Dataset Information

Assessment and simulation of land use and land cover change impacts on the land surface temperature of Chaoyang District in Beijing, China.

ABSTRACT: Rapid urbanization is changing the existing patterns of land use land cover (LULC) globally, which is consequently increasing the land surface temperature (LST) in many regions. The present study is focused on estimating current and simulating future LULC and LST trends in the urban environment of Chaoyang District, Beijing. Past patterns of LULC and LST were identified through the maximum likelihood classification (MLC) method and multispectral Landsat satellite images during the 1990-2018 data period. The cellular automata (CA) and stochastic transition matrix of the Markov model were applied to simulate future (2025) LULC and LST changes, respectively, using their past patterns. The CA model was validated for the simulated and estimated LULC for 1990-2018, with an overall Kappa (K) value of 0.83, using validation modules in IDRISI software. Our results indicated that the cumulative changes in built-up to vegetation area were 74.61 km2 (16.08%) and 113.13 km2 (24.38%) from 1990 to 2018. The correlation coefficient of land use and land cover change (LULCC), including vegetation, water bodies and built-up area, had values of r = - 0.155 (p > 0.005), -0.809 (p = 0.000), and 0.519 (p > 0.005), respectively. The results of future analysis revealed that there will be an estimated 164.92 km2 (-12%) decrease in vegetation area, while an expansion of approximately 283.04 km2 (6% change) will occur in built-up areas from 1990 to 2025. This decrease in vegetation cover and expansion of settlements would likely cause a rise of approximately ?10.74 °C and ?12.66 °C in future temperature, which would cause a rise in temperature (2025). The analyses could open an avenue regarding how to manage urban land cover patterns to enhance the resilience of cities to climate warming. This study provides scientific insights for environmental development and sustainability through efficient and effective urban planning and management in Beijing and will also help strengthen other research related to the UHI phenomenon in other parts of the world.

SUBMITTER: Amir Siddique M

PROVIDER: S-EPMC7224232 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Assessment and simulation of land use and land cover change impacts on the land surface temperature of Chaoyang District in Beijing, China.

Amir Siddique Muhammad M Dongyun Liu L Li Pengli P Rasool Umair U Ullah Khan Tauheed T Javaid Aini Farooqi Tanzeel T Wang Liwen L Fan Boqing B Rasool Muhammad Awais MA

PeerJ 20200511

Rapid urbanization is changing the existing patterns of land use land cover (LULC) globally, which is consequently increasing the land surface temperature (LST) in many regions. The present study is focused on estimating current and simulating future LULC and LST trends in the urban environment of Chaoyang District, Beijing. Past patterns of LULC and LST were identified through the maximum likelihood classification (MLC) method and multispectral Landsat satellite images during the 1990-2018 data ...[more]

PMID: 32435539

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Project description:The dynamic interplay between urbanization and its impacts on climate is a subject of recent concern, particularly in rapidly urbanizing cities of Pakistan. This research investigated the spatio-temporal effects of urban growth in terms of Land Use Land Cover changes on the thermal environment (Land Surface Temperature) of the Sialkot city, Pakistan using satellite data spanning four distinct time periods (1989, 2000, 2009 and 2020) and predicted changes for year 2030 by employing Cellular Automata Markov Chain Model. Satellite imagery (Landsat 5, 7 and 8) was processed, and maximum likelihood supervised classification was done to generate LULC maps for each of the aforementioned years. In addition to LULC classification, thermal bands of satellite data (for summer and winter) were processed to compute Land Surface Temperature (LST) of the city. The prediction of LULC changes and LST was done for year 2030 using Cellular Automata Markov Chain Model. The accuracy of classified and prediction maps was checked using Kappa Index. The LULC analysis revealed 4.14% increase in the built-up area and 3.43% decrease in vegetation cover of the city during 1989 to 2020. Both land covers are expected to change in the future (year 2030) by + 1.31% (built-up) and - 1.1% (vegetation). Furthermore, a declining trend in the barren land and water bodies was also observed over time. These LULC changes were found affecting the LST of study area. The transformation of vegetation cover into built-up area resulted in an increase in LST over time. A notable rise of 4.5 °C (summer) and 5.7 °C (winter) in the mean LST of Sialkot was observed during 1989 to 2020 and further increases are anticipated in year 2030. This study calls for attention of the policy makers to reduce human impact on the local climate of the city. The study will also help city developers in analyzing the urban population growth trend, finding suitable location to built new infrastructure by governmental authorities and how the rising temperature can affect energy demand and agriculture production of the city in future.

Project description:BackgroundDengue is a prioritized public health concern in China. Because of the larger scale, more frequent and wider spatial distribution, the challenge for dengue prevention and control has increased in recent years. While land use and land cover (LULC) change was suggested to be associated with dengue, relevant research has been quite limited. The "Open Door" policy introduced in 1978 led to significant LULC change in China. This systematic review is the first to review the studies on the impacts of LULC change on dengue dynamics in China. This review aims at identifying the research evidence, research gaps and provide insights for future research.MethodsA systematic literature review was conducted following the PRISMA protocol. The combinations of search terms on LULC, dengue and its vectors were searched in the databases PubMed, Web of Science, and Baidu Scholar. Research conducted on China published from 1978 to December 2019 and written in English or Chinese was selected for further screening. References listed in articles meeting the inclusion criteria were also reviewed and included if again inclusion criteria were met to minimize the probability of missing relevant research.Results28 studies published between 1978 and 2017 were included for the full review. Guangdong Province and southern Taiwan were the major regional foci in the literature. The majority of the reviewed studies observed associations between LULC change factors and dengue incidence and distribution. Conflictive evidence was shown in the studies about the impacts of green space and blue space on dengue in China. Transportation infrastructure and urbanization were repeatedly suggested to be positively associated with dengue incidence and spread. The majority of the studies reviewed considered meteorological and sociodemographic factors when they analyzed the effects of LULC change on dengue. Primary and secondary remote sensing (RS) data were the primary source for LULC variables. In 21 of 28 studies, a geographic information system (GIS) was used to process data of environmental variables and dengue cases and to perform spatial analysis of dengue.ConclusionsThe effects of LULC change on the dynamics of dengue in China varied in different periods and regions. The application of RS and GIS enriches the means and dimensions to explore the relations between LULC change and dengue. Further comprehensive regional research is necessary to assess the influence of LULC change on local dengue transmission to provide practical advice for dengue prevention and control.

Dataset Information

Assessment and simulation of land use and land cover change impacts on the land surface temperature of Chaoyang District in Beijing, China.

Publications

Assessment and simulation of land use and land cover change impacts on the land surface temperature of Chaoyang District in Beijing, China.

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