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.

Migratory waterbirds concentrated in freshwater ecosystems in mosaic environments rely on quality habitats for overwintering. At West Dongting Lake National Nature Reserve (WDLNNR), China, land-use change and hydrology alternation are compounding factors that have affected important wintering areas for migratory waterbirds. Presently, changes in the hydrology and landscape have reshaped natural wintering habitats and their availability, though the impact of hydrological management on habitat selection of wintering waterbirds is largely unknown. In this study, we classified differentially managed habitats and calculated their area using the normalized difference vegetation index (NDVI) to evaluate suitable habitat availability over the study period (2016–2017 and 2017–2018 wintering periods). We then used social behavioral association network (SBAN) model to compare habitat quality through species-species social interactions and species-habitat associations in lakes with different hydrological management. The results indicated that social interactions between and within species structured wintering waterbirds communities, which could be dominated by one or more species, while dominant species control the activities of other co-existing species. Analysis of variance (ANOVA) tests indicated significant differences in SBAN metrics between lakes (p = 0.0237) and habitat (p < 0.0001) levels. Specifically, lakes with managed hydrology were preferred by more species. The managed lakes had better habitat quality in terms of significantly higher habitat areas (p < 0.0001) and lower habitat transitions (p = 0.0113). Collectively, our findings suggest that proper hydrological management can provide continuous availability of quality habitats, especially mudflats and shallow waters, for a stable SBAN to ensure a wintering waterbirds community with more sympatric species in a dynamic environment.

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