2.21 km2), 6.9% (1.12 km2), and 5.8% (0.95 km2) of the study area, respectively.
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Aguhob, Jeruel, Waleed Hamza, Andreas Reul, Muna Musabih, Shahid Mustafa, and Maria Muñoz. "Baseline Habitat Setting for Future Evaluation of Environmental Status Quality of Jabal Ali Marine Sanctuary, Dubai, UAE." Sustainability 16, no. 6 (2024): 2374. http://dx.doi.org/10.3390/su16062374.
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Habitat mapping plays a crucial role in assessing marine protected areas (MPA) and implementing marine spatial management approaches. This study aims to present the spatial habitat distribution of the Jabal Ali Marine Sanctuary, considering the development projects implemented in its proximity. It serves as a reference for guiding conservation management efforts. The study focuses on in situ hyperspectral measurements of the optical properties of both the water column and the substrate. Additionally, a high density of geo-referenced spot checks were conducted, serving as sample points for ecological evaluation and ground-truthing. An “object-oriented” approach was adopted to generate the seabed map in two evaluated studies conducted in 2006 and 2017. While the 2017 survey identified 16 habitats, the 2006 study characterized only 10 habitats. These habitat maps serve as powerful tools for implementing mitigation measures and providing scientific support to mitigate the negative impact on the most crucial marine habitats within the context of a protected area management framework. Furthermore, monitoring the cover of the most important habitats provides an integrative indicator to maintain the good environmental status of the marine sanctuary. Based on this study, the information will be a reference for evaluating and synergizing the management approaches implemented by both the competent authority and the different stakeholders in the sanctuary.
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Lee, Sonny T. M., Michelle Kelly, Tim J. Langlois, and Mark J. Costello. "Baseline seabed habitat and biotope mapping for a proposed marine reserve." PeerJ 3 (December 10, 2015): e1446. http://dx.doi.org/10.7717/peerj.1446.
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Seabed mapping can quantify the extent of benthic habitats that comprise marine ecosystems, and assess the impact of fisheries on an ecosystem. In this study, the distribution of seabed habitats in a proposed no-take Marine Reserve along the northeast coast of Great Barrier Island, New Zealand, was mapped using underwater video combined with bathymetry and substratum data. As a result of the boundary extending to the 12 nautical mile Territorial Limit, it would have been the largest coastal Marine Reserve in the country. Recreational and commercial fisheries occur in the region and would be expected to affect species’ abundance. The seabed of the study area and adjacent coastal waters has been trawled up to five times per year. Benthic communities were grouped by multivariate cluster analysis into four biotope classes; namely (1) shallow water macroalgaeEckloniasp. andUlvasp. on rocky substrata (Eck.Ulv); and deeper (2) diverse epifauna of sponges and bryozoans on rocky substrata (Por.Bry), (3) brittle starAmphiurasp. and sea anemoneEdwardsiasp. on muddy sand (Amph.Edw), and (4) hydroids on mud (Hyd). In biotopes Por.Bry, Amph.Edw and Hyd, there where boulders and rocks were present, and diverse sponge, bryozoan and coral communities. Fifty species were recorded in the deep water survey including significant numbers of the shallow-water hexactinellid glass spongesSymplectella rowiDendy, 1924 andRossella ijimaiDendy, 1924, the giant pipe demospongeIsodictya cavicornutaDendy, 1924, black corals, and locally endemic gorgonians. The habitats identified in the waters to the northeast of Great Barrier Island are likely to be representative of similar depth ranges in northeast New Zealand. This study provides a baseline of the benthic habitats so that should the area become a Marine Reserve, any habitat change might be related to protection from fishing activities and impacts, such as recovery of epifauna following cessation of trawling. The habitat map may also be used to stratify future sampling that would aim to collect and identify epifauna and infauna for identification, and thus better describe the biodiversity of the area.
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Immordino, Francesco, Mattia Barsanti, Elena Candigliota, Silvia Cocito, Ivana Delbono, and Andrea Peirano. "Application of Sentinel-2 Multispectral Data for Habitat Mapping of Pacific Islands: Palau Republic (Micronesia, Pacific Ocean)." Journal of Marine Science and Engineering 7, no. 9 (2019): 316. http://dx.doi.org/10.3390/jmse7090316.
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Sustainable and ecosystem-based marine spatial planning is a priority of Pacific Island countries basing their economy on marine resources. The urgency of management coral reef systems and associated coastal environments, threatened by the effects of climate change, require a detailed habitat mapping of the present status and a future monitoring of changes over time. Here, we present a remote sensing study using free available Sentinel-2 imagery for mapping at large scale the most sensible and high value habitats (corals, seagrasses, mangroves) of Palau Republic (Micronesia, Pacific Ocean), carried out without any sea truth validation. Remote sensing ‘supervised’ and ‘unsupervised’ classification methods applied to 2017 Sentinel-2 imagery with 10 m resolution together with comparisons with free ancillary data on web platform and available scientific literature were used to map mangrove, coral, and seagrass communities in the Palau Archipelago. This paper addresses the challenge of multispectral benthic mapping estimation using commercial software for preprocessing steps (ERDAS ATCOR) and for benthic classification (ENVI) on the base of satellite image analysis. The accuracy of the methods was tested comparing results with reference NOAA (National Oceanic and Atmospheric Administration, Silver Spring, MD, USA) habitat maps achieved through Ikonos and Quickbird imagery interpretation and sea-truth validations. Results showed how the proposed approach allowed an overall good classification of marine habitats, namely a good concordance of mangroves cover around Palau Archipelago with previous literature and a good identification of coastal habitats in two sites (barrier reef and coastal reef) with an accuracy of 39.8–56.8%, suitable for survey and monitoring of most sensible habitats in tropical remote islands.
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McRea, James E., H. Gary Greene, Victoria M. O'Connell, and W. Waldo Wakefield. "Mapping marine habitats with high resolution sidescan sonar." Oceanologica Acta 22, no. 6 (1999): 679–86. http://dx.doi.org/10.1016/s0399-1784(00)88958-6.
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Fyfe, Jim, Steven A. Israel, Albert Chong, Norhadi Ismail, Catriona L. Hurd, and Keith Probert. "Mapping Marine Habitats in Otago, Southern New Zealand." Geocarto International 14, no. 3 (1999): 17–28. http://dx.doi.org/10.1080/10106049908542113.
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Noji, Thomas, Heye Rumohr, and Stephen J. Smith. "Sediment–biota interactions and mapping marine habitats: an Introduction." ICES Journal of Marine Science 66, no. 9 (2009): 2012. http://dx.doi.org/10.1093/icesjms/fsp213.
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Lim, Aaron, Andrew J. Wheeler, and Luis Conti. "Cold-Water Coral Habitat Mapping: Trends and Developments in Acquisition and Processing Methods." Geosciences 11, no. 1 (2020): 9. http://dx.doi.org/10.3390/geosciences11010009.
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Cold-water coral (CWC) habitats are considered important centers of biodiversity in the deep sea, acting as spawning grounds and feeding area for many fish and invertebrates. Given their occurrence in remote parts of the planet, research on CWC habitats has largely been derived from remotely-sensed marine spatial data. However, with ever-developing marine data acquisition and processing methods and non-ubiquitous nature of infrastructure, many studies are completed in isolation resulting in large inconsistencies. Here, we present a concise review of marine remotely-sensed spatial raster data acquisition and processing methods in CWC habitats to highlight trends and knowledge gaps. Sixty-three studies that acquire and process marine spatial raster data since the year 2000 were reviewed, noting regional geographic location, data types (‘acquired data’) and how the data were analyzed (‘processing methods’). Results show that global efforts are not uniform with most studies concentrating in the NE Atlantic. Although side scan sonar was a popular mapping method between 2002 and 2012, since then, research has focused on the use of multibeam echosounder and photogrammetric methods. Despite advances in terrestrial mapping with machine learning, it is clear that manual processing methods are largely favored in marine mapping. On a broader scale, with large-scale mapping programs (INFOMAR, Mareano, Seabed2030), results from this review can help identify where more urgent research efforts can be concentrated for CWC habitats and other vulnerable marine ecosystems.
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Morsy, S., A. B. Yánez Suárez, and K. Robert. "3D MAPPING OF BENTHIC HABITAT USING XGBOOST AND STRUCTURE FROM MOTION PHOTOGRAMMETRY." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023 (December 5, 2023): 1131–36. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-1131-2023.
