Land use pattern is a dominant factor causing carbon storage changes in terrestrial ecosystems and is crucial for maintaining the stability of carbon storage. Understanding the impact of land use on carbon storage variations in drylands is of great significance for local ecological protection and the sustainable management of land resources. Based on the land use data of the Gonghe Basin from 1990 to 2020, the InVEST model was applied to analyze the spatiotemporal changes in carbon storage, and the PLUS model was used to predict the changes in carbon storage under three different development scenarios in 2030. The results are as follows: (1) From 1990 to 2020, the main land use types in the Gonghe Basin were grassland and unused land, with an overall increase in grassland and a marked decrease in unused land. (2) The spatial distribution of carbon storage was generally characterized by being low in the center and high at the edge, and grassland was the most important land use type with the highest carbon storage. Over the past 30 years, it has shown an increase followed by a decline, with an overall increase of 1.84%. (3) The carbon storage under the natural trend, urban development, and ecological protection scenarios will be 158.80 × 10⁶ Mg, 158.66 × 10⁶ Mg, and 159.83 × 10⁶ Mg in 2030, respectively. The grassland and cropland areas were larger under the ecological protection scenario, which was more conducive to improving the carbon storage in this region. This study provides an effective reference for optimizing land use and achieving carbon neutrality (“dual carbon” goals) in drylands.

Ecological Security (ES) is an essential safeguard for regional sustainable development. Scientifically elucidating the multiscale evolution of ES patterns and their driving mechanisms is critical for ecological governance and conservation in Mountainous Urban Agglomerations (MUAs). Taking the central Yunnan Urban Agglomeration (CYUA) as a representative MUA, this study constructs a three-dimensional ES assessment framework integrating ecological health, ecological sensitivity, and ecological risk. By integrating ES slope-spectrum analysis with spatial autocorrelation, Geodetector, Multiscale Geographically Weighted Regression (MGWR), and machine learning, we analyze the spatiotemporal evolution of regional ES patterns and their driving mechanisms from a multiscale perspective. Results show that from 2000 to 2020, ES in the CYUA exhibited an overall improving trend with clear scale dependency. At the micro-scale, urban expansion intensified ecological fragmentation, whereas at the macro-scale, regional integration under policy guidance was evident. ES shows significant differentiation along slope gradients, forming a typical pattern of “low-slope–high-risk and high-slope–high-security,” with the 10–25° interval identified as a “conflict front” between ecological conservation and urban development, facing elevated degradation risks. Human Activity Intensity (HAI) is the dominant driver of ES spatial differentiation, with a critical pressure threshold of 0.29, and exhibits significant nonlinear interactive effects with slope and NDVI, with q-values exceeding 0.6. Overall, this study reveals complex human–environment interactions in MUAs and provides scientific evidence for balancing topographic constraints with urbanization, optimizing territorial spatial patterns, and promoting coordinated development of ecological conservation and high-quality urbanization.

Large-scale agricultural remote sensing monitoring is challenged by pronounced spatial heterogeneity arising from fragmented terrain, complex climatic backgrounds, and diverse cropping structures. However, existing agricultural zoning schemes generally lack an integrated consideration of remote sensing imaging mechanisms and key variable conditions such as atmospheric interference and crop phenology, limiting their direct utility in guiding region-specific sensor selection and classification algorithm calibration. To address this limitation, this study integrates multi-source earth observation data and agricultural statistical information to construct an Agricultural Remote-sensing Classification Difficulty Index (ARCDI) from multiple dimensions, including image availability, cropping structure, cropland fragmentation, and topographic environment. On this basis, a graph theory-based spatially constrained Skater clustering algorithm is introduced to establish a two-tier “cropland–major cereal crops” zoning framework oriented toward remote sensing applications. The results indicate that the proposed framework delineates five distinct first-tier cropland classification difficulty zones across China. This zoning scheme effectively quantifies the regional heterogeneities in monitoring challenges. Building upon this first-tier zoning, the framework is further refined into 50 second-tier major cereal crop classification difficulty zones, including 13 winter wheat zones, 21 maize zones, and 16 rice zones. Statistical tests and spatial analyses demonstrate that the proposed zoning scheme significantly outperforms conventional clustering approaches in balancing within-zone homogeneity and spatial continuity. This advantage is quantitatively reflected by consistently lower residual spatial autocorrelation (residual Moran’s I ≈ 0.10–0.11) and an approximately 20% reduction in within-zone variance compared with other spatially constrained methods. Extensive field-sample validation provides preliminary evidence of an inverse relationship between crop-type classification difficulty and accuracy. These results confirm the framework’s reliability in identifying regional difficulty and its decision-support value for selecting remote sensing strategies. Overall, this study systematically elucidates the spatial differentiation patterns of remote sensing classification difficulty for cropland and major cereal crops across China. The proposed framework provides robust scientific support for data selection, algorithm optimization, and differentiated strategy formulation in national-scale agricultural monitoring, thereby facilitating the operationalization of regional agricultural remote sensing applications.