Remote Sensing and GIS

Swordfish monitoring by a GIS-based spatial and temporal distribution analysis on harpoon fishery data: A case of study in the central Mediterranean Sea

Perzia P, Battaglia P, Consoli P, Andaloro F, Romeo T. Swordfish monitoring by a GIS-based spatial and temporal distribution analysis on harpoon fishery data: A case of study in the central Mediterranean Sea. Fisheries Research [Internet]. 2016 ;183:424 - 434. Available from: http://www.sciencedirect.com/science/article/pii/S016578361630217X
Freely available?: 
No
Summary available?: 
No
Type: Journal Article

The Geographic Information Systems (GIS) was used to support fishery analyses aimed to monitor the swordfish harpoon fishing and analyze temporal and spatial patterns of catch distribution, also in relation to the environmental parameter Sea Surface Temperature (SST). The study was carried out in an important reproductive area of the Mediterranean Sea, including the Strait of Messina and nearby areas (south-eastern Tyrrhenian Sea and north-western Ionian sea). These locations also represent the unique fishing ground of the Italian swordfish harpoon fishery.

Fishery, environmental and biological data were collected between 2002 and 2011 from fishermen’s logbooks and by observations on-board of fishing vessels during swordfish harpoon fishing season (May to August). Data were organized into a database and structured on a geographical reference to allow a quantitative multi-parameter modelling through the GIS tools.

The application of GIS allowed a good visualisation of catch distribution patterns, underlining annual and monthly differences in resource availability to fishery. The analysis of GIS maps showed a change in swordfish behaviour, likely related to SST anomalies and, as a consequence, difference in catch and effort distribution patterns. These differences were more evident if fishes in pair were considered. GIS turned out an important and powerful tool to analyze fishing information; the production of georeferentiated maps helps scientist to an easier interpretation of data on large pelagic resources. The proposed GIS-based analysis can add new information on swordfish and help decision makers in the Mediterranean swordfish management.

Fish identification from videos captured in uncontrolled underwater environments

Shafait F, Mian A, Shortis M, Ghanem B, Culverhouse PF, Edgington D, Cline D, Ravanbakhsh M, Seager J, Harvey ES. Fish identification from videos captured in uncontrolled underwater environments. ICES Journal of Marine Science [Internet]. 2016 :fsw106. Available from: http://icesjms.oxfordjournals.org/content/early/2016/07/14/icesjms.fsw106
Freely available?: 
No
Summary available?: 
No
Type: Journal Article

There is an urgent need for the development of sampling techniques which can provide accurate and precise count, size, and biomass data for fish. This information is essential to support the decision-making processes of fisheries and marine conservation managers and scientists. Digital video technology is rapidly improving, and it is now possible to record long periods of high resolution digital imagery cost effectively, making single or stereo-video systems one of the primary sampling tools. However, manual species identification, counting, and measuring of fish in stereo-video images is labour intensive and is the major disincentive against the uptake of this technology. Automating species identification using technologies developed by researchers in computer vision and machine learning would transform marine science. In this article, a new paradigm of image set classification is presented that can be used to achieve improved recognition rates for a number of fish species. State-of-the-art image set construction, modelling, and matching algorithms from computer vision literature are discussed with an analysis of their application for automatic fish species identification. It is demonstrated that these algorithms have the potential of solving the automatic fish species identification problem in underwater videos captured within unconstrained environments.

Detection of Coastline Deformation Using Remote Sensing and Geodetic Surveys

Sabuncu A, Dogru A, Ozener H, Turgut B. Detection of Coastline Deformation Using Remote Sensing and Geodetic Surveys. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences [Internet]. 2016 ;XLI-B8:1169 - 1174. Available from: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B8/1169/2016/
Freely available?: 
Yes
Summary available?: 
No
Type: Journal Article

The coastal areas are being destroyed due to the usage that effect the natural balance. Unconsciously sand mining from the sea for nearshore nourishment and construction uses are the main ones. Physical interferences for mining of sand cause an ecologic threat to the coastal environment. However, use of marine sand is inevitable because of economic reasons or unobtainable land-based sand resources. The most convenient solution in such a protection–usage dilemma is to reduce negative impacts of sand production from marine. This depends on the accurate determination of criteriaon production place, style, and amount of sand. With this motivation, nearshore geodedic surveying studies performed on Kilyos Campus of Bogazici University located on the Black Sea coast, north of Istanbul, Turkey between 2001-2002. The study area extends 1 km in the longshore. Geodetic survey was carried out in the summer of 2001 to detect the initial condition for the shoreline. Long-term seasonal changes in shoreline positions were determined biannually. The coast was measured with post-processed kinematic GPS. 

