Spatio-temporal exploratory analysis of urban surface temperature field in Shanghai, China

Abstract

The integrated application of remote sensing, geographic information system and quantitative analytical modeling can provide scientific and effective methods for monitoring and studying urban heat island, based on land surface temperature (LST) retrieved from thermal infrared channel data of sensors. In this paper, LST is retrieved from Landsat TM6 and ETM ? 6 data of Shanghai central city in 1989Shanghai central city in , 1997Shanghai central city in , 2000Shanghai central city in and 2002, by using the monowindow algorithm. Based on the data, global and local spatial autocorrelation analysis, and geostatistical methods are adopted to quantitatively describe the characteristics of spatial heterogeneity and temporal evolution of land surface thermal landscape at different scales and periods in Shanghai central city, by utilizing exploratory spatial data analysis. Results show that LST field in Shanghai central city tends to fragmentize and complicate with the development of Shanghai, and its global spatial difference becomes greater gradually. The spatial variance pattern of the change of LST field from 1997 to 2002 indicates that the dynamic change of LST presents a tendency of increase in circularity. LST declines distinctly in the districts of Puxi and Pudong near and inside the inner ring road, while it rises obviously outside the central city and near the out ring road. The extrema of temporal change in LST field have a characteristic of spatial clustering. Besides, as the city of Shanghai expands in a circular pattern as a whole, the directional difference of dynamic change of urban surface thermal landscape exists but is not very obvious.

The Effect of Urban Expansion on Urban Surface Temperature in Shenyang, China: an Analysis with Landsat Imagery

“…ρ λ is the at-satellite reflectance for the TM/ETM+ bands (1)(2)(3)(4)(5)7), θ denotes the solar elevation angle in degrees (Table 1), ESUN λ is the mean solar exoatmospheric irradiance (W/m 2 μm), and d is the Earth-Sun distance in astronomical units, which can be derived as d=1−0.01674 cos (0.9856×(JD-4)×π/180, where JD is the Julian day for the date of Landsat image acquisition. The normalized difference vegetation index (NDVI), normalized difference water index (NDWI) [8], and normalized difference built-up index NDBI [51] were used to characterize the LULC types in the current study and to interpret qualitatively the relationships between the LULC categories and UHI.…”

“…The UHI effect is closely related to several important issues, such as urban climatology, environmental change, and human-environment interactions [4,6,7,11,43]. The thermal variations within an urban area are primarily related to different land use/land cover (LULC) categories, surface materials, and air flushing rates [11,17,19,49].…”

Spatial Modeling of Urban Vegetation and Land Surface Temperature: A Case Study of Beijing

“…Since 1978, China has been experiencing unprecedented market-oriented urban growth [1,2]. The 8%-10% annual gross domestic product (GDP) growth rate during the past 30 years has been accompanied with an urbanization rate growth from 18% in 1978 to 45% in 2007 [3].…”

Application of random sets to model uncertainties of natural entities extracted from remote sensing images

“…Series of remotely sensed images as a major data source to observe the earth have been extensively integrated into spatialtemporal analysis in environmental research, for example for monitoring changes in lake sediment (Wu et al 2007), forest fire (Umamaheshwaran et al 2007), glacier coverage (Ye et al 2006) and urban heat island (Dai et al 2009). These images are only snapshots of processes, however, that neither directly represent geographical phenomena as entities, nor the changes in and interactions between the entities (Nixon and Aguado 2002).…”