Land | March 2025 - Browse Articles

19 pages, 4154 KiB

Open AccessArticle

Optimization of Land Use Patterns in a Typical Coal Resource-Based City Based on the Ecosystem Service Relationships of ‘Food–Carbon–Recreation’

by Wei-Ling Hsu, Zhicheng Zhuang, Cheng Li and Jie Zhao

Land 2025, 14(3), 661; https://doi.org/10.3390/land14030661 - 20 Mar 2025

Cited by 1 | Viewed by 376

Abstract

Imbalanced supplies and demands of ecosystem services (ESSD) can negatively affect human well-being. Optimizing land use patterns in cities and regions is, in fact, essential to mitigate this challenge and ensure sustainable development. In this context, the present study aims to analyze the [...] Read more.

Imbalanced supplies and demands of ecosystem services (ESSD) can negatively affect human well-being. Optimizing land use patterns in cities and regions is, in fact, essential to mitigate this challenge and ensure sustainable development. In this context, the present study aims to analyze the supply and demand of food production services (FPs), carbon sequestration services (CSs), and recreation services (RSs) in a typical coal resource-based city (Huainan) in China. In addition, the main influencing factors and their driving mechanisms were further explored using the geographical detector (Geo-Detector) and multi-scale geographic weighted regression (MGWR) models. Future land use changes were also predicted under traditional and constrained development scenarios using the GeoSOS-FLUS model. The obtained results indicated that: (1) the comprehensive ecosystem service (ES) supply index decreased from 1.42 to 0.84, while the comprehensive demand index increased from 0.74 to 0.95 during the 2010–2020 period; (2) the urban and rural areas had spatial disparities; (3) changes in the construction, ecological, and cultivated land strongly impacted ES; (4) implementing constrained development scenarios can effectively protect the ecological land, control urban expansion, and improve the ESSD relationships in Huainan City. This study provides a valuable theoretical foundation and a methodological framework for future urban and land use optimization efforts, as well as for enhancing the sustainability of ecosystem services and mitigating the imbalance between the supplies and demands of ecosystem services. Full article

(This article belongs to the Special Issue Land–Water–Energy–Food (LWEF) System Coupling and Sustainable Development of Watersheds)

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20 pages, 1605 KiB

Open AccessArticle

Effect of Artificial Intelligence on Chinese Urban Green Total Factor Productivity

by Yuanhe Zhang and Chaobo Zhou

Land 2025, 14(3), 660; https://doi.org/10.3390/land14030660 - 20 Mar 2025

Viewed by 433

Abstract

The manner of achieving high-quality economic development in China through artificial intelligence (AI) has become a focus of academic attention. On the basis of panel data of prefecture-level cities in China from 2010 to 2021, this research utilizes the exogenous impact of the [...] Read more.

The manner of achieving high-quality economic development in China through artificial intelligence (AI) has become a focus of academic attention. On the basis of panel data of prefecture-level cities in China from 2010 to 2021, this research utilizes the exogenous impact of the implementation of the National New Generation Artificial Intelligence Innovation and Development Pilot Zone (AIPZ) to explore the causal effect between AI and green total factor productivity (GTFP). The results are as follows: (1) AI has a significant enhancement effect on urban GTFP. After using a series of robustness tests, such as parallel trend sensitivity test, heterogeneity treatment effect test, and machine learning, this conclusion remains robust. (2) Subsequent mechanism analysis shows that the impact of AI on urban GTFP is mainly achieved by enhancing urban green innovation, promoting industrial structure upgrading, and reducing land resource misallocation. (3) Lastly, the effect of AI on urban GTFP is heterogeneous. AI has also markedly significant enhancement effects on high human capital, non-resource-based economies, and high levels of green consumption behavior. This study provides useful insights for China to develop AI and achieve green development. Full article

(This article belongs to the Special Issue Urban Transitions and Dynamics: Cultural, Ecological, Digital and Degrowth)

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21 pages, 2275 KiB

Open AccessArticle

Exploring the Perception Differences and Influencing Factors of Ecosystem Services Among Residents in Northeast China Tiger and Leopard National Park

by Huiyan Qin, Han Wang and Panwar Rajat

Land 2025, 14(3), 659; https://doi.org/10.3390/land14030659 - 20 Mar 2025

Viewed by 412

Abstract

Local residents’ satisfaction plays a crucial role in the successful management of national parks. However, limited attention has been paid to residents’ preferences in the management of national parks, which hinders the sustainable development and optimization of management systems. To address this gap, [...] Read more.

Local residents’ satisfaction plays a crucial role in the successful management of national parks. However, limited attention has been paid to residents’ preferences in the management of national parks, which hinders the sustainable development and optimization of management systems. To address this gap, we focused on the Dongning area of Northeast China Tiger and Leopard National Park (NCTLNP) as a case study and employed the importance–performance analysis (IPA) framework to assess residents’ perceptions and cognitive rankings of current ecosystem services. Additionally, we examined how demographic and socio-economic factors influence these perceptions. Our findings reveal that local residents prioritize ecosystem services that directly impact their livelihoods and that their material, social, spiritual, and cultural needs are not fully met. Satisfaction and importance ratings varied across regions, with significant influences occurring from the residents’ sex, occupations, and livelihoods. Based on these results, we recommend strengthening the institutional framework for national park management and enhancing the scientific effectiveness of management policies by incorporating residents’ perspectives into decision-making processes. Full article

(This article belongs to the Section Land, Biodiversity, and Human Wellbeing)

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24 pages, 53902 KiB

Open AccessFeature PaperArticle

Flood-Hazard Assessment in the Messapios River Catchment (Central Evia Island, Greece) by Integrating GIS-Based Multi-Criteria Decision Analysis and Analytic Hierarchy Process

by Vasileios Mazarakis, Konstantinos Tsanakas, Noam Greenbaum, Dimitrios-Vasileios Batzakis, Alessia Sorrentino, Ioannis Tsodoulos, Kanella Valkanou and Efthimios Karymbalis

Land 2025, 14(3), 658; https://doi.org/10.3390/land14030658 - 20 Mar 2025

Viewed by 1445

Abstract

This study presents a comprehensive flood-hazard assessment and mapping of the Messapios River catchment in Evia Island, Greece, utilizing a combination of Multi-Criteria Decision Analysis (MCDA) and Geographic Information Systems (GISs). Flood-prone zones were identified based on five critical factors, which were determined [...] Read more.

This study presents a comprehensive flood-hazard assessment and mapping of the Messapios River catchment in Evia Island, Greece, utilizing a combination of Multi-Criteria Decision Analysis (MCDA) and Geographic Information Systems (GISs). Flood-prone zones were identified based on five critical factors, which were determined to be the most influential in the watercourse when excessive discharge overwhelms the drainage network’s capacity: slope, elevation, proximity to stream channels, geological formations, and land cover. The Analytic Hierarchy Process (AHP) was applied to assign weights to these factors, while the final flood-hazard map was generated using the Weighted Linear Combination (WLC) method. The analysis revealed that 17.8% of the catchment, approximately 39 km², falls within a very high flood-hazard zone, while 18.02% (38.91 km²) is classified as highly susceptible to flooding. The flood-prone areas are concentrated in the central, southern, and western parts of the study area, particularly at the lower reaches of the catchment, on both sides of the main streams’ channels, and within the gently sloping, low-lying fan delta of the river. The study area has high exposure to flood hazards due to the significant population of approximately 9000 residents living within the flood-prone zones, a fact that contributes to the area’s potential vulnerability. Additionally, critical infrastructure, including five industrial facilities, the Psachna General High School, the local Public Power Corporation substation, about 21 km of the road network, and 21 bridges are located within the zones classified as having high and very high flood-hazard levels. Furthermore, about 35 km² of economically vital agricultural areas (such as parts of the Psachna and Triada plains) are situated in highly and very highly prone to floods zones. MCDA proved to be an effective and reliable approach for assessing and mapping flood-hazard distribution in the Messapios River catchment. The results provide valuable insights to assist decision-makers in prioritizing intervention areas and efficiently allocate resources. Full article

