With rapid urban population growth and industrial agglomeration, the urban land supply is becoming gradually tight. Improving land use quality (LUQ) is becoming increasingly critical. This study was carried out in the Luohe built-up zones between 2013 and 2021. The aim is to explore the growth characteristics of LUQ and determine the association between the inner urban location and the growth rate from the perspective of spatial heterogeneity. Therefore, based on a socio-economic-environmental framework, we selected an integration/GDP/population/artificial-surface Rate, and a remote-sensing-based ecological index to construct a LUQ assessment framework that is more stable and applicable for developing urban areas. Additionally, then, multiscale geographical weighted regression is adopted, which can better help us explore the scale of the location factors. The results show that: (1) The LUQ overall growth is gradually slowing. High-quality areas clustered in the urban center and subsystem elements spread outward along the national and provincial highways to drive boundary expansion; (2) In the W/E/SE direction, land use tends more towards physical sprawl than usual development and expansion; (3) Location factors were distinguished as global, semi-global, and local. The global factors constitute the homogenized locational space. Semi-global and local factors constitute a heterogeneous locational space. The latter is critical to guide LUQ growth. LUQ assessment can promote intensive land use. Exploring location factors can further guide the LUQ spatial growth and provide data in support of urban planning.

Urban Vitality (UV) is a critical indicator for measuring sustainable urban development and quality. It reflects the dynamic interactions and supply–demand coordination within urban systems, especially concerning the human–land relationship. This study aims to quantify the UV of Luohe City, China, for the year 2023, analyze its spatial characteristics, and investigate the driving patterns of socio-ecological land functions on UV intensity and heterogeneity under different scenarios. Utilizing multi-source data, including human mobility data from Baidu Location-Based Services (LBSs), Landsat-9, MODIS, and diverse geo-information datasets, we conducted factor screening and comprehensive assessments. Firstly, Self-Organizing Maps (SOMs) were employed to identify typical activity patterns, and the Urban Vitality Index (UVI) was calculated based on Human Mobility Intensity (HMI) data. Subsequently, a framework for quantity–quality–structure assessments weighted and aggregated sub-indicators to evaluate the Land Social Function (LSF) and Land Ecological Function (LEF). Following the screening process, a Multi-scale Geographically Weighted Regression (MGWR) was applied to analyze the scale and driving relationships between UVI and the land assessment sub-indicators. The results were as follows: (1) The UV distribution in Luohe City was highly uneven, with high vitality areas concentrated within the built-up regions. (2) UV showed significant correlations with both LSF and LEF. The influence of LSF on UV was stronger than that of LEF, with the effectiveness of LEF relying on the well-established provisioning of LSF. (3) Artificial Surface Ratio (ASR) and Corrected Night Lights (LERNCI) were identified as key drivers of UV across multiple scenarios. Under the weekend scenario, the Green Space Ratio (GSR) and the Vegetation Quality (VQ) notably enhanced the attractiveness of human activities. (4) The impacts of drivers varied at the urban, township, and street scales. The analysis focuses on factors with significant bandwidth changes across multiple scenarios: VQ, Remote-Sensing-based Ecological Index (RSEI), GSR, ASR, and ALSI. This study underscores the importance of socio-ecological land functions in enhancing urban vitality, offering valuable insights and data support for urban planning.

Understanding the nonlinear relationship between human activity intensity (HAI) and ecosystem resilience (ER) is crucial for sustainability, yet underdeveloped areas are often overlooked. This study examines the Xuzhou Urban Agglomeration (XZUA) from 2012 to 2022, creating a framework to assess both ER and HAI. Both frameworks utilize multi-source datasets, such as remote sensing, statistical yearbooks, and geospatial data. The ER framework uniquely combines dynamic and static indicators, while the HAI framework differentiates explicit and implicit human activity dimensions. We used spatial analysis, the Optimal Parameter Geodetector (OPGD), and Multi-Scale Geographically Weighted Regression (MGWR) to examine the nonlinear spatiotemporal interaction between HAI and ER. Results show the following: (1) ER exhibited a “shock-recovery” pattern with a net decline of 3.202%, while HAI followed a nonlinear “rise-fall” trend with a net decrease of 0.800%. (2) Spatial mismatches between HAI and ER intensified over time. (3) The negative correlation in high-HAI regions remained stable, whereas neighboring low-HAI areas deteriorated, indicating a spillover effect. (4) OPGD identified the change in HAI (Sen’s slope) as the primary driver of ER change (q = 0.512), with the strongest interaction observed between HAI Sen’s slope and precipitation (q = 0.802). (5) Compared to HAI intensity (mean), its temporal variation had a more spatially stable influence on ER. These findings offer insights for ecological management and sustainable planning in underdeveloped regions, highlighting the need for targeted HAI and ER interventions.

