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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), 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), 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.
Journal Cluster of Environmental Science: Sustainability, Land, Clean Technologies, Environments, Nitrogen, Recycling, Urban Science, Safety, Air, Waste and Aerobiology.

Impact Factor: 3.2 (2024); 5-Year Impact Factor: 3.4 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2073-445X

Latest Articles

29 pages, 2003 KB

Open AccessArticle

The Impact of Metropolitan Area Integration Policies on Urban Industrial Structure Upgrading: Evidence from China

by Kan Liu and Jinjun Duan

Land 2026, 15(1), 177; https://doi.org/10.3390/land15010177 (registering DOI) - 17 Jan 2026

As global production networks become increasingly regionalized, diversified, and resilience-oriented, metropolitan areas (MAs) have emerged as important spatial platforms for industrial development. This study examines whether China’s national-level metropolitan area integration policies promote urban industrial structure upgrading and, if so, through which channels. [...] Read more.

As global production networks become increasingly regionalized, diversified, and resilience-oriented, metropolitan areas (MAs) have emerged as important spatial platforms for industrial development. This study examines whether China’s national-level metropolitan area integration policies promote urban industrial structure upgrading and, if so, through which channels. We first develop a set of conceptual mechanisms and hypotheses, and then test them using panel data for 281 prefecture-level cities in China from 2012 to 2022. A staggered difference-in-differences (DID) model, complemented by a series of robustness checks, is employed to identify the policy effects. The baseline estimates indicate that the industrial structure of MA member cities is, on average, about 2.43 percentage points more advanced than that of non-MA cities. Mechanism analysis shows that the policies foster urban industrial upgrading through unified market formation, technological improvement, and optimization of factor endowments. However, the policies have only a very limited impact on breakthroughs in cutting-edge or frontier technologies. Based on these findings, we propose targeted policy recommendations to address the identified shortcomings. Full article

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21 pages, 1275 KB

Open AccessArticle

Naming as Resistance: Nahuatl Toponymy and Territorial Dispossession in San Antonio Cacalotepec, Mexico

by Melissa Schumacher, Andrea Galindo-Torres, Laura Romero and Sarah Herrejón-Montes

Land 2026, 15(1), 176; https://doi.org/10.3390/land15010176 (registering DOI) - 16 Jan 2026

The Indigenous community of San Antonio Cacalotepec, located in the region of Cholula in central Mexico, has been an active witness to territorial dispossession at the hands of powerful real estate capital. This small territory—where clean water once flowed, milpas and nopales were [...] Read more.

The Indigenous community of San Antonio Cacalotepec, located in the region of Cholula in central Mexico, has been an active witness to territorial dispossession at the hands of powerful real estate capital. This small territory—where clean water once flowed, milpas and nopales were cultivated, and Nahuatl was the everyday language—has now become the epicenter of predatory capitalism, manifested in gated communities, commercial zones, and exclusive residential developments. As a result, the original settlement and its small landholders have been segregated and excluded from the promises of modernity and progress. Nevertheless, in this last enclave, where traces of Nahuatl can still be heard, an Indigenous awareness has emerged, reclaiming identity and the right to continue naming the territory that has been lost as their own. Within this context, fieldwork carried out by the co-research group Colectiva Hilando Territorios has led to a series of community workshops with women from San Antonio Cacalotepec, together with architecture and anthropology students from Universidad de las Américas Puebla. These workshops mapped how Cacalotepec looked before massive urbanization and documented the toponyms in the Nahuatl language. The aim has been to make visible the memory of a living territory that persists, and that, despite the sale of exclusive, car-oriented commercial and residential spaces, is continually re-signified by the community as part of its identity and collective belonging in the face of dispossession. Full article

(This article belongs to the Special Issue Rural Social Movements and the Question of the Land in the 21st Century)

24 pages, 7504 KB

Open AccessArticle

Historical Trajectories of the Evolved Cropland Features and Their Reshaped Influences on Agricultural Landscapes and Ecosystem Services in China’s Sanjiang Commodity Grain Base

by Tao Pan, Kun Liu, Zherui Yin, Zexian Li and Lin Shi

Land 2026, 15(1), 175; https://doi.org/10.3390/land15010175 (registering DOI) - 16 Jan 2026

Drastic cropland expansion and its internal structural changes have had an obvious impact on agricultural landscapes and ecosystem services. However, a prolonged investigation of this effect is still lacking in China’s grain-producing bases, such as Sanjiang Plain. To address this issue, half a [...] Read more.

