Environmental factors and the detection of open surface water areas with X-band radar imagery

Henderson, Floyd M.

doi:10.1080/01431169508954567

Cited by 25 publications

(9 citation statements)

References 5 publications

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“…Compared to land, smooth open water shows lower backscatter intensities, as it acts as a specular reflector, reflecting most of the radar signal away from the sensor. The high dielectric constant of water also decreases the penetration depth of the signal, which results in low volume scattering and, thus, predominantly co-polarized reflection [26]. The discrimination of smooth open water from land is therefore a simple task [27].…”

Section: Polarimetric Sar Remote Sensing Of Small Reservoirs For Diffmentioning

confidence: 99%

Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach

et al. 2014

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Multipurpose small reservoirs are important for livelihoods in rural semi-arid regions. To manage and plan these reservoirs and to assess their hydrological impact at a river basin scale, it is important to monitor their water storage dynamics. This paper introduces a Bayesian approach for monitoring small reservoirs with radar satellite images. The newly developed growing Bayesian classifier has a high degree of automation, can readily be extended with auxiliary information and reduces the confusion error to the land-water boundary pixels. A case study has been performed in the Upper East Region of Ghana, based on Radarsat-2 data from November 2012 until April 2013. Results show that the growing Bayesian classifier can deal with the spatial and temporal variability in synthetic aperture radar (SAR) backscatter intensities from small reservoirs. Due to its ability to incorporate auxiliary information, the algorithm is able to delineate open water from SAR imagery with a low land-water contrast in the case of wind-induced Bragg scattering or limited vegetation on the land surrounding a small reservoir.

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Section: Polarimetric Sar Remote Sensing Of Small Reservoirs For Diffmentioning

confidence: 99%

Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach

et al. 2014

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“…Comparatively, the surrounding ground is rougher and thus produces a higher signal return. SAR images of floods in open areas appear as very dark zones, which are flooded ground, and brighter zones, which are non-flooded (Henderson 1995). Because of this property, a threshold procedure is sufficient, in most cases, to detect flooded areas (Brivio et al 2002, Malnes et al 2002.…”

Section: Natural Disastersmentioning

confidence: 99%

Earth observation using radar data: an overview of applications and challenges

Palmann¹,

Mavromatis²,

Hernández³

et al. 2008

International Journal of Digital Earth

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International audienceAbstract The first pictures of the earth were taken from a balloon in the mid-19th century and thus started ‘earth observation’. Aerial missions in the 20th century enabled the build-up of outstanding photographic libraries and then with Landsat-1, the first civilian satellite launched in 1972, digital images of the earth became an operational reality. The main roles of earth observation have become scientific, economic and strategic, and the role of synthetic aperture radar (SAR) is significant in this overall framework. Radar image exploitation has matured and several operational programs regularly use SAR data for input and numerous applications are being further developed. The technological development of interferometry and polarimetry has helped further develop these radar based applications. This paper highlights this role through a description of actual applications and projects, and concludes with a discussion of some challenges for which SAR systems may provide significant assistance

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“…Researchers across the globe have used different satellite data varying in spatial, spectral, and temporal characteristics to generate thematic maps of land use land cover or maps with special emphasis on water bodies [5][6][7][8][9][10][11]. At the same time, various techniques have been adopted to extract these features from satellite imagery and each method has its own merits and demerits.…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Water Bodies from Landsat Imagery Using Perceptron Model

Mishra

Prasad

2015

Journal of Computational Environmental Sciences

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Extraction of water bodies from satellite imagery has been widely explored in the recent past. Several approaches have been developed to delineate water bodies from different satellite imagery varying in spatial, spectral, and temporal characteristics. The current study puts forward an automatic approach to extract the water body from a Landsat satellite imagery using a perceptron model. Perceptron involves classification based on a linear predictor function that merges few characteristic properties of the object commonly known as feature vectors. The feature vectors, combined with the weights, sum up to provide an input to the output function which is a binary hard limit function. The feature vector in this study is a set of characteristic properties shown by a pixel of the water body. Low reflectance of water in SWIR band, comparison of reflectance in different bands, and a modified normalized difference water index are used as descriptors. The normalized difference water index is modified to enhance its reach over shallow regions. For this study a threshold value of 2 has been proved as best among the three possible threshold values. The proposed method accurately and quickly discriminated water from other land cover features.

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Environmental factors and the detection of open surface water areas with X-band radar imagery

Cited by 25 publications

References 5 publications

Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach

Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach

Earth observation using radar data: an overview of applications and challenges

Automatic Extraction of Water Bodies from Landsat Imagery Using Perceptron Model

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