Journal Description
Land
Land
is an international, cross-disciplinary, peer-reviewed, open access journal on land system science, landscape, soil and water, urban study, land–climate interactions, water–energy–land–food (WELF) nexus, biodiversity research and health nexus, land modelling and data processing, ecosystem services, multifunctionality and sustainability, and is published monthly online by MDPI. The International Association for Landscape Ecology (IALE), International Federation of Landscape Architects (IFLA), European Land-use Institute (ELI), Landscape Institute (LI) and Urban Land Institute (ULI) are affiliated with Land, and their members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SSCI (Web of Science), GEOBASE, PubAg, AGRIS, GeoRef, RePEc, and other databases.
- Journal Rank: JCR - Q2 (Environmental Studies) / CiteScore - Q1 (Nature and Landscape Conservation)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.5 days after submission; acceptance to publication is undertaken in 2.5 days (median values for papers published in this journal in the second half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journal: Drylands.
- Journal Cluster of Environmental Science: Sustainability, Land, Clean Technologies, Environments, Nitrogen, Recycling, Urban Science, Safety, Air, Waste, Aerobiology and Toxics.
Impact Factor:
3.2 (2024);
5-Year Impact Factor:
3.4 (2024)
Latest Articles
Spatiotemporal Analysis of Land Subsidence in the Sant’Eufemia Plain (Calabria Region, Italy) Using InSAR Techniques
Land 2026, 15(5), 836; https://doi.org/10.3390/land15050836 (registering DOI) - 14 May 2026
Abstract
Subsidence is the lowering of the ground surface caused by both natural processes, such as geological and tectonic dynamics, and anthropogenic activities related to land and resource use. Identifying and monitoring this phenomenon is essential for several reasons, including ensuring public safety, supporting
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Subsidence is the lowering of the ground surface caused by both natural processes, such as geological and tectonic dynamics, and anthropogenic activities related to land and resource use. Identifying and monitoring this phenomenon is essential for several reasons, including ensuring public safety, supporting the sustainable management of subsurface resources, and mitigating potential economic impacts. This study investigates ground deformation in an underexplored sector of the Calabria Region (Southern Italy), namely the Sant’Eufemia Plain. To this end, long-term Sentinel-1 datasets were processed using multi-temporal Synthetic Aperture Radar Interferometry techniques. Significant subsidence, reaching locally up to −17 mm/yr, was detected in the industrial area of San Pietro Lametino. Historical SAR datasets (ERS, ENVISAT) and optical imagery were used to reconstruct the long-term evolution of deformation since the 1990s. Satellite observations were integrated with rainfall records, piezometric data, and geotechnical modelling. A spatially distributed comparison between groundwater level variations and InSAR-derived deformation, supported by local time-series analysis, highlights weak and inconsistent correlations, indicating that groundwater fluctuations alone do not linearly control subsidence. The results suggest that subsidence is primarily associated with long-term consolidation processes affecting highly compressible Holocene deposits, likely enhanced by anthropogenic loading, while groundwater variations may contribute by modifying effective stress conditions within the subsoil. The relative contribution of these processes remains unquantified, highlighting the need for coupled hydro-mechanical investigations.
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(This article belongs to the Special Issue Digital Earth and Remote Sensing for Land Management, 2nd Edition)
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Manas River System Land Use Pattern Progressions: Drainage Divides to Riparian Regions
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Yuxuan Yang, Quanhua Hou, Jinxuan Wang, Xinyue Hou, Yazhen Du and Jiaji Li
Land 2026, 15(5), 835; https://doi.org/10.3390/land15050835 (registering DOI) - 13 May 2026
Abstract
In arid inland watersheds, the compounding impacts of climate change and intensive human activities have severely altered hydrological regimes and accelerated landscape degradation. However, conventional spatial planning often overlooks the critical coupling between subsurface hydrological processes and surface landscape dynamics. Taking the Manas
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In arid inland watersheds, the compounding impacts of climate change and intensive human activities have severely altered hydrological regimes and accelerated landscape degradation. However, conventional spatial planning often overlooks the critical coupling between subsurface hydrological processes and surface landscape dynamics. Taking the Manas River Watershed in northwestern China as a representative case, this research investigates the multi-scale dynamics of landscape patterns and their underlying spatial determinants. Integrating multi-period land-use data (2000–2020), landscape metrics, and the GeoDetector model, we diverge from conventional uniform buffer approaches by redefining riparian boundaries utilizing four distinct River–Groundwater Transformation (RGT) patterns. This methodological shift reveals critical eco-hydrological heterogeneities previously masked by fixed-width approaches. Our multi-scale analyses demonstrate that watershed-level landscapes exhibited a trajectory of declining diversity, transient recovery, and ultimately, intensified fragmentation, while riparian patches concurrently expanded and became increasingly homogenized. GeoDetector assessments indicate a fundamental shift in driving forces: early-stage variations were constrained by natural factors, whereas post-2010 dynamics became overwhelmingly dominated by socio-economic determinants, particularly agricultural expansion and GDP growth. Crucially, our RGT-coupled spatial analysis reveals a strong spatial association between agricultural sprawl and landscape risk hotspots concentrated within groundwater overflow zones—a pattern consistent with, but not directly demonstrating, disrupted vertical hydrological connectivity. Direct verification of subsurface mechanisms would require continuous piezometric monitoring beyond the scope of this study. Consequently, rather than generic zoning, we propose a multi-scale “hydro-spatial” governance framework featuring targeted interventions. By establishing strict agricultural redlines in vulnerable overflow zones and implementing eco-hydrological restoration tailored to specific RGT regimes, this paradigm delivers robust methodological insights for advancing precision spatial planning in fragile arid ecosystems.
