Impact of Urban Land-Cover Classification on Groundwater Recharge Uncertainty
Ampe, Eva × Vanhamel, Iris Salvadore, Elga Dams, Jef Bashir, Imtiaz Demarchi, Luca Cheung-Wai Chan, Jonathan Sahli, Hichem Canters, Frank Batelaan, Okke #
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing vol:5 issue:6 pages:1859-1867
Objective and detailed mapping of urban land-cover
types over large areas is important for hydrological modelling,as most man-made land-cover consist of sealed surfaces which strongly reduce groundwater recharge. Moreover, impervious surfaces are the predominant type in urbanized areas and can lead to increased surface runoff. Classification of man-made objects in urbanized areas is not straightforward due to similarity in spectral properties. This study examines the use of hyperspectral
CHRIS-Proba images for complex urban land-cover classification of the Woluwe River catchment, Brussels, Belgium. Two methods are compared: 1) a multiscale region-based classification approach, which is based on a causal Markovian model being defined on a Multiscale Region Adjacency Tree and a set of nonparametric dissimilarity measures; and 2) a pixel based classification method with a Mahalanobis distance classifier. Multiscale region-based classification results in a Kappa value of 0.95 while pixel-based classification has a slightly lower Kappa
value of 0.92. The impact of the classification method on the hydrology is estimated with the application of the WetSpass physically-based distributed water balance model. The model uncertainty is assessed with the use of a Monte Carlo simulation. Model results show that the region-based classification yields to a higher yearly recharge than the pixel-based classification. The overall uncertainty, quantified by the Monte Carlo method is lower for the region-based classification than for the pixel-based
classification. The presented study indicates that the selection of the classification technique is of critical importance for the outcome of hydrological models.