A MULTI-RESOLUTION STRATEGY FOR MAPPING SEALED SURFACES FOR
BRUSSELS AND SURROUNDINGS
T. De Roeck, T. Van de Voorde, F. Canters
Cartography and GIS Unit, Geography department Vrije
Universiteit Brussel, Brussels, Belgium
tim.de.roeck@vub.ac.be
Sealed surfaces
prevent water from infiltrating into the soil. They have a negative impact on
environmental conditions in urbanized watersheds. Mapping and monitoring the
extent of sealed surfaces has therefore become important in many applications.
A promising technique to obtain information about the distribution of sealed
surfaces is to extract its occurrence directly from satellite imagery. To
exploit the advantages of both medium and high-resolution data one can make use
of a multi-resolution strategy based on sub-pixel classification. In this
study, alternative sub-pixel classification models were developed and tested on
a Landsat ETM+ image for Brussels and surroundings. Training and validation of
the models was accomplished by extracting detailed information about land cover
from a classified Ikonos image covering part of the area.
When
classifying multispectral high-resolution satellite data (Ikonos, Quickbird) of
urban areas, traditional per-pixel classification methods do not produce
satisfying results. Besides the appearance of structural clutter in the
classified image, certain land-cover classes that are important for mapping
sealed surfaces are difficult to distinguish due to the limited spectral
resolution of the high-resolution imagery. Object-oriented classification is a
promising technique to tackle these problems. Instead of single pixels, this
method classifies image objects, obtained by image segmentation, making use of
information not only referring to the spectral characteristics of the object, yet
also to its texture, context and shape. In this study a hierarchical,
rule-based classification approach was applied to produce high-quality
reference data for the training and validation of sub-pixel classification
models. At each step in the hierarchical procedure a set of rules was defined
to separate one land-cover class from the remaining classes, using an optimal
subset of object-based features.
The
high-resolution classification result obtained this way was used as a reference
for training and validation of sub-pixel classification models. By counting the
amount of sealed Ikonos pixels present within the pixels of the co-registered
Landsat image, the proportion of sealed surfaces was obtained for a random
sample of medium-resolution pixels. Alternative models were developed to map
the multi-spectral signature of each pixel in the training set onto the
proportion of sealed surfaces at the 30 meter resolution. The accuracy of the
models was assessed by comparing sealed surface proportions produced for an
independent set of Landsat validation pixels with the high-resolution reference
classification. An average proportional error of about 10% was obtained, which
is comparable to the results found in similar studies.