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22 pages, 2412 KiB

Open AccessArticle

Evaluating Modified Soil Erodibility Factors with the Aid of Pedotransfer Functions and Dynamic Remote-Sensing Data for Soil Health Management

by Pooja Preetha and Naveen Joseph

Land 2025, 14(3), 657; https://doi.org/10.3390/land14030657 - 20 Mar 2025

Viewed by 258

Abstract

Soil erosion is a critical factor impacting soil health and agricultural productivity, with soil erodibility often quantified using the K-factor in erosion models such as the universal soil loss equation (USLE). Traditional K-factor estimation lacks spatiotemporal precision, particularly under varying soil moisture and [...] Read more.

Soil erosion is a critical factor impacting soil health and agricultural productivity, with soil erodibility often quantified using the K-factor in erosion models such as the universal soil loss equation (USLE). Traditional K-factor estimation lacks spatiotemporal precision, particularly under varying soil moisture and land cover conditions. This study introduces modified K-factor pedotransfer functions (Kmlr) integrating dynamic remotely sensed data on land use land cover to enhance K-factor accuracy for diverse soil health management applications. The Kmlr functions from multiple approaches, including dynamic crop and cover management factor (Cdynamic), high resolution satellite data, and downscaled remotely sensed data, were evaluated across spatial and temporal scales within the Fish River watershed in Alabama, a coastal watershed with significant soil–water interactions. The results highlighted that the Kmlr model provided more accurate sediment yield (SY) predictions, particularly in agricultural areas, where traditional models overestimated erosion by upto 59.23 ton/ha. SY analysis across the 36 hydrological response units (HRUs) in the watershed showed that the Kmlr model captured more accurate soil loss estimates, especially in regions with varying land use. The modified K-factor model (Kmlr-c) using Cdynamic and high-resolution soil surface moisture data outperformed the traditional USLE K-factors in predicting SY, with a strong correlation to observed SY data (R² = 0.980 versus R² = 0.911). The total sediment yield predicted by Kmlr-c (525.11 ton/ha) was notably lower than that of USLE-based estimates (828.62 ton/ha), highlighting the overestimation in conventional models. The identification of erosive hotspots revealed that 6003 ha of land was at high erosion risk (K-factor > 0.25), with an average soil loss of 24.2 ton/ha. The categorization of erosive hotspots highlighted critical areas at high risk for erosion, underscoring the need for targeted soil conservation practices. This research underscores the improvement of remotely sensed data-based models and perfects them for the application of soil erodibility assessments thus promoting the development of such models. Full article

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25 pages, 3016 KiB

Open AccessArticle

Low-Carbon City Policies and Employment in China: Impact Effects and Spatial Spillovers

by Lifei Ru and Yongling Yao

Land 2025, 14(3), 656; https://doi.org/10.3390/land14030656 - 20 Mar 2025

Viewed by 313

Abstract

This study examines the impact of low-carbon city policies on urban employment using panel data from 2006 to 2021. The findings reveal that these policies significantly enhance urban employment by promoting green technological innovation, which drives economic growth and creates new job opportunities. [...] Read more.

This study examines the impact of low-carbon city policies on urban employment using panel data from 2006 to 2021. The findings reveal that these policies significantly enhance urban employment by promoting green technological innovation, which drives economic growth and creates new job opportunities. Low-carbon policies also exhibit spatial spillover effects, benefiting neighboring cities within a 200 km radius. However, these effects vary non-linearly with distance. The key mechanisms include green technology adoption, industrial structure optimization, and the promotion of green consumption habits. These mechanisms accelerate industrial upgrading, foster the growth of tertiary and green industries, and expand job opportunities in emerging markets. Heterogeneity analysis shows more substantial employment effects in non-resource-based cities, provincial capitals, cities with high government innovation preferences, tertiary sector dominance, and higher urbanization rates. This highlights the need for policies tailored to specific urban characteristics. In conclusion, low-carbon policies integrate climate action with employment growth. Policymakers should invest in green technologies, support industrial transformation, and enhance public environmental awareness to maximize employment benefits, fostering sustainable urban development. Full article

(This article belongs to the Topic Ideas for Future Cities: Intelligent, Low-Carbon and Healthy)

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32 pages, 11794 KiB

Open AccessArticle

Urban Regeneration Through Circularity: Exploring the Potential of Circular Development in the Urban Villages of Chengdu, China

by Xinyu Lin, Marcin Dąbrowski, Lei Qu, Birgit Hausleitner and Roberto Rocco

Land 2025, 14(3), 655; https://doi.org/10.3390/land14030655 - 20 Mar 2025

Viewed by 438

Abstract

Research on circular development in China’s urban planning remains limited, particularly regarding marginalized groups’ actions. This study addresses the gap by examining circular practices within informal food systems in Chengdu’s urban villages. It highlights residents’ bottom-up initiatives in food production and consumption and [...] Read more.

Research on circular development in China’s urban planning remains limited, particularly regarding marginalized groups’ actions. This study addresses the gap by examining circular practices within informal food systems in Chengdu’s urban villages. It highlights residents’ bottom-up initiatives in food production and consumption and their interactions with the broader urban context. Using street interviews and Research through Design, it develops community-based visions to improve these actions and the needed planning tools for implementation. It also explores how circular development could support urban regeneration by recognizing overlooked resources and practices. Semi-structured expert interviews reveal barriers in China’s planning system to accommodate such visions. Findings indicate that local circular actions—driven by local labor and knowledge and efforts to tackle polluted land and idle spaces—offer valuable opportunities for circular development. However, deficiencies in planning tools for spatial planning, waste treatment, land contamination regulation, and vulnerability recognition create barriers to upscaling these initiatives. This study calls for integrating circular development into China’s spatial planning by strengthening top-down tools and fostering grassroots initiatives to promote sustainable resource flows, ecosystem health, and social equity. It also offers broader insights into promoting circular development by recognizing and integrating informal, bottom-up practices in cities undergoing informal settlement regeneration. Full article

(This article belongs to the Special Issue Strategic Spatial Planning in Urban and Regional Sustainability Transitions)

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22 pages, 5740 KiB

Open AccessArticle

Biocultural Diversity at Risk Amidst and Beyond Overtourism: The Decline in Wild Green Foraging in Corfu over the Past 50 Years

by Mousaab Alrhmoun, Naji Sulaiman, Irfan Ullah, Renata Sõukand and Andrea Pieroni

Land 2025, 14(3), 654; https://doi.org/10.3390/land14030654 - 20 Mar 2025

Viewed by 307

Abstract

This study focuses on the interplay between ecological, demographic, and developmental factors while examining the changes in wild greens (WGs) uses in Corfu from 1970 to 2024. A comparative analysis of historical and contemporary datasets reveals a decline in WG species from 58 [...] Read more.

