Assessing RADARSAT-2 for Mapping Shoreline Cleanup and Assessment Technique (SCAT) Classes in the Canadian Arctic

Banks, Sarah N.; King, Douglas J.; Merzouki, A.; Duffe, Jason

doi:10.1080/07038992.2014.968276

Cited by 17 publications

(64 citation statements)

References 49 publications

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Section: Potential For Shoreline Sensitivity Mapping Using Earth Obsementioning

confidence: 99%

“…The authors applied each classifier to the same six images used by Banks et al [11], and found that they could detect more land covers using the shallow and medium incidence angle images. In general though, classification results obtained by combining available SAR and optical data in the Maximum Likelihood classifier by Banks et al [11], were superior to results obtained with the polarimetric SAR classifiers. The authors also applied the Cloude-Pottier and Freeman-Durden decompositions [20][21][22] to characterize scattering behaviour, and assess the consistency of values between sites at like incidence angles.…”

Section: Potential For Shoreline Sensitivity Mapping Using Earth Obsementioning

confidence: 99%

“…Few studies have focused on assessing the potential for shoreline sensitivity mapping using Earth observation data, though of the studies that do exist, a number were undertaken in the Canadian Arctic [11][12][13]. Potential for this application has also been demonstrated in other regions [15,16].…”

Section: Potential For Shoreline Sensitivity Mapping Using Earth Obsementioning

confidence: 99%

“…Banks et al [11] assessed the potential to classify shore and near-shore land cover types over two study areas: Richards Island and Tuktoyaktuk Harbour, Northwest Territories, Canada. The authors acquired three Fine Quadrature Polarized (Quad Pol) RADARSAT-2 scenes over each site to assess the impact of incidence angle on class separability, and classification accuracy.…”

Section: Potential For Shoreline Sensitivity Mapping Using Earth Obsementioning

confidence: 99%

“…In light of this, there is interest in developing a semi-automated mapping approach using Earth observation data [11][12][13]. Accordingly, the purpose of this study was to assess the potential to operationalize shoreline sensitivity mapping over a large region, using a single model to classify data that have been demonstrated to provide relevant and complementary information for this application [11][12][13]; specifically, multiple RADARSAT-2 Synthetic Aperture Radar (SAR), and Landsat 5 optical scenes (images necessarily acquired on different dates and with different spatial footprints to provide full study site coverage), as well as a Digital Elevation Model (DEM). For this we used the Random Forest algorithm; a non-parametric classifier based on an ensemble of individual decision tree models [14].…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model

et al. 2015

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Abstract:The Random Forest algorithm was used to classify 86 Wide Fine Quadrature Polarized RADARSAT-2 scenes, five Landsat 5 scenes, and a Digital Elevation Model covering an area approximately 81,000 km 2 in size, and representing the entirety of Dease Strait, Coronation Gulf and Bathurst Inlet, Nunavut. The focus of this research was to assess the potential to operationalize shoreline sensitivity mapping to inform oil spill response and contingency planning. The impact of varying the training sample size and reducing model data load were evaluated. Results showed that acceptable accuracies could be achieved with relatively few training samples, but that higher accuracies and greater probabilities of correct class assignment were observed with larger sample sizes. Additionally, the number of inputs to the model could be greatly reduced without impacting overall performance. Optimized models reached independent accuracies of 91% for seven land cover types, and classification probabilities between 0.77 and 0.98 (values for latter represent per-class averages generated from independent validation sites). Mixed OPEN ACCESSRemote Sens. 2015, 7 13529 results were observed when assessing the potential for remote predictive mapping by simulating transferability of the model to scenes without training data.

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Section: Potential For Shoreline Sensitivity Mapping Using Earth Obsementioning

confidence: 99%

Section: Potential For Shoreline Sensitivity Mapping Using Earth Obsementioning

confidence: 99%

Section: Potential For Shoreline Sensitivity Mapping Using Earth Obsementioning

confidence: 99%

Section: Potential For Shoreline Sensitivity Mapping Using Earth Obsementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model

et al. 2015

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A Range of Earth Observation Techniques for Assessing Plant Diversity

Lausch

Heurich

Magdon

et al. 2020

Remote Sensing of Plant Biodiversity

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Vegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS.

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Characterizing Scattering Behaviour and Assessing Potential for Classification of Arctic Shore and Near-Shore Land Covers with Fine Quad-Pol RADARSAT-2 Data

Banks

King

Merzouki

et al. 2014

Canadian Journal of Remote Sensing

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Freeman-Durden and Cloude-Pottier decompositions were applied to polarimetric RADARSAT-2 data to characterize the scattering behaviour of shore and near-shore land cover types for two study areas in the Beaufort Sea, Canada: Richards Island and Tuktoyaktuk Harbour. The impact of incidence angle was evaluated through comparison of Single Look Complex Fine Quad-Pol images acquired at three angles: shallow (45.3 • -49.5 • ), medium (39.3 • -41.6 • ), and steep (20.9 • -24.2 • ), and the potential for accurate classification was assessed using three unsupervised polarimetric SAR classifiers: Wishart-entropy/alpha, Wishart-entropy/anisotropy/alpha, and Freeman-Wishart. Results indicate that overall, medium, and shallow incidence angle images provide more useful information than steep angle images and that these classifiers are capable of distinguishing some general land cover types, including substrates, wetlands, vegetated tundra, and water. Results from this analysis will inform future research related to classification and interpretation of RADARSAT-2 data for shoreline mapping. Résumé. Des décompositions Freeman-Durden etCloude-Pottier ontété appliquéesà des données polarimétriques RADARSAT-2 pour caractériser le comportement de diffusion des différents types de couverture terrestre des milieux littoraux et côtiers pour deux zones d'étude dans la mer de Beaufort, au Canada: l'île Richards et le port de Tuktoyaktuk. L'impact de l'angle d'incidence aétéévalué par comparaison des images singulières complexes <> quad-pol fines acquisesà trois angles; plus rasants (45.3 • -49.5 • ), moyens (39.3 • -41.6 • ) et abrupts (20.9 • -24.2 • ), tandis que le potentiel pour une classification précise aétéévaluéà l'aide de trois classificateurs RSO polarimétriques non dirigés: Wishart-entropie/alpha, Wishart-entropie/anisotropie/alpha et Freeman-Wishart.Les résultats indiquent que, dans l'ensemble, les images avec des angles d'incidence moyensà rasants fournissent des informations plus utiles que les images aux angles abrupts, et que ces classificateurs sont capables de distinguer certains types généraux de couverture terrestre, y compris: les substrats, les zones humides, la toundra végétalisée et l'eau. Les résultats de cette analyse seront utiles dans les recherches futures liéesà la classification età l'interprétation des données RADARSAT-2 pour la cartographie des rivages.

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Assessing RADARSAT-2 for Mapping Shoreline Cleanup and Assessment Technique (SCAT) Classes in the Canadian Arctic

Cited by 17 publications

References 49 publications

Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model

Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model

A Range of Earth Observation Techniques for Assessing Plant Diversity

Characterizing Scattering Behaviour and Assessing Potential for Classification of Arctic Shore and Near-Shore Land Covers with Fine Quad-Pol RADARSAT-2 Data

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