Besides, shoreline change has studied using Landsat imagery between the years 1986-2015. The data set of Landsat 5 imageries were dated 05.08.1986 and 31.08.2007 and Landsat 7 imageries were dated 21.07.2001 and 28.07.2015. Landcover types in the study area were analyzed on the basis of pixel based classification method. Firstly, unsupervised classification based on ISODATA (Iterative Self Organizing Data Analysis Technique) has been applied and spectral clusters have been determined that gives prior knowledge about the study area. In the second step, supervised classification was carried out by using the three different approaches which are minimum-distance, parallelepiped and maximum-likelihood. All pixel based classification processes were performed with ENVI 4.8 image processing software. Results of geodetic studies and classification outputs will be presented in this paper.

Mapping of the Seagrass Cover Along the Mediterranean Coast of Turkey Using Landsat 8 Oli Images

Bakirman T, Gumusay MU, Tuney I. 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 [Internet]. 2016 ;XLI-B8:1103 - 1105. Available from: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B8/1103/2016/
Freely available?: 
Yes
Summary available?: 
No
Type: Journal Article

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.

Oil Spill Detection and Monitoring of Abu Dhabi Coastal Zone Using KOMPSAT-5 SAR Imagery

Harahsheh HA. Oil Spill Detection and Monitoring of Abu Dhabi Coastal Zone Using KOMPSAT-5 SAR Imagery. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences [Internet]. 2016 ;XLI-B8:1115 - 1121. Available from: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B8/1115/2016/
Freely available?: 
Yes
Summary available?: 
No
Type: Journal Article

Abu Dhabi Government endorsed vision for its Maritime Strategy ‘A safe, secure and sustainable maritime domain for Abu Dhabi'. This research study share this vision using the concept of monitoring as tool for marine protection against any possible oil pollution. The best technology to detect and monitor oil pollution and in particularly oil spill is SAR imagery In this case study we chose KOMPSAT-5 SAR. 

KOMPSAT-5 carries X-band SAR for earth observation, and is capable of day-and-night imaging under all weather condition. It provides three operation modes: High Resolution Mode to provide 1 m resolution, Standard Mode to provide 3 m resolution and Wide Swath Mode to provide 20 m resolution with 100 km swath at 550 km altitude, with four modes of polarization. KOMPSAT-5 provides products for various applications; security and defense, mapping, and natural resource management, environmental monitoring, disaster monitoring and more. For our case study we chose to work with Wide Swath mode (WS) with Vertical polarization (VV) to cover a wide area of interest located to the north west of Abu Dhabi including some important islands like "Zirku Island", and areas with oil production activities. 

The results of data acquired on 4th May 2015 show some spot of oil spill with length estimated about 3 KM, and the daily satellite data acquisition over the period July 24 through July 31 shows serious and many oil spill events some are small, but many others are considered to be big with area size around 20 km2

In the context of oil spill pollution in the seas, we have to consider the development and increase of overseas transportation, which is an important factor for both social and economic sectors. The harmful effects of marine pollution are numerous, from the damage of marine life to the damage of the aquatic ecosystem as whole. As such, the need for oil slick detection is crucial, for the location of polluted areas and to evaluate slick drift to protect the coastline. Satellite-based oil spill monitoring system now can be used to take precautions and even to determine the possible polluter; it has a vital importance on the detection and protection of national and international waters from the possible damages of petroleum hazard. Finally, and as we suggested in previous studies, we recommend to the national authorities to establish a national near-real time oil spill monitoring system based on SAR satellite imagery, with the support of other tools like AIS and navigation radars.

Monitoring Seabirds and Marine Mammals by Georeferenced Aerial Photography

Kemper G, Weidauer A, Coppack T. Monitoring Seabirds and Marine Mammals by Georeferenced Aerial Photography. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences [Internet]. 2016 ;XLI-B8:689 - 694. Available from: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B8/689/2016/
Freely available?: 
Yes
Summary available?: 
No
Type: Journal Article