This study focuses on the interplay between ecological, demographic, and developmental factors while examining the changes in wild greens (WGs) uses in Corfu from 1970 to 2024. A comparative analysis of historical and contemporary datasets reveals a decline in WG species from 58 (belonging to 47 genera and 18 families) in 1971 to 42 species (37 genera, 16 families) in 2024. The reduction in cropland and, therefore, the herbaceous vegetation has significantly contributed to this loss, alongside urbanisation, demographic shifts, and the rise of tourism-driven economies. Changes in climatic factors, like a 1.5 °C increase in temperature and reduced rainfall, further affect plant biodiversity. Shifts in the occupations of local populations (from farming to touristic services), the declining role of women-centred foraging, and the pervasive influence of formal botanical education may have altered the cultural landscape of WG use. This study underlines the urgent need to integrate traditional ecological knowledge into conservation strategies to mitigate biodiversity loss and sustain cultural heritage. Full article

(This article belongs to the Special Issue Overtourism and the Disappearance of Sustainable Land Use and Local Knowledge Systems: A New Form of Colonialism?)

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21 pages, 11376 KiB

Open AccessArticle

Influence of Tree Community Characteristics on Carbon Sinks in Urban Parks: A Case Study of Xinyang, China

by Honglin Zhang, Qiutan Ren, Yuyang Zhou, Nalin Dong, Hua Wang, Yongge Hu, Peihao Song, Ruizhen He, Guohang Tian and Shidong Ge

Land 2025, 14(3), 653; https://doi.org/10.3390/land14030653 - 19 Mar 2025

Viewed by 336

Abstract

Cities are major contributors to global carbon emissions; however, urban parks offer substantial potential for carbon sinks. Research on factors influencing carbon capture in urban park vegetation is still limited. This study investigates 81 urban parks in Xinyang, Henan Province, to quantify woody [...] Read more.

Cities are major contributors to global carbon emissions; however, urban parks offer substantial potential for carbon sinks. Research on factors influencing carbon capture in urban park vegetation is still limited. This study investigates 81 urban parks in Xinyang, Henan Province, to quantify woody plant carbon storage (CS) and sequestration (CSG). By surveying all vegetation types and quantities in these parks, along with factors like park attributes, community structure, biodiversity, spatial distribution, woody plant connectivity, and spatial complexity, we create statistical models for CS and CSG. The results indicate that the average carbon storage density (CSD) in Xinyang’s urban parks is 4.01 kg/m², while the carbon sequestration density (CSGD) is 0.39 kg·C·m²·yr⁻¹. The dominant tree species are Ligustrum lucidum, Osmanthus fragrans, and Lagerstroemia indica, while species with higher carbon sequestration potential, such as Glyptostrobus pensilis, Populus deltoides, and Albizia kalkora, reveal a discrepancy between common and high-sequestration species. The study shows that park characteristics, community structure, and biodiversity are key factors impacting urban carbon sink capacity. By analyzing the relationship between these factors and carbon sinks in urban park vegetation, we create a comprehensive framework for assessing tree CS and CSG, offering quantitative support to improve carbon capture in urban parks. Full article

(This article belongs to the Section Landscape Ecology)

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30 pages, 21255 KiB

Open AccessArticle

Spatial and Temporal Changes and Influencing Factors of Mercury in Urban Agglomeration Land Patterns: A Case from Changchun Area, Old Industrial Base of Northeast China

by Zhe Zhang, Zhaojun Wang, Jing Zong, Hongjie Zhang, Yufei Hu, Yuliang Xiao, Gang Zhang and Zhenxin Li

Land 2025, 14(3), 652; https://doi.org/10.3390/land14030652 - 19 Mar 2025

Viewed by 271

Abstract

Mercury, a global pollutant with high biotoxicity, is widely distributed in soils, water bodies, and the atmosphere. Anthropogenic activities such as industrial emissions and coal combustion release large quantities of mercury into the environment, posing health risks to human populations. Strict implementation of [...] Read more.

Mercury, a global pollutant with high biotoxicity, is widely distributed in soils, water bodies, and the atmosphere. Anthropogenic activities such as industrial emissions and coal combustion release large quantities of mercury into the environment, posing health risks to human populations. Strict implementation of the Minamata Convention and innovative remediation technologies can mitigate escalating environmental and public health risks. This study investigated the spatiotemporal dynamics of mercury in soils and atmosphere across four spatial scales (central city, county, township, and village) within the Changchun urban agglomeration, China. During spring, summer, and autumn of 2023, surface soil and atmospheric mercury concentrations (at 0 cm and 100 cm) were measured using LUMEX RA-915+ at 361 sites. Soil mercury exhibited seasonal variability, with a mean concentration of 46.2 µg/kg, showing peak values in spring and troughs in summer; concentrations decreased by 29.40% from spring to summer, followed by a 27.85% rebound in autumn. Spatially, soil mercury concentrations exhibited a core–periphery decreasing gradient (central city > county > township > village). Average concentrations at county, township, and village levels were 9.92%, 35.07%, and 42.11% lower, respectively, than those in the central city. Atmospheric mercury displayed seasonal variations; mean concentrations at 0 cm and 100 cm heights were 6.13 ng/m³ and 6.75 ng/m³, respectively, both peaking in summer. At 0 cm, summer concentrations increased by 35.61% compared to spring, then declined by 35.96% in autumn; at 100 cm, summer concentrations rose by 49.39% from spring and decreased by 31.08% in autumn. Atmospheric mercury at both heights decreased from the central city to the peripheries, with reductions of approximately 40% at 0 cm and 37–39% at 100 cm. Atmospheric mercury dynamics were significantly correlated with meteorological parameters such as temperature and humidity. Spatial autocorrelation analysis revealed scale-dependent clustering patterns: soil mercury Moran’s I ranked central city > county > village > township, while atmospheric mercury followed township > village > county > central city. Structural equation modeling demonstrated that different spatial scales had a significant negative effect on soil mercury concentrations, atmospheric mercury concentrations at 0 cm and 100 cm, and mercury and its compounds emissions. Organic matter content had a significant positive effect on soil mercury content. Temperature and humidity positively influenced near-surface atmospheric mercury. This multi-scale approach elucidates urban agglomeration mercury dynamics, highlighting core–periphery pollution gradients and seasonal patterns, thereby providing empirical evidence for regional mercury transport studies and providing a scientific foundation for future heavy metal management strategies. Full article

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23 pages, 14330 KiB

Open AccessFeature PaperArticle

Prediction Capability of Analytical Hierarchy Process (AHP) in Badland Susceptibility Mapping: The Foglia River Basin (Italy) Case of Study

by Margherita Bianchini, Stefano Morelli, Mirko Francioni and Roberta Bonì

Land 2025, 14(3), 651; https://doi.org/10.3390/land14030651 - 19 Mar 2025

Viewed by 714

Abstract

Badland morphologies are prominent examples of linear erosion occurring on clay-rich slopes and are critical hotspots for sediment production. Traditional field-based mapping of these features can be both time-consuming and costly, particularly over larger basins. This research proposes a novel methodology for assessing [...] Read more.

