Urban Surface Biophysical Descriptors and Land Surface Temperature Variations

In remote sensing studies of land surface temperatures (LST), thematic land-use and land-cover (LULC) data are frequently employed for simple correlation analyses between LULC types and their thermal signatures. Development of quantitative surface descriptors could improve our capabilities for modeling urban thermal landscapes and advance urban climate research. This study developed an analytical procedure based upon a spectral unmixing model for characterizing and quantifying the urban landscape in Indianapolis, Indiana. A Landsat Enhanced Thematic Mapper Plus image of the study area, acquired on 22 June 2002, was spectrally unmixed into four fraction endmembers, namely, green vegetation, soil, high and low albedo. Impervious surface was then computed from the high and low albedo images. A hybrid classification procedure was developed to classify the fraction images into seven land-use and land-cover classes. Next, pixel-based LST measurements were related to urban surface biophysical descriptors derived from spectral mixture analysis (SMA). Correlation analyses were conducted to investigate land-cover based relationships between LST and impervious surface and green vegetation fractions for an analysis of the causes of LST variations. Results indicate that fraction images derived from SMA were effective for quantifying the urban morphology and for providing reliable measurements of biophysical variables such as vegetation abundance, soil, and impervious surface. An examination of LST variations within census block groups and their relationships with the compositions of LULC types, biophysical descriptors, and other relevant spatial data shows that LST possessed a weaker relation with the LULC compositions than with other variables (including urban biophysical descriptors, remote sensing biophysical variables, GIS-based impervious surface variables, and population density). Further research should be directed to refine spectral mixture modeling. The use of multi-temporal remote sensing data for urban time-space modeling and comparison of urban morphology in different geographical settings are also feasible. Introduction The receipt and loss of radiation of urban surfaces correspond closely to the distribution of land-use and land-cover (LULC) characteristics. Because of this correspondence, there Urban Surface Biophysical Descriptors and Land Surface Temperature Variations Qihao Weng, Dengsheng Lu, and Bingqing Liang has been a tendency to use thematic LULC data, not quantitative surface descriptors, to describe urban thermal landscapes (Voogt and Oke, 2003). This trend of qualitative description of thermal patterns and simple correlations between LULC types and their thermal signatures has slowed down the development of remote sensing of land surface temperature (LST) and thus surface temperature heat islands (Voogt and Oke, 2003). Clapham (2003) suggests using of a continuum-based classification for satellite imagery, which aims to provide continuous data for the “functional classes.” The idea of a continuum-based classification has long been pursued in urban landscape analysis. One of the major contributions is Ridd’s (1995) vegetation-impervious surfacesoil (V-I-S) model for characterizing urban environments. This model assumes that urban land-cover is a linear combination of three biophysical components: vegetation, impervious surface, and soil, and has recently been successfully implemented by using the technique of spectral mixture analysis (Ward et al., 2000; Madhavan et al., 2001; Rashed et al., 2001; Small, 2001; Phinn et al., 2002; Wu and Murray, 2003, Lu and Weng, 2004). The Ridd model provides the potential for a link between remote sensingderived urban biophysical components and LST, and may be applied to establish parameters for describing urban construction materials and fabrics to improve our understanding of urban surface energy budget and heat islands. The focus of this research is placed on the development of a methodology to examine the interplay between LST and urban morphology. A Landsat ETM image of 2000 that covers the City of Indianapolis, Indiana was used in conjunction with other types of spatial data for the analysis. Specific objectives of this research are: (a) to employ spectral mixture modeling to derive urban surface biophysical attributes, and to apply spectrally unmixed results to characterize the urban landscape; (b) to analyze the causes of LST variations, which were derived from Landsat thermal infrared data by linking LST with remotely sensed urban surface descriptors; and (c) to examine the spatial variations of LST at the census block group level, so that implications for urban planning may be explored. The City of Indianapolis, located in Marion County, Indiana (Figure 1), is the nation’s twelfth largest city, with approximately 0.8 million population (over 1.6 million in the metropolitan area). Situated in the middle of the country, Indianapolis possesses several other advantages that make it an appropriate choice. It has a single central city, and other large urban areas in the vicinity have not PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING Novembe r 2006 1275 Qihao Weng and Bingqing Liang are with the Department of Geography, Geology, and Anthropology, Indiana State University, Terre Haute, IN 47809 ([email protected]). Dengsheng Lu is with the Center for the Study of Institutions, Population, and Environmental Change, Indiana University, Bloomington, IN 47408 ([email protected]). Photogrammetric Engineering & Remote Sensing Vol. 72, No. 11, November 2006, pp. 1275–1286. 0099-1112/06/7211–1275/$3.00/0 © 2006 American Society for Photogrammetry and Remote Sensing 05-030 10/7/06 12:40 PM Page 1275