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Research Area 9 - Assessing urban heat island and its spatial and temporal patterns of Beijing, Shanghai, and Nanjing, China using thermal infrared of Landsat data

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Global trends in urbanization have witnessed a growing concentration of population in urban areas. In 2008 more than half of the world’s population were urban dwellers and urban population is expected to reach 81% by 2030 (UNFPA 2007). In the last decades population growth and economic expansion have been the primary drivers of land use and land cover change (LULCC) worldwide, especially in the developing countries. Since 1978, when China initiated economic reform and an opendoor policy, rapid land use and land cover change have taken place in most of its territory. The increase in industrialization and urbanization has resulted in the loss of a significant amount of agricultural land. Urbanization has also caused natural vegetation to be removed and replaced by impervious surfaces, such as metal, asphalt, and concrete. Research conducted by Li et al. (2009) has shown that both the extent and magnitude of urban heat island in Shanghai have undergone a significant increase during 1997 and 2004. Urban heat island (UHI) refers to the phenomenon of higher atmospheric and land surface temperatures (LST) occurring in urban areas than in the surrounding rural areas (Voogt and Oke 2003). Many of the earlier studies investigated the relative warmth of cities by measuring the air temperature employing landbased weather stations. Other studies have used indices such as normalized difference vegetation index (NDVI), Normalized Difference Built-up Index (NDBI), ratio vegetation index (RVI), difference vegetation index (DVI), and perpendicular vegetation index (PVI) to characterize the relationships between land use/cover types and UHI (Zha et al. 2003, Chen et al. 2006). The main objective of this study is to expose REU students to different methods of quantifying urban growth in selected cities in China (Beijing, Nanjing and Shanghai) using remote sensing, GIS, and GPS technologies. 

The specific objectives are to (1) examine the temporal and spatial pattern of LULCC of the study area, and (2) analyze land surface temperature differences in urban areas among different land cover types using thermal infrared (TIR) band of Landsat TM and ETM+ imageries (3) investigate the impact of LULCC on the intensity and spatial pattern of LST related to UHI of selected cities in China. Multi-temporal Landsat Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM+) imageries will be used to extract LULCC information and percent impervious surface of the study area. The thermal band (10.4-12.5 µm) of the same imageries will be used to derive LST to study the phenomena of UHI. To characterize the LST, converting radiance into surface temperature is essential. The procedures and algorithms described by Weng et al. (2004) and NASA (2010) will be adopted to convert the digital number (DN) of TIR band into radiant temperatures. In order to study the relationships between land use/cover types and UHI, NDVI and NDBI (Zha et al. 2003) will be extracted using band 1, 7, and 8 of the multi-temporal imageries. Additional information from literature reviews of past researches’ of the same study area will also be utilized as a reference. The image processing and feature extraction process will be conducted in collaboration with our partners at NFU, Shanghai Research Institute of Landscape Gardening, and AAMU. Outcomes of this research area will include accurate measurement of LUCCC, and extraction of LST from remotely sensed data using object-based classification algorithm. This will also enhance the effectiveness of urban landscape monitoring efforts and improve the understanding of links between land-use activity and environmental loading in the study area. The information derived from this research by the REU students will also be utilized to assess the effect of urbanization on the parameters described in some of the other research areas described here. REU students will be exposed to the spatial analysis and image classification related research. It is also expected that at least two presentations on the research findings will be given by REU participants at professional conferences.​