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Abstract. Benthic habitats mapping is essential to the management and conservation of marine ecosystems. The traditional methods of mapping benthic habitats, which involve multibeam data acquisition and manually collecting and annotating imagery data, are time-consuming. However, with technological advances, using machine learning (ML) algorithms with structure-from-motion (SfM) photogrammetry has become a promising approach for mapping benthic habitats accurately and at very high resolutions. This paper explores using SfM photogrammetry and extreme gradient boosting (XGBoost) classifier for benthic habitat 3D mapping of a vertical wall at the Charlie-Gibbs Fracture Zone in the North Atlantic Ocean. The classification workflow started with extracting frames from video footage. The SfM was then applied to reconstruct the 3D point cloud of the wall. Thereafter, nine geometric features were derived from the 3D point cloud geometry. The XGBoost classifier was then used to classify the vertical wall into rock, sponges, and corals (Case 1 - three classes). In addition, we separated the sponges class into three types of sponges: Demospongiae, Hexactinellida, and other Porifera (Case 2 - five classes). Moreover, we compared the results from XGBoost with the widely used ML classifier, random forest (RF). For Case 2, XGBoost achieved an overall accuracy (OA) of 74.45%, while RF achieved 73.10%. The OA improved by about 10% from both classifiers when the three types of sponges were combined into one class (Case 1). Results showed that the presented 3D mapping of benthic habitat has the potential to provide more detailed and accurate information about marine ecosystems.
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Peirano, Andrea, Mattia Barsanti, Ivana Delbono, et al. "Baseline Assessment of Ecological Quality Index (EQI) of the Marine Coastal Habitats of Tonga Archipelago: Application for Management of Remote Regions in the Pacific." Remote Sensing 15, no. 4 (2023): 909. http://dx.doi.org/10.3390/rs15040909.
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The loss of coral habitats and associated biodiversity have direct effects both on the physical dynamics of the coast and on natural resources, threatening the survival of local populations. Conservative actions, such as the creation of new Marine Protected Areas, are urgent measures needed to face climate change. Managers need fast and simple methods to evaluate marine habitats for planning conservation areas. Here, we present the application of an Ecological Quality Index (EQI), developed for regional-scale habitat maps of the Atlas of the Marine Coastal Habitats of the Kingdom of Tonga, by processing Copernicus Sentinel-2 imagery. Both the habitat mapping classification and the EQI application were focused on the importance of coral reef, seagrass and mangrove habitats, both as natural defense and sustenance for the local populations. Twelve main Pacific reef habitats were evaluated through a three-level EQI score assigned to six parameters: nursery ground, connectivity, species reservoir, fish attraction, biodiversity and primary production. The EQI was integrated into a developed georeferenced database associated to the QGIS software providing the ability to identify on the maps the area of interest and the associated habitats, and to quantify their ecological relevance. The EQI is proposed as a tool that can offer to stakeholders and environmental managers a simple and direct indicator of the value of the marine coastal environment. The index may be handled for management purposes of vast areas with remote and uninhabited islands.
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Mohamed, Hassan, Kazuo Nadaoka, and Takashi Nakamura. "Semiautomated Mapping of Benthic Habitats and Seagrass Species Using a Convolutional Neural Network Framework in Shallow Water Environments." Remote Sensing 12, no. 23 (2020): 4002. http://dx.doi.org/10.3390/rs12234002.
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Benthic habitats are structurally complex and ecologically diverse ecosystems that are severely vulnerable to human stressors. Consequently, marine habitats must be mapped and monitored to provide the information necessary to understand ecological processes and lead management actions. In this study, we propose a semiautomated framework for the detection and mapping of benthic habitats and seagrass species using convolutional neural networks (CNNs). Benthic habitat field data from a geo-located towed camera and high-resolution satellite images were integrated to evaluate the proposed framework. Features extracted from pre-trained CNNs and a “bagging of features” (BOF) algorithm was used for benthic habitat and seagrass species detection. Furthermore, the resultant correctly detected images were used as ground truth samples for training and validating CNNs with simple architectures. These CNNs were evaluated for their accuracy in benthic habitat and seagrass species mapping using high-resolution satellite images. Two study areas, Shiraho and Fukido (located on Ishigaki Island, Japan), were used to evaluate the proposed model because seven benthic habitats were classified in the Shiraho area and four seagrass species were mapped in Fukido cove. Analysis showed that the overall accuracy of benthic habitat detection in Shiraho and seagrass species detection in Fukido was 91.5% (7 classes) and 90.4% (4 species), respectively, while the overall accuracy of benthic habitat and seagrass mapping in Shiraho and Fukido was 89.9% and 91.2%, respectively.
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Bianchi, Carlo Nike, Annalisa Azzola, Silvia Cocito, et al. "Biodiversity Monitoring in Mediterranean Marine Protected Areas: Scientific and Methodological Challenges." Diversity 14, no. 1 (2022): 43. http://dx.doi.org/10.3390/d14010043.
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Biodiversity is a portmanteau word to indicate the variety of life at all levels from genes to ecosystems, but it is often simplistically equated to species richness; the word ecodiversity has thus been coined to address habitat variety. Biodiversity represents the core of the natural capital, and as such needs to be quantified and followed over time. Marine Protected Areas (MPAs) are a major tool for biodiversity conservation at sea. Monitoring of both species and habitat diversity in MPAs is therefore mandatory and must include both inventory and periodic surveillance activities. In the case of inventories, the ideal would be to census all species and all habitats, but while the latter goal can be within reach, the former seems unattainable. Species inventory should be commeasured to investigation effort, while habitat inventory should be based on mapping. Both inventories may profit from suitability spatial modelling. Periodic surveillance actions should privilege conspicuous species and priority habitats. Efficient descriptor taxa and ecological indices are recommended to evaluate environmental status. While it seems obvious that surveillance activities should be carried out with regular recurrence, diachronic inventories and mapping are rarely carried out. Time series are of prime importance to detect marine ecosystem change even in the absence of direct human impacts.
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Papenmeier, Svenja, Alexander Darr, Peter Feldens, and Rune Michaelis. "Hydroacoustic Mapping of Geogenic Hard Substrates: Challenges and Review of German Approaches." Geosciences 10, no. 3 (2020): 100. http://dx.doi.org/10.3390/geosciences10030100.
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Subtidal hard substrate habitats are unique habitats in the marine environment. They provide crucial ecosystem services that are socially relevant, such as water clearance or as nursery space for fishes. With increasing marine usage and changing environmental conditions, pressure on reefs is increasing. All relevant directives and conventions around Europe include sublittoral hard substrate habitats in any manner. However, detailed specifications and specific advices about acquisition or delineation of these habitats are internationally rare although the demand for single object detection for e.g., ensuring safe navigation or to understand ecosystem functioning is increasing. To figure out the needs for area wide hard substrate mapping supported by automatic detection routines this paper reviews existing delineation rules and definitions relevant for hard substrate mapping. We focus on progress reached in German approval process resulting in first hydroacoustic mapping advices. In detail, we summarize present knowledge of hard substrate occurrence in the German North Sea and Baltic Sea, describes the development of hard substrate investigations and state of the art mapping techniques as well as automated analysis routines.
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Legrand, H., P. Lenfant, I. S. Sotheran, R. L. Foster-Smith, R. Galzin, and J.-P. Maréchal. "Mapping marine benthic habitats in Martinique (French West Indies)." Caribbean Journal of Science 46, no. 2-3 (2010): 267–82. http://dx.doi.org/10.18475/cjos.v46i2.a15.
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Ilich, Alexander R., Jennifer L. Brizzolara, Sarah E. Grasty, et al. "Integrating Towed Underwater Video and Multibeam Acoustics for Marine Benthic Habitat Mapping and Fish Population Estimation." Geosciences 11, no. 4 (2021): 176. http://dx.doi.org/10.3390/geosciences11040176.
