Scaling Effect on the Relationship between Landscape Pattern and Land Surface Temperature: A Case Study of Indianapolis, United States

The objective of this paper is to examine the scaling-up effect on the relationship between landscape patterns and land surface temperatures based on a case study of Indianapolis, United States. The integration of remote sensing, GIS, and landscape ecology methods was used in this study. Four TERRA ASTER images were acquired to derive the land-use and land-cover (LULC) patterns and land surface temperatures (LST) in different seasons. Each LULC and LST image was resampled to eight spatial scales: 15, 30, 60, 90, 120, 250, 500, and 1,000 m. The scaling-up effect on the spatial and ecological characteristics of landscape patterns and LSTs were examined by the use of landscape metrics. Optimal spatial resolutions were determined on the basis of the minimum distance in the landscape metric spaces. The results show that the patch percentages of LULC and LST patches were not strongly affected by the scaling-up process in different seasons. The patch densities and landscape shape indices and LST patches kept decreasing across the scales without distinct seasonal differences. Thirty meters was found to be the optimal resolution in the study of the relationship between urban LULC and LST classes. Ninety meters was found to be the optimal spatial resolution for assessing the landscape-level relationship between LULC and LST patterns. This paper may provide useful information for urban planners and environmental practitioners to manage urban landscapes and urban thermal environments as a result of urbanization. Introduction Scale influences the examination of the landscape patterns in a region. The change of scale is relevant to the issues of data aggregation, information transfer, and the identification of appropriate scales for analysis (Krönert et al., 2001; Wu and Hobbs, 2002). Extrapolation of signals across spatial scales is a necessary research task (Turner, 1990). It is believed that spatial characteristics could be transferred Scaling Effect on the Relationship between Landscape Pattern and Land Surface Temperature: A Case Study of Indianapolis, United States Hua Liu and Qihao Weng across scales under specific conditions (Allen et al., 1984). Therefore, we need to know how the information is transferred from a fine scale to a broad scale (Krönert et al., 2001). In remote sensing studies, choosing various satellite sensors may result in different research results, since they usually have different spatial resolutions. Therefore, it is significant to examine the changes in spatial configuration of any landscape pattern as a result of different spatial resolutions of satellite imagery. Moreover, it is always necessary to find the optimal scale(s) for the study in which the environmental processes operate. Land-use and land-cover (LULC) pattern is regarded as an important determinant of ecosystem function, and can be considered as the representative of landscape pattern in an in situ area (Bain and Brush, 2004). LULC categories are linked to distinct behaviors of urban thermal environment (Voogt and Oke, 1997). Solar radiation and land surface temperature (LST) are important parameters for analysis of urban thermal behavior (Aguiar et al., 2002). LST reflects the result of surface-atmosphere interactions and energy fluxes between the ground and the atmosphere on the Earth (Wan and Dozier, 1996). Research is therefore needed to examine the relationship between LULC and LST (Weng et al., 2004; Weng et al., 2006). Furthermore, seasonal changes may have major impact on soil moisture and tree canopy components, and thus on the LULC-LST relationship. However, little research has so far been done to examine the scaling-up effect on the relationship between landscape pattern and LST. Landscape/LULC patches in a region may have different sizes, shapes and spatial arrangements, which contribute to the spatial heterogeneities of the landscape. In order to understand the dynamics of patterns and processes and their interactions in a heterogeneous landscape (such as the urban areas commented on by the authors), one must be able to accurately quantify the spatial pattern and its temporal changes of the landscape (Wu et al., 2000). In recent years, a series of landscape metrics have been developed to characterize the spatial patterns of landscapes and to compare ecological quality across the landscapes (McGarigal and Marks, 1995; Gustafson, 1998). However, there is lack of PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING March 2009 291 Hua Liu is with the Department of Political Science and Geography, Old Dominion University, Norfolk, VA, 23529, and formerly with the Center for Urban and Environmental Change, Department of Geography, Geology, and Anthropology, Indiana State University, Terre Haute, IN 47809. Qihao Weng is with the Center for Urban and Environmental Change, Department of Geography, Geology, and Anthropology, Indiana State University, Terre Haute, IN 47809 ([email protected]). Photogrammetric Engineering & Remote Sensing Vol. 75, No. 3, March 2009, pp. 291–304. 0099-1112/09/7503–0291/$3.00/0 © 2009 American Society for Photogrammetry and Remote Sensing 291-304_07-029.qxd 2/9/09 4:12 PM Page 291