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(This article belongs to the Special Issue Multiscale Geospatial and Remote Sensing Approaches for Landscape Ecology)
Open AccessArticle
Beyond Compliance: A Whole-Life-Cycle Governance Framework for Public Service Facilities in Urban Regeneration—An Exploratory Longitudinal Case Study of Guangzhou, China
by
Jianjun Li, Guangxian Lu and He Jin
Land 2026, 15(5), 834; https://doi.org/10.3390/land15050834 (registering DOI) - 13 May 2026
Abstract
In the context of the global urban transition towards stock-based regeneration (a shift from outward urban expansion to the redevelopment of existing built environments), the provision of public service facilities is facing a paradigm shift from mere physical spatial implementation to sustainable long-term
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In the context of the global urban transition towards stock-based regeneration (a shift from outward urban expansion to the redevelopment of existing built environments), the provision of public service facilities is facing a paradigm shift from mere physical spatial implementation to sustainable long-term operation. Traditional planning pathways heavily rely on static spatial allocation policies and upfront indicator compliance, yet systematically neglect the dynamic adaptability of facilities throughout their entire life cycles. Taking the megacity of Guangzhou, China, as a longitudinal case study, this paper reveals a typical compliance versus failure paradox—a situation where facilities strictly meet technical planning standards on paper but fail to deliver intended social welfare outcomes in practice. Using early comprehensive redevelopment projects like Liede Village as examples, the public service facilities strictly met the statutory allocation standard (5.7%) during the construction phase. However, after more than a decade of operation, these facilities have exhibited severe structural supply–demand mismatches and long-term operational dilemmas. To address this issue, this study proposes a four-dimensional governance framework—Value, Actor, Space, Institution (VASI)—that transcends traditional spatial perspectives. Through an in-depth analysis of the Guangzhou case using this framework, the research confirms that the root causes of the compliance failure lie in the absence of life-cycle costing (a method of assessing the total financial cost of facility ownership over its entire lifespan), the severe structural misalignment of rights and responsibilities between construction and operation actors, and the long-term void in post-occupancy evaluation feedback mechanisms. This paper argues that the planning of public service facilities in high-density megacities must achieve a theoretical leap from rigid upfront technical allocation to adaptive whole-life-cycle systemic governance, providing theoretical references and a practical guide for global cities facing similar stock-based regeneration challenges as they move towards equitable and socio-economically sustainable urban regeneration.
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(This article belongs to the Special Issue Participatory Land Planning: Theory, Methods, and Case Studies—Second Edition)
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Spatio-Temporal Reconstruction of MODIS LAI Using a Self-Supervised Framework for Vegetation Dynamics Monitoring Across China
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Huijing Wu, Ting Tian, Haitao Wei and Hongwei Li
Land 2026, 15(5), 833; https://doi.org/10.3390/land15050833 (registering DOI) - 13 May 2026
Abstract
Leaf Area Index (LAI) is a key biophysical parameter for characterizing terrestrial vegetation dynamics and land surface processes. Time-series MODIS LAI products are widely used in ecological and land-related research, but cloud contamination and sensor noise lead to widespread spatio-temporal gaps, limiting their
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Leaf Area Index (LAI) is a key biophysical parameter for characterizing terrestrial vegetation dynamics and land surface processes. Time-series MODIS LAI products are widely used in ecological and land-related research, but cloud contamination and sensor noise lead to widespread spatio-temporal gaps, limiting their ability to support long-term, consistent vegetation monitoring over large areas. To address this issue, this study proposes a novel self-supervised LAI reconstruction framework (SSLAI) for generating gap-free and ecologically consistent LAI datasets across China. The framework integrates cross-modal environmental fusion, multi-scale spatio-temporal modeling, and adaptive phenological constraints to ensure the reconstructed LAI aligns with realistic vegetation growth rhythms. SSLAI outperforms seven traditional and state-of-the-art deep learning methods, maintaining a root mean square error (RMSE) below 0.20 even with 16 missing time windows. Field validation confirms its high accuracy, with a coefficient of determination (R2) of 0.885 and an RMSE of 0.477. Furthermore, SSLAI’s response to meteorological changes aligns with ecological principles, demonstrating favorable physical interpretability and ecological rationality. The reconstructed LAI exhibits superior spatial completeness and temporal consistency compared with MODIS, VIIRS, and GLASS products, and performs robustly under variable climatic conditions. This study provides an effective self-supervised solution for MODIS LAI gap-filling over large regions, and the generated high-quality LAI dataset can serve as a reliable data foundation for vegetation dynamics monitoring, land surface modeling, and global change research.
