Sediment classification and surface type mapping in the Danish Wadden Sea by remote sensing

Bartholdy, Jesper; Folving, Sten

doi:10.1016/0077-7579(86)90001-3

Cited by 48 publications

(20 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moisture, porosity, organic content and high (>4%) or low silt and clay content could be detected with Landsat TM (Bartholdy and Folving 1986). These data could be classiWed in mud Xats (11%), muddy or mixed Xats (20%), wet or moist sand Xat (30%), dry sand (25%) and high sand (14%).…”

Section: Classiwcation Of Tidal Xatsmentioning

confidence: 98%

“…Landsat Thematic Mapper (TM) data (NASA 2009) was used to distinguish tidal Xats (64%) and water (36%) (Bartholdy and Folving 1986) and land, water, foreland and clouds (DoerVer and Murphy 1989). Radar and laser have more recently led to good results.…”

Section: Distinction Between Land and Watermentioning

confidence: 99%

“…However, the correlation between stability and Chl-a changes depending on the fraction of Wne sediment and even disappears in sandy sediments. Twenty years after Bartholdy and Folving (1986), it was possible to map all major properties on tidal Xats. Bare sand Xats, tidal Xats covered with diatoms, green macro-algae, red algae and sea grass (Kromkamp et al 2006) or Xats with the properties of macrophytes, several sediment types and mussel beds could be distinguished (Brockmann and Stelzer 2008) with data from Landsat TM images.…”

Section: Classiwcation Of Tidal Xatsmentioning

confidence: 99%

See 2 more Smart Citations

A review on substances and processes relevant for optical remote sensing of extremely turbid marine areas, with a focus on the Wadden Sea

et al. 2010

View full text Add to dashboard Cite

The interpretation of optical remote sensing data of estuaries and tidal Xat areas is hampered by optical complexity and often extreme turbidity. Extremely high concentrations of suspended matter, chlorophyll and dissolved organic matter, local diVerences, seasonal and tidal variations and resuspension are important factors inXuencing the optical properties in such areas. This review gives an overview of the processes in estuaries and tidal Xat areas and the implications of these for remote sensing in such areas, using the Wadden Sea as a case study area. Results show that remote sensing research in extremely turbid estuaries and tidal areas is possible. However, this requires sensors with a large ground resolution, algorithms tuned for high concentrations of various substances and the local speciWc optical properties of these substances, a simultaneous detection of water colour and land-water boundaries, a very short time lag between acquisition of remote sensing and in situ data used for validation and suYcient geophysical and ecological knowledge of the area.

show abstract

Section: Classiwcation Of Tidal Xatsmentioning

confidence: 98%

Section: Distinction Between Land and Watermentioning

confidence: 99%

Section: Classiwcation Of Tidal Xatsmentioning

confidence: 99%

See 1 more Smart Citation

A review on substances and processes relevant for optical remote sensing of extremely turbid marine areas, with a focus on the Wadden Sea

et al. 2010

View full text Add to dashboard Cite

show abstract

“…Mapping the type of sediment using satellite data (chiefly Landsat Multispectral Scanner, MSS), has been carried out by several authors, and has been aChieved mainly by means of multispectral classification techniques (Dennert-M6Uer, 1983;Folving, 1984;Wieland, 1984;Bartholdy & Folving, 1986). One major problem in deriving conclusions from satellite data is the ground resolution of the scanning radiometer, which is usually in the order of tens of metres.…”

Section: Introductionmentioning

confidence: 99%

Factor analysis and classification of remotely sensed data for monitoring tidal flats

Doerffer

Murphy

1989

Helgolander Meeresunters

View full text Add to dashboard Cite

Interest in using remote sensing techniques, principally those involving satellite, in Wadden Sea research has centred on attempting a classification of the various sediment surface types present. Unlike most recent studies which have used mainly Landsat Multispectral Scanner data, we have assessed the feasibility of using Landsat Thematic Mapper data, which in conjunction with time series aerial photography, forms the basis of a strategy for remotely sensing the Wadden Sea. This paper focusses on an approach for extracting potentially "hidden" within-pixel information from multispectral data sets. A hierarchical (unsupervised) classification of a Thematic Mapper image successfully classified five different classes, including land, saltmarsh, water, cloud and tidal flat areas. This procedure thus enabled a "masking-out" of all classes other than those classified as tidal flat, following which a factor analysis was used to determine the minimum number of independent factors necessary to explain the observed variation in the signal received by the satellite. Three factors accounted for a total of 82 % of the variation in all seven TM channels. Preliminary studies of the primary factor (score) image shows a good correlation with existing latterday cartographic data. Considering the proximate relationship between topography and other important biotic and abiotic sedimentary characteristics, this approach may prove valuable for future applications of satellite data for monitoring long-term change in physical and thus biological Wadden Sea characteristics. Ongoing research efforts are focussing on a classification and quantification of sub-pixel patchiness using aerial photography and ground surveys. The approaches taken and results obtained to date are discussed.

show abstract

“…Both satellite and airborne remote sensing techniques have become useful and effective tools to carry out coastal and oceanic pursuits (Folving 1984; Bartholdy and Folving 1986;Yachts et al 1993;Populus et al 1995a, b).…”

Section: Introductionmentioning

confidence: 99%

Sediment grain size estimation using airborne remote sensing, field sampling, and robust statistic

Castillo

Pereda

Luis

et al. 2011

Environ Monit Assess

View full text Add to dashboard Cite

Remote sensing has been used since the 1980s to study parameters in relation with coastal zones. It was not until the beginning of the twenty-first century that it started to acquire imagery with good temporal and spectral resolution. This has encouraged the development of reliable imagery acquisition systems that consider remote sensing as a water management tool. Nevertheless, the spatial resolution that it provides is not adapted to carry out coastal studies. This article introduces a new methodology for estimating the most fundamental physical property of intertidal sediment, the grain size, in coastal zones. The study combines hyperspectral information (CASI-2 flight), robust statistic, and simultaneous field work (chemical and radiometric sampling), performed over Santander Bay, Spain. Field data acquisition was used to build a spectral library in order to study different atmospheric correction algorithms for CASI-2 data and to develop algorithms to estimate grain size in an estuary. Two robust estimation techniques (MVE and MCD multivariate M-estimators of location and scale) were applied to CASI-2 imagery, and the results showed that robust adjustments give acceptable and meaningful algorithms. These adjustments have given the following R(2) estimated results: 0.93 in the case of sandy loam contribution, 0.94 for the silty loam, and 0.67 for clay loam. The robust statistic is a powerful tool for large dataset.

show abstract

Sediment classification and surface type mapping in the Danish Wadden Sea by remote sensing

Cited by 48 publications

References 7 publications

A review on substances and processes relevant for optical remote sensing of extremely turbid marine areas, with a focus on the Wadden Sea

A review on substances and processes relevant for optical remote sensing of extremely turbid marine areas, with a focus on the Wadden Sea

Factor analysis and classification of remotely sensed data for monitoring tidal flats

Sediment grain size estimation using airborne remote sensing, field sampling, and robust statistic

Contact Info

Product

Resources

About