Impact of Urban Land-Cover Classification on Groundwater Recharge Uncertainty

Ampe, Eva; Vanhamel, Iris; Salvadore, Elga; Dams, Jef; Bashir, Imtiaz; Demarchi, Luca; Chan, Jonathan Cheung-Wai; Sahli, Hichem; Canters, Frank; Batelaan, Okke

doi:10.1109/jstars.2012.2206573

Cited by 26 publications

(19 citation statements)

References 31 publications

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“…Attribute tables based on literature data are linked to the maps and transformed to distributed physical values via a GIS preprocessing step (Chormański and Michałowski, 2011). Several studies have demonstrated that WetSpa and its steady-state version WetSpass (Batelaan and De Smedt, 2007) are suited to integrate distributed remote sensing input data in the simulation of the hydrological processes (Poelmans et al, 2010;Dujardin et al, 2011;Ampe et al, 2012;Chormański, 2012;Demarchi et al, 2012;Dams et al, 2013).…”

Section: Hydrological Modelmentioning

confidence: 99%

Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Berezowski

Nossent

Chormański

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. As the availability of spatially distributed data sets for distributed rainfall-runoff modelling is strongly increasing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis method for spatial input data (snow cover fraction -SCF) for a distributed rainfall-runoff model to investigate when the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focussed on the relation between the SCF sensitivity and the physical and spatial parameters and processes of a distributed rainfall-runoff model. The methodology is tested for the Biebrza River catchment, Poland, for which a distributed WetSpa model is set up to simulate 2 years of daily runoff. The sensitivity analysis uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which employs different response functions for each spatial parameter representing a 4 × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by cooccurrence of different environmental factors such as geomorphology, soil texture, land use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for our spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model. The developed method can be easily applied to other models and other spatial data.

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Section: Hydrological Modelmentioning

confidence: 99%

Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Berezowski

Nossent

Chormański

et al. 2015

Hydrol. Earth Syst. Sci.

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“…And, for a specific hydrologic analysis, a study for the Woluwe catchment in Belgium [66] analyzed CHRIS-Probe imagery [67]. Researchers differentiated eight land cover classes-forest, agriculture and grassland, water, bare soil, construction site, white buildings, city buildup, and dark buildings.…”

Section: Hyperspectral Imagerymentioning

confidence: 99%

Land Use/Land Cover Monitoring and Geospatial Technologies: An Overview

Parece

Campbell

2015

The Handbook of Environmental Chemistry

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“…Our CRF model incorporates unary potential , local context potential ( is a neighboring region of ), and global pairwise potential . The energy distribution form of our CRF model is (2) The unary potential is computed independently for each pixel (patch) by a unary classifier that produces a distribution over the label assignment given image features. The unary classifier in our implementation incorporates backscattering intensity, texture, and the new supertexture, which are described in Section III.…”

Section: B Proposed Crf Modelmentioning

confidence: 99%

“…S YNTHETIC aperture radar (SAR) image classification or land-use/land-cover mapping plays an important role in many and diverse SAR applications [1], such as environment and resources monitoring [2], hydrology and agriculture modeling [3], and urban planning [4]. The availability of high-resolution (HR) SAR images from TerraSAR-X, COSMO-SkyMed, Radarsat-2, etc., has opened new opportunities in land-cover classification [1].…”

Section: Introductionmentioning

confidence: 99%

SAR Image Classification Based on CRFs With Integration of Local Label Context and Pairwise Label Compatibility

Ding

Wang

2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Context information plays a critical role in SAR image classification, as high-resolution SAR data provides more information on scene context and visual structures. This paper presents a novel classification method for SAR images based on conditional random fields (CRFs) with integration of low-level features, local label context, and pairwise label compatibility. First, we extract the low-level features used in the SVM-based unary classifier for SAR images. The supertexture is newly introduced as one of the low-level features to model the texture context between image patches. Then, we describe the context information, including local context potential and pairwise potential. Incorporation of the category context helps to resolve the ambiguities of the unary classifier. The performance of our approach in both accuracy and visual appearance for high-resolution SAR image classification is proved in the experiments.Index Terms-Conditional random fields (CRFs), context information, image classification, land cover, SAR.

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Impact of Urban Land-Cover Classification on Groundwater Recharge Uncertainty

Cited by 26 publications

References 31 publications

Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Land Use/Land Cover Monitoring and Geospatial Technologies: An Overview

SAR Image Classification Based on CRFs With Integration of Local Label Context and Pairwise Label Compatibility

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