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Open AccessArticle
The Impact of Rural Collective Property Rights System Reform on County-Level Urban–Rural Integration: Evidence from 1106 Counties in China
by
Xinyue Sun and Hengzhou Xu
Land 2026, 15(5), 832; https://doi.org/10.3390/land15050832 (registering DOI) - 13 May 2026
Abstract
The rural collective property rights system reform (RCPRSR) is a pivotal institutional innovation for revitalizing rural resources, optimizing factor allocation, and advancing urban–rural integration—a core goal of sustainable land use planning. This study evaluates the reform’s impact on county-level urban–rural integration using panel
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The rural collective property rights system reform (RCPRSR) is a pivotal institutional innovation for revitalizing rural resources, optimizing factor allocation, and advancing urban–rural integration—a core goal of sustainable land use planning. This study evaluates the reform’s impact on county-level urban–rural integration using panel data from 1106 Chinese county-level administrative units during 2013–2020. Treating the staggered rollout of reform pilots as a quasi-natural experiment, we employ a multi-period difference-in-differences approach. The results show that the RCPRSR significantly promotes urban–rural integration, a finding robust to a series of sensitivity checks. The policy effects exhibit marked heterogeneity: the dividends of narrowing the urban–rural development gap are more pronounced in poverty-stricken counties and areas with lower baseline integration levels. Mechanism analysis reveals two pathways—population agglomeration and industrial structure optimization—through which the reform operates, specifically manifested as enhanced county population carrying capacity, accelerated tertiary industry development, and deepened secondary–tertiary industrial integration. These findings provide empirical evidence for optimizing rural property rights reform and advancing sustainable urban–rural development.
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(This article belongs to the Special Issue Synergies in Land Use Planning: Advancing Urban–Rural Integration for Sustainable Development (2nd Edition))
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Modeling Employment Sectoral Distribution Using POI Data: Assessing Tourism Functions in Data-Scarce Destinations
by
Feng Xing and Sophia Shuang Chen
Land 2026, 15(5), 831; https://doi.org/10.3390/land15050831 (registering DOI) - 13 May 2026
Abstract
With the advancement of urbanization, the functions of cities continue to expand and deepen, among which the tourism function plays an increasingly important role in urban and regional economic development. To resolve the challenges in data acquisition for urban function classification and assessment,
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With the advancement of urbanization, the functions of cities continue to expand and deepen, among which the tourism function plays an increasingly important role in urban and regional economic development. To resolve the challenges in data acquisition for urban function classification and assessment, this study introduces POI data and machine learning methods to construct an employment sector distribution model. This enables the estimation of tourism-related employment data in Pacific Island countries. The tourism function of these cities is quantitatively evaluated based on two dimensions: functional scale and functional intensity. The results show that: (1) The constructed employment sector distribution model demonstrates strong predictive performance. The error rate for the total employed population in each island country is below 10%. The Bootstrap robustness test confirms that predicted values for all countries fall within the 95% confidence interval. The number of tourism employees shows a significant positive correlation with inbound tourist numbers and the count of tourism-related POIs at the 0.01 level. Empirical validation shows tourism-related sector error rates of 4.44% for Ningbo and 9.02% for Wuxi, both of which are under 10%. (2) Tourism in thirteen countries, including Samoa and Tonga, constitutes a fundamental function of the national economy, whereas in Papua New Guinea, tourism is a non-fundamental function, reflecting a lower degree of economic reliance on the tourism sector. (3) A provisional typology of tourism functions is proposed, identifying Fiji and The Cook Islands as robustly specialized, while Papua New Guinea remains characterized by stable low-specialization. The remaining 11 countries occupy transitional positions where classification is sensitive to prediction uncertainty. Subject to this caveat, the PICs are provisionally categorized into three groups: medium-to-large specialized (Fiji, Cook Islands, Vanuatu, and Samoa), small specialized (Tuvalu, Palau, Solomon Islands, and Tonga), and low-specialization (Papua New Guinea, Kiribati, Federated States of Micronesia, Nauru, Niue, and Marshall Islands). The classification results can guide these island nations in enhancing their tourism functions, fostering sound regional development, and enabling more effective participation in global governance.
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(This article belongs to the Special Issue Sustainable Urban and Rural Planning for Tourism: Designing a Better Future for Residents)
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Does Trade Union Participation Increase Rural–Urban Migrant Workers’ Willingness of Homestead Withdrawal?
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Wenfeng Fu, Yangshuo Bian, Jiahui Wan, Jie Guo and Minghao Ou
Land 2026, 15(5), 830; https://doi.org/10.3390/land15050830 (registering DOI) - 13 May 2026
Abstract
Enhancing the willingness of rural–urban migrant workers (RUMs) to pursue the withdrawal of rural homesteads is a key measure to deepen the reform of the rural land system and advance new-type urbanization. This study aims to examine the impact of trade union participation
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Enhancing the willingness of rural–urban migrant workers (RUMs) to pursue the withdrawal of rural homesteads is a key measure to deepen the reform of the rural land system and advance new-type urbanization. This study aims to examine the impact of trade union participation on RUMs’ willingness to withdraw from rural homesteads (WFRH). It further offers implications for improving trade union services and refining relevant institutional arrangements for homestead withdrawal. Based on valid questionnaire data from 1949 RUMs in Hefei, Anhui Province, China, analytical methods, including the ordered Probit model, Propensity Score Matching (PSM), and KHB model, are adopted for empirical analysis. The main conclusions are as follows: trade union participation significantly enhances RUMs’ willingness to WFRH. This conclusion remains robust after the replacement of explained variables, adjustment of econometric models, and use of the PSM method to correct for selection bias. Heterogeneity analysis based on an ordered probit model reveals that the impact of trade union participation on homestead withdrawal willingness is more pronounced among females, individuals under 45 years old, and those with a college degree or above. Mediation effect test based on the KHB model finds that urban identity and sense of social fairness play mediating roles between trade union participation and RUMs’ homestead withdrawal willingness. Trade union participation improves their withdrawal willingness by strengthening their urban identity and sense of social equity. Efforts should be made to enhance the willingness of RUMs to withdraw from homesteads by improving the service function system of “capacity cultivation + rights protection + emotional connection” of trade unions, expanding the effective coverage of trade union organizations, promoting the collaborative linkage between “trade unions and governments”, and strengthening the full process service support for homestead withdrawal. The study conclusions help optimize the allocation of rural land resources and advance the integration of urban and rural development.