Drastic cropland expansion and its internal structural changes have had an obvious impact on agricultural landscapes and ecosystem services. However, a prolonged investigation of this effect is still lacking in China’s grain-producing bases, such as Sanjiang Plain. To address this issue, half a century of study on the ‘land trajectory migration–landscape evolution–ecological effect,’ covering the period 1970–2020, was elucidated using the synergistic methodology of spatial analysis technology, the reclamation rate algorithm, the landscape indicator, and the newly established ecosystem service improvement model. Satellite observation results indicate that the cropland area exhibited a substantial expansion trend from 23,672.69 km² to 42,856.17 km² from 1970 to 2020, representing a net change of +19,183.48 km² and a huge growth rate of 81.04%, which led to an obvious improvement in the level of agricultural cultivation. Concurrently, the internal structure of the cropland underwent dramatic restructuring, with rice fields increasing from 6.46% to 53.54%, while upland fields decreased from 93.54% to 46.46%. In different regions, spatially heterogeneous improvements of 2.64–52.47% in agricultural cultivation levels across all cities were observed. From 1970 to 2020, the tracked cropland center of gravity trajectories exhibited a distinct biphasic pattern, initially shifting westward and then followed by a southward transition, accumulating a displacement of 19.39 km². As for the evolved agricultural landscapes, their integrity has improved (SHDI = −0.08%), accompanied by increased connectivity (CON = +8.82%) and patch edge integrity (LSI = −15.71%) but also by reduced fragmentation (PD = −48.14%). Another important discovery was that the evaluated ecosystem services continuously decreased from 2337.84 × 10⁸ CNY in 1970 to 1654.01 × 10⁸ CNY in 2020, a net loss of −683.84 × 10⁸ CNY and a huge loss rate of 33.65%, accompanied by a center–periphery gradient pattern whereby degradation propagated from the low-value central croplands to the high-value surrounding natural covers. These discoveries will play a significant role in guiding farmland structure reformation, landscape optimization, and ecosystem service improvement. Full article

(This article belongs to the Special Issue Monitoring Ecosystem Services and Biodiversity Under Land Use Change)

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24 pages, 3886 KB

Open AccessArticle

Disentangling Complexity and Performance: A Comparative Study of Deep Learning and Random Forest Models for Cropland Vulnerability Assessment in Bangladesh

by Arnob Bormudoi and Masahiko Nagai

Land 2026, 15(1), 174; https://doi.org/10.3390/land15010174 - 16 Jan 2026

Climate change increasingly threatens global food security through disrupted precipitation patterns and extreme weather events, requiring resilient systems for assessing agricultural vulnerability. This study developed and compared machine learning approaches for predicting cropland vulnerability using Earth Observation data, operationalized through NDVI anomalies as [...] Read more.

Climate change increasingly threatens global food security through disrupted precipitation patterns and extreme weather events, requiring resilient systems for assessing agricultural vulnerability. This study developed and compared machine learning approaches for predicting cropland vulnerability using Earth Observation data, operationalized through NDVI anomalies as a defensible biophysical metric. We employed both a dual-stream deep learning architecture and a Random Forest model to predict 2023 NDVI anomalies across Bangladesh croplands using a 22-year time series (2001–2023) of MODIS vegetation indices, ERA5 climate variables, and static environmental covariates. A spatially aware block cross-validation strategy ensured rigorous, independent performance evaluation. Results demonstrated that the Random Forest model (R² = 0.70, RMSE = 197.03) substantially outperformed the deep learning architecture (R² = 0.02, RMSE = 357.57), explaining 70% of cropland stress variance and enabling early detection of vulnerable areas three months before harvest. Feature importance analysis identified recent climate variables, March precipitation, February NDVI, and vapor pressure deficit as primary vulnerability drivers. Spatial analysis revealed distinct vulnerability patterns, with Natore and Magura districts exhibiting elevated stress consistent with 2023 drought conditions, threatening the productivity of the region’s critical irrigation-dependent rice cultivation. These findings demonstrate that simpler, interpretable models can sometimes outperform complex architectures while providing useful information for early warning systems and precision targeting of climate adaptation interventions. Full article

(This article belongs to the Special Issue Feature Papers for Land Innovations—Data and Machine Learning: 3rd Edition)

29 pages, 6496 KB

Open AccessArticle

Construction and Optimization of Ecological Network Based on SOM and XGBoost-SHAP: A Case Study of the Zhengzhou–Kaifeng–Luoyang Region

by Yunuo Chen, Pingyang Han, Pengfei Wang, Baoguo Liu and Yang Liu

Land 2026, 15(1), 173; https://doi.org/10.3390/land15010173 - 16 Jan 2026

The ecological network serves as a vital spatial strategy for addressing climate change, biodiversity loss, and habitat fragmentation. Addressing limitations in existing ecological network studies—such as strong subjectivity and insufficient accuracy in structural element identification, cross-regional integration, and resistance surface weighting—this research uses [...] Read more.