The assessment of anthropogenic impacts on the marine environment is challenged by the accessibility, accuracy and validity of biogeographical information. Offshore wind farm projects require large-scale ecological surveys before, during and after construction, in order to assess potential effects on the distribution and abundance of protected species. The robustness of site-specific population estimates depends largely on the extent and design of spatial coverage and the accuracy of the applied census technique. Standard environmental assessment studies in Germany have so far included aerial visual surveys to evaluate potential impacts of offshore wind farms on seabirds and marine mammals. However, low flight altitudes, necessary for the visual classification of species, disturb sensitive bird species and also hold significant safety risks for the observers. Thus, aerial surveys based on high-resolution digital imagery, which can be carried out at higher (safer) flight altitudes (beyond the rotor-swept zone of the wind turbines) have become a mandatory requirement, technically solving the problem of distant-related observation bias. A purpose-assembled imagery system including medium-format cameras in conjunction with a dedicated geo-positioning platform delivers series of orthogonal digital images that meet the current technical requirements of authorities for surveying marine wildlife at a comparatively low cost. At a flight altitude of 425 m, a focal length of 110 mm, implemented forward motion compensation (FMC) and exposure times ranging between 1/1600 and 1/1000 s, the twin-camera system generates high quality 16 bit RGB images with a ground sampling distance (GSD) of 2 cm and an image footprint of 155 x 410 m. The image files are readily transferrable to a GIS environment for further editing, taking overlapping image areas and areas affected by glare into account. The imagery can be routinely screened by the human eye guided by purpose-programmed software to distinguish biological from non-biological signals. Each detected seabird or marine mammal signal is identified to species level or assigned to a species group and automatically saved into a geo-database for subsequent quality assurance, geo-statistical analyses and data export to third-party users. The relative size of a detected object can be accurately measured which provides key information for species-identification. During the development and testing of this system until 2015, more than 40 surveys have produced around 500.000 digital aerial images, of which some were taken in specially protected areas (SPA) of the Baltic Sea and thus include a wide range of relevant species. Here, we present the technical principles of this comparatively new survey approach and discuss the key methodological challenges related to optimizing survey design and workflow in view of the pending regulatory requirements for effective environmental impact assessments.

GIS-Based Wind Farm Site Selection Model Offshore Abu Dhabi Emirate, UAE

Saleous N, Issa S, J. Mazrouei A. GIS-Based Wind Farm Site Selection Model Offshore Abu Dhabi Emirate, UAE. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences [Internet]. 2016 ;XLI-B8:437 - 441. Available from: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B8/437/2016/
Freely available?: 
Yes
Summary available?: 
No
Type: Journal Article

The United Arab Emirates (UAE) government has declared the increased use of alternative energy a strategic goal and has invested in identifying and developing various sources of such energy. This study aimed at assessing the viability of establishing wind farms offshore the Emirate of Abu Dhabi, UAE and to identify favourable sites for such farms using Geographic Information Systems (GIS) procedures and algorithms. Based on previous studies and on local requirements, a set of suitability criteria was developed including ocean currents, reserved areas, seabed topography, and wind speed. GIS layers were created and a weighted overlay GIS model based on the above mentioned criteria was built to identify suitable sites for hosting a new offshore wind energy farm. Results showed that most of Abu Dhabi offshore areas were unsuitable, largely due to the presence of restricted zones (marine protected areas, oil extraction platforms and oil pipelines in particular). However, some suitable sites could be identified, especially around Delma Island and North of Jabal Barakah in the Western Region. The environmental impact of potential wind farm locations and associated cables on the marine ecology was examined to ensure minimal disturbance to marine life. Further research is needed to specify wind mills characteristics that suit the study area especially with the presence of heavy traffic due to many oil production and shipping activities in the Arabian Gulf most of the year.

Foraging distribution overlap and marine reserve usage amongst sub-Antarctic predators inferred from a multi-species satellite tagging experiment

Patterson TA, Sharples RJ, Raymond B, Welsford DC, Andrews-Goff V, Lea MA, Goldsworthy SD, Gales NJ, Hindell M. Foraging distribution overlap and marine reserve usage amongst sub-Antarctic predators inferred from a multi-species satellite tagging experiment. Ecological Indicators [Internet]. 2016 ;70:531 - 544. Available from: http://www.sciencedirect.com/science/article/pii/S1470160X16302977
Freely available?: 
No
Summary available?: 
No
Type: Journal Article

