Comprehensive comparison of two models evaluating eco-environmental quality in Fangshan.

It is crucial to employ scientifically sound models for assessing the quality of the ecological environment and revealing the strengths and weaknesses of ecosystems. This process is vital for identifying regional ecological and environmental issues and devising relevant protective measures. Among the widely acknowledged models for evaluating ecological quality, the ecological index (EI) and remote sensing ecological index (RSEI) stand out; however, there is a notable gap in the literature discussing their differences, characteristics, and reasons for selecting either model. In this study, we focused on Fangshan District, Beijing, China, to examine the differences between the two models from 2017 to 2021. We summarized the variations in evaluation indices, importance, quantitative methods, and data acquisition times, proposing application scenarios for both models. The results indicate that the ecological environment quality in Fangshan District, Beijing, remained favorable from 2017 to 2021. There was a discernible trend of initially declining quality followed by subsequent improvement. The variation in the calculation results is evident in the overall correlation between the RSEI and EI. Particularly noteworthy is the significantly smaller correlation between EI and the RSEI in 2021 than in the other two years. This discrepancy is attributed to shifts in the contribution of the evaluation indices within the RSEI model. The use of diverse quantitative methods for evaluating indicators has resulted in several variations. Notably, the evaluation outcomes of the EI model exhibit a stronger correlation with land cover types. This correlation contributes to a more pronounced fluctuation in RSEI levels from 2017 to 2021, with the EI model's evaluation results in 2019 notably surpassing those of the RSEI model. Ultimately, the most prominent disparities lie in the calculation results for water areas and construction land. The substantial difference in water areas is attributed to the distinct importance assigned to evaluation indicators between the two models. Moreover, the notable difference in construction land arises from the use of different quantification methods for evaluation indicators. In general, the EI model has suggested to be more comprehensive and effectively captures the annual comprehensive status of the ecological environment and the multiyear change characteristics of the administrative region. On the other hand, RSEI models exhibit greater flexibility and ease of implementation, independent of spatial and temporal scales. These findings contribute to a clearer understanding of the models' advantages and limitations, offering guidance for decision makers and valuable insights for the improvement and development of ecological environmental quality evaluation models.

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

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.