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The west Florida shelf (WFS; Gulf of Mexico, USA) is an important area for commercial and recreational fishing, yet much of it remains unmapped and unexplored, hindering effective monitoring of fish stocks. The goals of this study were to map the habitat at an intensively fished area on the WFS known as “The Elbow”, assess the differences in fish communities among different habitat types, and estimate the abundance of each fish taxa within the study area. High-resolution multibeam bathymetric and backscatter data were combined with high-definition (HD) video data collected from a near-bottom towed vehicle to characterize benthic habitat as well as identify and enumerate fishes. Two semi-automated statistical classifiers were implemented for obtaining substrate maps. The supervised classification (random forest) performed significantly better (p = 0.001; α = 0.05) than the unsupervised classification (k-means clustering). Additionally, we found it was important to include predictors at a range of spatial scales. Significant differences were found in the fish community composition among the different habitat types, with both substrate and vertical relief found to be important with rock substrate and higher relief areas generally associated with greater fish density. Our results are consistent with the idea that offshore hard-bottom habitats, particularly those of higher vertical relief, serve as “essential fish habitat”, as these rocky habitats account for just 4% of the study area but 65% of the estimated total fish abundance. However, sand contributes 35% to total fish abundance despite comparably low densities due to its large area, indicating the importance of including these habitats in estimates of abundance as well. This work demonstrates the utility of combining towed underwater video sampling and multibeam echosounder maps for habitat mapping and estimation of fish abundance.
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Schmiing, Mara, Jorge Fontes, and Pedro Afonso. "Predictive mapping of reproductive fish habitats to aid marine conservation planning." Canadian Journal of Fisheries and Aquatic Sciences 74, no. 7 (2017): 1016–27. http://dx.doi.org/10.1139/cjfas-2015-0538.
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Postsettlement spillover from marine protected areas (MPAs) can support adjacent fished populations and has been subject of many scientific studies. The larval subsidy effect, on the contrary, is more challenging to study and less demonstrated, although it, arguably, provides key benefits for fisheries. We modeled and predicted the spatial distribution of fish spawning biomass and fecundity across a temperate insular MPA network (Azores archipelago, Northeast Atlantic) and identified potential single- and multispecies reproductive habitats (RHs) in shallow reefs. Reproductive strategies or skewed sex ratios influenced spatial patterns of potential spawning biomass and fecundity. Predicted multispecies RHs covered 5%–20% of the studied reef habitat. Given their potentially high reproductive output, we argue that such sites should be considered in marine conservation planning to increase chances of achieving fisheries and conservation benefits. Spatial patterns of the reproductive output may function as surrogates for larval subsidy when limited or no larval connectivity information is available and also may assist in identifying potential larval sources and priority sites for conservation.
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Muhamad, Muhammad Abdul Hakim, Rozaimi Che Hasan, Najhan Md Said, Mohd Shahmy Mohd Said, and Raiz Razali. "Marine Habitat Mapping using Multibeam Echosounder Survey and Underwater Video Observations: A Case Study from Tioman Marine Park." IOP Conference Series: Earth and Environmental Science 1240, no. 1 (2023): 012006. http://dx.doi.org/10.1088/1755-1315/1240/1/012006.
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Abstract In recent years, there has been an increasing trend of utilizing high-resolution multibeam echosounder (MBES) datasets and supervised classification via machine learning to create marine habitat maps. The purpose of current study was threefold: (1) to extract bathymetric and backscatter derivatives from a multibeam dataset, (2) to measure the correlation between bathymetric and backscatter derivatives, and (3) to generate a marine habitat map using the Random Forest (RF). Tioman Marine Park (TMP), which is situated Southeast China Sea. MBES surveyed area are encompassed an area of 406 km² and served as the location for the study. Based on results and analysis, fourteen (14) derivative were derived from bathymetry map and backscatter mosaic. The second step involved integrating variables and a total of 152 of habitat ground-truth data were used, derived from underwater imageries, and sediment samples, into an RF model to generate a map of the marine habitat. Based on marine habitat map, six habitat classes including sand, rock, gravel and sand, coral rubble, coral and rock, and coral were classified. The distribution of coral habitat was found to be correlated with the depth of the bathymetry in the shallow water region. Therefore, the study has reached the conclusion that the integration between MBES derivatives, ground-truth data, and RF machine learning algorithm is an effective in classifying the distribution of marine habitats, specifically the coral habitat.
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Bakirman, T., M. U. Gumusay, and I. Tuney. "MAPPING OF THE SEAGRASS COVER ALONG THE MEDITERRANEAN COAST OF TURKEY USING LANDSAT 8 OLI IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 1103–5. http://dx.doi.org/10.5194/isprs-archives-xli-b8-1103-2016.
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Benthic habitat is defined as ecological environment where marine animals, plants and other organisms live in. Benthic habitat mapping is defined as plotting the distribution and extent of habitats to create a map with complete coverage of the seabed showing distinct boundaries separating adjacent habitats or the use of spatially continuous environmental data sets to represent and predict biological patterns on the seafloor. Seagrass is an essential endemic marine species that prevents coast erosion and regulates carbon dioxide absorption in both undersea and atmosphere. Fishing, mining, pollution and other human activities cause serious damage to seabed ecosystems and reduce benthic biodiversity. According to the latest studies, only 5–10% of the seafloor is mapped, therefore it is not possible to manage resources effectively, protect ecologically important areas. In this study, it is aimed to map seagrass cover using Landsat 8 OLI images in the northern part of Mediterranean coast of Turkey. After pre-processing (e.g. radiometric, atmospheric, water depth correction) of Landsat images, coverage maps are produced with supervised classification using in-situ data which are underwater photos and videos. Result maps and accuracy assessment are presented and discussed.
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Bakirman, T., M. U. Gumusay, and I. Tuney. "MAPPING OF THE SEAGRASS COVER ALONG THE MEDITERRANEAN COAST OF TURKEY USING LANDSAT 8 OLI IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 1103–5. http://dx.doi.org/10.5194/isprsarchives-xli-b8-1103-2016.
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Benthic habitat is defined as ecological environment where marine animals, plants and other organisms live in. Benthic habitat mapping is defined as plotting the distribution and extent of habitats to create a map with complete coverage of the seabed showing distinct boundaries separating adjacent habitats or the use of spatially continuous environmental data sets to represent and predict biological patterns on the seafloor. Seagrass is an essential endemic marine species that prevents coast erosion and regulates carbon dioxide absorption in both undersea and atmosphere. Fishing, mining, pollution and other human activities cause serious damage to seabed ecosystems and reduce benthic biodiversity. According to the latest studies, only 5–10% of the seafloor is mapped, therefore it is not possible to manage resources effectively, protect ecologically important areas. In this study, it is aimed to map seagrass cover using Landsat 8 OLI images in the northern part of Mediterranean coast of Turkey. After pre-processing (e.g. radiometric, atmospheric, water depth correction) of Landsat images, coverage maps are produced with supervised classification using in-situ data which are underwater photos and videos. Result maps and accuracy assessment are presented and discussed.
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Scales, Kylie L., Peter I. Miller, Clare B. Embling, Simon N. Ingram, Enrico Pirotta, and Stephen C. Votier. "Mesoscale fronts as foraging habitats: composite front mapping reveals oceanographic drivers of habitat use for a pelagic seabird." Journal of The Royal Society Interface 11, no. 100 (2014): 20140679. http://dx.doi.org/10.1098/rsif.2014.0679.
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The oceanographic drivers of marine vertebrate habitat use are poorly understood yet fundamental to our knowledge of marine ecosystem functioning. Here, we use composite front mapping and high-resolution GPS tracking to determine the significance of mesoscale oceanographic fronts as physical drivers of foraging habitat selection in northern gannets Morus bassanus . We tracked 66 breeding gannets from a Celtic Sea colony over 2 years and used residence time to identify area-restricted search (ARS) behaviour. Composite front maps identified thermal and chlorophyll- a mesoscale fronts at two different temporal scales—(i) contemporaneous fronts and (ii) seasonally persistent frontal zones. Using generalized additive models (GAMs), with generalized estimating equations (GEE-GAMs) to account for serial autocorrelation in tracking data, we found that gannets do not adjust their behaviour in response to contemporaneous fronts. However, ARS was more likely to occur within spatially predictable, seasonally persistent frontal zones (GAMs). Our results provide proof of concept that composite front mapping is a useful tool for studying the influence of oceanographic features on animal movements. Moreover, we highlight that frontal persistence is a crucial element of the formation of pelagic foraging hotspots for mobile marine vertebrates.