The ecological network serves as a vital spatial strategy for addressing climate change, biodiversity loss, and habitat fragmentation. Addressing limitations in existing ecological network studies—such as strong subjectivity and insufficient accuracy in structural element identification, cross-regional integration, and resistance surface weighting—this research uses the Zhengzhou–Kaifeng–Luoyang region (ZKLR) as a case study. It introduces the self-organizing map (SOM) model to identify ecological sources and employs the XGBoost-SHAP model to optimize resistance surface weights, thereby reducing subjective weighting biases. Subsequently, the Linkage Mapper tool is utilized to construct the regional ecological network. The superiority of the SOM model for identifying ecological sources was confirmed by comparison with a traditional network based on morphological spatial pattern analysis (MSPA). Further integrating complex network topology theory, nodes attack the simulations-assessed network resilience and proposed optimization strategies. The results indicate the following: (1) The area of ecological sources identified by the SOM model is three times that of the MSPA model; (2) SHAP feature importance analysis revealed that elevation (DEM) exerted the greatest influence on the composite resistance surface, contributing over 40%, followed by land use and slope, with each contributing approximately 15%. High-resistance areas were primarily distributed in western and central mountainous regions and built-up urban areas, while low-resistance areas were concentrated in the central and eastern plains; (3) topological analysis indicates that the integrated ecological network (IEN) exhibits superior robustness compared to the structural ecological network (SEN). The edge-adding strategy generated 22 additional ecological corridors, significantly enhancing the overall resilience of the integrated ecological network; and (4) based on ecological network construction and optimization results, a territorial spatial protection strategy of “one belt, two cores, two zones, and three corridors” is proposed. This study provides a novel methodological framework for ecological network construction, with findings offering reference for ecological conservation and spatial planning in the ZKLR and similar areas. Full article

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15 pages, 2747 KB

Open AccessReview

Methodological Approaches to Assess the Resilience of Farming Systems to Climate Change: Examples from Latin America

by Clara I. Nicholls, Ángel Salazar-Rojas, Luis Vázquez, Rene Montalba, Mónica Machado, Inés Gazzano, Alejandro Henao and Miguel A. Altieri

Land 2026, 15(1), 172; https://doi.org/10.3390/land15010172 - 16 Jan 2026

The erratic nature, increasing prevalence, and intensity of extreme meteorological phenomena are forcing researchers and farmers to urgently develop adaptation practices to enhance the resilience of agroecosystems to climate change. It is strategically crucial to identify farming systems that have successfully endured recent [...] Read more.

The erratic nature, increasing prevalence, and intensity of extreme meteorological phenomena are forcing researchers and farmers to urgently develop adaptation practices to enhance the resilience of agroecosystems to climate change. It is strategically crucial to identify farming systems that have successfully endured recent climatic disturbances and understand the agroecological attributes that enabled them to resist and/or recover from droughts and hurricanes. This paper describes a number of methodologies utilized by Latin American researchers to assess agroecosystem resilience by estimating the vulnerability and the response capacity of selected farming systems to cope with climatic threats. The methodologies utilize a set of socio-ecological indicators that can be easily evaluated in the field, allowing farmers to determine whether their farms can withstand a drought or a major storm and, based on this information, select agroecological practices able to enhance the resiliency of their farms in preparation for future events. The principles and practices of resilience identified on successful, climate-resistant farms can be shared with thousands of producers, facilitating the broader adoption and scaling up of agroecological adaptation strategies. Full article

(This article belongs to the Special Issue Sustainable Development of the Territory and Use of Natural and Cultural Resources to Improve the Quality of Life of Residents, 2nd Edition)

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1 pages, 131 KB

Open AccessCorrection

Correction: Caslin et al. Tabonuco and Plantation Forests at Higher Elevations Are More Vulnerable to Hurricane Damage and Slower to Recover in Southeastern Puerto Rico. Land 2025, 14, 1324

by Michael W. Caslin, Madhusudan Katti, Stacy A. C. Nelson and Thrity Vakil

Land 2026, 15(1), 171; https://doi.org/10.3390/land15010171 - 15 Jan 2026

There was an error in the original publication [...] Full article

(This article belongs to the Special Issue The Dynamics of Biodiversity and Landscape Ecology: Patterns, Processes, and Planning (Second Edition))

23 pages, 3276 KB

Open AccessArticle

Multi-Scenario Assessment of Ecological Network Resilience and Community Clustering in the Yellow River Delta

by Yajie Zhu, Zhaohong Du, Yunzhao Li, Chienzheng Yong, Jisong Yang, Bo Guan, Fanzhu Qu and Zhikang Wang

Land 2026, 15(1), 170; https://doi.org/10.3390/land15010170 - 15 Jan 2026

The rapid economic and urban development in the Yellow River Delta Efficient Ecological Economic Zone (YRDEEZ) has intensified land use changes and aggravated ecological patch fragmentation. Constructing ecological networks (ENs) can reconnect fragmented patches and enhance ecosystem services. This study simulated land use [...] Read more.

