Studying the genetic diversity and population structure of natural forest populations is essential for evaluating their ability to survive under future environmental changes and establishing conservation strategies. Pinus koraiensis is a conifer species with high ecological and economic value in Northeast China. However, its natural forests have been greatly reduced in recent years, mostly due to over exploitation and over utilization. Here, we evaluated the genetic diversity and population structure of seven populations of P. koraiensis located throughout its native distribution. A total of 204 samples were genotyped with nine polymorphic nuclear SSR (simple sequence repeat) markers. The results showed high genetic diversity in all populations, with an average expected heterozygosity of 0.610, and the northern-most populations (Dailin (DL) and Fenglin (FL)) showed slightly higher diversity than the other five populations. The level of genetic differentiation among populations was very low (F_ST = 0.020). Analysis of molecular variance (AMOVA) showed that only 2.35% of the genetic variation existed among populations. Moreover, STRUCTURE analysis clearly separated the seven populations into two clusters. Populations DL and FL from the Xiaoxinganling Mountains comprised cluster I, while cluster II included the five populations from the Changbai Mountains and adjacent highlands. Our research on the genetic diversity and population structure of P. koraiensis in natural forests of China can provide a basis for the implementation of programs for the conservation and utilization of P. koraiensis genetic resources in the future.

It is well established that China’s rapid urban expansion has led to a substantial loss of cropland. However, few studies have examined how different urban expansion patterns contribute to cropland consumption, which has hindered the formulation of sustainable urban development and cropland protection policies. To fill this gap, we analyzed the occupation of cropland under three urban expansion patterns (leap-frogging, edge-spreading, and interior filling) in China from 1990 to 2020, using long-term land use data. The dominant driving forces of cropland loss were then explored using the XGBoost model and SHAP values. Our findings indicate that urban expansion in China from 1990 to 2020 resulted in a 6.3% reduction in cropland, with edge-spreading (4.0%) contributing the most, followed by leap-frogging (2.1%) and interior filling (0.2%). Change in urban intensity (CUI) proved to be the most critical driver of cropland loss, with SHAP values of 0.38, 0.28, and 0.37 for edge-spreading, leap-frogging, and interior filling, respectively. Over time, the driving forces evolved from a single demographic-economic dominance to a more diversified and integrated set of drivers. Based on these findings, we propose tailored planning and policies for different urban expansion patterns; for regions dominated by edge-spreading, stricter controls on urban boundaries and stronger land use planning constraints are required. For regions with prominent interior filling expansion, efforts should be made to improve internal land use efficiency while preserving existing cropland spaces. In regions characterized by leap-frogging expansion, further optimization of construction land allocation is needed to reduce the occupation of productive suburban cropland. These findings not only offer new empirical evidence for understanding the interplay between urban expansion and cropland conservation but also provide transferable insights that can inform sustainable land-use planning and cropland protection strategies in other rapidly urbanizing regions facing similar challenges.

Cropland use stability is vital for sustaining the productive and ecological functions of cropland. Existing multidimensional assessment frameworks rarely distinguish between quantity change and spatial change, nor do they examine their interactions across hierarchical scales. Using annual 30 m land cover data for Guangdong Province, China (1990–2024), this study developed a two-dimensional stability framework integrating quantity and spatial indicators, using a pixel-tracking strategy applied within each five-year interval to capture interannual dynamics. Analyses were conducted at provincial, agricultural zone, and city scales. Results showed that provincial stability exhibited a fluctuating upward trajectory, with the lowest point in 2005–2009. Regional patterns diverged markedly, and city-level stability exhibited spatial heterogeneity, with pronounced disparities between high- and low-stability cities. Grade transition and quadrant analyses further revealed that quantity and spatial stability do not always move in tandem, and single-dimension assessments may provide an incomplete picture of cropland use stability. These findings suggest that incorporating both dimensions into monitoring frameworks could support more differentiated cropland management strategies.

Urban agriculture plays a critical yet increasingly complex role in sustainable urban development, especially in high-density regions undergoing rapid transformation. Accurate mapping of its spatial distribution and functional composition remains a methodological challenge due to its fragmented landscape, small plot sizes, and multifunctional nature. This study addresses this gap by developing and applying a novel hierarchical classification framework that integrates agricultural land cover types with key socio-economic functions to map urban agriculture in the Pearl River Delta (PRD), China. This framework is structured around agricultural land categories (i.e., cropland, garden, forest, grass, and water body) and further delineated by two primary production functions, planting and breeding, with a third functional dimension, leisure activities, proposed as a conceptual extension for future research. Using unmanned aerial vehicle (UAV) imagery and high-resolution satellite data, we constructed a spatial sample database for urban agriculture. The random forest algorithm was applied to classify urban agriculture with Gaofen-2 imagery, generating detailed spatial distribution maps across the study area, with consistently reliable overall accuracy (79.07–81.82%), though this may be slightly optimistic due to potential spatial autocorrelation between training and testing samples. While the framework performed exceptionally well for spectrally and spatially distinct classes such as water bodies and perennial plantations, challenges remained in discriminating among annual field crops due to spectral similarity. These findings underscore the potential of integrating multi-temporal remote sensing data to capture phenological variations for improved classification. This study provides a replicable, functionally informed mapping approach that not only advances the methodological toolkit for urban agriculture characterization but also offers a valuable evidence base for land use planning, agricultural policy, and sustainable urban development in rapidly urbanizing regions.