Spatial heterogeneity of the thermal environment based on the urban expansion of natural cities using open data in Guangzhou, China

Yang, Zhiwei; Chen, Yingbiao; Wu, Zhifeng; Qian, Qinglan; Zheng, Zihao; Huang, Qingyao

doi:10.1016/j.ecolind.2019.05.032

Cited by 33 publications

(9 citation statements)

References 39 publications

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“…In order to analyze the spatial patterns of the thermal environment from 2000 to 2016, we quantified the temperature variations during the research period by reclassifying the LST in the four years of 2000, 2006, 2012, and 2016. For this reclassification, we adopted the mean-standard deviation method which was used for thermal environmental research for the urban expansion [51], or land cover response to the LST in the Nanjing metropolitan region [52]. The mean-standard deviation method and the variables for reclassification are outlined in Table 2 to show how we classify the LST images into five LST zones.…”

Section: Land Surface Temperature Pattern Classificationsmentioning

confidence: 99%

Assessing the Link between Human Modification and Changes in Land Surface Temperature in Hainan, China Using Image Archives from Google Earth Engine

et al. 2020

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In many areas of the world, population growth and land development have increased demand for land and other natural resources. Coastal areas are particularly susceptible since they are conducive for marine transportation, energy production, aquaculture, marine tourism and other activities. Anthropogenic activities in the coastal areas have triggered unprecedented land use change, depletion of coastal wetlands, loss of biodiversity, and degradation of other vital ecosystem services. The changes can be particularly drastic for small coastal islands with rich biodiversity. In this study, the influence of human modification on land surface temperature (LST) for the coastal island Hainan in Southern China was investigated. We hypothesize that for this island, footprints of human activities are linked to the variation of land surface temperature, which could indicate environmental degradation. To test this hypothesis, we estimated LST changes between 2000 and 2016 and computed the spatio-temporal correlation between LST and human modification. Specifically, we classified temperature data for the four years 2000, 2006, 2012 and 2016 into 5 temperature zones based on their respective mean and standard deviation values. We then assessed the correlation between each temperature zone and a human modification index computed for the year 2016. Apart from this, we estimated mean, maximum and the standard deviation of annual temperature for each pixel in the 17 years to assess the links with human modification. The results showed that: (1) The mean LST temperature in Hainan Island increased with fluctuations from 2000 to 2016. (2) The moderate temperature zones were dominant in the island during the four years included in this study. (3) A strong positive correlation of 0.72 between human modification index and mean and maximum LST temperature indicated a potential link between human modification and mean and maximum LST temperatures over the 17 years of analysis. (4) The mean value of human modification index in the temperature zones in 2016 showed a progressive rise with 0.24 in the low temperature zone, 0.33 in the secondary moderate, 0.45 in the moderate, 0.54 in the secondary high and 0.61 in the high temperature zones. This work highlighted the potential value of using large and multi-temporal earth observation datasets from cloud platforms to assess the influence of human activities in sensitive ecosystems. The results could contribute to the development of sustainable management and coastal ecosystems conservation plans.

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Section: Land Surface Temperature Pattern Classificationsmentioning

confidence: 99%

Assessing the Link between Human Modification and Changes in Land Surface Temperature in Hainan, China Using Image Archives from Google Earth Engine

et al. 2020

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“…Especially in China, urban agglomerations are not only areas with the fastest urban expansion speed, but also areas with highly sensitive ecological environment. These urban agglomerations concentrate more than 3/4 of the Chinese pollution output, and their environmental pollution and resource degradation are very serious [19], which hinders the sustainable development of ecological environment. Therefore, studying the impact of urban expansion forms in urban agglomerations on ESs is of great significance to regional sustainable development.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Urban Expansion Forms on Ecosystem Services in Urban Agglomerations: A Case Study of Shanghai-Hangzhou Bay Urban Agglomeration