The rapid economic and urban development in the Yellow River Delta Efficient Ecological Economic Zone (YRDEEZ) has intensified land use changes and aggravated ecological patch fragmentation. Constructing ecological networks (ENs) can reconnect fragmented patches and enhance ecosystem services. This study simulated land use patterns for 2040 under three scenarios: Natural Development (NDS), Ecological Protection (EPS), and Urban Development (UDS). Results indicated a consistent decline in agricultural land and an expansion of urban land across all scenarios, with the most pronounced urban growth under UDS (6.79%) and the largest ecological land area under EPS (5178.96 km²). Since 2000, the number of EN sources and corridors had decreased, with sources mainly concentrated along coastal areas. The source and corridor under UDS exhibited the highest area ratio (20.08%), while NDS showed the lowest (18.72%), with UDS demonstrating the strongest resilience. Through community detection, the UDS EN was divided into five ecological clusters, encompassing 127 intra-cluster corridors (2285.95 km) and 34 inter-cluster corridors (1171.32 km), among which the cluster near the Yellow River estuary was determined to be the most critical (Level 1). These findings will provide valuable insights for managing landscape fragmentation and biological habitat protection in YRDEEZ. Meanwhile, the multi-scenario simulations of ENs could play an important role in constructing ecological security patterns and protecting ecosystems. Full article

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26 pages, 5222 KB

Open AccessArticle

Identification of Potential Supplementary Cultivated Land Based on a Markov-FLUS Model and Cultivation Suitability Evaluation Under the New Occupation and Compensation Balance Policy: A Case Study of Jiangsu Province

by Yanan Liu, Kening Wu, Wei Zou, Hao Su, Xiaoliang Li, Xiao Li and Rui Shi

Land 2026, 15(1), 169; https://doi.org/10.3390/land15010169 - 15 Jan 2026

The identification of supplementary cultivated land as a reserve resource is of great significance for ensuring implementation of the new mechanism of land occupation and compensation balance in China. Using Jiangsu Province as a case study, here, we use a “multi-period land use [...] Read more.

The identification of supplementary cultivated land as a reserve resource is of great significance for ensuring implementation of the new mechanism of land occupation and compensation balance in China. Using Jiangsu Province as a case study, here, we use a “multi-period land use change patterns–multi-scenario land use simulation–cultivation suitability evaluation–identification of supplementary cultivated land” framework to explore identification of supplementary cultivated land. A single land use dynamic index and a land use transfer matrix were used to analyze land use pattern changes in Jiangsu Province and showed that the area of cultivated land in Jiangsu Province decreased significantly, mainly by being converted into land used for buildings, and waters and conservancy facilities. A Markov-FLUS model was used to simulate and predict land use quantity and spatial distribution under four scenarios: an inertial development scenario, a cultivated land protection scenario, an economic development priority scenario, and an ecological protection priority scenario. Sixteen factor indicators were selected from the four dimensions of natural land quality, social economy, management, and the ecological condition of the land, and the degree of suitability of cultivated land in Jiangsu was evaluated by multi-factor stepwise correction. The southern and central parts of Jiangsu had higher suitability, while the northern part had lower suitability. By superimposing these data on current land use data from 2023, the plots of land that were converted to or from cultivated land were identified. Combined with the suitability degree, the potential three major categories and eight types of sources for supplementary cultivated land, totaling 29,015.92 km², were identified, along with their distribution. A time sequence arrangement for these sources was initially set up. Corresponding management suggestions were proposed based on the adaptability of different supplementary cultivated land sources, with the aim of providing scientific references for the acquisition of supplementary cultivated land sources in the implementation of the national and local government’s farmland balance management. Full article

(This article belongs to the Special Issue Agricultural Land Management to Meet Future Global Food Demand (Second Edition))

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20 pages, 11247 KB

Open AccessArticle

A Methodological Proposal for Implementing Dark Infrastructure Within the Ecological Network of an Urban Forest

by Julien Wynants and Serge Schmitz

Land 2026, 15(1), 168; https://doi.org/10.3390/land15010168 - 15 Jan 2026

Ecological continuity cannot be limited to spatial surfaces and corridors without consideration of the day–night cycle. This integration is achieved through the implementation of dark infrastructure, a field that remains comparatively understudied. The present article proposes a methodological approach for implementing dark infrastructure [...] Read more.

Ecological continuity cannot be limited to spatial surfaces and corridors without consideration of the day–night cycle. This integration is achieved through the implementation of dark infrastructure, a field that remains comparatively understudied. The present article proposes a methodological approach for implementing dark infrastructure within an urban forest near Liège (Belgium), where the ecological network has recently been characterised, but nocturnal landscape-specificities are not included. Several sources of information were combined, including satellite imagery, a point-light map and the existing ecological network. The research seeks to illustrate conflicts between ecological requirements and human activities, with particular emphasis on public, and private lighting. In the discussion, the paper identifies methodological limitations, characterises the nature of light pollution, and lists planning issues for the cohabitation of human activities and nocturnal species’ habitats. Full article

(This article belongs to the Special Issue Spatial Planning and Land-Use Management: 2nd Edition)

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30 pages, 5796 KB

Open AccessArticle

Beyond Physical Upgrades: Reconfiguring Original Residents’ Belongingness in Informal Settlements with a Network–Node–Subject Lens

by Xianyue Tang, Mohan Wang, Kai Liu, Hang Ma and Jinqi Li

Land 2026, 15(1), 167; https://doi.org/10.3390/land15010167 - 14 Jan 2026

Informal settlements are widely present and important urban spaces, providing valuable living spaces for many migrant populations, low-income groups, and indigenous peoples. However, urbanization faces a common challenge: the transformation of informal settlements often destroys social structures and belongingness. Despite its importance, few [...] Read more.