Full article
(This article belongs to the Special Issue State of the Art in Agriculture in Rural Areas: For Sustainable Land Management, 2nd Edition)
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Understanding Spatial Patterns and Drivers of Outdoor Recreation Participation in Southeastern National Forests
by
Rosny Jean and Kozma Naka
Land 2026, 15(5), 829; https://doi.org/10.3390/land15050829 (registering DOI) - 13 May 2026
Abstract
This study examines the spatial patterns and key drivers of outdoor recreation participation in the 14 National Forests (NFs) of USDA Forest Service Region 8—covering thirteen southeastern states and El Yunque NF in Puerto Rico (15 forest units in total)—based on data from
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This study examines the spatial patterns and key drivers of outdoor recreation participation in the 14 National Forests (NFs) of USDA Forest Service Region 8—covering thirteen southeastern states and El Yunque NF in Puerto Rico (15 forest units in total)—based on data from the USDA National Visitor Use Monitoring (NVUM) 2010–2014 microdata cycle. We postulated that spatial autocorrelation is statistically significant for individual recreation drivers, particularly around forest boundaries and major road networks. We test for spatial autocorrelation using Global Moran’s I and Local Indicators of Spatial Association (LISA) and identify hot spots with Getis–Ord Gi* statistics. Spatial regression models (OLS, spatial lag, and spatial error) are estimated to assess the effects of proximity to major roads and distance from forest boundaries on population-normalised visitation intensity. We find significant spatial autocorrelation in overall visitation intensity (Global Moran’s I = 0.312, p < 0.001), with high clusters observed within 50 km of forest boundaries and along major Interstate highway corridors. At least four of five key recreation drivers are significantly clustered. Our results provide spatially specific, statistically robust evidence to inform NF management.
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(This article belongs to the Special Issue The Role of Land Policy in Shaping Tourism Development: 2nd Edition)
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Dynamic Weighted Monitoring of Surface Deformation in Mining Areas Based on Multi-Source Remote Sensing from Space, Airborne, and Ground Platforms
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Zijian Wang, Youfeng Zou, Weibing Du, Yingying Su, Hebing Zhang, Huabin Chai, Xiaofei Mi, Litao Xu, Caifeng Guo and Junlin Zhu
Land 2026, 15(5), 828; https://doi.org/10.3390/land15050828 (registering DOI) - 13 May 2026
Abstract
Coal mines constitute a vital component of the national security system, where the extraction and utilisation of coal resources directly impact mine stability and engineering safety. Therefore, addressing the surface deformation issues caused by repeated mining activities across multiple coal seams at the
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Coal mines constitute a vital component of the national security system, where the extraction and utilisation of coal resources directly impact mine stability and engineering safety. Therefore, addressing the surface deformation issues caused by repeated mining activities across multiple coal seams at the Daliuta Mine, this study proposes a multi-source remote sensing monitoring technology system, which aims to improve the accuracy of surface deformation in the mining area. At the mining area scale, optimised Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology utilised 168 Sentinel-1A image scenes to generate 789 interferometric image pairs. This extracted the long-term surface deformation field of the Daliuta mining area, revealing the spatiotemporal evolution patterns of surface subsidence under repeated mining activities. To further enhance monitoring accuracy and reliability, this study proposed a Satellite Aerial-Prior Weighting (SA-PW) method. This approach integrated satellite-based time-series InSAR, aerial Pixel Offset Tracking (POT), and unmanned aerial vehicle light detection and ranging (UAV LiDAR) data through a dynamic priority weighting model. This enabled the synergistic inversion of high-precision surface deformation parameters for repeatedly mined areas. Results demonstrated that compared to SBAS-InSAR alone, the SA-PW method achieved a 10% improvement in surface deformation parameter accuracy. By constructing a dynamic priority-weighted model, this approach systematically integrated multi-source data to achieve collaborative inversion of high-precision surface deformation parameters in repeatedly mined areas. Results demonstrated that compared to SBAS-InSAR and UAV LiDAR methods, SA-PW data fusion yielded higher accuracy, with MAE and RMSE values of 60 mm and 90 mm on the A line, and 57 mm and 83 mm on the H line, respectively. This method facilitates the establishment of integrated air–space–ground real-time monitoring networks for mining areas, enables subsidence hazard early warning and management, identifies key zones for ecological restoration, explores synergistic mechanisms between repeated mining and ecological rehabilitation, and promotes safe and green mining operations and development.
