Evaluation of C-band polarization diversity and polarimetry for wetland mapping

Brisco, Brian; Kapfer, Mark; Hirose, T.; Tedford, Bill; Liu, J.

doi:10.5589/m11-017

Cited by 93 publications

(81 citation statements)

References 16 publications

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“…Addition of phase information may improve wetland classification [102,103], while fully polarimetric data allow an array of polarimetric decomposition techniques that have been successfully used for characterization and classification of wetlands [60,104]. In addition, interferometric data can provide improved topographic information or vegetation surface model data that could improve classifications.…”

Section: Random Forest Classifier Performance and Variable Importancementioning

confidence: 99%

“…The predictor variables listed in Table 3 also include the coefficient of variation from a 3 × 3-pixel moving window applied to the HH and HV PALSAR images; it was used as a texture metric to evaluate how spatial heterogeneity in backscatter intensity can contribute to improving the discrimination of the wetland classes [58,59]. PALSAR L-band polarization intensity ratio HV/HH was selected for its effectiveness in discriminating flooded from non-flooded vegetation and water [47], while HH/HV demonstrated similar characteristics using SAR C-band data [60]. Full description of metrics with associated citations is given in [55].…”

Section: Palsarmentioning

confidence: 99%

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Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

et al. 2017

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Abstract:The Dabus Wetland complex in the highlands of Ethiopia is within the headwaters of the Nile Basin and is home to significant ecological communities and rare or endangered species. Its many interrelated wetland types undergo seasonal and longer-term changes due to weather and climate variations as well as anthropogenic land use such as grazing and burning. Mapping and monitoring of these wetlands has not been previously undertaken due primarily to their relative isolation and lack of resources. This study investigated the potential of remote sensing based classification for mapping the primary vegetation groups in the Dabus Wetlands using a combination of dry and wet season data, including optical (Landsat spectral bands and derived vegetation and wetness indices), radar (ALOS PALSAR L-band backscatter), and elevation (SRTM derived DEM and other terrain metrics) as inputs to the non-parametric Random Forest (RF) classifier. Eight wetland types and three terrestrial/upland classes were mapped using field samples of observed plant community composition and structure groupings as reference information. Various tests to compare results using different RF input parameters and data types were conducted. A combination of multispectral optical, radar and topographic variables provided the best overall classification accuracy, 94.4% and 92.9% for the dry and wet season, respectively. Spectral and topographic data (radar data excluded) performed nearly as well, while accuracies using only radar and topographic data were 82-89%. Relatively homogeneous classes such as Papyrus Swamps, Forested Wetland, and Wet Meadow yielded the highest accuracies while spatially complex classes such as Emergent Marsh were more difficult to accurately classify. The methods and results presented in this paper can serve as a basis for development of long-term mapping and monitoring of these and other non-forested wetlands in Ethiopia and other similar environmental settings.

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Section: Random Forest Classifier Performance and Variable Importancementioning

confidence: 99%

Section: Palsarmentioning

confidence: 99%

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

et al. 2017

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“…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%

“…In dual-channel SAR systems the signal can be both co-polarized (transmitted and received energy as HH or VV) and cross-polarized (transmitted and received as HV or VH). With the advancement of fully polarimetric satellite systems such as RADARSAT-2 the satellite can transmit and receive energy in all four planes (HH, VV, HV and VH), maintaining the phase and allowing for mapping of the different scattering mechanisms within a wetland [49], rather than just the difference between low and high backscatter values (Figure 1). The phase measures the time it takes for the radar signal sent from the satellite to interact with the target on the ground and return to the satellite [50].…”

Section: Introductionmentioning

confidence: 99%

A Collection of SAR Methodologies for Monitoring Wetlands

et al. 2015

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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.

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“…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. The 'all-weather' data collection capability combined with the information content makes SAR an attractive sensor for wetland applications.…”

Section: Introductionmentioning

confidence: 99%

Compact polarimetry assessment for rice and wetland mapping

Brisco

Tedford

et al. 2012

International Journal of Remote Sensing

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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.

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Evaluation of C-band polarization diversity and polarimetry for wetland mapping

Cited by 93 publications

References 16 publications

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

A Collection of SAR Methodologies for Monitoring Wetlands

Compact polarimetry assessment for rice and wetland mapping

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