Introduction
Urban spatial areas have expanded in an accelerated speed during the last five decades, and rates of urban population growth are higher than the overall growth in most countries because urban areas are the locus of economic activity and transportation nodes (Masek et al., 2000). Expanded urbanized areas encroached on surrounding valuable natural lands such as paddy fields, forestlands, or wetlands (Xu et al.,2000). Urban areas are dominated by built-up lands with impervious surfaces, and therefore the conversion of the nature lands into these impervious built-up lands may have significant impacts on the ecosystem, hydrologic system, biodiversity, and local climate which can result in the negative aspects such as the urban heat island phenomenon. The study of urban spatial expansion and the resultant urbanheat 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