Inappropriate soil management accelerates soil erosion and thus poses a serious threat to food security and biodiversity. Due to poor data availability and fragmented terrain, the landscape pattern of garden erosion in tropical Xishuangbanna is not clear. In this study, by integrating multi-source satellite imagery, field investigation and visual interpretation, we realized high-resolution mapping of gardens and soil conservation measures at the landscape scale. The Chinese Soil Loss Equation (CSLE) model was then performed to estimate the garden erosion rates and to identify critical erosion-prone areas; the landscape pattern of soil erosion was further discussed. Results showed the following: (1) For the three major plantations, teas have the largest degree of fragmentation and orchards suffer the highest soil erosion rate, while rubbers show the largest patch area, aggregation degree and soil erosion ratio. (2) The average garden erosion rate is 1595.08 t·km⁻²a⁻¹, resulting in an annual soil loss of 9.73 × 10⁶ t. Soil erosion is more susceptible to elevation and vegetation cover rather than the slope gradient. Meanwhile, irreversible erosion rates only occur in gardens with fraction vegetation coverage (FVC) lower than 30%, and they contribute 68.19% of total soil loss with the smallest land portion, indicating that new plantations are suffering serious erosion problems. (3) Garden patches with high erosion intensity grades and aggregation indexes should be recognized as priorities for centralized treatment. For elevations near 1900 m and lowlands (<950 m), the decrease in the fractal dimension index of erosion-prone areas indicates that patches are more regular and aggregated, suggesting a more optimistic conservation situation.

As well as very high resolution (VHR) remote sensing technology and deep learning, methods for detecting changes in buildings have made great progress. Despite this, there are still some problems with the incomplete detection of change regions and rough edges. To this end, a change detection network for building VHR remote sensing images based on Siamese EfficientNet B4-MANet (Siam-EMNet) is proposed. First, a bi-branches pretrained EfficientNet B4 encoder structure is constructed to enhance the performance of feature extraction and the rich shallow and deep information is obtained; then, the semantic information of the building is input into the MANet decoder integrated by the dual attention mechanism through the skip connection. The position-wise attention block (PAB) and multi-scale fusion attention block (MFAB) capture spatial relationships between pixels in the global view and channel relationships between layers. The integration of dual attention mechanisms ensures that the building contour is fully detected. The proposed method was evaluated on the LEVIR-CD dataset, and its precision, recall, accuracy, and F1-score were 92.00%, 88.51%, 95.71%, and 90.21%, respectively, which represented the best overall performance compared to the BIT, CDNet, DSIFN, L-Unet, P2V-CD, and SNUNet methods. Verification of the efficacy of the suggested approach was then conducted.

Investigating spatio-temporal differentiation patterns of land-use conflicts in mountainous and flatland regions provides critical insights for optimizing spatial regulation strategies and advancing sustainable regional development. Using the Urban Agglomeration in Central Yunnan (UACY) as a case study, the production–living–ecological space (PLES) was classified through land-use functional dominance analysis based on 2010–2020 geospatial datasets. Spatio-temporal evolution patterns and mountain–dam differentiation were analyzed using spatial superposition, dynamic degree analysis, transfer matrices, and geospatial TuPu methods. A multi-scale conflict index incorporating landscape metrics was developed to assess PLES conflict intensities across spatial scales, with contribution indices identifying key conflict-prone spatial types. Analysis revealed distinct regional differentiation in PLES distribution and evolutionary trajectories during 2010–2020. Forest Ecological Space (FES) and Agricultural Production Space (APS) dominated both the entire study area and mountainous zones, with APS exhibiting particular dominance in dam regions. Grassland Ecological Space (GES) and Other Ecological Space (OES) experienced rapid conversion rates, contrasting with stable or gradual expansion trends in other space types. Change intensity was significantly greater in mountainous zones compared to flatland area (FA). PLES conflict exhibited marked spatial heterogeneity. FA demonstrated substantially higher conflict levels than mountainous zones, with evident scale-dependent variations. Maximum conflict intensity occurred at the 4000 m scale, with all spatial scales demonstrating consistent escalation trends during the study period. ULS, FES, and WES predominantly occurred in low-conflict zones characterized by stability, whereas APS, Industrial and Mining Production Space (IMPS), RLS, GES, and OES were primarily associated with high-conflict areas, constituting principal conflict sources.

Non-grain conversion threatens food security in karst mountainous regions where fragmented terrain and shallow soils create unique agricultural challenges. This study examines Yunnan Province (28% karst coverage) in the Yunnan-Guizhou Plateau, where cultivated land faces distinct pressures from limited soil depth (average < 30 cm in karst areas) and poor water retention capacity. Using multi-source data (2001–2021) and an integrated Dynamic Spatial-Temporal Clustering Model (DSTCM), we quantify non-grain conversion through a clearly defined Non-Grain Conversion Index (NGCI = 0.35 × CPI + 0.25 × LUI + 0.20 × RSI + 0.20 × PSI). Results reveal the NGCI declined from 45.91 to 21.05, indicating a 54% intensification in conversion (lower values = higher conversion intensity). Spatial analysis shows significant clustering (Moran’s I = 0.57, p < 0.001), with karst areas experiencing 23% higher conversion rates than non-karst regions. Key drivers include soil fertility limitations (t = 2.35, p = 0.027), crop type transitions (t = 3.12, p = 0.047), and economic pressures (t = 2.88, p = 0.012). Model predictions (accuracy: 92.51% ± 2.3%) forecast continued intensification with NGCI reaching 9.31 by 2035 under current policies. Spatial distribution mapping reveals concentrated conversion hotspots in southeastern karst regions, with 73% of high-intensity conversion occurring in areas with >30% karst coverage. This research provides critical insights for managing cultivated land in karst landscapes facing unique geological constraints.

Soil erosion on sloping cropland is a major threat to agricultural sustainability and ecological security in mountainous regions, yet its spatial distribution and landscape-level structural characteristics remain insufficiently quantified. Taking Zhaotong in northeastern Yunnan, China, as a typical mountainous agricultural region in the upper Yangtze River Basin, this study integrated Sentinel-2 imagery, high-resolution reference data, field survey information, the Google Earth Engine platform, a random forest classifier, the Chinese Soil Loss Equation, and landscape pattern metrics to assess soil erosion on sloping cropland. The land use classification achieved an overall accuracy of 90.70% and a Kappa coefficient of 0.88, providing a reliable basis for sloping cropland extraction. Sloping cropland covered 4218.77 km², accounting for 84.64% of total cropland area, but contributed 1.84 × 10⁷ t·yr⁻¹ of annual soil loss, equivalent to 97.51% of total cropland erosion. The mean erosion rate of sloping cropland reached 4260.50 t·km⁻²·yr⁻¹, and 95.84% of sloping cropland exceeded the soil loss tolerance threshold. County-level analysis revealed strong spatial heterogeneity, with high erosion risks concentrated in northern and eastern mountainous counties. Intensive, Severe, and Extreme erosion occupied only 26.14% of the sloping cropland area but contributed 62.21% of total soil loss. Landscape metrics further showed that Moderate erosion had the highest patch density and edge density, indicating a critical fragmentation stage in erosion development. These findings support a tiered conservation strategy in which high-intensity patches are prioritized for immediate sediment reduction, while fragmented Moderate-erosion (2500–5000 t·km⁻²·yr⁻¹) areas receive preventive management. The proposed framework provides a useful approach for identifying erosion hotspots and supporting targeted soil and water conservation in mountainous agricultural landscapes.