et al. 2021

Remote Sensing

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Exploring impacts of urban expansion on ecosystem services has become a hot topic for regional sustainable development, while analyzing the ecological effects of urban expansion forms under different expansion intensities and city sizes is relatively rare. Therefore, taking a typical urban agglomeration, Shanghai-Hangzhou Bay Urban Agglomeration, as a case study, this study first analyzed the dynamics of urban expansion forms (leapfrogging, edge-expansion, and infilling) and four critical ecosystem services (carbon sequestration, food supply, habitat quality, and soil retention) in three periods from 1990 to 2019. The multiple linear regression model and zonal statistics analysis model were used to quantitatively identify the impacts of urban expansion forms on ecosystem services, taking into account different expansion intensities and city sizes. The results showed that the urban expansion trend in the study area experienced a morphological change from integration to diffusion and then to integration in 1990–2019; edge-expansion was the dominant expansion form. Food supply decreased continuously while other ecosystem services had fluctuating changes, and they all had spatial heterogeneity. The leapfrogging, edge-expansion, and infilling all had negative impacts on ecosystem services, and among them, the edge-expansion intensity had the highest influence degree in the early expansion, and the leapfrogging intensity occupied the dominant position in all influences with the expansion of urban scales. For different city sizes, the impact of edge-expansion in large-scale cities was greater than in small-scale cities in the early expansion, and the impact of leapfrogging in large-scale cities exceeded the edge-expansion in the subsequent expansion. These findings will help further understand the influential mechanisms between urban expansion and ecosystem services and provide a scientific basis for formulating reasonable urban planning.

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“…In order to make a comparative analysis in the spatial patterns of the UHI that may be contributed by the expansion of urban growth, the study calculated LST from satellite data of the years 2005 and 2019. The raster layers of calculated LST have been reclassified by the mean-standard deviation method that has been applied by many researchers [61,62]. In this method, the LST regime has been classified into five LST zones based on mean and standard deviation as given in table 2.…”

Section: Spatio-temporal Change In Lstmentioning

confidence: 99%

Spatio-temporal Analysis of Environmental Criticality: Planned Versus Unplanned Urbanization

Mallik

Bhutia

Roy

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The economic boom in the Indian cities is causing rapid urban growth, mostly in an unplanned fashion. The growth of urban built-up is primarily taking place by replacing vegetation and other low radiative surfaces, increasing the magnitude and spatial extent of heat concentrations. The resulting phenomenal increase in temperature, known as Urban Heat Island (UHI), raises environmental criticality. Satellite remote sensing provides a breakthrough for monitoring the spatiotemporal variations of UHI by estimating Land Surface Temperature (LST) and surface biophysical parameters. The objective of this work is to compare the changing pattern of LST that resulted from urban growth and associated biophysical characteristics in a planned city (Kalyani city) and an unplanned city (Barasat city) in West Bengal, India from 2005 to 2019. Using Landsat data, the study retrieved summer LST along with the prepared vegetation index (NDVI) and built-up index (NDBI) for the years 2005 and 2019. The Environmental Criticality Index (ECI) was calculated for the periods from LST, NDVI, and NDBI datasets. The long-term (1988-2019) LST has been derived using the cloud computation technique to analyze the trend. Over the years, though the average LST of Kalyani is relatively high from Barasat, a rapid increase in LST is noticed for Barasat city. Between 2005 – 2019, the rapid unplanned growth in Barasat city has not only increased the LST but also raised the concern for environmental criticality as compared to Kalyani City. The correlation of LST with NDVI and NDBI suggests that urban heating is significantly controlled by the surface characteristics that need to modify through proper planning for urban sustainability. This study may assist planners, administrators, and researchers in decision-making.

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Spatial heterogeneity of the thermal environment based on the urban expansion of natural cities using open data in Guangzhou, China

Cited by 33 publications

References 39 publications

Assessing the Link between Human Modification and Changes in Land Surface Temperature in Hainan, China Using Image Archives from Google Earth Engine

Assessing the Link between Human Modification and Changes in Land Surface Temperature in Hainan, China Using Image Archives from Google Earth Engine

Impacts of Urban Expansion Forms on Ecosystem Services in Urban Agglomerations: A Case Study of Shanghai-Hangzhou Bay Urban Agglomeration

Spatio-temporal Analysis of Environmental Criticality: Planned Versus Unplanned Urbanization

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