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(This article belongs to the Section Land – Observation and Monitoring)
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Spatiotemporal Interaction of Diverse Agricultural Business Entities and Arable Land Transfer: An Empirical Study of 30 Provinces in China During 2012–2020
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Zhengtong Wei, Guanghao Li, Liming Liu and Guanyi Yin
Land 2026, 15(5), 827; https://doi.org/10.3390/land15050827 (registering DOI) - 13 May 2026
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To investigate the heterogeneous interactions between various agricultural business entities (abbreviated as ABEs, including farmers, cooperatives and enterprises) and agricultural land transfer (abbreviated as ALT) in China, this study constructs a spatial simultaneous equation model based on the GS3SLS method and applied to
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To investigate the heterogeneous interactions between various agricultural business entities (abbreviated as ABEs, including farmers, cooperatives and enterprises) and agricultural land transfer (abbreviated as ALT) in China, this study constructs a spatial simultaneous equation model based on the GS3SLS method and applied to data from 30 provinces in 2012–2020. The results show the following: (1) ABEs and ALT demonstrate significant bidirectional positive correlations at the intra-regional level, especially among farmers, while cooperatives and enterprises exhibit more pronounced spatial spillover effects. (2) Despite overall positive correlations, negative interactions emerge in specific entities of some regions (e.g., central China’s ALT among farmers vs. central China’s ABEs among farmers, and eastern China’s ABEs among enterprises vs. neighboring ABEs among enterprises). Conversely, cooperatives maintain universally positive ABE-ALT interactions, peaking in central/western regions. (3) The co-development of ABEs and ALT exhibits temporal heterogeneity: the growth in the number of farmer ABEs lags behind their agricultural land transfer (ALT), whereas for cooperatives and agricultural enterprises, ALT lags behind their growth in numbers. This indicates that the relationship between agricultural operators (“human”) and land transfer (“land”) needs to be reconfigured. The heterogeneous interactive relationships revealed in this study provide a solid theoretical basis for formulating differentiated and precise policies on the transfer of agricultural land and the coordination of various operating entities, so as to efficiently promote agricultural modernization.
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Impacts of Alpine Grassland Degradation on Soil Aggregate Distribution and Stability in the Qinghai Lake Basin, Qinghai–Tibetan Plateau
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Jie Ma, Wei Wang, Yuan Han, Guoqing Niu, Xiaolong Li, Yuanjie Hu, Ping Zhang, Jifu Zhang and Xiang Liu
Land 2026, 15(5), 826; https://doi.org/10.3390/land15050826 (registering DOI) - 12 May 2026
Abstract
Under the influence of climate change and human activities, alpine grasslands in the Qinghai Lake basin have undergone a degradation trend over recent decades. In this context, investigating the distribution and stability of soil aggregates across varying degradation degrees of alpine grasslands, along
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Under the influence of climate change and human activities, alpine grasslands in the Qinghai Lake basin have undergone a degradation trend over recent decades. In this context, investigating the distribution and stability of soil aggregates across varying degradation degrees of alpine grasslands, along with their driving factors, is critical for formulating sustainable management strategies to maintain grassland health and soil structural resilience in this ecologically sensitive region. In this study, plant and soil samples (0–20 cm) were collected at nine sites in the Qinghai Lake basin, each encompassing a non-degraded (ND), a lightly degraded (LD), and a heavily degraded (HD) grassland plot. The distribution and stability of mechanically stable aggregates and water-stable aggregates were evaluated using the dry-sieving and wet-sieving methods, respectively. The results showed that grassland degradation led to declines in plant above-ground and below-ground biomass, soil carbon, nitrogen, phosphorus, and microbial biomass carbon contents, and β-1,4-nacetylglucosaminidase activity, alongside an increase in soil pH. However, soil β-1,4-glucosidase and alkaline phosphatase activities exhibited no significant changes. The 2–0.25 mm fraction is the primary component of mechanically stable aggregates in alpine grasslands across three degradation levels. After degradation, neither the distribution nor the stability of mechanically stable aggregates exhibited significant changes. In terms of water-stable aggregates, the 2–0.25 mm fraction constituted the primary component in ND and LD, whereas the <0.053 mm fraction predominated in HD. Additionally, the mass proportions of the >2 mm and 2–0.25 mm size fractions were significantly lower in HD compared to ND, while the mass fraction of the <0.053 mm fraction was notably higher. The altered distribution of water-stable aggregates resulted in a significant decrease in mean weight diameter and a notable increase in the percentage of aggregate destruction, suggesting a reduced resistance of the soil to water erosion. Plant below-ground biomass, soil total organic carbon, and total nitrogen were identified as crucial factors modulating the dynamics of aggregate stability during grassland degradation. The findings of this study suggest that alpine grassland degradation in the Qinghai Lake basin reduces the water stability rather than the mechanical stability of soil aggregates.
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(This article belongs to the Section Land Use, Impact Assessment and Sustainability)
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The BES–GDP Nexus: A Panel Econometric and Machine Learning Analysis of Italian Regions
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Angelo Leogrande, Massimo Arnone, Carlo Drago, Alberto Costantiello and Fabio Anobile
Land 2026, 15(5), 825; https://doi.org/10.3390/land15050825 (registering DOI) - 12 May 2026
Abstract
The study investigates the interrelationship between the performance of the regional economy in Italy and the multidimensionality of wellbeing, as defined by the ISTAT Benessere Equo e Sostenibile (BES) model. Based on panel data from 19 Italian regions and 2 autonomous provinces—Trentino and
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The study investigates the interrelationship between the performance of the regional economy in Italy and the multidimensionality of wellbeing, as defined by the ISTAT Benessere Equo e Sostenibile (BES) model. Based on panel data from 19 Italian regions and 2 autonomous provinces—Trentino and Bolzano (2012–2023)—the research aims to explore whether there is a link between regional GDP and the three BES dimensions: Benessere (B), Equità (E), and Sostenibilità (S). The innovative contribution of this paper is not the creation of a novel theoretical model, but a multilayered empirical approach that combines panel data methods, machine learning, and clustering. This approach makes it possible to reveal nonlinearities, complex interactions, and regional heterogeneity in BES–GDP relationships. The analysis of the Benessere dimension based on k-Nearest Neighbors reveals nonlinear dynamics related to health, mobility, security, digital access, and socio-economic conditions. Furthermore, cluster analysis identifies territorial development regimes according to the Benessere dimension. The Equità dimension is estimated using boosting regression and clustering models that emphasize the role of income, poverty risk, healthcare pressure, labour-market participation, youth exclusion, deprivation, and access to essential services. Finally, the Sostenibilità dimension is explored using boosting regression and random forest models to estimate interactions among environmental quality, climate stress, energy transition, innovation, digital skills, service reliability, and regional economic performance. The findings demonstrate a structural connection between well-being, equity, sustainability, and the economic performance of Italian regions. The results also confirm the hypothesis that Italy has multiple development regimes that differ geographically.