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Nababan, Bisman, La Ode Khairum Mastu, Nurul Hazrina Idris, and James P. Panjaitan. "Shallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses." Remote Sensing 13, no. 21 (2021): 4452. http://dx.doi.org/10.3390/rs13214452.
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Spatial information on benthic habitats in Wangiwangi island waters, Wakatobi District, Indonesia was very limited in recent years. However, this area is one of the marine tourism destinations and one of the Indonesia’s triangle coral reef regions with a very complex coral reef ecosystem. The drone technology that has rapidly developed in this decade, can be used to map benthic habitats in this area. This study aimed to map shallow-water benthic habitats using drone technology in the region of Wangiwangi island waters, Wakatobi District, Indonesia. The field data were collected using a 50 × 50 cm squared transect of 434 observation points in March–April 2017. The DJI Phantom 3 Pro drone with a spatial resolution of 5.2 × 5.2 cm was used to acquire aerial photographs. Image classifications were processed using object-based image analysis (OBIA) method with contextual editing classification at level 1 (reef level) with 200 segmentation scale and several segmentation scales at level 2 (benthic habitat). For level 2 classification, we found that the best algorithm to map benthic habitat was the support vector machine (SVM) algorithm with a segmentation scale of 50. Based on field observations, we produced 12 and 9 benthic habitat classes. Using the OBIA method with a segmentation value of 50 and the SVM algorithm, we obtained the overall accuracy of 77.4% and 81.1% for 12 and 9 object classes, respectively. This result improved overall accuracy up to 17% in mapping benthic habitats using Sentinel-2 satellite data within the similar region, similar classes, and similar method of classification analyses.
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Valentine, Page C., Guy R. Cochrane, and Kathryn M. Scanlon. "Mapping the Seabed and Habitats in National Marine Sanctuaries—Examples from the East, Gulf and West Coasts." Marine Technology Society Journal 37, no. 1 (2003): 10–17. http://dx.doi.org/10.4031/002533203787537465.
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The National Marine Sanctuary System requires seabed and habitat maps to serve as a basis for managing sanctuary resources and for conducting research. NOAA, the agency that manages the sanctuaries, and the USGS have conducted mapping projects in three sanctuaries (Stellwagen Bank NMS, Flower Garden Banks NMS, and Channel Islands NMS) with an emphasis on collaboration of geologists and biologists from the two agencies and from academic institutions. Mapping of seabed habitats is a developing field that requires the integration of geologic and biologic studies and the use of swath imaging techniques such as multibeam and sidescan sonar. Major products of swath mapping are shaded-relief topographic imagery which shows seabed features in great detail, and backscatter imagery which provides an indication of the types of materials that constitute the seabed. Sea floor images provide an excellent basis for conducting the groundtruthing studies (using video, photo, and sampling techniques) that are required to collect the data necessary for making meaningful interpretative maps of the seabed. The compilation of interpretive maps showing seabed environments and habitats also requires the development of a sea floor classification system that will be a basis for comparing, managing, and researching characteristic areas of the seabed. Seabed maps of the sanctuaries are proving useful for management and research decisions that address commercial and recreational fishing, habitat disturbance, engineering projects, tourism, and cultural resources.
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Georgiou, Nikos, Xenophon Dimas, and George Papatheodorou. "Integrated Methodological Approach for the Documentation of Marine Priority Habitats and Submerged Antiquities: Examples from the Saronic Gulf, Greece." Sustainability 13, no. 21 (2021): 12327. http://dx.doi.org/10.3390/su132112327.
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The rising human activities and resource exploitation have increased pressure in the coastal zone and the marine environment, risking the very existence of Marine Priority Habitats (MPH) and Underwater Cultural Heritage (UCH). The delimitation of these two priority areas in a time- and cost-effective way is essential for the sustainable management and exploitation of sea resources and natural-cultural heritage preservation. We propose an Integrated Methodological Approach for the Detection and Mapping of MPH and UCH. To achieve this, we used a downscale methodological approach of increasing spatial resolution based on three main methodological axes: (i) desk-based research, (ii) marine geophysics/seafloor classification, and (iii) in-depth visual inspection/3D mapping. This methodological scheme was implemented at the Saronic Gulf and focused on Aegina island. The methodology proposed, which combines existing and new techniques, proved successful in detecting and mapping the MPH and UCH in detail, while it compiled the information necessary for the establishment of Marine Spatial Planning (MSP) maps. Finally, the MSP map constructed for the Saronic Gulf demonstrated the lack of holistic coastal zone management plans due to impacts on UCH linked to anthropogenic intervention and the sparsity of marine habitats owing to marine pollution.
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Teixeira, João Batista, Agnaldo Silva Martins, Hudson Tercio Pinheiro, Nelio Augusto Secchin, Rodrigo Leão de Moura, and Alex Cardoso Bastos. "Traditional Ecological Knowledge and the mapping of benthic marine habitats." Journal of Environmental Management 115 (January 2013): 241–50. http://dx.doi.org/10.1016/j.jenvman.2012.11.020.
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Kenny, A. J., I. Cato, M. Desprez, G. Fader, R. T. E. Schüttenhelm, and J. Side. "An overview of seabed-mapping technologies in the context of marine habitat classification☆." ICES Journal of Marine Science 60, no. 2 (2003): 411–18. http://dx.doi.org/10.1016/s1054-3139(03)00006-7.
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Abstract A wide range of seabed-mapping technologies is reviewed in respect to their effectiveness in discriminating benthic habitats at different spatial scales. Of the seabed attributes considered important in controlling the benthic community of marine sands and gravel, sediment grain size, porosity or shear strength, and sediment dynamics were highlighted as the most important. Whilst no one mapping system can quantify all these attributes at the same time, some may be estimated by skilful interpretation of the remotely sensed data. For example, seabed processes or features, such as bedform migration, scour, slope failure, and gas venting are readily detectable by many of the mapping systems, and these characteristics in turn can be used to assist a habitat classification (and monitoring) of the seabed. We tabulate the relationship between “rapid” continental shelf sedimentological processes, the seabed attributes affecting these processes, and the most suitable mapping system to employ for their detection at different spatial scales.
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de los Ángeles Liceaga-Correa, María, Eduardo Cuevas, and Mauricio Garduño-Andrade. "Spatial characterization of a foraging area for immature hawksbill turtles (Eretmochelys imbricata) in Yucatan, Mexico." Amphibia-Reptilia 28, no. 3 (2007): 337–46. http://dx.doi.org/10.1163/156853807781374683.
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AbstractThe submarine habitats in the Rio Lagartos Sea Turtle Sanctuary, Mexico, are an important feeding and development area for juvenile Hawksbill turtles (Eretmochelys imbricata). The characterization of these critical habitats is an important and urgent issue to attend for the conservation of this species in Mexico. The objective of this study is to identify, locate and describe the marine benthic habitats in this area, and explain the distribution of the juvenile Hawksbill turtles in the study area. We used submarine techniques such as videotransects and spot checks to characterize the bottom types in the area, and geostatistic techniques to elaborate thematic maps of the different benthic components, integrating all of them into a GIS. We obtained the bottom type map with eight submarine habitats at different depth ranges. We found juvenile Hawksbill turtles distributed mainly on hard bottom sites covered by octocorals, such as Pseudopterogorgia, and sponges of the genera Chondrilla and Spheciospongia. We estimated habitat ranges for the captured turtles and the bottom types occupied by them, then we calculated the distribution densities of juvenile Hawksbill turtles by bottom type. We concluded that the combination of spatial techniques and multivariate statistics is efficient for mapping the marine bottom types in the area, and recommend the generation of specific conservation strategies to protect this area because of the natural marine resources and process ocurring in it.
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Warner, Philip, Jamel Banton, David Smith, and Renée McDonald Lyn Shue. "ROCK ARMOUR: A BENTHIC HABITAT PROVIDING VALUABLE ECOSYSTEM SERVICES IN THE CARIBBEAN SEA." Coastal Engineering Proceedings, no. 37 (September 1, 2023): 95. http://dx.doi.org/10.9753/icce.v37.management.95.
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This paper describes our findings from ecological surveys at several shoreline and beach enhancement projects in the Caribbean. Using standard fish abundance and diversity surveys and benthic habitat mapping protocols, we track how rock armour transitions from a barren substrate at installation to a diverse marine ecosystem over time. The ecosystem services provided by the rock armour have been evaluated and compare well with naturally occurring habitats. Using available economic benefit analyses, we estimate the value of these ecosystem services at various stages as the rock armour habitat matures.