Badland morphologies are prominent examples of linear erosion occurring on clay-rich slopes and are critical hotspots for sediment production. Traditional field-based mapping of these features can be both time-consuming and costly, particularly over larger basins. This research proposes a novel methodology for assessing badland susceptibility through a multi-criteria decision-making framework known as the Analytical Hierarchy Process (AHP). This methodology, developed and tested in the Foglia River basin of the Marche region (Italy), facilitates the identification and mapping of badland areas. More in detail, our study resulted in the creation of a comprehensive badland inventory and susceptibility map for the 102 km² study area, identifying 276 badlands using a combination of satellite imagery, historical orthophotos, existing regional inventories, and field inspections. Key predisposing factors, including geological, land use, topographical, and hydrometric elements, were systematically analyzed using the AHP approach. The research findings indicate that badlands develop in medium to steep slopes oriented towards the southern quadrants and in proximity to watercourses; their formation is predominantly influenced by clayey–sandy lithology. The resulting inventory and susceptibility map serve as relevant tools for monitoring, preventing, and mitigating slope instability risks within the region. Full article

(This article belongs to the Special Issue Understanding Disasters in a Changing Landscape: Causes, Impacts and Mitigation Strategies)

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20 pages, 9176 KiB

Open AccessArticle

Effects of Natural Factors and Human Activities on the Spatio-Temporal Distribution of Net Primary Productivity in an Inland River Basin

by Fenghua Sun, Bingming Chen, Jianhua Xiao, Fujie Li, Jinjin Sun and Yugang Wang

Land 2025, 14(3), 650; https://doi.org/10.3390/land14030650 - 19 Mar 2025

Viewed by 240

Abstract

Net primary productivity (NPP) is a critical indicator for evaluating the carbon sequestration potential of an ecosystem and regional sustainable development, as its spatiotemporal dynamics are jointly influenced by natural and anthropogenic factors. This study investigated the Sangong River Basin, an inland watershed [...] Read more.

Net primary productivity (NPP) is a critical indicator for evaluating the carbon sequestration potential of an ecosystem and regional sustainable development, as its spatiotemporal dynamics are jointly influenced by natural and anthropogenic factors. This study investigated the Sangong River Basin, an inland watershed located in northwestern China. By employing the Carnegie–Ames–Stanford Approach (CASA) model and the Geodetector method, integrated with remote sensing data and field surveys, we systematically analyzed the spatiotemporal evolution and driving mechanisms of NPP from 1990 to 2020. Our results reveal an average annual basin-wide NPP increase of 2.33 g C·m⁻²·a⁻¹, with plains experiencing significantly greater increases (2.86 g C·m⁻²·a⁻¹) than mountains (1.71 g C·m⁻²·a⁻¹). Land use intensity (LUI) explained 31.44% of the NPP variability in the plains, whereas climatic factors, particularly temperature (71.27% contribution rate), primarily governed the NPP dynamics in mountains. Soil properties exhibited strong associations with NPP. Specifically, a 1 g·kg⁻¹ increase in soil organic content elevated NPP by 99.04 g C·m⁻²·a⁻¹, while a comparable rise in soil salinity reduced NPP by 123.59 g C·m⁻²·a⁻¹. These findings offer spatially explicit guidance for ecological restoration and carbon management in arid inland basins, underscoring the need for a strategic equilibrium between agricultural intensification and ecosystem conservation to advance carbon neutrality objectives. Full article

(This article belongs to the Special Issue Land Use/Cover Change and Its Impacts on Regional Sustainable Development)

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26 pages, 48126 KiB

Open AccessArticle

Multi-Source Attention U-Net: A Novel Deep Learning Framework for the Land Use and Soil Salinization Classification of Keriya Oasis in China with RADARSAT-2 and Landsat-8 Data

by Yang Xiang, Ilyas Nurmemet, Xiaobo Lv, Xinru Yu, Aoxiang Gu, Aihepa Aihaiti and Shiqin Li

Land 2025, 14(3), 649; https://doi.org/10.3390/land14030649 - 19 Mar 2025

Viewed by 400

Abstract

Soil salinization significantly impacts global agricultural productivity, contributing to desertification and land degradation; thus, rapid regional monitoring of soil salinization is crucial for agricultural production and sustainable management. With advancements in artificial intelligence, the efficiency and precision of deep learning classification models applied [...] Read more.

Soil salinization significantly impacts global agricultural productivity, contributing to desertification and land degradation; thus, rapid regional monitoring of soil salinization is crucial for agricultural production and sustainable management. With advancements in artificial intelligence, the efficiency and precision of deep learning classification models applied to remote sensing imagery have been demonstrated. Given the limited feature learning capability of traditional machine learning, this study introduces an innovative deep fusion U-Net model called MSA-U-Net (Multi-Source Attention U-Net) incorporating a Convolutional Block Attention Module (CBAM) within the skip connections to improve feature extraction and fusion. A salinized soil classification dataset was developed by combining spectral indices obtained from Landsat-8 Operational Land Imager (OLI) data and polarimetric scattering features extracted from RADARSAT-2 data using polarization target decomposition. To select optimal features, the Boruta algorithm was employed to rank features, selecting the top eight features to construct a multispectral (MS) dataset, a synthetic aperture radar (SAR) dataset, and an MS + SAR dataset. Furthermore, Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and deep learning methods including U-Net and MSA-U-Net were employed to identify the different degrees of salinized soil. The results indicated that the MS + SAR dataset outperformed the MS dataset, with the inclusion of the SAR band resulting in an Overall Accuracy (OA) increase of 1.94–7.77%. Moreover, the MS + SAR MSA-U-Net, in comparison to traditional machine learning methods and the baseline model, improved the OA and Kappa coefficient by 8.24% to 12.55% and 0.08 to 0.15, respectively. The results demonstrate that the MSA-U-Net outperformed traditional models, indicating the potential of integrating multi-source data with deep learning techniques for monitoring soil salinity. Full article

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30 pages, 6392 KiB

Open AccessArticle

Language Culture and Land Use: A Case Study of the Dialect Cultural Regions in Anhui Province, China

by Xiyu Chen, Guodong Fang, Jia Kang, Bo Hong, Ziyou Wang and Wuyun Xia

Land 2025, 14(3), 648; https://doi.org/10.3390/land14030648 - 19 Mar 2025

Viewed by 370

Abstract

The unity of material and spiritual civilization is among the important criteria for sustainable development and modernization construction. However, defining the relationship between the two has posed a challenge to researchers. In terms of spiritual civilization, many studies on dialect maps reflect the [...] Read more.

The unity of material and spiritual civilization is among the important criteria for sustainable development and modernization construction. However, defining the relationship between the two has posed a challenge to researchers. In terms of spiritual civilization, many studies on dialect maps reflect the dialect characteristics and cultural features of different regions. Regarding material civilization, changes in land use and behavior have attracted the attention of many scholars, who have extensively discussed their regional heterogeneity. However, few studies have focused on the connection between the two, and discussions on the possible bidirectional interaction between dialects and land use have been limited. Thus, in order to bridge the gap between the spiritual civilization related to language and the material civilization related to land use, this study proposes an interactive theoretical framework and conducts an in—depth analysis by taking Anhui Province in China as an example. Firstly, it comprehensively identifies the dialect types within Anhui Province and maps the dialects. This fundamental work provides a crucial basis for understanding the distribution of different dialect regions. Subsequently, a profound analysis of the spatiotemporal changes in land use in this province over time is carried out. To further explore the characteristics of land use behaviors, this study employs the Latent Dirichlet Allocation (LDA) model to mine the latent semantic topics in the land use-related data, thus enabling a more detailed understanding of the diverse patterns of land use behaviors in different regions. Finally, by uncovering the characteristics of land use changes and behavior differences in different dialect regions, this study explores the possible bidirectional interaction mechanisms. The results show that significant spatial heterogeneity in land use behavior and its driving factors can be observed within different dialect regions. Its bidirectional interaction is manifested in land use behaviors regulating people’s activities through constructing “fields” and forming habits that influence regional dialects and cultures. Meanwhile, under mobility mechanisms, new dialect systems replace indigenous languages in immigration destinations. Land use methods from emigration areas are spread through convenient communication, affecting the cultural psychology and land use behaviors of social groups in immigration destinations. This study expands the boundaries of linguistic and cultural geography, offering a new perspective for the identification of spatial differentiation and new ideas for the governance of spatial differences. Full article

(This article belongs to the Special Issue Global Commons Governance and Sustainable Land Use)

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25 pages, 7300 KiB

Open AccessArticle

Spatiotemporal Patterns and Determinants of Cropland Abandonment in Mountainous Regions of China: A Case Study of Sichuan Province

by Buting Hong, Jicheng Wang, Jiangtao Xiao, Quanzhi Yuan and Ping Ren

Land 2025, 14(3), 647; https://doi.org/10.3390/land14030647 - 18 Mar 2025

Cited by 1 | Viewed by 463

Abstract

Cropland abandonment (CA) is an increasingly severe global issue, with significant implications for achieving the Sustainable Development Goal of Zero Hunger. In China, widespread CA is particularly evident in remote mountainous regions. However, the rugged terrain and highly fragmented cropland pose significant challenges [...] Read more.

