Estimating Flow Resistance of Wetlands Using SAR Images and Interaction Models

Salvia, M.; Franco, Mariano; Grings, F.; Perna, P.; Martino, Román; Karszenbaum, H.; Ferrazzoli, P.

doi:10.3390/rs1040992

Cited by 15 publications

(12 citation statements)

References 19 publications

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“…Carbon storage, flood dampening, and habitat for wildlife are among key wetland ecosystem services (Järvellä 2003, Baigun et al 2008, Kandus et al 2010, Vicari et al 2011) that are affected by varying fire severity (Lal 2007, Salvia et al 2009a.…”

Section: Introductionmentioning

confidence: 99%

Post-Fire Effects in Wetland Environments: Landscape Assessment of Plant Coverage and Soil Recovery in the Paraná River Delta Marshes, Argentina

et al. 2012

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During 2008, under a region-wide drought, there were a large number of simultaneous fires in the Paraná River Delta region: the most affected vegetation was in marshes dominated by Schoenoplectus californicus (C.A.Mey.) Soják or Cyperus giganteus Vahl. The objective of this paper was to study fire severity in terms of fire effect on vegetation cover and soil properties, and the recovery of those properties after one growing season, using optical remote sensing techniques and fieldwork data. To this aim, we performed unsupervised classification of Landsat TM imagery and conducted vegetation censuses and soil sampling in November 2008 and May 2009. Our results show that we could identify three fire severity classes: low severity, medium severity, and high severity. These classes are characterized by a remnant vegetation cover of approximately 75 %, 25 %, and 5 %, respectively, and a diminution of soil organic carbon and nitrogen of 66 % and 59 % in the case of medium severity and high severity. Fire had almost no effect over pH and a slight effect on electrical conductivity. After one growing season, vegetation recovery is dependent on fire severity and hydrological condition, while soil properties did not show signs of recovery. This is one of the first studies of fire effects and recovery on fluvial herbaceous wetlands.

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Section: Introductionmentioning

confidence: 99%

Post-Fire Effects in Wetland Environments: Landscape Assessment of Plant Coverage and Soil Recovery in the Paraná River Delta Marshes, Argentina

et al. 2012

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“…For instance, Straatsma et al [12] and Forzieri et al [20] used a fusion of airborne spectral and altimetry data sets to estimate roughness input parameters such as vegetation height and vegetation density, and subsequently used these data as input into a hydrodynamic model to compute flow resistance values of a local river floodplain. It is foreseen that in the forthcoming years it will become possible to upscale these approaches using spaceborne instruments, e.g., in combination with SAR data [21,76,77]. The advantages of relying on spaceborne optical data are ample; it offers a standardized, spatially-explicit and repeatable monitoring scheme that can cover complete river catchments with high spatial detail.…”

Section: Towards Spaceborne River Floodplain Monitoringmentioning

confidence: 99%

Mapping Vegetation Density in a Heterogeneous River Floodplain Ecosystem Using Pointable CHRIS/PROBA Data

2012

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River floodplains in the Netherlands serve as water storage areas, while they also have the function of nature rehabilitation areas. Floodplain vegetation is therefore subject to natural processes of vegetation succession. At the same time, vegetation encroachment obstructs the water flow into the floodplains and increases the flood risk for the hinterland. Spaceborne pointable imaging spectroscopy has the potential to quantify vegetation density on the basis of leaf area index (LAI) from a desired view zenith angle. In this respect, hyperspectral pointable CHRIS data were linked to the ray tracing canopy reflectance model FLIGHT to retrieve vegetation density estimates over a heterogeneous river floodplain. FLIGHT enables simulating top-of-canopy reflectance of vegetated surfaces either in turbid (e.g., grasslands) or in 3D (e.g., forests) mode. By inverting FLIGHT against CHRIS data, LAI was computed for three main classified vegetation types, 'herbaceous', 'shrubs' and 'forest', and for the CHRIS view zenith angles in nadir, backward (−36°) and forward (+36°) scatter direction. The −36° direction showed most LAI variability within the vegetation types and was best validated, closely followed by the nadir direction. The +36° direction led to poorest LAI retrievals. The class-based inversion process has been implemented into a GUI toolbox which would enable the river manager to generate LAI maps in a semiautomatic way.

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“…They also provide an important buffer between land and reef, as they filter land runoff and improve the quality of water. 4,5 Saltmarshes have been subject to extensive exploitation, modification, and destruction due to the effects of human activity. 6 A significant proportion of the Australian eastern coast has been developed since European settlement.…”

Section: Introductionmentioning

confidence: 99%

Improving image classification in a complex wetland ecosystem through image fusion techniques

Kumar¹,

Sinha²,

Taylor³

2014

J. Appl. Remote Sens

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The aim of this study was to evaluate the impact of image fusion techniques on vegetation classification accuracies in a complex wetland system. Fusion of panchromatic (PAN) and multispectral (MS) Quickbird satellite imagery was undertaken using four image fusion techniques: Brovey, hue-saturation-value (HSV), principal components (PC), and Gram-Schmidt (GS) spectral sharpening. These four fusion techniques were compared in terms of their mapping accuracy to a normal MS image using maximum-likelihood classification (MLC) and support vector machine (SVM) methods. Gram-Schmidt fusion technique yielded the highest overall accuracy and kappa value with both MLC (67.5% and 0.63, respectively) and SVM methods (73.3% and 0.68, respectively). This compared favorably with the accuracies achieved using the MS image. Overall, improvements of 4.1%, 3.6%, 5.8%, 5.4%, and 7.2% in overall accuracies were obtained in case of SVM over MLC for Brovey, HSV, GS, PC, and MS images, respectively. Visual and statistical analyses of the fused images showed that the Gram-Schmidt spectral sharpening technique preserved spectral quality much better than the principal component, Brovey, and HSV fused images. Other factors, such as the growth stage of species and the presence of extensive background water in many parts of the study area, had an impact on classification accuracies. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Estimating Flow Resistance of Wetlands Using SAR Images and Interaction Models

Cited by 15 publications

References 19 publications

Post-Fire Effects in Wetland Environments: Landscape Assessment of Plant Coverage and Soil Recovery in the Paraná River Delta Marshes, Argentina

Post-Fire Effects in Wetland Environments: Landscape Assessment of Plant Coverage and Soil Recovery in the Paraná River Delta Marshes, Argentina

Mapping Vegetation Density in a Heterogeneous River Floodplain Ecosystem Using Pointable CHRIS/PROBA Data

Improving image classification in a complex wetland ecosystem through image fusion techniques

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