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. 2020 May 11:8:e9115.
doi: 10.7717/peerj.9115. eCollection 2020.

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

Muhammad Amir Siddique  1 , Liu Dongyun  1 , Pengli Li  1 , Umair Rasool  2 , Tauheed Ullah Khan  3 , Tanzeel Javaid Aini Farooqi  4 , Liwen Wang  1 , Boqing Fan  1 , Muhammad Awais Rasool  3
Affiliations

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

Muhammad Amir Siddique et al. PeerJ. .

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.

Keywords: Land surface temperature; Land use and land cover change; Markov model; Urban dynamics; Urban green vegetation; Urban planning.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Conceptual framework for explaining the UHI phenomenon in city canyons.
Figure 2
Figure 2. Map representing the geostrategic importance of the study area: (A) People’s Republic of China, (B) Beijing County, (C) Digital elevation model (DEM) of Chaoyang District showing elevation.
Figure 3
Figure 3. Methodology flow chart of the study.
Figure 4
Figure 4. Land use and land cover change (LULCC) maps for (A) 1990, (B) 1997, (C) 2004, (D) 2011 and (E) 2018 in Chaoyang, Beijing.
Figure 5
Figure 5. Land surface temperature (LST) maps for (A) 1990, (B) 1997, (C) 2004, (D) 2011 and (E) 2018 of Chaoyang, Beijing.
Figure 6
Figure 6. The chord diagram explicates the portion of land use land cover changes (LULCC) concerning the time series 1990–2018.
Figure 7
Figure 7. Proportional changes in LULCC in the study area between 1990 and 2018.
Green bars represent the increment, and blue bars show the decrease in area (km2).
Figure 8
Figure 8. Land surface temperature (LST) during 1990–2018 in Chaoyang.
Figure 9
Figure 9. Corr-plot representing the linear correlation between the LULCC and mean LST for the period 1990–2018.
The color bar represents the value of R.
Figure 10
Figure 10. Markov chain’s Stochastic Transition Matrix structure of predictive analysis for LST-2025.
Figure 11
Figure 11. Projected map of land use land cover change (LULCC) for 2025 by CA-Markove.
Figure 12
Figure 12. Relative temperature of various land use and landcover change (LULCC), during the study period 1990-2018 mentioned on axis.
The color bar represents the mean temperature value of each segment.
See this image and copyright information in PMC

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