Developing countries have been undergoing dramatic urban growth over the past three decades. It is essential to understand and simulate the urban growth process for smart urban planning and sustainable development purposes. Cellular automata (CA) modeling is an efficient approach to simulating urban land use/cover change; however, the traditional CA method has limitations in simulating the various urban growth patterns and processes. This study aims to analyze the influences of different urban growth characteristics on the effectiveness of CA modeling by conducting a case study over the area in the Pearl River Delta of Southern China. We used the growth rate, landscape expansion index, and spatial dependency to quantify the urban growth characteristics. The effectiveness of CA modeling was measured through a comparison of the simulation results with the reference data. The simulation results and validation analyses reveal that the traditional CA is not applicable for the following three situations: (1) the urban growth pattern characterized by less growth area or a higher ratio of outlying expansion; (2) the urban region that includes several subregions with disparate growth characteristics; and (3) the existence of temporal differences in growth characteristics over a long period.

Urban green space (UGS) is crucial for urban sustainability, yet equity in access to urban green space remains a global challenge amidst rapid urbanization. Guangzhou, a megacity in China’s Pearl River Delta, exemplifies this challenge, with its population increasing from 5.9 million in 1990 to 18.7 million in 2020, leading to intensified land use conflicts. This study analyzes the changes in urban green space across three strategic zones—Old Guangzhou, New Guangzhou, and Future Guangzhou—compared to other areas, and equity over time using land use transfer matrices, landscape indices, hotspot analysis, and the Gini coefficient. It also explores the driving factors behind these changes. The results show a 16% decline in total UGS area from 1990 to 2020, with fragmentation peaking between 2000 and 2010, as indicated by a 24% increase in patch density and an 18% decrease in connectivity; growing spatial inequity, with the overall Gini coefficient rising over time—Old Guangzhou exhibited severe inequality (0.806) due to the scarcity of community-level urban green space, while Future Guangzhou demonstrated a relatively balanced distribution (0.523); and a shift in policy focus from basic greening to ecological prioritization after 2010, which slowed UGS loss but failed to address the disparities between core and peripheral areas. These findings are significant not only for Guangzhou but also reflect broader global issues faced by urban areas undergoing rapid urbanization. This study provides a comprehensive framework for balancing urban growth with equity, offering valuable insights for rapidly urbanizing regions worldwide.

Accurate mapping of built-up land is essential for urbanization monitoring and ecosystem research. At present, remote sensing is one of the primary means used for real-time and accurate surveying and mapping of built-up land, due to the long time series and multi-information advantages of existing remote sensing images and the ability to obtain highly precise year-by-year built-up land maps. In this study, we obtained feature-enhanced data regarding built-up land from Landsat images and phenology-based algorithms and proposed a method that combines the use of the Google Earth Engine (GEE) and deep learning approaches. The Res-UNet++ structural model was improved for built-up land mapping in Guangdong from 1991 to 2020. Experiments show that overall accuracy of built-up land map in the study area in 2020 was 0.99, the kappa coefficient was 0.96, user accuracy of built-up land was 0.98, and producer accuracy was 0.901. The trained model can be applied to other years with good results. The overall accuracy (OA) of the assessment results every five years was above 0.97, and the kappa coefficient was above 0.90. From 1991 to 2020, built-up land in Guangdong has expanded significantly, the area of built-up land has increased by 71%, and the proportion of built-up land has increased by 3.91%. Our findings indicate that the combined approach of GEE and deep learning algorithms can be developed into a large-scale, long time-series of remote sensing classification techniques framework that can be useful for future land-use mapping research.

Understanding the interdependencies among production, living, and ecological spaces (PLESs) is critical for sustainable regional development. Urban fringe areas, shaped by rapid urbanization and conflicting land-use demands, are particularly vulnerable to spatial tensions. This study analyzes the spatiotemporal dynamics and drivers of PLES conflicts in Zengcheng District, Guangzhou, a representative urban fringe region. Using land-use data from 2010 to 2020, the study applies the optimal parameter geographic detector, chosen for its ability to untangle complex spatial interactions, to quantify conflict intensity and identify key drivers. This method was chosen over other spatial analysis techniques due to its ability to effectively capture nonlinear relationships and interaction effects between variables, which traditional regression-based or spatial autocorrelation methods often fail to fully address. The results indicate that production and ecological lands dominated the landscape, while living space expansion slowed, leading to escalating conflicts, particularly in the southern and central regions. The PLES conflict index shows that severe conflict units rose from 0.89% in 2010 to 2.15% in 2020, despite over 80% of spatial units remaining stable. Moderate conflicts peaked in 2015 before declining, while stronger conflicts intensified, especially in rapidly urbanizing areas. Conflict hotspots were most pronounced in rapidly urbanizing zones, particularly at the interface of urban expansion and ecological conservation areas. Moreover, the driving forces behind these conflicts transitioned from economic and urbanization factors to a multifaceted interplay of natural and social determinants, underscoring the growing intricacy of spatial dynamics. These findings offer crucial insights into the mechanisms driving PLES conflicts, guiding urban planners and policymakers in developing strategies to balance competing land-use priorities. By quantifying conflicts and identifying key drivers, this study helps prioritize interventions that mitigate tensions between production, living, and ecological spaces, supporting policies that reconcile urban expansion with ecological preservation for sustainable development in urban fringe areas.

Satellite-derived environmental parameters play important roles in environmental research on global changes and regional resources. Atmosphere effects and sensor limitations often lead to data products that vary in quality. The main goal of time series data reconstruction is to use various statistical and numerical analysis methods and to stimulate changing seasonal or annual parameters, providing more complete data sets for correlational research. This paper aims to develop a time series reconstruction algorithm for LST based on data assimilation according to the current problems of unstable precision and unsatisfactory results, and the simplistic effects of evaluation methods while using remote sensing-derived LST data as the basic parameters and the daily LST data derived from the static meteorological satellite GMS-5 as the input data. The data assimilation system used the Kalman filter as the assimilation algorithm. A complete set of global refined LST time series data sets were obtained by constantly correcting the LST values according to the regional ground-based observations. This method was implemented using MATLAB software (version R2017a), and was applied and validated through partitioning using the principal elevation in the Beijing, Tianjin, and Hebei regions. The results show that the accuracy of the reconstructed LST data series improved significantly in terms of the mean and standard deviation. Better consistency was achieved between the variables obtained over a year from the reconstructed LST data and the ground observations from the LST data set.