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Jurkus, Egidijus, Ramūnas Povilanskas, Artūras Razinkovas-Baziukas, and Julius Taminskas. "Current Trends and Issues in Applications of Remote Sensing in Coastal and Marine Conservation." Earth 3, no. 1 (2022): 433–47. http://dx.doi.org/10.3390/earth3010026.
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The background of this feature article is a necessity to systematize a vast array of issues pertinent to the latest applications of remote sensing in coastal and marine conservation. Hence the purpose of this study: stocktaking of cutting-edge research articles in this field and eliciting the essential trends and issues shaping the knowledge and future research and technical development perspectives in coastal and marine nature conservation, which is pivotal for meeting the United Nations Sustainable Development Goals till 2030. A hierarchical cluster analysis was undertaken with the KH Coder 3.0 tool to elicit topical co-occurrence networks for thematic words in academic papers from 2015 to 2021 on the topic quarried from Scholar Google. The article’s main findings are the elicited four main trending themes in applications of remote sensing in coastal and marine conservation: (1) Remote Sensing-Based Classification and Modelling; (2) Conservation of Tropical Coastal and Marine Habitats; (3) Mapping of Habitats and Species Distribution; (4) Ecosystem and Biodiversity Conservation and Resource Management. The main conclusion of the article is that habitat vulnerability is a key factor to take into consideration for the future hybrid applications of remote sensing and “citizen science” inputs.
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Nandika, Muhammad Rizki, Azura Ulfa, Andi Ibrahim, and Anang Dwi Purwanto. "Assessing the Shallow Water Habitat Mapping Extracted from High-Resolution Satellite Image with Multi Classification Algorithms." Geomatics and Environmental Engineering 17, no. 2 (2023): 69–87. http://dx.doi.org/10.7494/geom.2023.17.2.69.
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Remote sensing technology is reliable in identifying the distribution of seabed cover yet there are still challenges in retrieving the data collection of shallow water habitats than with other objects on land. Classification algorithms based on remote sensing technology have been developed for application to map benthic habitats, such as Maximum Likelihood, Minimum Distance, and Support Vector Machine. This study focuses on examining those three classification algorithms to retrieve information on the benthic habitat in Pari Island, Jakarta using visual interpretation data for classification, and data field measurements for accuracy testing. This study used five classes of benthic objects, namely sand, sand-seagrass, rubble, seagrass, and coral. The results show how the proposed approach in this study provides an overall good classification of marine habitat with an accuracy produced 63.89–81.95%. The Support Vector Machine algorithm produced the highest accuracy rate of about 81.95%. The Support Vector Machine algorithm at a very high spatial resolution is considered to be capable of identifying, monitoring, and performing the rapid assessment of benthic habitat objects.
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Bekkby, Trine, and Martin Isæus. "Mapping large, shallow inlets and bays: modelling a Natura 2000 habitat with digital terrain and wave-exposure models." ICES Journal of Marine Science 65, no. 2 (2008): 238–41. http://dx.doi.org/10.1093/icesjms/fsn005.
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Abstract Bekkby, T., and Isæus, M. 2008. Mapping large, shallow inlets and bays: modelling a Natura 2000 habitat with digital terrain and wave-exposure models. – ICES Journal of Marine Science, 65: 238–241. EU member countries are obliged to protect a certain share of Natura 2000 habitats. Hence, these habitats must be mapped. This paper is an attempt to provide a tool for modelling one of the Natura 2000 habitat, the “large shallow inlets and bays” (Natura 2000 habitat 1160), using a Norwegian archipelagic area as a case study. The Natura 2000 definition of the habitat is interpreted into criteria used for modelling, and a spatial prediction is presented on a map. The effect of scale, regarding both spatial resolution of data and methodology, is also tested. This is the first publicly accessible attempt to model the Natura 2000 habitat. It shows that the result of the modelling depends on the spatial resolution of the data and the methods used in the modelling process. Using data at a 10-m and a 25-m resolution provides good results, and even the model based on the 50-m data provided an acceptable overall picture.
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da Silveira, Camila Brasil Louro, Gil Marcelo Reuss Strenzel, Mauro Maida, Ana Lídia Bertoldi Gaspar, and Beatrice Padovani Ferreira. "Coral Reef Mapping with Remote Sensing and Machine Learning: A Nurture and Nature Analysis in Marine Protected Areas." Remote Sensing 13, no. 15 (2021): 2907. http://dx.doi.org/10.3390/rs13152907.
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Mapping habitats is essential to assist strategic decisions regarding the use and protection of coral reefs. Coupled with machine learning (ML) algorithms, remote sensing has allowed detailed mapping of reefs at meaningful scales. Here we integrated WorldView-3 and Landsat-8 imagery and ML techniques to produce a map of suitable habitats for the occurrence of a model species, the hydrocoral Millepora alcicornis, in coral reefs located inside marine protected areas in Northeast Brazil. Conservation and management efforts in the region were also analyzed, integrating human use layers to the ecological seascape. Three ML techniques were applied: two to derive base layers, namely geographically weighted regressions for bathymetry and support vector machine classifier (SVM) for habitat mapping, and one to build the species distribution model (MaxEnt) for Millepora alcicornis, a conspicuous and important reef-building species in the area. Additionally, human use was mapped based on the presence of tourists and fishers. SVM yielded 15 benthic classes (e.g., seagrass, sand, coral), with an overall accuracy of 79%. Bathymetry and its derivative layers depicted the topographical complexity of the area. The Millepora alcicornis distribution model identified distance from the shore and depth as topographical factors limiting the settling and growth of coral colonies. The most important variables were ecological, showing the importance of maintaining high biodiversity in the ecosystem. The comparison of the habitat suitability model with species absence and human use maps indicated the impact of direct human activities as potential inhibitors of coral development. Results reinforce the importance of the establishment of no-take zones and other protective measures for maintaining local biodiversity.
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Forsey, D., B. Leblon, A. LaRocque, M. Skinner, and A. Douglas. "EELGRASS MAPPING IN ATLANTIC CANADA USING WORLDVIEW-2 IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 685–92. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-685-2020.
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Abstract. Eelgrass (Zostera marina L.) is a marine angiosperm plant that grows throughout coastal areas in Atlantic Canada. Eelgrass meadows provide numerous ecosystem services, and while they have been acknowledged as important habitats, their location, extent, and health in Atlantic Canada are poorly understood. This study examined the effectiveness of WorldView-2 optical satellite imagery to map eelgrass presence in Tabusintac Bay, New Brunswick (Canada), an estuarine lagoon with extensive eelgrass coverage. The imagery was classified using two supervised classifiers: the parametric Maximum Likelihood Classifier (MLC) and the non-parametric Random Forests (RF) classifier. While Random Forests was expected to produce higher classification accuracies, it was shown not to be much better than MLC. The overall validation accuracy was 97.6% with RF and 99.8% with MLC.
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Talpaert Daudon, J., M. Contini, I. Urbina-Barreto, et al. "GEOAI FOR MARINE ECOSYSTEM MONITORING: A COMPLETE WORKFLOW TO GENERATE MAPS FROM AI MODEL PREDICTIONS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W7-2023 (June 22, 2023): 223–30. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w7-2023-223-2023.
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Abstract. Mapping and monitoring marine ecosystems imply several challenges for data collection and processing: water depth, restricted access to locations, instrumentation costs or weather constraints for sampling, among others. Nowadays, Artificial Intelligence (AI) and Geographic Information System (GIS) open source software can be combined in new kinds of workflows, to annotate and predict objects directly on georeferenced raster data (e.g. orthomosaics). Here, we describe and share the code of a generic method to train a deep learning model with spatial annotations and use it to directly generate model predictions as spatial features. This workflow has been tested and validated in three use cases related to marine ecosystem monitoring at different geographic scales: (i) segmentation of corals on orthomosaics made of underwater images to automate coral reef habitats mapping, (ii) detection and classification of fishing vessels on remote sensing satellite imagery to estimate a proxy of fishing effort (iii) segmentation of marine species and habitats on underwater images with a simple geolocation. Models have been successfully trained and the models predictions are displayed with maps in the three use cases.
