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Mapping without the sun
Zhang, Jixian

Liming Jiang' 2 *, Hui Lin 1 , Mingsheng Liao 2 , Limin Yang 1,3
'institute of Space and Earth Information Science, Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China
2 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan, China
3 Raytheon Information Technology and Scientific Services (ITSS), EROS Data Center, U.S. Geological Survey, Sioux
Falls, SD 57198, U.S.A.
KEY WORDS: Urban impervious surface, Classification and regression tree (CART); ERS-1/2 InSAR data; SPOT5 HRG imagery;
Data fusion, Hong Kong
A wide range of urban ecosystem studies, including urban hydrology, urban climate, land use planning and resource management
require current and accurate geospatial data of urban impervious surfaces. In this study, the potential of the synergistic use of optical
and InSAR data in urban impervious surface mapping was investigated. A case study in Hong Kong was conducted for this purpose
by applying a CART-based ISP estimate approach to the SPOT 5 HRG imagery and the ERS-1/2 SLC SAR data. Validated by
reference data derived from the high-resolution CIR aerial photographs, our results showed that the addition of InSAR feature
information can improve the performance of SPOT-derived ISP estimation, average error (AE) value decreased from 15.51% to
12.93% and correlation coefficient (R2) value increased from 0.71 to 0.77.
Impervious surfaces are usually defined as anthropogenic
features through which water cannot infiltrate the soil, typically
including buildings, roads, parking lots, sidewalks, and other
built surfaces. Due to the close correlation with the spatial
extent and intensity of urban development, impervious surface
cover has been recently recognized as a key environmental
indicator in assessing urban ecological condition and utilized to
investigate urban hydrology, urban climate, land use planning,
and resource management(Schueler 1994; Arnold and Gibbons
1996; Brabec 2002).
Over the past decade, extensive research efforts have been
carried out to map impervious surfaces cost-effectively with
satellite remote sensing data, especially multi-spectral optical
images (e.g. Landsat TM/ETM+ or SPOT imagery). However,
an accurate representation of impervious surface is still a
challenge using these middle-resolution optical remote sensing
data, because of the complexity of urban/suburban landscapes
and the spectral confusions among different land-use/cover
types (such as between barren land and parking lots)(Yang,
Huang et al. 2003). The spectral confusion as well as the
presence of mixed pixel may result in an overestimation of
impervious surface distribution in the less-developed areas, but
underestimation in the well-developed areas(Yang 2003; Wu
2004; Lu and Weng 2006). Unlike optical images that represent
the spectral reflectivity of the targets illuminated by sun light,
Synthetic Aperture Radar (SAR) images are very sensitive to
the surface roughness, shape, structure, dielectric properties of
the illuminated ground features and can provide information
complementary to optical data(Henderson and Xia.Z 1997). In
particular, several recent study progresses in radar remote
sensing have demonstrated that the use of feature images (e.g.
coherence, average intensity and intensity temporal change)
derived from SAR interferometry (InSAR) pairs can improve
the capability to distinguish natural features with man-made
features(Bruzzone, Marconcini et al. 2004; Liao 2007).
The main aim of this study is to explore potentials of the
synergistic use of optical and radar remote sensing data in
mapping impervious surface cover. In particular, the CART-
based approach was adopted to quantify urban impervious
surfaces as a continuous variable (impervious surface
percentage, ISP) by fusion of multi-sensor and multi-source
datasets. The approach produces a rule-based model for
prediction of ISP based on training data, and can allow
impervious surface cover to be mapped at sub-pixel level of
medium remote sensing data. A case study has been conducted
for impervious surface mapping in Hong Kong by using the
combined SPOT 5 HRG imagery and ERS-1/2 InSAR data.
Results show that the fusion of the optical and InSAR data can
significantly improve the performance of ISP estimation.
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Figure 1. Location of the study area in Hong Kong
* Corresponding author: Liming Jiang. Email: JIANGLIMING@CUHK.EDU.HK