Full article
(This article belongs to the Special Issue Decentralization and Development: Territorial Dimension and Spatial Disparities Mitigation)
Open AccessArticle
Energy-Aware AI for Landscape-Scale Conservation: A Digital Twin Architecture for the Greater Yellowstone Ecosystem
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Harsh Deep Singh Narula
Land 2026, 15(5), 824; https://doi.org/10.3390/land15050824 (registering DOI) - 12 May 2026
Abstract
Conservation management of large, multi-species landscapes requires integrating heterogeneous data streams—such as satellite imagery, GPS telemetry, camera traps, bioacoustic sensors, weather stations, and field reports—into a unified model capable of simulating ecosystem dynamics and generating actionable recommendations. This paper proposes a tiered, energy-aware
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Conservation management of large, multi-species landscapes requires integrating heterogeneous data streams—such as satellite imagery, GPS telemetry, camera traps, bioacoustic sensors, weather stations, and field reports—into a unified model capable of simulating ecosystem dynamics and generating actionable recommendations. This paper proposes a tiered, energy-aware AI architecture for constructing ecosystem digital twins that enables prescriptive, rather than merely descriptive or predictive, landscape-scale conservation management. The framework classifies conservation tasks across three computational tiers: classical machine learning for continuous environmental monitoring and species distribution prediction, deep learning for perception-oriented tasks such as computer vision and bioacoustic analysis, and foundation models for cross-domain synthesis and stakeholder interaction. We apply this architecture to a comprehensive digital twin of the Greater Yellowstone Ecosystem, anchored in the ongoing conservation crisis of the Sublette Pronghorn Herd—a population that crashed from 43,000 to 24,000 animals in a single winter due to compounding severe weather and a Mycoplasma bovis outbreak. We formalize a coupled change model linking population dynamics, forage condition, corridor permeability, winter severity, and disease pressure, and demonstrate how a prescriptive recommendations engine can generate goal-conditioned management actions for the herd’s 165-mile “Path of the Pronghorn” migration corridor. A comparative energy footprint analysis, grounded in hardware-level energy measurements using Intel RAPL instrumentation and the CodeCarbon framework, estimates that the tiered architecture reduces computational energy consumption by approximately 34% relative to a deep-learning-everywhere baseline and by over three orders of magnitude relative to a foundation-model-centric baseline. The architecture provides a replicable blueprint for resource-constrained conservation organizations seeking to deploy AI-powered ecosystem management at landscape scale.
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(This article belongs to the Special Issue GenAI-Enabled Land Use Mapping as the Base for Modelling and Earth-Oriented Digital Twins)
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Matching Supply and Demand of Ecosystem Services in the Pinglu Canal Economic Zone from the Perspective of the Water–Energy–Food Nexus
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Yurou Liang, Baoqing Hu, Xiangying Kong and Yinyin Lao
Land 2026, 15(5), 823; https://doi.org/10.3390/land15050823 (registering DOI) - 12 May 2026
Abstract
Global climate change and rapid socio-economic development have increasingly exacerbated the imbalance between ecosystem service (ES) supply and demand. Taking the Pinglu Canal Economic Zone as a case study and employing a water–energy–food (WEF) nexus perspective, this study selected three key ESs—water yield,
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Global climate change and rapid socio-economic development have increasingly exacerbated the imbalance between ecosystem service (ES) supply and demand. Taking the Pinglu Canal Economic Zone as a case study and employing a water–energy–food (WEF) nexus perspective, this study selected three key ESs—water yield, carbon sequestration, and food supply. The InVEST model, supply–demand index (SDI), Pearson correlation analysis, and four-quadrant model were integrated to systematically reveal the spatiotemporal patterns, correlation characteristics, and spatial matching of ES supply and demand from 2005 to 2020. Scale effects and appropriate management scales were clarified through municipal, county, and grid scale comparisons, and a comprehensive management zoning scheme was constructed using a “zoning–classification–grading” framework. The results show that water yield and food supply exhibited an overall increasing trend, while carbon sequestration supply remained stable. Demand for all three services showed continuous growth, with a spatial pattern of “high in the central area and low in the surrounding areas”, consistent with population and economic agglomerations. The county scale was the most effective at capturing local supply–demand characteristics. A “zoning–classification–grading” spatial governance system was constructed based on dominant functions, supply–demand status, and control priority. This study can provide a scientific basis for territorial spatial planning and integrated ecosystem management in the Pinglu Canal Economic Zone and similar regions.
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(This article belongs to the Section Water, Energy, Land and Food (WELF) Nexus)
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Open AccessArticle
Spatiotemporal Effects of Urban Park Features on Walking and Running: Evidence from a Long-Term Observational Study in Shanghai
by
Junqi Chen, Zheng Tao, Wenrui Wu, Yi Wen, Ling Wang and Dan Chen
Land 2026, 15(5), 822; https://doi.org/10.3390/land15050822 (registering DOI) - 12 May 2026
Abstract
As urban parks become vital settings for public health, understanding how spatial features influence physical activity is essential. This study addresses a research gap by examining how park attributes affect walking and running across different space types (paths vs. plazas) and time periods.
