Remote sensing as a tool for bathymetric mapping of coral reefs in the Red Sea (Hurghada – Egypt)

Vanderstraete, Tony; Goossens, Rudi; Ghabour, T. K.

doi:10.4000/belgeo.16652

Cited by 17 publications

(8 citation statements)

References 6 publications

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“…Seafloor bottom reflectance is an important component of the overall water-leaving signal in coastal waters [24]. The assumption of a uniform bottom type is generally not appropriate in coastal areas due to the dynamic environment near the shoreline characterised by complex sediment and oceanographic patterns.…”

Section: Performance Of This Study's Most Accurate Predictor (Ked-ln)mentioning

confidence: 99%

“…A variety of empirical models have been proposed, from linear functions [20], band ratios to log transformed regression models [21], and non-linear inverse models [22] with varying degrees of corrections applied (atmospheric, sun glint, seafloor). The evolution of empirical models has been largely linked to the chronology of satellite platforms: coarse spatial resolution using Landsat TM [13,23,24]; medium spatial resolution SPOT images in shallow waters [15,25] or RapidEye in lakes [26]; and high spatial resolution with the use of commercial satellites such as WorldView [27,28], QuickBird [29,30] and IKONOS [31].…”

Section: Introductionmentioning

confidence: 99%

“…Although [32] have demonstrated an artificial neural network that is not influenced by bottom type or vegetation on the seabed. The Lyzenga method [20] is not restricted to imagery from a single satellite-imagery from Landsat TM [13,21,23,24], QuickBird [29,30], SPOT [15,25] and WorldView-2 [27,28] have all been employed. Nor is the source of the imagery limited to satellites-Flener [33] have performed a comparison between mobile laser scanners and UAV imagery for bathymetric surveys of rivers.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

et al. 2015

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Abstract:The coastal shallow water zone can be a challenging and costly environment in which to acquire bathymetry and other oceanographic data using traditional survey methods. Much of the coastal shallow water zone worldwide remains unmapped using recent techniques and is, therefore, poorly understood. Optical satellite imagery is proving to be a useful tool in predicting water depth in coastal zones, particularly in conjunction with other standard datasets, though its quality and accuracy remains largely unconstrained. A common challenge in any prediction study is to choose a small but representative group of predictors, one of which can be determined as the best. In this respect, exploratory analyses are used to guide the make-up of this group, where we choose to compare a basic non-spatial model versus four spatial alternatives, each catering for a variety of spatial effects. Using one instance of RapidEye satellite imagery, we show that all four spatial models show better adjustments than the non-spatial model in the water depth predictions, with the best predictor yielding a correlation coefficient of actual versus predicted at 0.985. All five predictors also factor in the influence of bottom type in explaining water depth variation. However, the prediction ranges are too large to be used in high accuracy bathymetry products such as navigation charts; nevertheless, they are considered beneficial in a variety of other OPEN ACCESSRemote Sens. 2015, 7 13783 applications in sensitive disciplines such as environmental monitoring, seabed mapping, or coastal zone management.

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Section: Performance Of This Study's Most Accurate Predictor (Ked-ln)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

et al. 2015

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“…Because of the unique marine and terrestrial environmental conditions along its coasts, more interest for mapping ESI has been focused on the Sinai Peninsula and Red Sea coasts. This is including a high biological diversity such as coral reefs, mangroves, rare wildlife species and a variety of coastal geomorphology from rocky to sandy, with both low and high relief topology with stable physical characters such as salinity, temperature and water quality [3,4,6,27]. Oil production and shipping always threaten the coasts of the Red Sea and Sinai with oil pollution, especially the Gulf of Suez where the majority of oil exploration and production is located.…”

Section: Figure 1 Location Of the Study Areamentioning

confidence: 99%

Evaluation of environmental sensitivity of the coastal plains shoreline to oil spills: Southwestern Sinai coastal plain, Egypt

Wahid¹,

Madden²

2017

EESRJ

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Assessing the coastal Environmental Sensitivity Index (ESI) is very important for contingency planning for oil spills, especially for regions undergoing urban development. This study is an attempt to identify the sensitivity of the southeastern portion of the Gulf of Suez shoreline; a very promising area for economic development. Enhancing a methodology for calculating ESI in this work obtains more efficient results for environmental sensitivity assessment. This is accomplished by adding more detailed physical factors that may affect the ESI. The coastline is ranked according to factors controlling coastal resiliency to oil spills. These factors are categorized in three sets of data: 1) physical habitats of the coastline that determine the persistence of oil pollution and ease of cleaning and reclamation such as beach types, degree of exposure to waves, beach slope, wave directions and tidal flats (physical factors); 2) biological resources sensitive to oil (ecosystem factors); and 3) the types of human-use resources (socio-economic factors). Data used to determine these factors were collected from satellite images, field work and documentary information and were manipulated in a GIS environment using a geospatial arithmetic modeling to produce spatial environmental sensitivity maps of coastal shorelines. The studied shoreline of the southwestern Sinai coastal plain of Egypt has a wide range of sensitivity to oil spills ranges from very low to very high. The northern part of the shoreline is characterized by low sensitive beaches where the morphology and beach type play the dominant rule in reducing the sensitivity. It is composed mainly of high slope rocky beaches. The southern part is dominant by sandy tidal flat beaches and coral reefs communities that extend wider and shallower. This makes the southern beaches relatively more environmentally sensitive to oil spills.

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“…This way, on each photograph an adequate number of GCPs should be identified, evenly distributed across the whole photograph and mostly located on human landscape features such as crossroads. If the photographs are intended to be used for calculating shoreline rates of change as accurate as possible, Root Mean Square Error (RMSE) for the GCPs should generally be less than one-half the pixel size of the image (Vanderstraete et al 2003) according to the US National Map Accuracy Standards (Stathopoulou and Cartalis 2009). After this process is completed, geometrically corrected photographs and orthophotographs from the same areas are superimposed on GIS environment in order to assess temporal changes in shoreline position.…”

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confidence: 99%

Error determination in the photogrammetric assessment of shoreline changes

Río

Prieto

2012

Nat Hazards

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The evaluation of error or uncertainty in shoreline change studies is an issue of prime importance for providing an adequate framework for calculated rates of change, and to allow the establishment of threshold values above which the rates would be significant. In this note a practical, easy-to-use method is presented to estimate error involved in the calculation of shoreline changes on aerial photographs, including the three most used types of shoreline indicators: high water line, dune/cliff toe and cliff top. This approach takes into account the specific characteristics of each shoreline proxy, such as relief in the case of the cliff top or tidal oscillations in the case of the high water line. At the same time it includes the error components that are independent from the proxy, basically related to the technical aspects of the process such as photo scanning and georeferencing. A practical example of application of the method is provided for several types of data inputs, based on shoreline changes around the Bay of Cadiz (SW Spain).

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Remote sensing as a tool for bathymetric mapping of coral reefs in the Red Sea (Hurghada – Egypt)

Cited by 17 publications

References 6 publications

Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

Evaluation of environmental sensitivity of the coastal plains shoreline to oil spills: Southwestern Sinai coastal plain, Egypt

Error determination in the photogrammetric assessment of shoreline changes

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