Ecosystem balance is an important factor that affects healthy and sustainable urban development. The traditional cellular automata (CA) model considers only a few ecological factors, however, the MCR model can account for ecological factors. In previous studies, few ecological factors were added to the CA model. Thus, the minimal cumulative resistance (MCR) model is combined with the CA and Markov models for the simulation of urban expansion. To verify the reliability of the method, the Wuhan metropolitan area was selected as a representative urban area, and its expansion in the past and future was simulated. Firstly, seven influential factors were selected from the perspective of location theory. The transformation rules of the comprehensive resistance surface followed by the modified CA–Markov model were constructed on the basis of the MCR model. The expansion of the Wuhan metropolitan area in 2013 was simulated on the basis of the 1996 and 2006 maps of land-use status, and the kappa coefficient was used as an index to evaluate the accuracy of the proposed method. Then, the expansion of the Wuhan metropolitan area in 2020 was simulated. Finally, the simulation results obtained with and without the MCR model were compared and analysed from the macro- and micro levels. Results show that the prediction accuracy of the two models differed for ecological regions, such as woodlands and water bodies. The similarities between the regions that were overestimated and underestimated by the MCR-modified CA–Markov model and non-MCR model may be attributed to solution of the land-use transfer matrix with the Markov model. The accuracy of the MCR-modified CA–Markov model for predicting forests, water and other ecological regions was higher than that of the Markov model. Therefore, the proposed MCR-modified CA–Markov model has potential applications in environmentally-conscious urban expansion.

Rapid urbanization and climate change have exacerbated urban heat stress, underscoring the importance of research on human thermal comfort for sustainable urban development. This study analyzes the spatiotemporal variation and driving factors of apparent temperature in the Chang–Zhu–Tan urban agglomeration, China. The Humidex index, representing apparent temperature, was derived from multi-source remote sensing data (Landsat 8, MODIS) and meteorological variables (ERA5-Land reanalysis), employing atmospheric correction, random forest modeling, and path analysis. The results indicate pronounced spatiotemporal heterogeneity: apparent temperature reached its maximum in urban centers during summer (mean 52.9 °C) and its minimum in winter (mean 5.99 °C), following a decreasing gradient from urban core to periphery. Land cover emerged as a key driver, with vegetation (NDVI, r = −0.938) showing a strong negative correlation and built-up areas (NDBI, r = +0.8) a positive correlation with apparent temperature. Uniquely, in the Chang–Zhu–Tan region’s persistently high humidity, water bodies (MNDWI, r = +0.616) exhibited a positive correlation with apparent temperature, likely due to humidity-enhanced thermal perception in summer and relatively warmer water temperature in winter. Path analysis revealed that air temperature exerts the strongest direct positive influence on apparent temperature, while relative humidity and NDVI primarily act through indirect pathways. These findings provide scientific evidence to guide climate-adaptive urban planning and enhance human living conditions in humid environments.