Informal settlements are widely present and important urban spaces, providing valuable living spaces for many migrant populations, low-income groups, and indigenous peoples. However, urbanization faces a common challenge: the transformation of informal settlements often destroys social structures and belongingness. Despite its importance, few studies have examined how redevelopment is correlated with original residents’ belongingness. To address this gap, this study proposes a research framework of belongingness structured along the logical chain of “network–node–subject”. Social network analysis (SNA) is employed to identify the existing outdoor activity space network and its key nodes. Exploratory factor analysis (EFA) is then conducted to determine the key environmental features of outdoor activity spaces that influence original residents’ belongingness. Furthermore, K-means clustering is applied to explore the correlation mechanism between space and belongingness across different age groups. This study identifies differentiated and universal elements by analyzing the clustered conflict factors, in order to provide precise policy insights. The findings provide actionable insights for enhancing residents’ belongingness during the redevelopment of informal settlements in cities. Full article

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21 pages, 2331 KB

Open AccessArticle

The Mechanism by Which Jobs–Housing Mismatch Affects Urban Land Resource Allocation Efficiency Under External Shocks: An Excess Commuting Perspective

by Dan Wan, Lindan Zhao, Xiaoli Chong and Yanzhe Cui

Land 2026, 15(1), 166; https://doi.org/10.3390/land15010166 - 14 Jan 2026

Excess commuting reflects the inefficiency of urban land resource allocation, generating additional greenhouse gas emissions and social costs, and has therefore become a central concern in the pursuit of sustainable cities. While exogenous shocks inevitably alter the efficiency of land resource allocation, it [...] Read more.

Excess commuting reflects the inefficiency of urban land resource allocation, generating additional greenhouse gas emissions and social costs, and has therefore become a central concern in the pursuit of sustainable cities. While exogenous shocks inevitably alter the efficiency of land resource allocation, it remains unclear how such shocks affect overall urban efficiency. To address this gap, this paper proposes a generalized framework for measuring excess commuting that accounts for imbalances between the numbers of jobs and residences. Drawing on mobile signaling big data, we trace the daily commuting patterns of more than 900,000 residents in Beijing, comparing the pre-pandemic period (March–October 2019) with the pandemic period (March–October 2020). The results show that: (1) Excess commuting increased significantly after the outbreak of COVID-19, with the observed average commuting distance (Tact) of the full sample rising from 6267 m to 10,058 m (an increase of 59%), indicating a decline in urban land resource allocation efficiency; (2) A more pronounced center-periphery pattern emerged at the metropolitan scale: the average Jobs–Housing Ratio (JHR) increased from 1.08 to 1.11, and its standard deviation rose from 0.54 to 0.70, with the JHR of central urban areas decreasing by 3% and that of suburban areas increasing by 20%—suggesting a marked increase in commuting distances; (3) Heterogeneous impacts were observed across age groups: the Difference-in-Differences (DID) regression confirmed a significant negative interaction term (Group × COVID-19 = −0.2991 **, p < 0.05), indicating that older adults experienced a greater increase in commuting inefficiency than younger adults. These findings reveal the dynamic mechanisms linking exogenous shocks, jobs–housing mismatch, and urban land resource allocation efficiency and provide policy implications for improving spatial resource allocation in the post-pandemic era. Full article

(This article belongs to the Section Land Use, Impact Assessment and Sustainability)

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25 pages, 5084 KB

Open AccessReview

The Impacts of Extreme Weather Events on Soil Contamination by Heavy Metals and Polycyclic Aromatic Hydrocarbons: An Integrative Review

by Traianos Minos, Alkiviadis Stamatakis, Evangelia E. Golia, Chrysovalantou Adamantidou, Pavlos Tziourrou, Marios-Efstathios Spiliotopoulos and Edoardo Barbieri

Land 2026, 15(1), 165; https://doi.org/10.3390/land15010165 - 14 Jan 2026

Floods and wildfires are two extreme environmental events with significant yet different impacts on soil health and on two particularly important soil pollutants, heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs), which are directly associated with ishytoxic properties and their ability to enter [...] Read more.