Cropland abandonment (CA) is an increasingly severe global issue, with significant implications for achieving the Sustainable Development Goal of Zero Hunger. In China, widespread CA is particularly evident in remote mountainous regions. However, the rugged terrain and highly fragmented cropland pose significant challenges in mapping abandoned cropland with high precision using remote sensing technology. Moreover, CA is the result of multi-level factors, yet previous studies have primarily analyzed its driving factors from a single level, leading to a lack of comprehensive understanding of the underlying mechanisms. We took Sichuan Province, located in the mountainous regions of Western China, as a case study, utilizing satellite-derived high-precision CA maps to reveal the spatiotemporal patterns of CA. Additionally, we employed hierarchical linear model to explore the determinants of CA and their interactions at both county and municipal levels. The results indicate that the CA rate decreased continuously from 6.75% in 2019 to 4.47% in 2023, with abandoned cropland exhibiting significant spatial clustering. High-value clusters were predominantly concentrated in the western mountainous areas, and hotspots of CA exhibited a general migration trend from the northeast to the southwest. Furthermore, we found that CA is influenced by multi-level factors, with 61% and 39% of the variance in CA being explained at the county and municipal levels, respectively. The agglomeration index of cropland (AI) is a key determinant at the county level, with the Digital Elevation Model (DEM) and the distance to roads also playing significant roles. At the municipal level, urbanization rate and the proportion of non-agricultural employment (PNAE) are dominant factors, and an increase in PNAE weakens the negative impact of AI on CA rates. To curb CA in mountainous areas, we recommend implementing land consolidation projects, improving rural land transfer markets, and strengthening legal mechanisms to combat CA. Our study has broad application prospects, providing critical support for assessing the ecological and environmental consequences of CA and exploring the potential of reutilizing abandoned cropland for food production, bioenergy, and carbon sequestration. Full article

(This article belongs to the Section Land Socio-Economic and Political Issues)

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22 pages, 8169 KiB

Open AccessReview

Structural Equation Model in Landscape Performance Research: Dimensions, Methodologies, and Recommendations

by Xiao Han, Zhe Li, Haini Chen, Mengyao Yu and Yi Shi

Land 2025, 14(3), 646; https://doi.org/10.3390/land14030646 - 18 Mar 2025

Viewed by 406

Abstract

The scientific evaluation of landscape performance has become a critical focus in promoting landscape architecture and urban quality research. Structural equation modeling (SEM) is widely applied in digital assessments and performance studies, offering robust analytical capabilities. However, further progress requires a systematic review [...] Read more.

The scientific evaluation of landscape performance has become a critical focus in promoting landscape architecture and urban quality research. Structural equation modeling (SEM) is widely applied in digital assessments and performance studies, offering robust analytical capabilities. However, further progress requires a systematic review to synthesize past findings and identify emerging opportunities. This study reviews 245 articles that utilize SEM in landscape performance research, analyzing publication trends, research dimensions, methodologies, and data sources. The results indicate that SEM-based studies are predominantly focused on cognitive environmental performance based on subjective evaluation data. SEM can be applied to analyze the correlation mechanisms between landscape performance and influencing factors, examine the mediating effects among multiple factors, and conduct comparative analyses across different sample groups. Future research should prioritize integrating subjective and objective assessments, developing open-source databases, and promoting practical applications of SEM technologies. These efforts will enhance policy-making and improve the precision of performance evaluations, strengthening the scientific foundation of landscape architecture and quality enhancement research. Full article

(This article belongs to the Topic Biophilic Cities and Communities: Human-Environment Interaction and Sustainable Governance)

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25 pages, 41068 KiB

Open AccessArticle

Configuration of Green–Blue–Grey Spaces for Efficient Cooling of Urban Physical and Perceptual Thermal Environments

by Wenxia Zeng, Kun Yang, Shaohua Zhang, Changyou Bi, Jing Liu, Xiaofang Yang, Yan Rao and Yan Ma

Land 2025, 14(3), 645; https://doi.org/10.3390/land14030645 - 18 Mar 2025

Viewed by 355

Abstract

Blue and green spaces are well-known for their benefits in improving urban thermal environments. However, the optimal configuration of green, blue, and grey spaces (GBGSs) for the physical and mental health of urban residents remains unclear. Therefore, we employed land surface temperature (LST), [...] Read more.

Blue and green spaces are well-known for their benefits in improving urban thermal environments. However, the optimal configuration of green, blue, and grey spaces (GBGSs) for the physical and mental health of urban residents remains unclear. Therefore, we employed land surface temperature (LST), near-surface air temperature (SAT), and Humidex to analyze the optimal configuration of GBGS. The results indicated the following: (1) The spatial distribution of Perceptual Urban Thermal Environments (PTEs) is consistent with that of Surface Urban Thermal Environments (STEs). However, the temperature of most perceptual indicators is lower than the daytime LST and higher than the SAT. (2) Blue spaces have higher cooling efficiency than green spaces. (3) The coverage of grey space is less than 40%, at least 35% for green space, and blue space covers between 15% and 25%, which is the optimal configuration to balance the thermal environment. Moreover, increasing blue space and simplifying green spaces is recommended where grey space coverage is below 30%. In areas with 30–40% grey space, enhancing the complexity and fragmentation of blue space boundaries is more effective. Maintaining at least 30% blue space and optimizing green space aggregation improves cooling efficiency where grey space coverage is over 40%. This study provides the scientific foundation for configuration of GBGSs in urban development and renovations. Full article

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22 pages, 11154 KiB

Open AccessArticle

When to Use What: A Comparison of Three Approaches to Quantify Relationships Among Ecosystem Services

by Zhen Zhong, Bochuan Zhou, Lingqiang Kong and Xuening Fang

Land 2025, 14(3), 644; https://doi.org/10.3390/land14030644 - 18 Mar 2025

Viewed by 295

Abstract

Sustainable landscape management requires accurately identifying the trade-offs and synergies among ecosystem services (ES). Three commonly utilized approaches to quantify ES trade-off/synergy relationships include the space-for-time approach, landscape background-adjusted space-for-time approach, and temporal trend approach. However, the similarities and differences among these three [...] Read more.