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Teixeira, L., M. Nilsson, J. Hedley, and A. Shapiro. "BENTHIC HABITAT MAPPING AND BIODIVERSITY ANALYSIS IN THE PRIMEIRAS AND SEGUNDAS ARCHIPELAGO RESERVE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 1009–16. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-1009-2015.
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The Primeiras and Segundas Archipelago Reserve is a recently established marine protected area, the largest in Africa, located in the waters of Northern Mozambique. This protected area is of significant local economic importance and global ecological relevance, containing the southernmost coral reefs in Eastern Africa. However, information related to 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 in the Segundas Archipelago. The resulting map allows 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. Average overall accuracy was above 50%. The high variability of the optical properties on the reef systems, in large due to the connectivity with the mainland via plumes, while interesting from an ecological perspective increases the challenges for remote sensing of bottom cover. New information indicates the presence of deep benthic cover extending from the atolls, suggesting the need for further research on Coastal Eastern African corals, namely on their resilience and connectivity, and supporting current knowledge of the existence of an almost continuous coral reef from Kenya to Mozambique. Coral and fish biodiversity data have been analysed together with the satellite-derived maps. Results support the local perception that ecosystems are in decline and uncover new information about biodiversity’s spatial patterns. Our work provides a detailed depiction of marine habitats that may aid the management of the protected area, namely in the definition of fishing zones and coral cover monitoring.
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Fakiris, Elias, Philippe Blondel, George Papatheodorou, et al. "Multi-Frequency, Multi-Sonar Mapping of Shallow Habitats—Efficacy and Management Implications in the National Marine Park of Zakynthos, Greece." Remote Sensing 11, no. 4 (2019): 461. http://dx.doi.org/10.3390/rs11040461.
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In this work, multibeam echosounder (MBES) and dual frequency sidescan sonar (SSS) data are combined to map the shallow (5–100 m) benthic habitats of the National Marine Park of Zakynthos (NMPZ), Greece, a Marine Protected Area (MPA). NMPZ hosts extensive prairies of the protected Mediterranean phanerogams Posidonia oceanica and Cymodocea nodosa, as well as reefs and sandbanks. Seafloor characterization is achieved using the multi-frequency acoustic backscatter of: (a) the two simultaneous frequencies of the SSS (100 and 400 kHz) and (b) the MBES (180 kHz), as well as the MBES bathymetry. Overall, these high-resolution datasets cover an area of 84 km2 with ground coverage varying from 50% to 100%. Image texture, terrain and backscatter angular response analyses are applied to the above, to extract a range of statistical features. Those have different spatial densities and so they are combined through an object-based approach based on the full-coverage 100-kHz SSS mosaic. Supervised classification is applied to data models composed of operationally meaningful combinations between the above features, reflecting single-sonar or multi-sonar mapping scenarios. Classification results are validated against a detailed expert interpretation habitat map making use of extensive ground-truth data. The relative gain of one system or one feature extraction method or another are thoroughly examined. The frequency-dependent separation of benthic habitats showcases the potentials of multi-frequency backscatter and bathymetry from different sonars, improving evidence-based interpretations of shallow benthic habitats.
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Lavrinenko, I. A. "Approaches of european ecologists to typology and mapping of habitats." Geobotanical mapping, no. 2020 (December 2020): 51–77. http://dx.doi.org/10.31111/geobotmap/2020.51.
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The presence of appropriate habitats is a necessary condition for the existence of flora and fauna. In recent decades, it has become obvious that monitoring of a state not only populations of rare species, and, first, habitats which for a biota are vital, is necessary (Galdenzi et al., 2012; Rodríguez et al., 2012; Izco, 2015; Keith et al., 2015; etc.). The paper discusses the main European programs and projects devoted to the classification, mapping and inventory of habitats, the formation of lists of biotopes in need of protection (Berne Convention, Habitats Directive, Nature 2000, CORINE, EUNIS, etc.). The implementation of these projects makes a significant contribution to the state environmental policy of European countries. Since the Berne Convention for the Conservation of European Wildlife and Natural Habitats, adopted in 1979 and to date, great importance has been given to the protection of natural habitats in the countries of the European Union. Given their diversity, the most important tasks of diagnostics and monitoring of habitats were the development of classification schemes, inventory and allocation of biotopes that require protection. In 1985, the European Commission initiated the CORINE (Coordination of Information on the Environment) project on the inventory of habitats or biotopes. Its main goal was information support for pan-European and national policies in the field of environmental management, control of their consequences and the proposal of corrective measures. The CORINE habitat classification was first published in 1991 and served as the basis for the selection of habitats listed in Annex I of the 1992 (EU Habitat Directive 92/43/EEC). Since the adoption of this document, known as the “Habitats Directive”, there has been a turning point in the EU in the prospects for the management of biological diversity and the transition of scientific research in the field of ecology and nature protection to a qualitatively new level. Annex I lists 233 European types of natural habitats, including 71 priority ones, that are at risk of extinction and whose natural areas mainly distributed into the EU. The most important achievement of the Habitats Directive is the creation of a network of protected areas in Europe, known as Natura 2000, the world’s largest ecological network created to protect the rarest and most endangered species of plants, animals and habitats in Europe. Natura 2000 is not a Specially Protected Natural Areas system, but also includes all EU protected areas, although most of the land in this network is privately owned. EU member States have an obligation to ensure the sustainable management of all Natura 2000 facilities, regardless of ownership, both environmentally and economically. In 1995, the International Seminar in Paris recognized the need to develop an improved European classification. Since that time, the development of the EUNIS (European Union Nature Information System) habitat classification has begun. This classification covers all European habitats: marine, terrestrial and freshwater, natural, semi-natural and anthropogenic and is currently one of the main systems developed for the study and protection of European Union habitats. Despite its shortcomings, EUNIS serves as a pan-European integration system with certain standards for the identification and characterization of habitats. It ensures comparability between national and international classifications. The system provides extensive opportunities to establish relationships (crosswalks) of habitat categories with other pan-European (Habitat Directive, NATURE 2000, CORINE, Palaearctic Habitats) and national classifications. Approaches to mapping habitats at different scales are considered — display on the map physiognomically and topographically expressed territorial units of vegetation using remote sensing, reflecting the ecological characteristics and originality of the earth’s surface. It is noted that the diagnostics and inventory of habitats, their mapping are based, first of all, on the materials of geobotanical mapping. The mapping of habitat types and categories can be presented at different scales. For inventory and monitoring purposes, large-scale mapping is most widely used, which is based on the allocation and diagnosis of territorial units of vegetation. A review of several dozen European projects showed that the scale from 1 : 5 000 to 1 : 50 000 is mainly used to display habitat types on the map (Terrestrial ..., 2014). A smaller scale is used to map the distribution of individual habitat types and categories within wide areas. In this case, a grid with cells of different sizes (from 1 to 10 km2) is often used. One of the main tasks in the study and evaluation of the current state of habitats is the identification and organization of monitoring of biotopes that are under the threat of degradation or complete disappearance under the impact, first of all, of human activities. For this purpose, pan-European and national Red Lists of Habitats are being created, which are taken into account in the development plans of the territories of the EU countries. The problems of classification, mapping and inventory of habitats, the preparation of Red Lists of Habitats are currently among the priority areas of environmental science and make a significant contribution to the state environmental policy of the EU countries. The high efficiency of this approach at all levels, from municipal to European, is evidenced by the large number of programs implemented in Europe in the last decade, which were funded both at the national and EU level. Considering that such an approach underlies the formation of an environmentally oriented economy and environmental protection activities in many European countries, it seems promising to implement it in the Russian Federation and, first of all, for the Arctic, as the territory most vulnerable to the impact of anthropogenic and climatic factors. This region is currently experiencing a significant anthropogenic impact due to the development of dozens of hydrocarbon deposits, the construction of industrial facilities and infrastructure elements, and numerous oil and gas pipelines. At the same time, the Russian Arctic is a territory of high concentration of species of biota in need of protection, numerous populations of waterfowl and mammals (polar bear, walrus), many of which have international protection status. Due to the high sensitivity to anthropogenic and climatic factors, not only individual protected areas, but the entire territory of the Russian Arctic should be under constant control by the state. In the context of industrial development of Arctic territories, it is necessary to monitor the state not only of populations of certain species of plants and animals in need of protection, but especially habitats that are vital to them. Among the top-priority tasks is the preparation of a list of habitats in the Russian Arctic requiring constant control and protection by the state (Red List of Russian Arctic Habitats). These include areas of concentration of populations of Red Data Book species, the most important resource species and species that are of high importance for maintaining homeostasis and supporting the potential of Arctic ecosystems. The creation of such a list will significantly complement the system of protected areas in the Arctic, since it will allow taking into account and monitoring the state of not only the natural complexes of the existing reserves, but also habitats outside this system, which are of great importance for the preservation of arctic ecosystems and biota. Giving them the status of protected areas of a certain rank, given their multiplicity and dispersion in the territory, is certainly impractical. However, when planning economic activities, special attention will need to be paid to the presence of Red List Habitats and to monitor their condition. The latter, taking into account Remote Sensing technologies, is now quite feasible, and allows for large-scale monitoring in such a vast area.