Floods and wildfires are two extreme environmental events with significant yet different impacts on soil health and on two particularly important soil pollutants, heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs), which are directly associated with ishytoxic properties and their ability to enter the food chain. The present study includes a methodological approach that was based on a literature review of published studies conducted worldwide regarding these two phenomena. The main forms of both pollutants, their possible sources and inevitable deposition onto the soil surface, along with their behavior–transport–mobility, and their residence time in soil were investigated. Furthermore, the changes that both HMs and PAHs induce in the physicochemical properties of post-flood and post-fire soils (in soil pH, Cation Exchange Capacity (CEC), organic matter content, porosity, mineralogical alterations, etc.), are investigated after a literature review of various case studies. Wildfires, in contrast to floods, can more easily remove large quantities of heavy metals into the soil ecosystem, most likely due to the intense erosion they cause. At the same time, floods appear to significantly burden soils with PAHs. In wildfires, the largest mean increases were observed for Mn (386%), Zn (300%), and Cu (202%). In floods, Pb showed the highest mean increase (534%), with Cd also rising substantially (236%). Regarding total PAHs, mean post-event concentrations reached 482.3 μg/kg after wildfires, compared to 4384 μg/kg after floods. Changes in the structure and chemical composition of flooded and burned soils may also affect the mobility and bioavailability of the pollutants under study. Overall, these two phenomena significantly alter soil quality, affecting both ecological processes and potential health impacts. Full article

(This article belongs to the Section Land Use, Impact Assessment and Sustainability)

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26 pages, 5391 KB

Open AccessArticle

Quantifying Urban Expansion and Its Driving Forces in the Indus River Basin Using Multi-Source Spatial Data

by Wenfei Luan, Jingyao Zhu, Wensheng Wang, Chunfeng Ma, Qingkai Liu, Yu Wang, Haitao Jing, Bing Wang and Hui Li

Land 2026, 15(1), 164; https://doi.org/10.3390/land15010164 - 14 Jan 2026

Urban expansion and its driving factors are frequently analyzed within administrative regions to inform regional urban planning, yet such analyses often fall short at the natural basin scale (referring to the spatial extent defined by hydrological drainage boundaries) due to the scarcity of [...] Read more.

Urban expansion and its driving factors are frequently analyzed within administrative regions to inform regional urban planning, yet such analyses often fall short at the natural basin scale (referring to the spatial extent defined by hydrological drainage boundaries) due to the scarcity of statistical data. Geographic and socio-economic spatial data can offer more detailed information across various research scales compared to traditional data (such as administrative statistical data, survey-based data, etc.), providing a potential solution to this limitation. Thus, this study took the Indus Basin as an example to reveal its urban expansion patterns and driving mechanism based on natural–economic–social time-series (2000–2020) spatial data, landscape expansion index, and geographical detector model (GDM). Future urban expansion distribution under different scenarios was also projected using Cellular Automata and Markov model (CA-Markov). The results indicated the following: (1) The Indus River Basin experienced rapid urban expansion during 2000–2020 dominated by edge-expansion, with urban expansion intensity showing a continuous increase. (2) Between 2000 and 2010 as well as 2010 and 2020, the dominant factor influencing urban expansion shifted from altitude to population (Pop), while the strongest interacting factors shifted from fine particulate matter (PM_2.5) and altitude to Gross Domestic Product (GDP) and Pop. (3) Future urban expansion probably occupies substantial mountainous area under the normal scenario, while the expansion region shifts towards the central plains to protect more ecological zones under a sustainable development scenario. Findings in this study would deepen the understanding of urban expansion characteristics of the Indus Basin and benefit its future urban planning. Full article

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23 pages, 18378 KB

Open AccessArticle

Innovative Spatial Equity Assessment in Healthcare Services: Integrating Travel Behaviors with Supply–Demand Coupling

by Wenge Xu, Jianxiong He, Yuhuan Yang, Wenfang Gao, Jiangjiang Xie and Yang Rui

Land 2026, 15(1), 163; https://doi.org/10.3390/land15010163 - 14 Jan 2026

Spatial equity of healthcare services is a critical concern in social equity and spatial justice research. Despite the availability of various methods to measure this equity, few studies have integrated the supply–demand coupling perspective with the analysis of impacts of residents’ travel behaviors’ [...] Read more.

Spatial equity of healthcare services is a critical concern in social equity and spatial justice research. Despite the availability of various methods to measure this equity, few studies have integrated the supply–demand coupling perspective with the analysis of impacts of residents’ travel behaviors’ on equity. This study develops and applies a Travel Behavior-based Coupling Coordination Degree (TB-CCD) method to assess the spatial equity of healthcare services in the Xi’an region. The results show the following: (1) Traditional single-mode models may fail to accurately assess this equity, whereas the TB-CCD model provides a more realistic evaluation. (2) Public transportation and driving provide a more equitable distribution of healthcare services compared to walking and cycling modes. The spatial equity of healthcare services exhibits a distinct core–periphery pattern, where accessibility and equity levels are significantly higher in city centers than in suburban areas. (3) The distribution of inequity ‘deserts’ and ‘oases’ in healthcare services is found to be travel-mode dependent, with the walking and public transportation modes exhibiting the highest incidence of these classifications. These findings provide valuable insights for urban planners and policymakers to formulate strategies and spatial plans aimed at enhancing equity in healthcare services. Full article

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21 pages, 2300 KB

Open AccessArticle

Integration of Landscape Ecological Risk Assessment and Circuit Theory for Ecological Security Pattern Construction in the Pinglu Canal Economic Belt

by Jiayang Lai, Baoqing Hu and Qiuyi Huang

Land 2026, 15(1), 162; https://doi.org/10.3390/land15010162 - 14 Jan 2026

Against the backdrop of rapid urbanization and land development, the degradation of regional ecosystem services and the intensification of ecological risks have become prominent challenges. This study takes the Pinglu Canal Economic Belt—a region characterized by the triple pressures of “large-scale engineering disturbance, [...] Read more.

