Radar detection of wetland ecosystems: a review

Henderson, Floyd M.; Lewis, Anthony

doi:10.1080/01431160801958405

Cited by 233 publications

(152 citation statements)

References 70 publications

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“…The importance of cross-polarization (HV) backscatter was consistently higher than HH for all RF models. In a review paper, Henderson and Lewis [18] noted that in a number of studies HV provided better results than HH for wetland mapping. More specifically, in this study wet-season HV backscatter was an important predictor for all land cover classes, but HH was generally more important in distinguishing wetland classes than terrestrial classes.…”

Section: Random Forest Classifier Performance and Variable Importancementioning

confidence: 99%

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%

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

et al. 2017

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“…Differences between information classes were higher in the cross-polarization band (HV) than in co-polarization bands (HH or VV). This issue has been pointed out in [11]: the depolarizing character of the vegetation differs according to vegetation structure, biomass distribution and flooding state.…”

Section: Description Of the Scenes And Field Samplesmentioning

confidence: 99%

Mapping Plant Functional Types in Floodplain Wetlands: An Analysis of C-Band Polarimetric SAR Data from RADARSAT-2

et al. 2016

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Abstract:The inclusion of functional approaches on wetland characterizations and on biodiversity assessments improves our understanding of ecosystem functioning. In the Lower Paraná River floodplain, we assessed the ability of C-band polarimetric SAR data of contrasting incidence angles to discriminate wetland areas dominated by different plant functional types (PFTs). Unsupervised H/α and H/A/α Wishart classifications were implemented on two RADARSAT-2 images differing in their incidence angles (FQ24 and FQ08). Obtained classes were assigned to the information classes (open water, bare soil and PFTs) by a priori labeling criteria that involved the expected interaction mechanisms between SAR signal and PFTs as well as the relative values of H and α. The product obtained with the shallow incidence angle scene had a higher accuracy than the one obtained with the steep incidence angle product (61.5% vs. 46.2%). We show how a systematic analysis of the H/A/α space can be used to improve the knowledge about the radar polarimetric response of herbaceous vegetation. The map obtained provides novel ecologically relevant information about plant strategies dominating the floodplain. Since the obtained classes can be interpreted in terms of their functional features, the approach is a valuable tool for predicting vegetation response to floods, anthropic impacts and climate change.

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“…The 'all-weather' data collection capability combined with the information content makes SAR an attractive sensor for wetland applications. Henderson and Lewis (2008) provide a review of radar and wetlands.…”

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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Radar detection of wetland ecosystems: a review

Cited by 233 publications

References 70 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

Mapping Plant Functional Types in Floodplain Wetlands: An Analysis of C-Band Polarimetric SAR Data from RADARSAT-2

Compact polarimetry assessment for rice and wetland mapping

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