Rapid urban expansion in tropical coastal cities has intensified vegetation moisture stress, compromising urban resilience and ecological stability. This study investigates the spatial drivers of the Moisture Stress Index (MSI) in Makassar City, Indonesia, by integrating biophysical indicators and land-use characteristics through multi-scale regression analyses. Utilizing dry-season satellite composites (May–August 2025), the research derived Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Built-up Index (NDBI). MSI was modeled using Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) across 240 m, 480 m, and 960 m grids. Results indicate that MSI is highly sensitive to urban morphology and land-use configuration. High moisture stress was concentrated in commercial–industrial and dense residential zones characterized by extreme population densities exceeding 28,000 people/km² and elevated NDBI. In contrast, agricultural zones and open/green spaces provided significant cooling and moisture retention. Comparative performance analysis reveals that the local GWR model significantly outperformed the global OLS model, achieving a substantial reduction in AICc (−10,475.81) and resolving significant spatial autocorrelation to achieve random residuals (z-score = 1.55). The study further confirms that NDBI is the most robust biophysical predictor of MSI. Spatial heterogeneity analysis demonstrated that land-use influences are geographically contingent, with institutional areas showing varied effects based on campus design and canopy presence. These findings emphasize the necessity of scale-aware, climate-adaptive urban planning and demonstrate that GWR provides a high-fidelity tool for identifying neighborhood-level “micro-hotspots” overlooked by global modeling frameworks.

The Balaton region is Hungary’s most important recreational area, known for Central Europe’s largest freshwater lake and its traditional vineyard and horticultural landscapes. Since 1990, vineyard and orchard abandonment and intensified shoreline urbanization have increasingly threatened both landscape character and ecological balance. This study analyses land-use changes in the Balaton hinterland and evaluates the effectiveness of regional land-use regulation between 1990 and 2018, with a focus on the 2000 Balaton Law (BKÜRT), which sought to preserve traditional land uses by permitting construction only where at least 80% of vineyard parcels remained cultivated. Spatial–temporal analysis was based on CORINE Land Cover (CLC) data from 1990 to 2018, supplemented by change layers from the Copernicus Land Monitoring Service. The CORINE Land Cover classification is a three-level hierarchical system (5 Level-1 groups, 15 Level-2 classes, and 44 Level-3 classes) developed by the EEA to provide standardized, satellite-based land cover information across Europe. Land cover was aggregated into major categories (using Level-1 and Level-2 classes) relevant to the Hungarian landscape. To address CLC limitations related to representing vineyards as relatively homogeneous units despite substantial differences in the density and scale of built structures, detailed case studies were conducted in three C1 vineyard zones—Alsóörs, Paloznak, and Szentantalfa—using historical aerial photographs, Google Earth imagery, and the Hungarian Ecosystem Map (NÖSZTÉP). Despite the restrictive regulatory framework, the CLC database showed that the share of vineyards in the vineyard regulation zone (C-1, C-2) decreased between 1990 and 2018 from 45.4% to 35.8% (the share of gardens and fruit plantations had changed from 9.7% to 15.5%). In the whole Balaton region, there was an approximately 18% decline in vineyard areas. Considering the M-2 horticultural zone, the garden coverage increased from 18.9% in 1990 (17.7% in 2000) to 30.5% (share of vineyards changed from 54.3% (54.6% in 2000) to 38.8%). At the regional level, gardens and fruit plantations had a smaller decrease (3.2%). Although overall trends were more favorable than at the national level, regulatory measures proved insufficient to prevent the conversion of vineyards and orchards in sensitive areas, particularly on slopes overlooking the lake, in proximity to tourist hubs, and in areas exposed to strong development pressure. By 2018, the C1 zone had expanded spatially but became less targeted, as the proportion of vineyards within it decreased. Boundary refinements failed to substantially improve regulatory precision or effectiveness. The case studies reveal a gradient of regulatory strictness reflecting differing landscape protection priorities and stages of vineyard transformation, with Alsóörs responding to long-standing, partly irreversible changes while attempting to slow further landscape alteration. To counter ongoing negative trends, more targeted and enforceable regulations are required, including a clearer separation of cultivated and recreational land uses, a maximum building size of 80 m² for recreational properties, and a reassessment of vineyard zone boundaries to better reflect active cultivation and protect sensitive landscapes.