Fast and low-cost method for VBES bathymetry generation in coastal areas

Sánchez-Carnero, Noela; Aceña, S.; Rodríguez‐Pérez, Daniel; Couñago, Elena; Fraile, P.; Freire, Juan

doi:10.1016/j.ecss.2012.08.018

Cited by 26 publications

(14 citation statements)

References 37 publications

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“…These methods, and especially the MBES and ALB, can provide highly accurate information in multiple scales. However, depending on the extent of the project area, they require a large amount of effort 2 of 12 and are costly [5] when compared with newly adopted approaches such as the Satellite-derived bathymetry (SDB).…”

Section: Introductionmentioning

confidence: 99%

Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry

et al. 2019

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High spatial and temporal resolution satellite remote sensing estimates are the silver bullet for monitoring of coastal marine areas globally. From 2000, when the first commercial satellite platforms appeared, offering high spatial resolution data, the mapping of coastal habitats and the extraction of bathymetric information have been possible at local scales. Since then, several platforms have offered such data, although not at high temporal resolution, making the selection of suitable images challenging, especially in areas with high cloud coverage. PlanetScope CubeSats appear to cover this gap by providing their relevant imagery. The current study is the first that examines the suitability of them for the calculation of the Satellite-derived Bathymetry. The availability of daily data allows the selection of the most qualitatively suitable images within the desired timeframe. The application of an empirical method of spaceborne bathymetry estimation provides promising results, with depth errors that fit to the requirements of the International Hydrographic Organization at the Category Zone of Confidence for the inclusion of these data in navigation maps. While this is a pilot study in a small area, more studies in areas with diverse water types are required for solid conclusions on the requirements and limitations of such approaches in coastal bathymetry estimations.

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Section: Introductionmentioning

confidence: 99%

Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry

et al. 2019

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“…Additionally, ship-borne surveys are time consuming and swath widths can be very narrow in shallow waters. Although single beam surveys are less costly than their multi-beam alternative, their low sampling density requires advanced interpolation (or prediction) techniques [6] to estimate seabed topography, and, therefore, are generally not used for high accuracy surveys but rather commissioned by local interest groups as an interim measure. A second non-imaging method known as satellite altimetry can be used to measure the geoidal height and marine gravity field which, in turn, can be used to determine the water depth from the linear relationship between the gravity anomaly and square of the depth [7].…”

Section: Introductionmentioning

confidence: 99%

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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“…Traditionally, the active sensors used for estimating bathymetry are the echo sounders (Multi-Beam Echo Sounders, Single-Beam Echo Sounders) or LIDAR devices installed on vessels following a course along specially designed sailing lines that have a specific geometry (Kearns and Breman, 2010). Beyond the fact that these methods are very accurate, they are also very time consuming and costly (McCaffrey, 1981;Sánchez-Carnero et al, 2012). Airborne LIDAR is another method that has been implemented for estimating the bathymetry which revealed to be cost effective compared onboard the vessels providing high area coverage faster (Irish and Lillycrop, 1999;Muirhead and Cracknell, 1986;Ogawa et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Bathymetric maps from multi-temporal analysis of Sentinel-2 data: the case study of Limassol, Cyprus

et al. 2019

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Abstract. During the last years, various methods and techniques have been used to estimate the seafloor topography with high accuracy. The topography is considered an important component for marine applications since several activities and infrastructures are being carried out at the bottom, column and surface of the sea. In this, study, freely and open distributed optical satellite images were used to obtain bathymetric data for shallow waters, (until 30 m below sea level) based on timeseries analysis of multispectral Sentinel-2 datasets. The ratio transform algorithm was implemented for twelve (12) monthly images covering thus a whole year. The different generated Digital Terrain Models (DTMs) were compared with high resolution LIDAR measurements. The results showed that bathymetry can be obtained from satellite data within a Root Mean Square Error (RMSE) ranging from 1.39 up to 2.56 m while more accurate results were generated during the summer period.

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Fast and low-cost method for VBES bathymetry generation in coastal areas

Cited by 26 publications

References 37 publications

Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry

Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry

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

Bathymetric maps from multi-temporal analysis of Sentinel-2 data: the case study of Limassol, Cyprus

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