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Stevens, Tim, and Rod M. Connolly. "Local-scale mapping of benthic habitats to assess representation in a marine protected area." Marine and Freshwater Research 56, no. 1 (2005): 111. http://dx.doi.org/10.1071/mf04233.
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Macrobenthic habitat types were classified and mapped using a compact video array at 78 sites spaced 5 km apart in Moreton Bay, Australia. The area mapped was about 2400 km2 and extended from estuarine shallow subtidal waters to offshore areas to the 50-m isobath. Nine habitat types were recognised, with only one on hard substrate, and their representation within an existing marine protected area was assessed. Only two habitat types were represented in highly protected (no-take) zones, with less than 3% of the total area of each habitat type included. The habitat mapping characterised several habitat types not previously described in the area and located deep-water algal and soft coral reefs not previously reported. Seagrass beds were encountered in several locations where their occurrence was either unknown or had not previously been quantified. The study represents the most spatially comprehensive survey of epibenthos undertaken in Moreton Bay, with over 40 000 m2 sampled. Derived habitat maps provide a robust basis for inclusion of representative examples of all habitat types in marine protected area planning in, and adjacent to, Moreton Bay. The utility of video data to conduct a low-cost habitat survey over a comparatively large area was also demonstrated. The method used has potentially wide application for the survey and design of marine protected areas.
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Knudby, Anders, Ellsworth LeDrew, and Candace Newman. "Progress in the use of remote sensing for coral reef biodiversity studies." Progress in Physical Geography: Earth and Environment 31, no. 4 (2007): 421–34. http://dx.doi.org/10.1177/0309133307081292.
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Coral reefs are hotspots of marine biodiversity, and their global decline is a threat to our natural heritage. Conservation management of these precious ecosystems relies on accurate and up-to-date information about ecosystem health and the distribution of species and habitats, but such information can be costly to gather and interpret in the field. Remote sensing has proven capable of collecting information on geomorphologic zones and substrate types for coral reef environments, and is cost-effective when information is needed for large areas. Remote sensing-based mapping of coral habitat variables known to influence biodiversity has only recently been undertaken and new sensors and improved data processing show great potential in this area. This paper reviews coral reef biodiversity, the influence of habitat variables on its local spatial distribution, and the potential for remote sensing to produce maps of these habitat variables, thus indirectly mapping coral reef biodiversity and fulfilling information needs of coral reef managers.
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Lucieer, V. L. "Object‐oriented classification of sidescan sonar data for mapping benthic marine habitats." International Journal of Remote Sensing 29, no. 3 (2007): 905–21. http://dx.doi.org/10.1080/01431160701311309.
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Populus, Jacques, Ana Maria Rodrigues, Fergal McGrath, et al. "Preface to “MeshAtlantic: Mapping Atlantic area seabed habitats for better marine management”." Journal of Sea Research 100 (June 2015): 1. http://dx.doi.org/10.1016/j.seares.2015.06.007.
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Huang, Zhi, Brendan P. Brooke, and Peter T. Harris. "A new approach to mapping marine benthic habitats using physical environmental data." Continental Shelf Research 31, no. 2 (2011): S4—S16. http://dx.doi.org/10.1016/j.csr.2010.03.012.
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Bullimore, Ross D., Nicola L. Foster, and Kerry L. Howell. "Coral-characterized benthic assemblages of the deep Northeast Atlantic: defining “Coral Gardens” to support future habitat mapping efforts." ICES Journal of Marine Science 70, no. 3 (2013): 511–22. http://dx.doi.org/10.1093/icesjms/fss195.
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Abstract Bullimore, R. D., Foster, N. L., and Howell, K. L. 2013. Coral-characterized benthic assemblages of the deep Northeast Atlantic: defining “Coral Gardens” to support future habitat mapping efforts – ICES Journal of Marine Science, 70: 511–522. Providing statistically robust maps of habitat distributions on which to base spatial planning and management of the marine area is reliant upon established and agreed descriptions and definitions of habitats. “Coral Gardens” is an Oslo–Paris Convention (OSPAR) listed habitat, which currently cannot be reliably mapped as a result of poorly developed deep-sea habitat classification systems and habitat definitions. The aim of this study is to assess and inform development of the current definition of this habitat to support future mapping efforts. This study uses multivariate community analysis of video data to identify deep-sea benthic assemblages characterized by coral taxa and thus constituting a potential “coral gardens” habitat. Assemblages are assessed against a set of qualifying criteria, derived from current definitions of “coral gardens”, first at the assemblage level then sample by sample. The current definition of “coral gardens” captures a range of benthic assemblages, thus “Coral Gardens” cannot be considered a single “habitat”. While 19 assemblages are identified as being characterized by one or more coral garden taxa, only 8 meet the qualifying criteria. It is suggested that the current definition incorporates descriptions of the different “Coral Gardens” assemblages together with guidance on threshold densities for coral species specific to each assemblage type.
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Wan, Jiaxin, Zhiliang Qin, Xiaodong Cui, et al. "MBES Seabed Sediment Classification Based on a Decision Fusion Method Using Deep Learning Model." Remote Sensing 14, no. 15 (2022): 3708. http://dx.doi.org/10.3390/rs14153708.
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High-precision habitat mapping can contribute to the identification and quantification of the human footprint on the seafloor. As a representative of seafloor habitats, seabed sediment classification is crucial for marine geological research, marine environment monitoring, marine engineering construction, and seabed biotic and abiotic resource assessment. Multibeam echo-sounding systems (MBES) have become the most popular tool in terms of acoustic equipment for seabed sediment classification. However, sonar images tend to consist of obvious noise and stripe interference. Furthermore, the low efficiency and high cost of seafloor field sampling leads to limited field samples. The factors above restrict high accuracy classification by a single classifier. To further investigate the classification techniques for seabed sediments, we developed a decision fusion algorithm based on voting strategies and fuzzy membership rules to integrate the merits of deep learning and shallow learning methods. First, in order to overcome the influence of obvious noise and the lack of training samples, we employed an effective deep learning framework, namely random patches network (RPNet), for classification. Then, to alleviate the over-smoothness and misclassifications of RPNet, the misclassified pixels with a lower fuzzy membership degree were rectified by other shallow learning classifiers, using the proposed decision fusion algorithm. The effectiveness of the proposed method was tested in two areas of Europe. The results show that RPNet outperforms other traditional classification methods, and the decision fusion framework further improves the accuracy compared with the results of a single classifier. Our experiments predict a promising prospect for efficiently mapping seafloor habitats through deep learning and multi-classifier combinations, even with few field samples.
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Paganelli, Daniela, Paola La Valle, Marina Pulcini, et al. "Towards an evaluation of physical loss pressure in the Italian seas for the implementation of the marine strategy framework directive." Journal of the Marine Biological Association of the United Kingdom 98, no. 1 (2017): 61–69. http://dx.doi.org/10.1017/s0025315417000911.
