The cumulative effects of urban expansion on land surface temperatures in metropolitan JingjinTang, China

Abstract

Rapid urbanization has resulted in the permanent conversion of large areas of cropland and natural vegetation to impervious surfaces and therefore greatly modified land surface properties and land‐atmosphere interactions. This study sought to examine the urbanization process using Landsat images from 2001 to 2010 in metropolitan JingjinTang (JJT), a rapidly expanding urban cluster in northern China. We aggregated the original results of land use data as fractional cover information in 1 km and 10 km grids. Annual and seasonal land surface temperatures (LSTs) were processed from Moderate Resolution Imaging Spectroradiometer products. We used moving window and gradient analysis methods to examine the differences in LST between urban and other land types, further identifying LST increases in gradients of urbanization levels. Urban extent increased by 1.6 times, and approximately 45% newly developed areas were converted from croplands during this process. Emerging urban land in JJT has caused approximately 0.85 ± 0.68°C warming in terms of annual mean LST, and the greatest warming occurred in the summer. An increase in urban land of 10% in a 1 km grid in JJT would cause approximately a 0.21°C increase in annual LST. Urbanization also led to increases in daytime LSTs and nighttime LSTs by approximately 1.03 ± 1.38°C and 0.78 ± 1.02°C, respectively. The warming trend induced by urbanization exhibits clear seasonal and diurnal differences, and this warming trend is most likely caused by the cumulative effects of changes in land properties, radiation storage, and anthropogenic heat release by urbanization.

Urban Growth Dynamics in Perth, Western Australia: Using Applied Remote Sensing for Sustainable Future Planning

“…However, accurate identification and temporal monitoring of urban land is frequently precluded due to the coarse resolution (300 m-1 km) of a number of commonly used remotely sensed datasets including night time lights (1 km) and the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (0.083 • ) [22,24]. Whilst 30 m resolution data (e.g., Landsat) are more suitable to detect nuances of urban development the majority of studies and classified products which have used these finer resolution products implement large temporal windows, negating the possibility of detailed temporal urban characterisation e.g., GloeLand30 [25][26][27][28][29]. This research provides the first comprehensive temporal evolution analysis quantifying land cover change and associated urban expansion for the Perth Metropolitan Region (PMR) using 30 m Landsat imagery, the longest temporal record of medium spatial resolution imagery, for seven sequential time snapshots between 1990 and 2015.…”

Urbanisation-Induced Land Cover Temperature Dynamics for Sustainable Future Urban Heat Island Mitigation

“…The association between urban area and LST was established through the Surface UHI Intenisty (SUHII) which compares the temperature of urban surfaces to that of surfaces comprising of different land cover types [11,26,70,71]: (1) where is the surface temperature of a MODIS pixel defined as being urban and is the temperature of an alternative land cover type (e.g., forest) to be differenced from urban. This formula can be applied either as a global average or on a per pixel basis.…”

“…Contrastingly, southern cities (e.g., Dallas) utilised more energy (net deficit) as a consequence of UHI within warmer climates [17]. Similarly, research from Romanian [25], Chinese [26] and Asian cities [27] established more pronounced daytime SUHIs, whilst Italian [28], other Chinese [29,30] and USA [31] cities established both more distinct nighttime surface and atmospheric UHIs. Identifying the localised spatio-temporal impacts of urban expansion on the UHI effect is therefore essential to help prevent further socio-ecological impacts and inform targeted policies for sustainable future development [17,[32][33][34].…”

“…To derive air temperature from LST additional factors including surface properties, atmospheric conditions and solar angles are incorporated to calculations (assuming local data is available during satellite overpass) [8,47]. Therefore, temperature directly extracted from satellite imagery enables universal comparison, averting issues associated with scaling to air temperature over large spatial extents, typically used within UHI studies [8,26,28,30,48].…”

“…Transformation of land during urbanisation encompasses a broad range of changes within metropolitan regions which are frequently excluded from temperature analysis, and this needs consideration to address the aforementioned limitations. For example, whilst Xie et al (2012) and identified urban to represent the greatest increase in land cover, their analysis focused on the difference between averaged land cover temperatures producing singular metropolitan values that is unable to identify which land cover change induced the greatest temperature change [26,56]. Exclusion of the holistic impact of land cover change on temperature, alongside the universal assumption of urban homogeneity, which disregards the variability between high and low albedo urban land covers obtaining diverse thermal characteristics, has precluded targeted policies in mitigating changes that most influence surface temperature across metropolitan regions [5,30,[48][49][50][52][53][54]57].…”