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As urban parks become vital settings for public health, understanding how spatial features influence physical activity is essential. This study addresses a research gap by examining how park attributes affect walking and running across different space types (paths vs. plazas) and time periods. Analyzing 30 spatial units in three Shanghai parks (2021–2023) via OLS regression, the research identifies several key findings. Results indicate that paths facilitate significantly higher activity levels than plazas. While safety facilities, single-layer vegetation, and stone paving consistently promote activity, seat density and complex vegetation show divergent effects across space types. Temporally, connectivity, lighting, and simple vegetation structures encourage activity throughout the day, whereas high choice values and long entrance distances consistently act as suppressors. Other features, such as sky openness and water proximity, exhibit time-specific influences. These findings provide empirical evidence of the dynamic, context-dependent relationship between park design and exercise, offering actionable insights for urban planners to optimize green spaces for public health.
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(This article belongs to the Special Issue Urban Landscape and Greenway Planning)
Open AccessArticle
Effects of Land Use on Soil Parameters and Carbon Dynamics in Surface Soil of Ecosystems of Rila Mountains, Bulgaria
by
Lora Stoeva and Elena Tsvetkova
Land 2026, 15(5), 821; https://doi.org/10.3390/land15050821 (registering DOI) - 12 May 2026
Abstract
This study quantifies how different land-use types influence surface soil characteristics (0–5 cm) and the dynamics of soil organic carbon (SOC) and nitrogen in the mountainous ecosystems of the Rila Mountains. Across 54 forest and agricultural plots, pH, bulk density, coarse fraction, C:N
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This study quantifies how different land-use types influence surface soil characteristics (0–5 cm) and the dynamics of soil organic carbon (SOC) and nitrogen in the mountainous ecosystems of the Rila Mountains. Across 54 forest and agricultural plots, pH, bulk density, coarse fraction, C:N ratio, SOC, total nitrogen (TN), and their respective stocks were assessed using standard analytical methods and statistical tests (Shapiro–Wilk, ANOVA, Kruskal–Wallis, correlation and regression analysis). Land use significantly affected all soil parameters except pH. Forest soil showed lower bulk density and lower SOC stocks compared with grasslands. Unmown meadows exhibited the highest SOC and TN concentrations and stocks, while potato fields recorded the highest bulk density and elevated TN stocks, reflecting intensive management impacts on surface soil properties. Forest soils displayed species-specific patterns, with Scots pine and Silver fir showing comparatively lower SOC and TN stocks attributable to historical degradation and site limitations. As the study focused on the uppermost soil layer (0–5 cm), the results should be interpreted more as indicators of surface soil dynamics rather than as estimates of total topsoil carbon and nutrient storage. Correlation analysis revealed strong positive relationships among SOC, TN, and the C:N ratio, and strong negative relationships between SOC and both bulk density and coarse fraction in managed agricultural lands. The findings demonstrate that minimizing soil disturbance and maintaining permanent vegetation cover—particularly through conservation of unmanaged grasslands—offer great capacity for enhancing the soil organic matter accumulation in mountainous ecosystems.
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(This article belongs to the Special Issue Sustainable Land Use and Governance in Forest and Grassland Ecosystems)
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Open AccessArticle
Evaluating Sustainable Development and Coupling Coordination in Western China Under the SDG Framework
by
Min Wu, Qirui Chen, Zihan Hu and Huimin Wang
Land 2026, 15(5), 820; https://doi.org/10.3390/land15050820 (registering DOI) - 12 May 2026
Abstract
Achieving the Sustainable Development Goals (SDGs) requires not only aggregate progress but also more balanced coordination across social, economic, and ecological systems. This issue is especially salient in western China, where development catch-up, ecological fragility, and pronounced intraregional heterogeneity coexist. This study constructs
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Achieving the Sustainable Development Goals (SDGs) requires not only aggregate progress but also more balanced coordination across social, economic, and ecological systems. This issue is especially salient in western China, where development catch-up, ecological fragility, and pronounced intraregional heterogeneity coexist. This study constructs a localized SDG evaluation framework for 12 provincial units of western China from 2000 to 2018, reorganizing the 17 SDGs into social, economic, and ecological subsystems with 106 indicators. The analysis combines entropy-weighted TOPSIS, coupling coordination analysis, regional disparity analysis, spatial autocorrelation analysis, and integrated forecasting. Results show that the composite sustainable development index increased from 0.225 to 0.430, yet subsystem progress was uneven: social sustainability improved fastest, economic sustainability also increased substantially, while ecological sustainability lagged significantly. SDG5, SDG6, SDG10, SDG12, SDG13, and SDG15 emerged as the principal lagging goals. Coupling coordination among the three subsystems improved from near disorder to primary coordination, but economic–ecological and social–ecological links stayed weaker than the social–economic relationship. Provincial disparities were moderate overall but ecological sustainability exhibited greater interprovincial divergence. Spatially, the three subsystems followed distinct trajectories: ecological sustainability shifted from early clustering to a low-level dispersed state, economic sustainability developed an entrenched club-convergence pattern, and social sustainability remained spatially random. Forecasts to 2030 indicate continued social and economic gains alongside persistent ecological lag and subsystem imbalance. These findings indicate that the main sustainability challenge in western China has shifted from general development insufficiency to structural imbalance across goals, subsystems, and provinces, and that regional SDG assessments must move beyond aggregate metrics to identify subsystem coordination, territorial heterogeneity, and spatially differentiated governance pathways.
