Urbanization challenges city sustainability by aggravating uneven population migration and land exploitation. Understanding the characteristics and dynamics of this imbalance is crucial for promoting sustainable development. With a focus on population-related land change, this study analyzes the urban–rural imbalance characterized by settlement expansion from 1985 to 2019, using nighttime light (NTL) remote sensing imagery and global settlement distribution data, with Guangdong province, China, as a case study. The key findings reveal significant spatiotemporal differences in settlement expansion between the urban and rural regions. The urban settlements experienced faster expansion from 1985 to 2005, which slowed post-2005, while the rural settlements maintained a stable growth rate throughout the study period. The economic and environmental conditions were identified as major drivers of expansion diversity, with economic factors playing a dominant role in the urban regions and both factors influencing the rural regions. A linear regression analysis highlighted the diverse quantity relationships between the urban and rural settlements across different spatial extents; the urban settlements dominated in quantity at the provincial level, primarily due to the contributions of the core Pearl River Delta (PRD) region. In contrast, the rural settlements outnumbered the urban ones in most of the other prefectures, a trend that continued to deepen across Guangdong province. The findings of this study provide deeper insights into the characteristics and evolvement of the urban–rural imbalance, policy implications and actionable strategies are offered for equitable and sustainable city development.

Understanding spatiotemporal mismatches between urban expansion and population dynamics is essential for guiding sustainable development in rapidly urbanizing regions. Using multi-source nighttime light (NTL) images and global settlement layers, this study investigates the settlement growth pattern and potential spatiotemporal mismatch with population distribution in Guangdong, China, from 1995 to 2019 at a 5-year interval. Specifically, population spatialization in urban and rural areas is separately mapped by adopting a population-based thresholding method, achieving strong agreement with the census record. Our analysis reveals distinct expansion patterns and mismatch conditions across Guangdong’s Core, Belt, and District subzones. The Core and District subzones primarily experienced infilling and edge-expansion urban growth, while the Belt subzone exhibited more dispersed spatial patterns. Notably, only 5 of 21 prefectures exhibited faster population growth than urban expansion, likely due to sustained migration driven by economic opportunities and advanced urbanization. Quantitatively, both urban expansion and population growth followed a Core, Belt, District order. Spatially, population-dominated areas were primarily clustered within 10 km of urban centers, while the District subzone extensively displayed overfilled settlements, indicating low-efficient land use. Temporally, urban growth relative to population in the Core subzone turned from slower pre-2000 to faster post-2000, followed by gradual deceleration, while the Belt subzone maintained balanced growth throughout the study period. The District subzone sustained faster urban growth from 2000 to 2019. Findings of the study provide an important reference for scientific urban planning and sustainable regional development, not only in Guangzhou but other rapidly urbanizing regions globally.

Shale oil/gas extraction has expanded rapidly in the last two decades due to the rising energy prices and the advancement of technologies. Its development can have huge impacts on and, at the same time, is also deeply affected by energy markets, especially in an era with high economic uncertainty. Understanding and monitoring shale oil/gas development over large regions are critical for both energy policies and environmental protection. However, there are currently no applicable methods to track the spatio-temporal dynamics of shale oil/gas development. To fill this gap, we propose a new NDVI Trajectroy Matching algorithm to track shale oil/gas development using the annual Landsat NDVI composite time series from 2000 to 2020. The results reveal that our algorithm can accurately extract the location and time of shale oil/gas exploitation in Eagle Ford and Three Forks, with an accuracy of 83.80% and 81.40%, respectively. In the Eagle Ford area, accuracy for all disturbance year detection was greater than 66.67%, with the best in 2011 and 2019 at 90.00%. The lowest accuracy in the Three Forks area was 63.33% in 2002, while the highest accuracy was 93.33% in 2019. In conclusion, the algorithm can effectively track shale oil/gas development with considerable accuracy and simplicity. We believe that the algorithm has enormous potential for other applications, such as built-up regions, forests, farmlands, and water body expansion and contraction involving vegetation damage.