Sustainable landscape management requires accurately identifying the trade-offs and synergies among ecosystem services (ES). Three commonly utilized approaches to quantify ES trade-off/synergy relationships include the space-for-time approach, landscape background-adjusted space-for-time approach, and temporal trend approach. However, the similarities and differences among these three approaches in identifying ES relationships in the same area remain unclear. Thus, we conducted a case study in the rapidly urbanizing Yangtze River Delta region, comparing the three approaches based on annual data spanning from 2001 to 2020 for 12 types of ES. We found that: (1) the ES trade-off/synergy relationships detected by the three approaches exhibit significant divergence, with only 1.45% consistency among the 66 pairs of ES relationships. (2) All three approaches can overlook ES trade-offs, miss ES synergies, and erroneously detect interactions where none exist. (3) The mechanisms contributing to the misidentification of ES relationships by the three approaches include: neglecting the underlying assumptions of different approaches, insufficient time interval length, short time series of ES data, data aggregation effects, non-linear changes in ESs, time lag effects of ES relationships, among others. Our results indicate that each of the three approaches has its own advantages and disadvantages in identifying ES relationships. Prior to selecting an approach for identifying relationships between ESs in a specific study area, careful consideration of the availability of time series data, the characteristics of the chosen ES type, and thorough examination of the underlying assumptions and uncertainties of each approach are imperative. Full article

(This article belongs to the Special Issue Spatial-Temporal Evolution Analysis of Land Use)

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17 pages, 12359 KiB

Open AccessArticle

Obtaining a Land Use/Cover Cartography in a Typical Mediterranean Agricultural Field Combining Unmanned Aerial Vehicle Data with Supervised Classifiers

by Ioannis A. Nikolakopoulos and George P. Petropoulos

Land 2025, 14(3), 643; https://doi.org/10.3390/land14030643 - 18 Mar 2025

Viewed by 367

Abstract

The mapping of land use/cover (LULC) types is a crucial tool for natural resource management and monitoring changes in both human and physical environments. Unmanned aerial vehicles (UAVs) provide high-resolution data, enhancing the capability for accurate LULC representation at potentially very high spatial [...] Read more.

The mapping of land use/cover (LULC) types is a crucial tool for natural resource management and monitoring changes in both human and physical environments. Unmanned aerial vehicles (UAVs) provide high-resolution data, enhancing the capability for accurate LULC representation at potentially very high spatial resolutions. In the present study, two widely used supervised classification methods, namely the Maximum Likelihood Classification (MLC) and Mahalanobis Distance Classification (MDC), were applied to analyze image data collected by UAVs from a typical Mediterranean site located in Greece. The study area, characterized by diverse land uses (urban, agricultural, and natural areas), served as an ideal field for comparing the two classification methods. Although both methods produced comparable results, MLC outperformed MDC, with an overall accuracy of 96.58% and a Kappa coefficient of 0.942, compared to MDC for which an overall accuracy of 92.77% and a Kappa coefficient of 0.878 were reported. This study highlights the advantages of using UAVs to produce robust information on the geospatial variability of land use/cover in a given area at very high spatial resolution in a cost-efficient, timely, and on-demand manner. Such information can help in decision- and policy-making for ensuring a more sustainable physical environment. This study’s limitations, including the small and relatively homogeneous study area, are acknowledged. Future research could potentially focus on exploring the use of advanced classification techniques, such as deep learning and more diverse Mediterranean landscapes, which would assist in enhancing the present’s approach applicability. Full article

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21 pages, 2237 KiB

Open AccessArticle

Framework Construction and Application of Gross Ecosystem Product (GEP) in the Three-River-Source National Park (TRSNP) in China

by Peihong Jia, Jing Chen, Diangong Gao, Yuxin Zhu and Xinyue Wang

Land 2025, 14(3), 642; https://doi.org/10.3390/land14030642 - 18 Mar 2025

Viewed by 367

Abstract

Assessing the value of ecosystem products over time can reflect the effectiveness of ecosystem protection and serve as a measurable indicator in national park management. This study focuses on the Three-River-Source National Park (TRSNP), located in the Tibetan Plateau, the “Water Tower of [...] Read more.

Assessing the value of ecosystem products over time can reflect the effectiveness of ecosystem protection and serve as a measurable indicator in national park management. This study focuses on the Three-River-Source National Park (TRSNP), located in the Tibetan Plateau, the “Water Tower of China”. We developed an accounting system for ecosystem products in the TRSNP and assessed their value for 2015 and 2020. Key findings include the following. (1) The validation of the system’s scientific basis with a comprehensive indicator framework covering material products, regulating services, and cultural services. (2) The total value of ecosystem products in TRSNP increased by 31.19% from 2015 to 2020. Driven by policies such as grazing bans and the restoration of grasslands, the value of material products saw a decrease during the same period, while the value of regulating services experienced an increase. Notably, among the regulating services, the value associated with soil conservation emerged as the highest. (3) The value of regulating services varies across different regions due to the influences of land use types and soil erosion types. Among these, the value of regulating services per unit area is highest in the Lancang River source region, followed by the Yellow River source region, and the value was the lowest in the Yangtze River source region. (4) Recommendations include enhancing the value of agricultural and animal husbandry products to increase the overall agro-pastoral income, focusing on soil protection and restoration in the Yangtze and Yellow River source regions, and exploring strategies for the trading of ecological resource rights for soil retention in the Lancang River source region. This research offers a pertinent case study for ecosystem product value assessment, contributes a scientific ecological protection effect evaluation system for TRSNP, and provides a relevant scientific basis for the management of TRSNP. Full article

(This article belongs to the Special Issue Sustainable Agricultural Land Management towards a Net-Zero Pathway)

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19 pages, 3728 KiB

Open AccessArticle

Spatiotemporal Evolution and Influencing Factors of Carbon Footprint in Yangtze River Economic Belt

by Zhehan Shao, Xiaoshun Li, Jiangquan Chen, Yiwei Geng, Xuanyu Zhai, Ke Zhang and Jie Zhang

Land 2025, 14(3), 641; https://doi.org/10.3390/land14030641 - 18 Mar 2025

Viewed by 339

Abstract

As an important engine of China’s development, the Yangtze River Economic Belt faces the dual contradiction of economic growth and ecological protection. Addressing the insufficient analysis of the spatiotemporal evolution and driving mechanisms of city-level carbon footprints, this study delves into the concept [...] Read more.

As an important engine of China’s development, the Yangtze River Economic Belt faces the dual contradiction of economic growth and ecological protection. Addressing the insufficient analysis of the spatiotemporal evolution and driving mechanisms of city-level carbon footprints, this study delves into the concept of carbon footprint from the perspective of ecological footprint theory and carbon cycle dynamics. Using ODIAC and NPP data, it systematically evaluates carbon footprints across 130 cities and examines their spatiotemporal evolution and driving factors using kernel density estimation and the Kaya-LMDI model. The results show (1) a significant growth trend in carbon footprint, with rapid expansion from 2000 to 2012, followed by fluctuating growth from 2012 to 2022; (2) a west-to-east “low–high” spatial pattern, where disparities have narrowed but absolute gaps continue to widen, leading to polarization; and (3) economic growth and urban expansion as the primary drivers of carbon footprint growth, while ecological land use pressure and carbon sequestration capacity played a major role in mitigation, with the impact of carbon sequestration foundations remaining limited. This study conducts precise regional carbon sink accounting and offers a new perspective on the quantitative analysis of carbon footprint drivers. The findings provide insights for low-carbon governance and sustainable urban development in the Yangtze River Economic Belt. Full article

(This article belongs to the Special Issue Global Commons Governance and Sustainable Land Use)

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21 pages, 7561 KiB

Open AccessArticle

Spatiotemporal Change of Crop Yield and Its Response to Planting Structural Shifts in Northeast China from 2001 to 2021

by Xu Lin, Yaqun Liu and Jieyong Wang

Land 2025, 14(3), 640; https://doi.org/10.3390/land14030640 - 18 Mar 2025

Viewed by 379

Abstract

As a pivotal region for safeguarding China’s food security, Northeast China requires a quantitative evaluation of crop yield dynamics, planting structure shifts, and their interdependent mechanisms. Leveraging MODIS NPP data and remote sensing-derived crop classification data from 2001 to 2021, this study established [...] Read more.