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(This article belongs to the Special Issue Optimisation of Environmental, Economic and Social Impacts in Natural Resource Management)
Open AccessArticle
Resilience of Deforestation Reduction Policies Across Land Tenure Regimes: Evidence from the Post-2019 Policy Shock in the Brazilian Amazon
by
Roxana Juliá
Land 2026, 15(5), 819; https://doi.org/10.3390/land15050819 (registering DOI) - 12 May 2026
Abstract
This paper examines the resilience of deforestation control policies across land tenure regimes in the Brazilian Legal Amazon, using Brazil’s post-2019 shift in environmental governance policies as a quasi-natural experiment. Combining high-resolution deforestation data with detailed tenure classifications, the analysis evaluates how land
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This paper examines the resilience of deforestation control policies across land tenure regimes in the Brazilian Legal Amazon, using Brazil’s post-2019 shift in environmental governance policies as a quasi-natural experiment. Combining high-resolution deforestation data with detailed tenure classifications, the analysis evaluates how land governance mediates deforestation outcomes under weakened enforcement and regulatory rollback. Using a difference-in-differences framework with spatial panel data and event-study specifications, the results reveal substantial heterogeneity across tenure regimes. Areas characterized by strong legal recognition—particularly homologated Indigenous territories and strictly protected conservation units—remain comparatively resilient, exhibiting stable or declining deforestation. In contrast, lands with weaker or incomplete property rights, including non-homologated Indigenous territories, agrarian settlements, and untitled public lands, experience significant increases in deforestation. The findings also highlight important within-land category variation, underscoring the role of formal recognition and cadastral validation in shaping environmental outcomes. Overall, the results demonstrate that the durability of deforestation reductions depends critically on the strength of land tenure institutions in the face of changing policy regimes.
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(This article belongs to the Special Issue Sustainable Land Management Practices in the Face of Climate Change)
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Open AccessArticle
Phased Planning as a Land-Use Governance Instrument: Evidence from the Serbian Planning System
by
Marija Lalošević, Milica Hadži Arsenović and Nataša Danilović Hristić
Land 2026, 15(5), 818; https://doi.org/10.3390/land15050818 (registering DOI) - 12 May 2026
Abstract
Contemporary planning systems are increasingly exposed to pressures for accelerated decision-making, while remaining bound to legally rigid and procedurally formalized frameworks. In this context, phased planning has emerged in Serbia as a mechanism for structuring the preparation and adoption of planning documents. However,
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Contemporary planning systems are increasingly exposed to pressures for accelerated decision-making, while remaining bound to legally rigid and procedurally formalized frameworks. In this context, phased planning has emerged in Serbia as a mechanism for structuring the preparation and adoption of planning documents. However, its role as a land-use governance instrument remains conceptually underdefined and unevenly operationalized in practice. This paper examines how phased planning is interpreted and implemented within the Serbian statutory planning system through a qualitative comparative analysis of two planning processes in Belgrade. The selected cases represent two distinct procedural models: amendment-based phasing through successive modifications of a single planning document, and document-based phasing in which phases are adopted as separate but interrelated plans. The analysis focuses on key governance dimensions relevant to land-use planning, including integration of planning scales, coordination among institutional actors, procedural transparency, and the risk of fragmentation across planning phases. The findings indicate that while phased planning can introduce a degree of procedural flexibility, it also tends to reproduce or intensify coordination gaps and information asymmetries in legally rigid systems. The paper contributes to a better understanding of how phased planning operates as a governance mechanism in land-use planning and identifies conditions under which it may support—or constrain—coherent spatial development.
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(This article belongs to the Section Land Planning and Landscape Architecture)
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Open AccessArticle
Identification of Obstacles and Optimization Pathways for Sustainable Tourism in Southern Xinjiang: A Deep Learning Approach Based on GRU Sentiment Analysis
by
Fujian Han, Faming Huang, Liang Song, Xiaomin Dai and Liangping Wang
Land 2026, 15(5), 817; https://doi.org/10.3390/land15050817 (registering DOI) - 12 May 2026
Abstract
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With the rapid expansion of the tourism industry in Xinjiang, which received a record 328 million tourists in 2025, identifying development bottlenecks is crucial for regional sustainability. This study aims to identify the core obstacles hindering sustainable tourism in Southern Xinjiang—the region’s fastest-growing
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With the rapid expansion of the tourism industry in Xinjiang, which received a record 328 million tourists in 2025, identifying development bottlenecks is crucial for regional sustainability. This study aims to identify the core obstacles hindering sustainable tourism in Southern Xinjiang—the region’s fastest-growing sector—and proposes evidence-based optimization pathways. Utilizing a deep learning approach, we deployed a Gated Recurrent Unit (GRU) sentiment analysis model to parse 5800 online reviews from 38 representative A-level scenic spots. The analysis identified 28 distinct obstacle clusters across three categories: landscape, cultural, and comprehensive destinations. The results reveal significant site-specific differentiation: natural landscape sites like Bayanbulak are primarily constrained by environmental risks and safety hazards, while high-traffic cultural sites like the Ancient City of Kashgar face acute challenges from over-commercialization and cultural erosion. Based on these findings, this study introduces a macro-level diagnostic tool and proposes targeted optimization strategies within the ESG (Environmental, Social, and Governance) framework. These insights offer actionable references for policymakers to enhance tourism resilience and achieve high-quality sustainable development in sensitive frontier regions.
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