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Scaling of impervious surface area and vegetation as indicators to urban land surface temperature using satellite data

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Vegetation and impervious surface as indicators of urban land surface temperature (LST) across a spatial resolution from 30 to 960 m were investigated in this study. Enhanced thematic mapper plus (ETM+) data were used to retrieve LST in Nanjing, China. A land cover map was generated using a decision tree method from IKONOS imagery. Taking the normalized difference vegetation index (NDVI) and percent vegetation area (V) to present vegetated cover, and the normalized difference building index (NDBI) and percent impervious surface area (I) to present impervious surface, the correlation coefficients and linear regression models between the LST and the indicators were simulated. Comparison results indicated that vegetation had stronger correlation with the LST than the impervious surface at 30 and 60 m, a similar magnitude of correlation at 120 and 240 m, and a much lower correlation at 480 and 960 m. In total, the impervious surface area was a slightly better indicator to the LST than the vegetation because all of the correlation coefficients were relatively high (>0.5000) across the spatial resolution from 30 to 960 m. The indicators of LST, V and I are slightly better than the NDVI and NDBI, respectively, based on the correlation coefficients between the LST and the four indices. The strongest correlation of the LST and vegetation at the resolution of 120 m, and the strongest correlation between the LST and impervious surface at 120, 480 and 960 m, denoted the operational scales of LST variations.
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Document Type: Research Article

Affiliations: 1: Institute of Remote Sensing & Information System Application, Zhejiang University, Hangzhou 310029, China,International Institute for Earth System Science, Nanjing University, Nanjing 210093, China 2: School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China 3: Institute of Remote Sensing & Information System Application, Zhejiang University, Hangzhou 310029, China 4: Department of Geography, Texas A & M University, USA

Publication date: January 1, 2009

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