As a pivotal region for safeguarding China’s food security, Northeast China requires a quantitative evaluation of crop yield dynamics, planting structure shifts, and their interdependent mechanisms. Leveraging MODIS NPP data and remote sensing-derived crop classification data from 2001 to 2021, this study established a crop yield estimation model. By integrating the Theil–Sen median slope estimator and Mann–Kendall trend analysis, we systematically investigated the spatiotemporal characteristics of maize, rice, and soybean yields. Phased attribution analysis was further employed to quantify the effects of crop type conversions on total regional yield. The results revealed: (1) strong consistency between estimated yields and statistical yearbook data, with validation R² values of 0.76 (maize), 0.69 (rice), and 0.81 (soybean), confirming high model accuracy; (2) significant yield growth areas that spatially coincided with the core black soil zone, underscoring the productivity-enhancing role of conservation tillage practices; (3) all three crops exhibited upward yield trends, with annual growth rates of 1.33% (maize), 1.20% (rice), and 1.68% (soybean). Spatially, high-yield maize areas were concentrated in the southeast, rice productivity peaked along river basins, and soybean yields displayed a distinct north-high-south-low gradient; (4) crop type transitions contributed to a net yield increase of 35.9177 million tons, dominated by soybean-to-maize conversions (50.41% contribution), while maize-to-soybean shifts led to a 2.61% yield reduction. This study offers actionable insights for optimizing planting structures and tailoring grain production strategies in Northeast China, while providing a methodological framework for crop yield estimation in analogous regions. Full article

(This article belongs to the Special Issue Land Use Policy and Food Security: 2nd Edition)

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19 pages, 16891 KiB

Open AccessFeature PaperArticle

Integration of Historical and Contemporary Data Sources in Understanding the Extent and Types of Disruptions in the Syrdarya Delta Land Use/Land Cover

by Zohar Zofnat, Leah Orlovsky and Isaac A. Meir

Land 2025, 14(3), 639; https://doi.org/10.3390/land14030639 - 18 Mar 2025

Viewed by 386

Abstract

The Syrdarya Delta, located in semi-arid and arid Central Asia, is an important water source for fertile landscapes. The environmental history of the Syrdarya Delta (SD) during the 19th and 20th centuries is a diverse and understudied subject, and its natural and anthropogenic [...] Read more.

The Syrdarya Delta, located in semi-arid and arid Central Asia, is an important water source for fertile landscapes. The environmental history of the Syrdarya Delta (SD) during the 19th and 20th centuries is a diverse and understudied subject, and its natural and anthropogenic aspects changed drastically during this period. As a result of the Syrdarya Delta’s location, on the shores of the former Aral Sea, there is a vital need to expand our understanding of the phases and policies that led to the current condition. This study argues that by integrating methods from social and natural sciences and applying them to selected historical materials, among them, former classified materials from the Cold War period, we can expand our understanding regarding the extent and types of disruptions in the Syrdarya Delta ecological system. The main findings of this study show that between the second part of the 19th and the 21st centuries, a period of roughly a hundred and fifty years, the SD changed drastically in aspects of urban areas, which increased during the Soviet period, changes in land use and hydrography, with changes in the amounts, size and flowing directions of water streams in the SD. The findings also present changes in vegetative cover and amounts parallel to salinization of the soil, which increased in the 1970s–1980s, and changes in the meeting point of the former Aral Sea with the SD. The findings of the study indicate that most of these changes can be attributed to anthropogenic factors, which have taken place mainly since the 1960s–1970s under the USSR regime. As this study presents, such materials can assist in reconstructing land use and land cover from the years to which our data are limited by integrating them with modern satellite image analysis, thus being able to quantify and estimate the amounts and types of these changes regarding salinization, land use and land cover and hydrology, which are crucial for studying deltas located in arid and semi-arid landscapes, such as the SD. This study presents evidence and argues that these data are of pivotal importance and should be used when attempting to rehabilitate and manage today’s Syrdarya Delta landscapes and hydrology. Full article

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19 pages, 6547 KiB

Open AccessArticle

Predicting Suitable Spatial Distribution Areas for Urban Trees Under Climate Change Scenarios Using Species Distribution Models: A Case Study of Michelia chapensis

by Chenbin Shen, Xi Chen, Chao Zhou, Lingzi Xu, Mingyi Qian, Hongbo Zhao and Kun Li

Land 2025, 14(3), 638; https://doi.org/10.3390/land14030638 - 18 Mar 2025

Viewed by 575

Abstract

Climate change has presented considerable challenges in the management of urban forests and trees. Varieties of studies have predicted the potential changes in species distribution by employing single-algorithm species distribution models (SDMs) to investigate the impacts of climate change on plant species. However, [...] Read more.

Climate change has presented considerable challenges in the management of urban forests and trees. Varieties of studies have predicted the potential changes in species distribution by employing single-algorithm species distribution models (SDMs) to investigate the impacts of climate change on plant species. However, there is still limited quantitative research on the impacts of climate change on the suitable distribution ranges of commonly used urban tree species. Therefore, our study aims to optimize traditional SDMs by integrating multiple machine learning algorithms and to propose a framework for identifying suitable distribution ranges of urban trees under climate change. We took Michelia chapensis, a tree species of particular significance in southern China, as a pilot tree species to investigate the evolution of its suitable distribution range in the context of two future climate scenarios (SSP126 and SSP585) across four periods (2030s, 2050s, 2070s, and 2090s). The findings indicated that the ensemble SDM showed strong predictive capacity, with an area under the curve (AUC) value of 0.95. The suitable area for Michelia chapensis is estimated at 15.9 × 10⁵ km² currently and it will expand in most areas under future climate scenarios according to the projection. However, it will contract in southeastern Yunnan, central Guangdong, the Sichuan Basin, northern Hubei, and Jiangxi, etc. The central location of the current suitable distribution area is located in Hengyang, Hunan (27.36° N, 112.34° E), and is projected to shift westward with climate change in the future. The migration magnitude is positively correlated with the intensity of climate change. These findings provide a scientific basis for the future landscape planning and management of Michelia chapensis. Furthermore, the proposed framework can be seen as a valuable tool for predicting the suitable distribution ranges of urban trees in response to climate change, providing insights for proactive adaptation to climate change in urban planning and landscape management. Full article

(This article belongs to the Special Issue Urban Forestry Dynamics: Management and Mechanization)

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19 pages, 15138 KiB

Open AccessArticle

Trade-Offs and Synergies Between Ecosystem Services and Their Ecological Security Patterns in the Guanzhong–Tianshui Economic Zone

by Jing Zhou, Jianhua Xiao, Daiying Yin and Yu Ren

Land 2025, 14(3), 637; https://doi.org/10.3390/land14030637 - 18 Mar 2025

Viewed by 378

Abstract

The Guanzhong–Tianshui economic zone is a strategic link in China’s Belt and Road network, faces the contradiction between ecological protection and economic development, and urgently needs to construct an ecological security pattern based on ecosystem services to permit sustainable development. In this study, [...] Read more.