Satellite telemetry data was used to predict at sea spatial usage of five top order and meso-predators; Antarctic fur seals (Arctocephalus gazella), macaroni penguins (Eudyptes chrysolophus), king penguins (Aptenodytes patagonicus), black browed albatross (Diomedea melanophrys), and light mantled albatross (Phoebetria palpebrata). All were tagged at Heard Island in the Southern Ocean over a single summer season collecting over 5000 tracking days for 178 individuals. We aimed to predict areas of likely high foraging value from tracking environmental data and also to quantify overlap in foraging range between species. Hidden Markov models were used to differentiate between bouts of Area Restricted Search (ARS) assumed to be associated with areas of higher foraging value, and transit behaviours. Oceanographic and distance metrics associated with ARS activity were then used to calculate a habitat electivity index. A combined bootstrap/Monte Carlo scheme was employed to propagate uncertainty from the Hidden Markov models into the habitat prediction scheme. Distinct differences were predicted in the spatial distribution of foraging locations in different species, reflecting different dispersive abilities and foraging strategy. The extent of usage and foraging distribution was largely contained within Australian the Economic Exclusion Zone (EEZ). In comparison, the smaller Australian Commonwealth Marine Protected Areas (MPAs) contained <20% of the predicted foraging distributions.

Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning

de Souza EN, Boerder K, Matwin S, Worm B. Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning. PLOS ONE [Internet]. 2016 ;11(7):e0158248. Available from: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0158248
Freely available?: 
Yes
Summary available?: 
No
Type: Journal Article

A key challenge in contemporary ecology and conservation is the accurate tracking of the spatial distribution of various human impacts, such as fishing. While coastal fisheries in national waters are closely monitored in some countries, existing maps of fishing effort elsewhere are fraught with uncertainty, especially in remote areas and the High Seas. Better understanding of the behavior of the global fishing fleets is required in order to prioritize and enforce fisheries management and conservation measures worldwide. Satellite-based Automatic Information Systems (S-AIS) are now commonly installed on most ocean-going vessels and have been proposed as a novel tool to explore the movements of fishing fleets in near real time. Here we present approaches to identify fishing activity from S-AIS data for three dominant fishing gear types: trawl, longline and purse seine. Using a large dataset containing worldwide fishing vessel tracks from 2011–2015, we developed three methods to detect and map fishing activities: for trawlers we produced a Hidden Markov Model (HMM) using vessel speed as observation variable. For longliners we have designed a Data Mining (DM) approach using an algorithm inspired from studies on animal movement. For purse seiners a multi-layered filtering strategy based on vessel speed and operation time was implemented. Validation against expert-labeled datasets showed average detection accuracies of 83% for trawler and longliner, and 97% for purse seiner. Our study represents the first comprehensive approach to detect and identify potential fishing behavior for three major gear types operating on a global scale. We hope that this work will enable new efforts to assess the spatial and temporal distribution of global fishing effort and make global fisheries activities transparent to ocean scientists, managers and the public.

GIS-based multi-criteria analysis of breeding habitats for recolonising species: New Zealand sea lions

MacMillan H, Moore AB, Augé AA, B. Chilvers L. GIS-based multi-criteria analysis of breeding habitats for recolonising species: New Zealand sea lions. Ocean & Coastal Management [Internet]. 2016 ;130:162 - 171. Available from: http://www.sciencedirect.com/science/article/pii/S096456911630117X
Freely available?: 
No
Summary available?: 
No
Type: Journal Article

The New Zealand sea lion (Phocarctos hookeri) is a threatened endemic species, with only three breeding colonies in the sub-Antarctic islands. Since 1993, there has been evidence for recolonisation of mainland New Zealand. Yet the coast that the sea lion has returned to only has fragmented and unevenly distributed potential habitats due to coastal urbanisation and development. Therefore, the need to identify and protect potential breeding habitats for recolonisation is a priority for management.

A GIS-based multi-criteria analysis was used to identify potential suitable habitats for a 1600 km length of the NZ South Island coast based on distance to anthropogenic disturbance (urban areas, roads), distance to desirable environmental features (beaches, estuaries) and presence of suitable habitat/land access. From this model, we identified preliminary suitable habitat for breeding sites on the Otago Peninsula (east coast) and Catlins Coast (south). We independently detected some of the current dominant areas used by recolonising sea lions as well as identifying some promising new sites.

We discuss the limitation of the results of this case study and the need for further data to be added to the model in the face of limited data availability. Overcoming this data limitation will meet an increasing need for a New Zealand-wide study for determining potential habitat for NZ sea lions. The results of such a study would identify areas to allow real-world management (protection or restoration) of the limited potential breeding sites for New Zealand sea lions. This new method could also be used for other recolonising species and encourage management of areas most likely to be recolonized by them.

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