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All over the world marine waters are under increasing pressure from human activities affecting marine ecosystems. Several EU Directives require assessment of the condition of marine environments; in particular the Marine Strategy Framework Directive (MSFD) applies an ecosystem approach to the management of human activities. In this context, the mapping of pressures in a standardized and comparable way is a critical step to assess the spatial pattern, the temporal change and the ecological impacts of human pressures. Within the MSFD, one of the stressors directly affecting the seafloor is the Physical Loss (PL) pressure, representing the permanent or long-term alteration of marine habitats. The main purpose of this study was to propose a method to estimate the spatial extent of PL pressure in the framework of the Initial Assessment phase of the MSFD. Furthermore, considering that human activities PL-related cause the loss of benthic habitats, and that the Mediterranean sea is characterized by sensitive and protected habitats such as the biogenic substrates sensu MSFD, the distribution of PL pressure was overlaid with the distribution of the seabed habitats to estimate the loss of biogenic substrates. This study represents a useful tool for establishing the baseline condition for PL pressure, to compare future conditions and to evaluate different management scenarios. Moreover, it allows identification of the areas where pressure tends to accumulate as ‘hot spots’ on which to focus in future impact analyses and the areas where few stressors are present.
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Rende, F. S., A. D. Irving, A. Lagudi, et al. "PILOT APPLICATION OF 3D UNDERWATER IMAGING TECHNIQUES FOR MAPPING POSIDONIA OCEANICA (L.) DELILE MEADOWS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5/W5 (April 9, 2015): 177–81. http://dx.doi.org/10.5194/isprsarchives-xl-5-w5-177-2015.
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Seagrass communities are considered one of the most productive and complex marine ecosystems. Seagrasses belong to a small group of 66 species that can form extensive meadows in all coastal areas of our planet. <i>Posidonia oceanica</i> beds are the most characteristic ecosystem of the Mediterranean Sea, and should be constantly monitored, preserved and maintained, as specified by EU Habitats Directive for priority habitats. Underwater 3D imaging by means of still or video cameras can allow a detailed analysis of the temporal evolution of these meadows, but also of the seafloor morphology and integrity. Video-photographic devices and open source software for acquiring and managing 3D optical data rapidly became more and more effective and economically viable, making underwater 3D mapping an easier task to carry out. 3D reconstruction of the underwater scene can be obtained with photogrammetric techniques that require just one or more digital cameras, also in stereo configuration. In this work we present the preliminary results of a pilot 3D mapping project applied to the <i>P. oceanica</i> meadow in the Marine Protected Area of Capo Rizzuto (KR, Calabria Region &ndash; Italy).
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Marx, Denise, Agata Feldens, Svenja Papenmeier, et al. "Habitats and Biotopes in the German Baltic Sea." Biology 13, no. 1 (2023): 6. http://dx.doi.org/10.3390/biology13010006.
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To maintain or enhance biodiversity and sea floor integrity, mapping benthic habitats is a mandatory requirement in compliance with the Marine Strategy Framework Directive (MSFD). The EU Commission Decision distinguishes between Broad Habitat Types (BHTs) and Other Habitat Types (OHTs). At the regional level, biotopes in the Baltic Sea region are classified according to the HELCOM underwater biotope and habitat classification (HUB). In this study, the habitats and their benthic communities were mapped for the entire German Baltic Sea at a high spatial resolution of 1 km. In two nature conservation areas of the Exclusive Economic Zone (EEZ) as well as selected focus areas in the coastal waters, the resolution we provide is even more detailed at 50 × 50 m. Hydroacoustic data recording and benthological surveys (using bottom grabs, underwater towing camera technology, and diver sampling) helped identify biotopes in high resolution. Based on these data, together with additional data acquired since 2010 (a total of over 7000 stations and transect sections), we were able to spatially delineate benthic biotopes and their communities via predictive habitat modelling. The results are provided as full-coverage maps each for BHT, OHT, and HUB (9 classes of BHTs, 5 classes of OHTs, and 84 classes of HUB) with a level of spatial detail that does not yet exist for the Baltic Sea, and they form an essential basis for future monitoring, status assessments, and protection and management measures.
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Iampietro, Pat J., Rikk G. Kvitek, and Erica Morris. "Recent Advances in Automated Genus-specific Marine Habitat Mapping Enabled by High-resolution Multibeam Bathymetry." Marine Technology Society Journal 39, no. 3 (2005): 83–93. http://dx.doi.org/10.4031/002533205787442495.
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There is a great need for accurate, comprehensive maps of seafloor habitat for use in fish stock assessments, marine protected area design, and other resource management pursuits. Recent advances in acoustic remote sensing technology have made it possible to obtain high-resolution (meter to sub-meter) digital elevation models (DEMs) of seafloor bathymetry that can rival or surpass those available for the terrestrial environment. The acquisition and processing of these data are expensive, however, requiring specialized equipment, expertise, and large amounts of both field and laboratory effort per unit area mapped. Further, the interpretation and classification of these data into maps of habitat type is typically (and appropriately) performed only by trained experts that are familiar with both seafloor geomorphology and the nature and limitations of the data sources. Because it is done visually, this interpretation can be very time-consuming and may yield subjective results that are not comparable from site-to-site or between individual interpreters.We applied an algorithmic terrain analysis approach to efficiently and objectively classify seafloor habitats using the quantifiable landscape metric Topographic Position Index (TPI). We used high-resolution multibeam bathymetry, together with precisely geolocated (± 5 m) ROV observations of fish distribution, to produce a preliminary genus-specific habitat suitability model for eight rockfish (Sebastes) species in the Del Monte shale beds of Monterey Bay, California. A high-resolution (2 m) multibeam bathymetry Digital Elevation Model (DEM) was generated and used to produce a derived TPI surface model using repeatable, algorithmic methods. This data layer, together with the positions and counts by species from 229 rockfish observations (2892 total fish) was then used to create preliminary predictive models of habitat suitability and fish distribution, as well as stock estimates for the study area. A second, independent fish observation data set was used to validate the models.
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Br Ginting, Devica Natalia, and Anang Dwi Purwanto. "Semi-Automatic Classification Model on Benthic Habitat Using Spot-7 Imagery in Penerusan Bay, Bali." Jurnal Segara 17, no. 3 (2021): 185. http://dx.doi.org/10.15578/segara.v17i3.9771.
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Benthic habitats are one of the interesting marine resources and its existence must be preserved. Provision of up-to-date benthic habitat information requires a relatively large amount of time and money. The use of remote sensing technology is one of the best solution. This study aims to develop a semi-automatic processing model that is fast, accurate, and with broad spatial coverage. The satellite image data used is the SPOT-7 image acquired on April 11th, 2018. The method used is a supervised classification with a decision tree algorithm. The analysis was carried out using a script developed in the open-source R application. The results showed that the model used was able to accelerate the processing of benthic habitat extracted from the initial process to the classification. The model developed is able to classify habitat classes based on the training sample data provided so that it does not affect the user’s ability to determine the habitat class. The resulting model accuracy is 93.6%. The validation of the resulting classification showed an overall accuracy of 59% and a kappa accuracy of 0.46. It is necessary to carry out further research by increasing quality and quantity of training samples from each object of benthic habitats and developing scripts in order to produce better mapping accuracy.
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Le Quilleuc, Alyson, Antoine Collin, Michael F. Jasinski, and Rodolphe Devillers. "Very High-Resolution Satellite-Derived Bathymetry and Habitat Mapping Using Pleiades-1 and ICESat-2." Remote Sensing 14, no. 1 (2021): 133. http://dx.doi.org/10.3390/rs14010133.
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Accurate and reliable bathymetric data are needed for a wide diversity of marine research and management applications. Satellite-derived bathymetry represents a time saving method to map large shallow waters of remote regions compared to the current costly in situ measurement techniques. This study aims to create very high-resolution (VHR) bathymetry and habitat mapping in Mayotte island waters (Indian Ocean) by fusing 0.5 m Pleiades-1 passive multispectral imagery and active ICESat-2 LiDAR bathymetry. ICESat-2 georeferenced photons were filtered to remove noise and corrected for water column refraction. The bathymetric point clouds were validated using the French naval hydrographic and oceanographic service Litto3D® dataset and then used to calibrate the multispectral image to produce a digital depth model (DDM). The latter enabled the creation of a digital albedo model used to classify benthic habitats. ICESat-2 provided bathymetry down to 15 m depth with a vertical accuracy of bathymetry estimates reaching 0.89 m. The benthic habitats map produced using the maximum likelihood supervised classification provided an overall accuracy of 96.62%. This study successfully produced a VHR DDM solely from satellite data. Digital models of higher accuracy were further discussed in the light of the recent and near-future launch of higher spectral and spatial resolution satellites.