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Improving Distributed Runoff Prediction in Urbanized Catchments with Remote Sensing based Estimates of Impervious Surface Cover

View Article: PubMed Central - PubMed

ABSTRACT

The amount and intensity of runoff on catchment scale are strongly determined by the presence of impervious land-cover types, which are the predominant cover types in urbanized areas. This paper examines the impact of different methods for estimating impervious surface cover on the prediction of peak discharges, as determined by a fully distributed rainfall-runoff model (WetSpa), for the upper part of the Woluwe River catchment in the southeastern part of Brussels. The study shows that detailed information on the spatial distribution of impervious surfaces, as obtained from remotely sensed data, produces substantially different estimates of peak discharges than traditional approaches based on expert judgment of average imperviousness for different types of urban land use. The study also demonstrates that sub-pixel estimation of imperviousness may be a useful alternative for more expensive high-resolution mapping for rainfall-runoff modelling at catchment scale.

No MeSH data available.


Scatter plot of NDVI of a random sample of ETM+ pixels and the average NDVI of constituent Ikonos pixels before (left) and after temporal filtering (right).
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f3-sensors-08-00910: Scatter plot of NDVI of a random sample of ETM+ pixels and the average NDVI of constituent Ikonos pixels before (left) and after temporal filtering (right).

Mentions: A clear relationship was observed between ETM+ NDVI values and Ikonos mean NDVI values (Figure 3, left). Two clusters of respectively high and low NDVI values can be seen in the graph. They represent vegetation and non vegetation end-members. Some pixels, however, deviate from the observed overall trend: they have a substantially lower NDVI value in the ETM+ image than in the Ikonos image. These pixels represent pixels that are covered with vegetation in the Ikonos image of June, but were bare eight months before on the Landsat ETM+ image of October. Other pixels have a higher NDVI on the ETM+ image. These pixels represent agricultural fields that carry crops with a growing season that starts later in the year.


Improving Distributed Runoff Prediction in Urbanized Catchments with Remote Sensing based Estimates of Impervious Surface Cover
Scatter plot of NDVI of a random sample of ETM+ pixels and the average NDVI of constituent Ikonos pixels before (left) and after temporal filtering (right).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3927506&req=5

f3-sensors-08-00910: Scatter plot of NDVI of a random sample of ETM+ pixels and the average NDVI of constituent Ikonos pixels before (left) and after temporal filtering (right).
Mentions: A clear relationship was observed between ETM+ NDVI values and Ikonos mean NDVI values (Figure 3, left). Two clusters of respectively high and low NDVI values can be seen in the graph. They represent vegetation and non vegetation end-members. Some pixels, however, deviate from the observed overall trend: they have a substantially lower NDVI value in the ETM+ image than in the Ikonos image. These pixels represent pixels that are covered with vegetation in the Ikonos image of June, but were bare eight months before on the Landsat ETM+ image of October. Other pixels have a higher NDVI on the ETM+ image. These pixels represent agricultural fields that carry crops with a growing season that starts later in the year.

View Article: PubMed Central - PubMed

ABSTRACT

The amount and intensity of runoff on catchment scale are strongly determined by the presence of impervious land-cover types, which are the predominant cover types in urbanized areas. This paper examines the impact of different methods for estimating impervious surface cover on the prediction of peak discharges, as determined by a fully distributed rainfall-runoff model (WetSpa), for the upper part of the Woluwe River catchment in the southeastern part of Brussels. The study shows that detailed information on the spatial distribution of impervious surfaces, as obtained from remotely sensed data, produces substantially different estimates of peak discharges than traditional approaches based on expert judgment of average imperviousness for different types of urban land use. The study also demonstrates that sub-pixel estimation of imperviousness may be a useful alternative for more expensive high-resolution mapping for rainfall-runoff modelling at catchment scale.

No MeSH data available.