heat island phenomenon always needs accurate data on urban
built-up areas such as the size, shape, and spatial context.
Therefore, a technique is required to quickly reveal the data.
Timely availability of the data is of great importance for
urban planners and decision makers. Fortunately, satellite
remote sensing technology offers considerable promise to
meet this requirement. With different spatial and spectral
resolutions, the satellite observations can provide globally
consistent and repetitive measurements of the Earth’s surface
conditions. The objective of this study is to develop a new
technique to extract urban built-up land features from Landsat
Thematic Mapper (TM) and Enhanced Thematic Mapper Plus
(ETM) imagery. This would allow urban planner and decision
makers to timely understand and evaluate urban growth with
related land-cover changes and be aware of the sustainable
usage of the invaluable nature lands.
Many researchers have made use of remote sensing
imagery to discriminate urban lands from non-urban lands.
A popular method for the definitions of urban areas started
with conventional multispectral classification. However, this
may not produce satisfactory accuracy, normally less than
80 percent, due to spectral confusion of the heterogeneous
urban built-up land class. Therefore, many studies have not
only used a single classification method to extract the urban
built-up lands but also combined different methods to
improve the extraction. Masek et al. (2000) identified urban
built-up areas of the Washington D.C. metropolitan area from
multi-date Landsat images based on an NDVI-differencing
approach with the assistance of an unsupervised classification
and achieved overall accuracy of 85 percent. Xu (2002)
extracted urban built-up lands of Fuqing City in southeastern
China by a combination of signature analysis and supervised
classification. Based on the analysis of spectral response
differences between built land and various non-built classes
within multispectral bands, urban land information was
extracted and then integrated with a classification layer to
generate a final product with improved accuracy. Zhang et al.
(2002) integrated a road density layer with spectral bands for
the post-classification change detection of Beijing, China.
This greatly reduced spectral confusion and increased accuracy
of the change detection. Zha et al. (2003) proposed the
Normalized Difference Built-up Index using TM4 and TM5 and
applied it in extracting urban areas of Najing City of China