Spatio-Temporal Features of Urban Heat Island and Its Relationship with Land Use/Cover in Mountainous City: A Case Study in Chongqing

Chunxia Liu, Yuechen Li
2018 Sustainability  
The urban heat island (UHI) becomes more and more serious with the acceleration of urbanization. Many researchers have shown interest in studying the UHI by using remote sensing data, but these studies rarely examine the mountainous cities. Studies on UHI in mountainous cities often used empirical parameters to estimate the land surface temperature (LST), and lacked satellite-ground synchronous experiments to test the accuracy. This paper revised the parameters in the mono-window algorithm used
more » ... ndow algorithm used to retrieve the LST according to the characteristics of mountainous cities. This study examined the spatial and temporal patterns of the UHI intensity in Chongqing, a typical mountainous city, and its relationship with land cover from 2007 to 2016 based on the Landsat 5 TM and Landsat 8 TIRS data and the improved method. The accuracy of the LST derivation increased by about 1 • C compared to the traditional method. The high LST areas increased and extended from the downtown to suburban area each year, but the rate of change decreased. The UHI is dramatically impacted by the rivers. There is a good relationship between the urban sprawl and the UHI. The LST was reduced by about 1 • C within a 300 m distance from large urban fringe green spaces. The urban landscape parks had a strong effect relieving the UHI at a 100 m distance. The LST was reduced by about 0.5 • C. This study greatly improves the accuracy of LST derivation, and provides reliable parameters for the UHI researched in mountainous cities. 2 of 15 urban expansion [7] . Ogashawara et al. found that the distribution of UHIs in São José dos Campos has expanded rapidly from 1986 to 2010. The correlations between the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) and temperature were low (<0.5); a moderate correlation was found between the Normalized Difference Built-up Index (NDBI) and temperature [8] . Many other researchers also have shown interest in estimating the magnitude of UHI, analyzing its spatio-temporal evolution features, understanding its implication with respect to a broad set of environmental factors, and looking for the measures to reduce its detrimental effects [9] [10] [11] [12] [13] [14] [15] [16] . These studies on the UHI phenomenon can be divided into two types: ground-based observed air temperature in urban and rural weather stations and remotely-sensed data-based land surface temperature [17] . It is well known that the air temperature defined UHI can be used to analyze the long-term trend, but considerable efforts must be made to correct air temperature biases when comparing UHI effects across different regions. It is difficult to analyze the UHI phenomenon in the regions where there are not enough weather stations. The remote sensing data with the advantages of large area, synchronization, and spatial coverage has become the effective tool for studying UHI phenomenon [18] [19] [20] [21] [22] . Rao studied the UHI effects by using thermal remote sensing data for the first time in 1972 [23]. Since then, many scientists turned to the use of remotely-sensed data for UHI effect analysis [8] . The remote sensing data, such as NOAA (National Oceanic and Atmospheric Administration), MODIS (Moderate Resolution Imaging Spectroradiometer), TM (Landsat Thematic Mapper), ETM+ (Enhanced Thematic Mapper Plus), etc., were widely used to examine the UHI effects [12, 18, 19] . Many algorithms for UHI based on remote sensing were also be developed by the scientists [24] . Among many UHI algorithms based on remote sensing, the mono-window algorithm, because it is easy and feasible, is widely used in the studies of UHI [9, 15, [22] [23] [24] [25] [26] . While these detailed studies provide an excellent basis for understanding UHI, there are also some limitations: (1) These studies rarely examined the mountainous cities; (2) Few studies on UHI in mountainous cities based on remote sensing data often used empirical parameters to estimate the land surface temperature (LST). It is therefore hampered by the lack of proper parameters to retrieve the LST in mountainous cities; (3) Many studies lacked satellite-ground synchronous experiment to test the accuracy. Chongqing, a typical mountainous city, is located at the valley between Mount Tongluo and Mount Zhongliang. The eco-environmental problems caused by UHI are becoming increasingly acute with the acceleration of urbanization. Ren found that the UHI intensity of Chongqing increased year by year. The UHI effect was stronger in winter and at night [27] . Li et al. indicated that the UHI was contemporaneous with the Urban Wet Island (UWI) in Chongqing. The vertical structure of UHI was shown as the city being covered by warm moist air at 200-300 m above the river level [28] . He et al. discovered that the UHI was influenced by the mountainous terrain features and city layout in Chongqing [29] . Dan et al. thought that the Yangtze River and Jialing River had important effects on the UHI of Chongqing [30] . Some other scholars also did some research on UHI in Chongqing [31, 32] . Although Chongqing's UHI effect has been well studied, most of the literature is based on weather station data, which neglects the spatial extension and distribution of UHI. These studies also lack necessary verification of their precision. Furthermore, it is necessary to better understand how the impact factors, especially urban green spaces, influence the UHI effect in Chongqing. In this study, we rectify the parameters used in the mono-window algorithm according to the terrain features of Chongqing. We use a combination of Landsat TM and Landsat 8 TIRS data, automatic weather station data, and satellite-ground synchronous experiment data to estimate the LST, test its accuracy, and examine the relationship between the impact factors and the UHI variations in Chongqing. The main objective of this study is to provide a suitable quantitative method to study UHI in mountainous cities. Sustainability 2018, 10, 1943 3 of 15 Materials and Methods Study Area Chongqing municipality, one of the fastest developing cities in China over the past twenty years, is situated in southwest China. Characterized by rugged hills, Chongqing is a typical mountainous city and is also one of the three famous "stoves" of China. The study area is located between 106 • 23 2.4"E-106 • 41 45.6"E and 29 • 20 31.2"N-29 • 43 26.4"N in the southwest of Chongqing municipality, with a total area of approximately 780 km 2 (Figure 1 ). It has a humid subtropical monsoon climate, and receives an average annual precipitation of 1000 mm. The average annual temperature is 18.4 • C. The Yangtze River and Jialing River go through the area roughly from south to north and from west to east respectively, uniting in the city center. Separated by the terrain and the rivers, naturally, the study area forms a multi-center structure. The UHI phenomenon in the study area is obvious because of the air circulation caused by the terrain barrier, and the high population and building density caused by rapid urbanization [33] .
doi:10.3390/su10061943 fatcat:aqbhwqribvddvpmhfecdfpv3lq