Against the backdrop of rapid urbanization and land development, the degradation of regional ecosystem services and the intensification of ecological risks have become prominent challenges. This study takes the Pinglu Canal Economic Belt—a region characterized by the triple pressures of “large-scale engineering disturbance, karst ecological vulnerability, and port economic agglomeration”—as a case study. Based on remote sensing image data from 2000 to 2020, a landscape ecological risk index was constructed, and regional landscape ecological risk levels were assessed using ArcGIS spatial analysis tools. On this basis, ecological sources were identified by combining the InVEST model with morphological spatial pattern analysis (MSPA),and an ecological resistance surface was constructed by integrating factors such as land use type, elevation, slope, distance to roads, distance to water bodies, and NDVI. Furthermore, the circuit theory method was applied to identify ecological corridors, ecological pinch points, and barrier points, ultimately constructing the ecological security pattern of the Pinglu Canal Economic Belt. The main findings are as follows: (1) Ecological risks were primarily at low to medium levels, with high-risk areas concentrated in the southern coastal region. Over the past two decades, an overall optimization trend was observed, shifting from high risk to lower risk levels. (2) A total of 15 ecological sources (total area 1313.71 km²), 31 ecological corridors (total length 1632.42 km), 39 ecological pinch points, and 15 ecological barrier points were identified, clarifying the key spatial components of the ecological network. (3) Based on spatial analysis results, a zoning governance plan encompassing “ecological protected areas, improvement areas, restoration areas, and critical areas” along with targeted strategies was proposed, providing a scientific basis for ecological risk management and pattern optimization in the Pinglu Canal Economic Belt. Full article

(This article belongs to the Section Landscape Ecology)

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22 pages, 3607 KB

Open AccessArticle

Spatiotemporal Evolution and Pathways for Enhancing Urban Competitiveness in China

by Nuoya Wu and Jinqun Wu

Land 2026, 15(1), 161; https://doi.org/10.3390/land15010161 - 14 Jan 2026

Urban competitiveness encapsulates a city’s comprehensive capacity for development. Utilizing panel data for 282 prefecture-level cities from 2012 to 2020, this study constructs an evaluation index system of urban competitiveness and applies kernel density estimation, standard deviational ellipse, trend-surface analysis, and Dagum Gini [...] Read more.

Urban competitiveness encapsulates a city’s comprehensive capacity for development. Utilizing panel data for 282 prefecture-level cities from 2012 to 2020, this study constructs an evaluation index system of urban competitiveness and applies kernel density estimation, standard deviational ellipse, trend-surface analysis, and Dagum Gini coefficient decomposition to examine its spatiotemporal evolution and regional disparities. The results indicate that: (1) urban competitiveness exhibits a V-shaped recovery, with intensified polarization after 2016, widening innovation advantages in the East, and persistent decline in the Northeast; (2) the spatial configuration follows a “dual-gradient, polycentric” structure, characterized by an inverted-U pattern along the east–west axis and an expanding gradient gap along the north–south axis; (3) club convergence and hierarchical entrenchment coexist, as polarization deepens in the East while the Northeast tends toward internal balance; and (4) the competitiveness center shifts southeastward, accompanied by a pronounced fragmentation trend in the Northeast. Based on these findings, the paper proposes differentiated spatial governance, the development of multi-tier innovation networks, and the promotion of green and sustainable development as integrated strategies to systematically enhance urban competitiveness. Full article

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37 pages, 3409 KB

Open AccessArticle

Regionalized Life Cycle Analysis of Ecosystem External Cost Associated with Land-Use Change in Photovoltaic Systems

by Andrea Molocchi, Giulio Mela, Elisabetta Brivio and Pierpaolo Girardi

Land 2026, 15(1), 160; https://doi.org/10.3390/land15010160 - 13 Jan 2026

This article presents a methodology for assessing the ecosystem external costs linked to land-use changes caused by utility-scale photovoltaic systems using a regionalized life cycle approach. The core scientific challenge is to integrate a typically non-site-specific method—life cycle assessment—with a site-specific evaluation of [...] Read more.

