Existing studies on eco-efficiency evaluation often neglect the social and ecological benefits of land use, as well as the spatial heterogeneity in the effects of natural factors. This study aims to establish a systematic eco-efficiency evaluation framework and examine the dynamic impacts of various factors on land use eco-efficiency at the county scale. An evaluation framework integrating economic, ecological, and social outputs was constructed using multi-source remote sensing data. The super-efficiency Slack-Based Measure model and exploratory spatial data analysis method were employed to analyze the land use eco-efficiency and its spatiotemporal characteristics in the Huaihai Economic Zone from 2000 to 2022. Furthermore, the enhanced Geographically and Temporally Weighted Regression model was applied to examine the spatiotemporal heterogeneity of industrial structure, ecological background, land use, and other factors influencing land use eco-efficiency. The results indicated the following: (1) The average land use eco-efficiency in the Huaihai Economic Zone followed a pronounced “U-shaped” pattern, initially decreasing before increasing. At the county level, efficiency values generally showed an upward trend, with municipal districts or county-level cities accounting for more than half of the areas. (2) A weak positive spatial agglomeration of land use eco-efficiency was observed at the county level. High-efficiency areas shifted from a dispersed pattern to a “T-shaped” spatial distribution, while low-efficiency areas gradually converged toward the southern regions, evolving from an “H-shaped” cluster. (3) The influence of industrial structure, urbanization, environmental regulation, ecological factors, and land use on ecological efficiency exhibited clear spatial and temporal heterogeneity. The multi-dimensional land use eco-efficiency evaluation offers an effective tool for comprehensively assessing ecological conservation, environmental sustainability, and socio-economic development. It also provides valuable insights for improving land use eco-efficiency at the county level.

Accurate land use/land cover (LULC) maps generated through cloud computing can support large-scale land management. Leveraging the rich resources of Google Earth Engine (GEE) is essential for developing historical maps that facilitate the analysis of regional LULC dynamics. We implemented the best-performing scheme on GEE to produce 30 m LULC maps for the Shandong Peninsula urban agglomeration (SPUA) and to detect LULC changes, while closely observing the spatio-temporal trends of landscape patterns during 2004–2024 using the Shannon Diversity Index, Patch Density, and other metrics. The results indicate that (a) Gradient Tree Boost (GTB) marginally outperformed Random Forest (RF) under identical feature combinations, with overall accuracies consistently exceeding 90.30%; (b) integrating topographic features, remote sensing indices, spectral bands, land surface temperature, and nighttime light data into the GTB classifier yielded the highest accuracy (OA = 93.68%, Kappa = 0.92); (c) over the 20-year period, cultivated land experienced the most substantial reduction (11,128.09 km²), accompanied by impressive growth in built-up land (9677.21 km²); and (d) landscape patterns in central and eastern SPUA changed most noticeably, with diversity, fragmentation, and complexity increasing, and connectivity decreasing. These results underscore the strong potential of GEE for LULC mapping at the urban agglomeration scale, providing a robust basis for long-term dynamic process analysis.

Land use changes within urban agglomerations exhibit significant spatiotemporal heterogeneity and regional diversity. In urban agglomeration land simulation, traditional models often struggle to systematically capture these variations. We introduce the GCTP, a novel framework that integrates guided Geographical zoning, Convolutional Neural Networks (CNN)-Transformer, and the Patch-generating Land Use Simulation (PLUS) model. Initially, guided K-means clustering was employed for geographic zoning to characterize regional spatial non-stationarity. Then, a CNN-Transformer network leveraged self-attention mechanisms to capture multi-scale spatial correlations, obtaining pixel-level development probabilities. Finally, these probabilities were fused with PLUS- Land Expansion Analysis Strategy (LEAS) outputs to drive PLUS- Cellular Automata with multi-type Random Seeds (CARS) for patch-level simulation. The results demonstrate the following: (1) The embedding of guided zoning enabled the model to achieve an Overall Accuracy (OA) of 0.941, effectively mitigating global simulation bias. (2) The optimal simulation performance occurred at a fusion weight of 0.81, yielding a Kappa of 0.8917 and an Figure of Merit (FoM) of 0.3830, significantly exceeding a single model. (3) The 2030 simulation indicates that the GCTP model effectively reduces isolated pixels at urban fringes. The GCTP generates neighborhood patterns with high spatial compactness and geographic consistency. This study highlights the significant advantages of integrating long-range spatial perception with geographical heterogeneity constraints in the land expansion simulation of urban agglomerations. The findings support more precise territorial spatial planning practices.

Quantifying landscape ecological risk (LER) using multi-period land use data and ecological indicators is essential for understanding regional ecological dynamics. However, LER assessment is sensitive to spatial delineation, introducing uncertainty. This study developed an integrated LER model that incorporates the remote sensing ecological index and abundance index, and evaluated spatial unit effects through comparative analyses of fishnet, hexagonal, sub-watershed, and county units. LER dynamics in the Shandong Peninsula Urban Agglomeration (SPUA) from 2004 to 2024 were analyzed, and a boosted regression trees model was applied to quantify the relative importance of influencing factors and their nonlinear effects. The results indicate that: (1) sub-watershed units showed greater robustness and stability across multiple evaluation indicators, supporting their suitability for LER assessment; (2) LER in the SPUA exhibited a fluctuating but overall slightly increasing trend over the past two decades, with a persistent west-low and east-high spatial pattern; and (3) relief degree (29.13%) and nighttime light (17.78%) were the dominant factors shaping LER, showing an inverted U-shaped response and a saturating nonlinear increase, respectively. This study supports the use of sub-watershed units as an appropriate spatial unit for LER assessment and provides insights into terrain-sensitive conservation and sustainable land-use management in urbanizing regions.