Urban heat island monitoring and analysis using a non-parametric model: A case study of Indianapolis |
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| Authors: | Umamaheshwaran Rajasekar Qihao Weng |
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| Institution: | 1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;2. University of Chinese Academy of Sciences, Beijing 100101, China;3. Sanya Institute of Remote Sensing, Sanya 572029, China;4. College of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China;1. Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China;2. Department of Engineering, University of Perugia, via G. Duranti 93, 06125 Perugia, Italy;1. College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China;2. National Engineering Laboratory of Forest Ecology and Applied Technology for Southern China, and Central South University of Forest and Technology, Changsha 410004, China;3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China |
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| Abstract: | A procedure for the monitoring an urban heat island (UHI) was developed and tested over a selected location in the Midwestern United States. Nine counties in central Indiana were selected and their UHI patterns were modeled. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) images taken in 2005 were used for the research. The images were sorted based on cloud cover over the study area. The resulting 94 day and night images were used for the modeling. The technique of process convolution was then applied to the images in order to characterize the UHIs. This process helped to characterize the LST data into a continuous surface and the UHI data into a series of Gaussian functions. The diurnal temperature profiles and UHI intensity attributes (minimum, maximum and magnitude) of the characterized images were analyzed for variations. Skin temperatures within any given image varied between 2–15 °C and 2–8 °C for the day and night images, respectively. The magnitude of the UHI varied from 1–5 °C and 1–3 °C over the daytime and nighttime images, respectively. Three dimensional (3-D) models of the day and night images were generated and visually explored for patterns through animation. A strong and clearly evident UHI was identified extending north of Marion County well into Hamilton County. This information coincides with the development and expansion of northern Marion County during the past few years in contrast to the southern part. To further explore these results, an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 2004 land use land cover (LULC) dataset was analyzed with respect to the characterized UHI. The areas with maximum heat signatures were found to have a strong correlation with impervious surfaces. The entire process of information extraction was automated in order to facilitate the mining of UHI patterns at a global scale. This research has proved to be promising approach for the modeling and mining of UHIs from large amount of remote sensing images. Furthermore, this research also aids in 3-D diachronic analysis. |
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