This article presents a methodology for assessing the ecosystem external costs linked to land-use changes caused by utility-scale photovoltaic systems using a regionalized life cycle approach. The core scientific challenge is to integrate a typically non-site-specific method—life cycle assessment—with a site-specific evaluation of ecosystem services affected by land-use changes. The methodology does not model specific agricultural practices. The approach is applied to three configurations of solar-tracking photovoltaic plants installed on arable land: ground-mounted photovoltaics, elevated agrivoltaics, and spaced agrivoltaics. For each configuration, the external costs or benefits per megawatt-hour (MWh) produced are estimated, allowing a comparative life cycle analysis. The findings show that the elevated agrivoltaic system is the only configuration resulting in a net loss of ecosystem service value, albeit marginal (−0.2 EUR/MWh). In contrast, the ground-mounted system yields a net benefit (approximately 1 EUR/MWh), followed by spaced agrivoltaics (0.1 EUR/MWh). These outcomes are mainly driven by the construction and operational phases, while the impacts from component production, transport, and end-of-life stages are significantly lower. The methodology offers a replicable framework for integrating the monetary evaluation of ecosystem services into life cycle assessments of land-intensive renewable energy systems. Full article

(This article belongs to the Topic Sustainable Water–Energy–Environment Nexus System and Power)

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40 pages, 5686 KB

Open AccessArticle

Digital–Intelligent Transformation and Urban Carbon Efficiency in the Yellow River Basin: A Hybrid Super-Efficiency DEA and Interpretable Machine-Learning Framework

by Jiayu Ru, Jiahui Li, Lu Gan and Gulinaer Yusufu

Land 2026, 15(1), 159; https://doi.org/10.3390/land15010159 - 13 Jan 2026

The goal of this scientific study is to clarify whether and how digital–intelligent integration contributes to urban carbon efficiency and to identify the conditions under which this contribution becomes nonlinear and policy-relevant. Focusing on 39 prefecture-level cities in the middle reaches of the [...] Read more.

The goal of this scientific study is to clarify whether and how digital–intelligent integration contributes to urban carbon efficiency and to identify the conditions under which this contribution becomes nonlinear and policy-relevant. Focusing on 39 prefecture-level cities in the middle reaches of the Yellow River Basin during 2011–2022, we adopt an integrated measurement–modelling approach that combines efficiency evaluation, machine-learning interpretation, and dynamic–spatial validation. Specifically, we construct two super-efficiency DEA indicators: an undesirable-output SBM incorporating CO₂ emissions and a conventional super-efficiency CCR index. We then estimate nonlinear city-level relationships using XGBoost and interpret the marginal effects with SHAP, while panel vector autoregression (PVAR) and spatial diagnostics are employed to validate the dynamic responses and spatial dependence. The results show that digital–intelligent integration is positively associated with both carbon-related and conventional efficiency, but its marginal contribution is strongly conditioned by human capital, urbanisation, and environmental regulation, exhibiting threshold-type behaviour and diminishing returns at higher digitalisation levels. Green efficiency reacts more strongly to short-run shocks, whereas conventional efficiency follows a steadier improvement trajectory. Heterogeneity across urban agglomerations and evidence of spatial clustering further suggest that uniform policy packages are unlikely to perform well. These findings highlight the importance of sequencing and policy complementarity: investments in digital infrastructure should be coordinated with institutional and structural measures such as green finance, environmental standards, and industrial upgrading and place-based pilots can help scale effective digital applications toward China’s dual-carbon objectives. The proposed framework is transferable to other regions where the digital–climate nexus is central to smart and sustainable urban development. Full article

(This article belongs to the Special Issue Innovative Strategies for Sustainable Smart Cities and Territories)

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27 pages, 7181 KB

Open AccessArticle

Towards Trustworthy Urban Land Use Classification: A Synergistic Fusion of Deep Learning and Explainable Machine Learning with a Nanning Case Study

by Yusheng Zheng, Xinying Huang and Huanmei Yao

Land 2026, 15(1), 158; https://doi.org/10.3390/land15010158 - 13 Jan 2026

While artificial intelligence (AI) has advanced urban land use classification, its application in high-stakes decision making, such as urban planning, demands not only high accuracy but also transparency and interpretability. This study evaluates the potential of Google Satellite Embeddings (GSE), a ready-to-use dataset [...] Read more.

While artificial intelligence (AI) has advanced urban land use classification, its application in high-stakes decision making, such as urban planning, demands not only high accuracy but also transparency and interpretability. This study evaluates the potential of Google Satellite Embeddings (GSE), a ready-to-use dataset of AI-generated numerical features that capture deep land cover characteristics, for land use classification in the central urban area of Nanning in 2022. A synergistic analytical framework was constructed by integrating the 64 high-dimensional features of GSE data with the feature attribution of Shapley Additive Explanations (SHAP), merging deep learning features with explainable machine learning. The results demonstrate that the XGBoost model (OA = 85.00% ± 2.24%) significantly outperformed the Random Forest (RF) model (OA = 81.87% ± 1.72%) overall. Key abstract features were successfully interpreted as comprehensible geographic semantics, with A51 and A36 corresponding to built-up intensity and vegetation cover, respectively. Moreover, XGBoost enabled more refined decisions than Random Forest (RF) due to its superior ability to distinguish between functionally distinct classes that have similar physical appearances. This framework provides a scalable and transferable analytical solution for the challenges of feature limitations and insufficient model transparency in urban land use classification. Full article

(This article belongs to the Special Issue Feature Papers for Land Innovations—Data and Machine Learning: 3rd Edition)

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