Abstract:Fresh water is arguably the most vital resource for many aspects of a healthy and stable environment. Monitoring the extent of surface water enables resource managers to detect perturbations and long term trends in water availability, and set consumption guidelines accordingly. Potential end-users of water-related observations are numerous and reflect society as a whole. They encompass scientists and managers at all levels of government, aboriginal groups, water/ power utility managers, farmers, planners, engi… Show more
“…Adding the beta parameter to the Cloude-Pottier decomposition could improve the classification to a similar level of the Freeman-Durden as was found for rice classification (Li, Brisco, and Yun 2012) Note, however, that both the C-L CP and fully polarimetric data produced operationally suitable wetland classification accuracies. It is not surprising that the full polarimetric data outperformed other data sets as it did for rice due to the increased information content (Brisco et al 2008Touzi 2007). …”
Section: Resultsmentioning
confidence: 99%
“…Synthetic aperture radar (SAR) has been recognized as an important source of data for water resource applications (Brisco et al 2008). For surface water and flooding applications, the lack of backscatter from the specular water surface allows for easy delineation of open water, while the flooded vegetation in wetlands results in an enhanced backscatter due to double-bounce scattering (Hess, Melack, and Simonett 1990;Kasischke and Bourgeau-Chavez 1997;Pope et al 1997;Brisco et al 2009Brisco et al , 2011.…”
Polarimetric RADARSAT-2 data of rice and wetlands are used to simulate compact polarimetry (CP) mode data from the upcoming RADARSAT Constellation Mission (RCM). The simulated CP data are then used to evaluate the information content for rice and wetland mapping using supervised classification, and the results are compared for linear and circular polarization combinations and polarimetric decompositions from the fully polarimetric data and the simulated CP data. The results are consistent for both rice and wetlands and show that the classification accuracy increases as one goes up the polarization hierarchy. The circular polarizations produced the best classification results for the polarization combinations. This result requires further research to verify. Although the CP data did not perform as well as the fully polarimetric data, the results were better than for dual polarization, and this mode may offer the best option for rice and wetland mapping applications because of swath coverage. Note that both the compact simulations and the fully polarimetric data produced operationally suitable classification accuracies. Additional research is underway to evaluate the monitoring capability of this new CP mode. This article describes the approach used for the analyses and the classification results for both rice and wetlands.
“…Adding the beta parameter to the Cloude-Pottier decomposition could improve the classification to a similar level of the Freeman-Durden as was found for rice classification (Li, Brisco, and Yun 2012) Note, however, that both the C-L CP and fully polarimetric data produced operationally suitable wetland classification accuracies. It is not surprising that the full polarimetric data outperformed other data sets as it did for rice due to the increased information content (Brisco et al 2008Touzi 2007). …”
Section: Resultsmentioning
confidence: 99%
“…Synthetic aperture radar (SAR) has been recognized as an important source of data for water resource applications (Brisco et al 2008). For surface water and flooding applications, the lack of backscatter from the specular water surface allows for easy delineation of open water, while the flooded vegetation in wetlands results in an enhanced backscatter due to double-bounce scattering (Hess, Melack, and Simonett 1990;Kasischke and Bourgeau-Chavez 1997;Pope et al 1997;Brisco et al 2009Brisco et al , 2011.…”
Polarimetric RADARSAT-2 data of rice and wetlands are used to simulate compact polarimetry (CP) mode data from the upcoming RADARSAT Constellation Mission (RCM). The simulated CP data are then used to evaluate the information content for rice and wetland mapping using supervised classification, and the results are compared for linear and circular polarization combinations and polarimetric decompositions from the fully polarimetric data and the simulated CP data. The results are consistent for both rice and wetlands and show that the classification accuracy increases as one goes up the polarization hierarchy. The circular polarizations produced the best classification results for the polarization combinations. This result requires further research to verify. Although the CP data did not perform as well as the fully polarimetric data, the results were better than for dual polarization, and this mode may offer the best option for rice and wetland mapping applications because of swath coverage. Note that both the compact simulations and the fully polarimetric data produced operationally suitable classification accuracies. Additional research is underway to evaluate the monitoring capability of this new CP mode. This article describes the approach used for the analyses and the classification results for both rice and wetlands.
