Elga Salvadore

Impact of Urban Land-Cover Classification on Groundwater Recharge Uncertainty

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.

A Wavelet Approach for Estimating Chlorophyll-A From Inland Waters With Reflectance Spectroscopy

This letter presents an application of continuous wavelet analysis, providing a new semi-empirical approach to estimate Chlorophyll-a (Chl-a) in optically complex inland waters. Traditionally spectral narrow band ratios have been used to quantify key diagnostic features in the remote sensing signal to estimate concentrations of optically active water quality constituents. However, they cannot cope easily with shifts in reflectance features caused by multiple interactions between variable absorption and backscattering effects that typically occur in optically complex waters. We use continuous wavelet analysis to detect Chl-a features at various wavelengths and frequency scales. Using the wavelet decomposition, we build a 2-D correlation scalogram between in situ pond reflectance spectra and in situ Chl-a concentration. By isolating the most informative wavelet regions via thresholding, we could relate all five regions to known inherent optical properties. We select the optimal feature per region and compare them to three well-known narrow band ratio models. For this experimental application, the wavelet features outperform the NIR-red models, while fluorescence line height (FLH) yield comparable results. Because wavelets analyze the signal at different scales and synthesize information across bands, we hypothesize that the wavelet features are less sensitive to confounding factors, such as instrument noise, colored dissolved organic matter, and suspended matter.