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.

Evaluation and projection of mean surface temperature using CMIP6 models over East Africa

“…The result of the present study shows a higher warming tendency relative to the outputs of CMIP5 models. In agreement with other studies, Almazroui et al (2020a) (Fan et al, 2020). A higher rate of warming is likely to result in extreme events over most parts of the globe (Huang et al, 2017;Lehner et al, 2017;Dosio et al, 2018;Kharin et al, 2018).…”

“…For instance, the PDFs for projected change during the 2071 -2100 period relative to the baseline period (1961 -1990) over EA shows that the mean temperature will be 25.2 and 26.7 under RCP4.5 and RCP8.5 scenarios, while the present study demonstrates that the projected change during the same period will be 25.6 and 27.7 under SSP2-4.5 and SSP5-8.5 scenarios, respectively (Figure 11 and Table 4). The results of the present study are in harmony with projected changes in T2m using CMIP6 over most regions (Grose et al, 2020;Fan et al, 2020;Tokarska et al, 2020;Almazroui et al, 2020a, b, c;Karim et al, 2021). The findings from this study illustrate higher warming in the latest model outputs of CMIP6 relative to its predecessor, despite identical instantaneous radiative forcing (Wyser et al, 2020).…”

Evaluation and Projection of Mean Surface Temperature Using CMIP6 Models Over East Africa

“…For instance, the PDFs for projected change during the 2071 -2100 period relative to the baseline period (1961 -1990) over EA shows that the mean temperature will be 25.2 and 26.7 under RCP4.5 and RCP8.5 scenarios, while the present study demonstrates that the projected change during the same period will be 25.6 and 27.7 under SSP2-4.5 and SSP5-8.5 scenarios, respectively (Figure 11 and Table 4). The results of the present study are in harmony with projected changes in T2m using CMIP6 over most regions (Grose et al, 2020;Fan et al, 2020;Tokarska et al, 2020;Almazroui et al, 2020a, b, c;Karim et al, 2021). The findings from this study illustrate higher warming in the latest model outputs of CMIP6 relative to its predecessor, despite identical instantaneous radiative forcing (Wyser et al, 2020).…”

Future Changes in Seasonal Temperature Over Pakistan in CMIP6

“…The impacts of anthropogenic activities on surface temperature are essential in understanding the present and future climate change, environment, and sustainable development (Siddique et al, 2020). In Pakistan, rapid growth in transportation, industrialization, urbanization, deforestation, waste, agriculture, livestock, and energy use are the main drivers of GHG emissions and will result in countrywide temperature rise (Hussain et al, 2018).…”