“…This allows the user to decompose the SAR backscatter being returned from the objects being sensed into four common scattering types: (1) specular scattering (no return to the SAR), which occurs from smoother surfaces such as calm water or bare soil; (2) rough scattering, which results when there is a single bounce return to the SAR from surfaces such as small shrubs or rough water; (3) volume scattering, which is when the signal is backscattered in multiple directions from features such as vegetation canopies; and (4) double-bounce or dihedral scattering, which results when two smooth surfaces create a right angle that deflects the incoming radar signal off both surfaces such that most of the energy is returned to the sensor. This latter scattering case typically occurs when vertical emergent vegetation is surrounded by a visible, smooth water surface [32,[47][48][49][50][51]. Flooded vegetation can also have a combination of double-bounce and volume backscattering [50,51].…”
Section: Introductionmentioning
confidence: 99%
“…Synthetic Aperture Radar (SAR) technology can be effective for monitoring changes in surface water [32,33] and wetlands both seasonally and annually [34−37]. SAR has many characteristics that make it ideal for mapping and monitoring water and wetlands over time.…”
Abstract:Wetlands are an important natural resource that requires monitoring. A key step in environmental monitoring is to map the locations and characteristics of the resource to better enable assessment of change over time. Synthetic Aperture Radar (SAR) systems are helpful in this way for wetland resources because their data can be used to map and monitor changes in surface water extent, saturated soils, flooded vegetation, and changes in wetland vegetation cover. We review a few techniques to demonstrate SAR capabilities for wetland monitoring, including the commonly used method of grey-level thresholding for mapping surface water and highlighting changes in extent, and approaches for polarimetric decompositions to map flooded vegetation and changes from one class of land cover to another. We use the Curvelet-based change detection and the Wishart-Chernoff Distance approaches to show how they substantially improve mapping of flooded vegetation and flagging areas of change, respectively. We recommend that the increasing availability SAR data and the proven ability of these data to map various components of wetlands mean SAR should be considered as a critical component of a wetland monitoring system.
“…Water monitoring using Synthetic Aperture Radar (SAR) has been the object of study for many researchers [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Due to its all-weather capabilities, and its image acquisition capacity during day or night or in cloudy conditions, SAR imagery offers better alternatives for water mapping than optical imagery [5].…”
Traditional on-site methods for mapping and monitoring surface water extent are prohibitively expensive at a national scale within Canada. Despite successful cost-sharing programs between the provinces and the federal government, an extensive number of water features within the country remain unmonitored. Particularly difficult to monitor are the potholes in the Canadian Prairie region, most of which are ephemeral in nature and represent a discontinuous flow that influences water pathways, runoff response, flooding and local weather. Radarsat-2 and the Radarsat Constellation Mission (RCM) offer unique capabilities to map the extent of water bodies at a national scale, including unmonitored sites, and leverage the current infrastructure of the Meteorological Service of Canada to monitor water information in remote regions. An analysis of the technical requirements of the Radarsat-2 beam mode, polarization and resolution is presented. A threshold-based procedure to map locations of non-vegetated water bodies after the ice break-up is used and complemented with a texture-based indicator to capture the most homogeneous water areas and automatically delineate their extents. Some strategies to cope with the radiometric artifacts of noise inherent to Synthetic Aperture Radar (SAR) images are also discussed. Our results show that Radarsat-2 Fine mode can capture 88% of the total water area in a fully automated way. This will greatly improve current operational procedures for surface water monitoring information and impact a number of applications including weather forecasting, hydrological modeling, and drought/flood predictions.
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