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An advantage of this CART-based approach over other ISP
estimation approaches based on spectral clustering (e.g.,
spectral mixture analysis) is their applicability to use multi
source and multi-sensor remote sensing data which are
characterized with different statistical distribution and different
physical feature. In addition, it permits the exploration of large-
area impervious surface information at sup-pixel level in a
repeatable and objective way. In this study, the process of
mapping impervious surface using the CART-based approach
involves the following steps (as shown in Figure 3): 1)
development of training/test data using 33cm-resolution digital
aerial CIR photography, 2) design of predictive variables,
establishment and assessment of final regression tree modelling,
3) spatial extendibility of ISP prediction modelling with
middle-resolution remote sensing dataset, and 4) accuracy
assessment of ISP mapping.
33-cm resolution CIR
aerial images
CART-based classification
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ISP estimation (0.33m)
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Middle-resolution
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SPOT-4.
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modeling using Regression Tree
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(training data and features)
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ISP prediction
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ISP mapping based on
prediction model
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mapping
Accuracy assessment
Figure 3 Block scheme of impervious surface mapping with
remote sensing data and a regression tree model
3.1 Development of ISP training/test data
Successful ISP prediction modelling using CART algorithm
relies on the quality of training-test data. In this study, training
and test data were derived from four subset patches of the
33cm-resolution CIR aerial image. These image subsets were
approximately 2000 x 1500 m each and were visually selected
to avoid areas where land cover changes occurred among the
CIR aerial images, the SPOT imagery and the InSAR products.
Initially, the selected CIR aerial images were classified using
the CART machine learning algorithm. Each pixel was
classified as one of five land cover classes: impervious surface,
vegetated areas, bared soil, water, and shadow. The
classifications were further modified by screen digitizing and
recoding to reduce misclassification. Validated by the in situ
data, the classification results archived overall accuracy 87.56%
and Kappa coefficient 0.811. Once the final classification
results were obtained, all 33-cm resolution pixels mapped as
impervious surface were tallied using a 10 x 10 m grid
geographically aligned with the middle-resolution data pixels
(10m resolution corresponding to SPOT and InSAR data in this
study) to compute percent impervious surfaces. This resulted in
10-m resolution raster image of percent imperviousness. It is
noted that the shadow class was excluded from the calculation
of imperviousness in this process since the class could not be
unambiguously merged with any single land cover class.
Figure5 illustrates one of the four CIR aerial image subsets and
its ISP image with 10-m spatial resolution.
3.2 ISP prediction model building and ISP mapping
An ISP prediction model based on the CART regression tree
algorithm was developed and calibrated by using the
training/test data above obtained from high-resolution CIR
aerial image as dependent variable (target variable). In addition,
the independent variables (predictor variable) were derived
from the six layer including three SPOT multi-spectral images
and three InSAR feature images. In this study, 2000 training
samples and 2000 testing samples were randomly selected from
the layers of target and predictor variables. It is worth nothing
that the training samples were independent to the test samples.
The training/test samples were utilized to build and improve the
rule-based ISP prediction model by using the CART regression
tree algorithm. The prediction model was composed of rule sets
where each rule was defined by one or more conditions and
corresponding confidence-level under which a multivariate
linear regression model was established. Once the final ISP
prediction model was built, it was applied to all pixels of the six
middle-resolution remote sensing dataset to map percent
impervious surfaces.
3.3 Accuracy assessment of ISP estimation
For accuracy assessment of ISP estimate, the reference data
should ideally be collected from the field work based on a
statistical sampling design. Due to time and resource constraints,
the 10-m resolution ISP derived from the CIR aerial image was
Figure 4 the CIR aerial imagery with 0.33m resolution and estimated ISP with 10m resolution from CIR imagery
used for this purpose. To ensure the validity of the assessment,