The Guanzhong–Tianshui economic zone is a strategic link in China’s Belt and Road network, faces the contradiction between ecological protection and economic development, and urgently needs to construct an ecological security pattern based on ecosystem services to permit sustainable development. In this study, we evaluated the ecological services of net primary productivity (NPP), water yield (WY), soil conservation (SC), habitat quality (HQ), and food production (FP). We explored the trade-offs and synergies between services using correlation analysis and geographically weighted regression and constructed an ecological security pattern through circuit theory. NPP, WY, SC, and FP increased during the study period, whereas HQ decreased. The NPP × WY, WY × SC, and WY × HQ shifted from synergies to trade-offs; NPP × SC, NPP × HQ, and SC × HQ were always synergies; NPP × FP, SC × FP, and FP × HQ were always trade-offs; and WY × FP shifted from trade-offs to synergies. We selected service bundles with significant synergies among NPP, SC, and HQ as ecological sources, which were mainly in the Qinling and Weibei mountains, comprising 47 ecological patches. We identified 58 ecological corridors, 330.83 km² of pinch points, and 401.30 km² of barriers, which form a mesh structure covering the study area, proposing a development pattern of six zones and one belt. Our results provide a framework for ecological protection and restoration, which may serve as a scientific foundation for upcoming regional land management initiatives. Full article

(This article belongs to the Special Issue Land Use/Cover Change and Its Impacts on Regional Sustainable Development)

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22 pages, 4669 KiB

Open AccessArticle

Evaluation of Sustainable Development Objectives in the Production of Protected Geographical Indication Legumes

by Betty Carlini, Javier Velázquez, Derya Gülçin, Cristina Lucini and Víctor Rincón

Land 2025, 14(3), 636; https://doi.org/10.3390/land14030636 - 18 Mar 2025

Viewed by 351

Abstract

The Mediterranean Diet is a highly sustainable diet, and legumes are among the products that best characterize this concept. This study evaluates the environmental sustainability of the Protected Geographical Indication (PGI) legume Phaseolus vulgaris L. cultivated in the Asturias region, Spain. Employing a [...] Read more.

The Mediterranean Diet is a highly sustainable diet, and legumes are among the products that best characterize this concept. This study evaluates the environmental sustainability of the Protected Geographical Indication (PGI) legume Phaseolus vulgaris L. cultivated in the Asturias region, Spain. Employing a multi-indicator approach, the study aims to define and measure certain biodiversity indicators useful for assessing the ecological quality and sustainability of the agroecosystems under consideration. Spatial analyses were conducted with GIS-based methodologies, integrating the Analytic Hierarchy Process (AHP) to generate a Sustainability Index (SI). The study found that a significant positive spatial autocorrelation was observed using Moran’s I test (Moran’s I = 0.74555, p < 0.01), indicating that the SI values were not equally distributed but clustered around particular regions. Furthermore, the Getis-Ord Gi* analysis determined statistically significant hotspots, mainly distributed in the western and southwestern areas, including regions near Cangas del Narcea and Tineo. This paper highlights the importance of integrating spatial analysis for environmental assessments to develop sustainability approaches. Soil quality, water use, biodiversity, and land management are some of the factors that affect sustainability outcomes in the region. The results underscore the role of PGI in promoting sustainable agricultural practices by meeting geographical and quality requirements for local production. Full article

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26 pages, 13816 KiB

Open AccessArticle

Evaluating Territorial Space Use Efficiency: A Geographic Data Envelopment Model Considering Geospatial Effects

by Minrui Zheng, Yin Ma, Xinqi Zheng, Xvlu Wang, Li Li, Feng Xu, Xiaoyuan Zhang, Fuping Gan, Jianchao Wang and Zhengkun Zhu

Land 2025, 14(3), 635; https://doi.org/10.3390/land14030635 - 17 Mar 2025

Viewed by 365

Abstract

Accurately evaluating territorial space use efficiency is a prerequisite for promoting the realization of high-quality development. Existing efficiency evaluation models all treat decision making units (DMUs) as independent individuals, ignoring geospatial effects between geographical spaces, which leads to unreliable results. This study proposes [...] Read more.

Accurately evaluating territorial space use efficiency is a prerequisite for promoting the realization of high-quality development. Existing efficiency evaluation models all treat decision making units (DMUs) as independent individuals, ignoring geospatial effects between geographical spaces, which leads to unreliable results. This study proposes a geographic data envelopment analysis (GeoDEA) model, integrating a spatially constrained multivariate clustering model with generalized data envelopment analysis (DEA). The GeoDEA model reconstructs evaluation and reference sets considering spatial adjacency, cluster numbers, and socio-economic indicators and then applies a slack-based measure (SBM) super-efficient formula. It is verified that the efficiency value evaluated using the GeoDEA model is higher than that of the traditional DEA model, but it is also more consistent with cognition and more reliable. This is mainly explained by the fact that the GeoDEA model takes into account the geospatial effect and selects DMUs with relatively close geographic distance and higher levels of development as the reference frontier for efficiency evaluation. The GeoDEA model optimizes the traditional DEA model and avoids the problem that the efficiency of DMU is underestimated when the geographical background and development mode of DMU are very different from the reference frontier. It enhances the reliability of the evaluation of territorial space use efficiency. Full article

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

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29 pages, 19581 KiB

Open AccessArticle

Integrating Blue–Green Infrastructure with Gray Infrastructure for Climate-Resilient Surface Water Flood Management in the Plain River Networks

by Liqing Zhu, Chi Gao, Mianzhi Wu and Ruiming Zhu

Land 2025, 14(3), 634; https://doi.org/10.3390/land14030634 - 17 Mar 2025

Viewed by 520

Abstract

Along with the progression of globalized climate change, flooding has become a significant challenge in low-lying plain river network regions, where urban areas face increasing vulnerability to extreme climate events. This study explores climate-adaptive land use strategies by coupling blue–green infrastructure (BGI) with [...] Read more.

Along with the progression of globalized climate change, flooding has become a significant challenge in low-lying plain river network regions, where urban areas face increasing vulnerability to extreme climate events. This study explores climate-adaptive land use strategies by coupling blue–green infrastructure (BGI) with conventional gray infrastructure, forming blue–green–gray infrastructure (BGGI), to enhance flood resilience at localized and regional scales. By integrating nature-based solutions with engineered systems, this approach focuses on flood mitigation, environmental co-benefits, and adaptive land-use planning. Using the Minhang District in Shanghai as a case study, the research employs geospatial information system (GIS) analysis, hydrological modeling, and scenario-based assessments to evaluate the performance of BGGI systems under projected climate scenarios for the years 2030, 2050, and 2100. The results highlight that coupled BGGI systems significantly improve flood storage and retention capacity, mitigate risks, and provide ecological and social benefits. Water surface-to-catchment area ratios were optimized for primary and secondary catchment areas, with specific increases required in high-risk zones to meet future flood scenarios. Ecological zones exhibited greater adaptability, while urban and industrial areas required targeted interventions. Scenario-based modeling for 2030, 2050, and 2100 demonstrated the scalability, feasibility, and cost-effectiveness of BGI in adapting to climate-induced flooding. The findings contribute to the existing literature on urban flood management, offering a framework for climate-adaptive planning and resilience building with broader implications for sustainable urban development. This research supports the formulation of comprehensive flood management strategies that align with global sustainability objectives and urban resilience frameworks. Full article

(This article belongs to the Special Issue Computational Design and Planning for Socio-Environmental Sustainability of Landscapes and Communities)

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26 pages, 4804 KiB

Open AccessArticle

Research on the Spatio-Temporal Evolution and Impact of China’s Digital Economy and Green Innovation

by Chunshan Zhou, Xiaoli Wei, Xiangjun Dai and Guojun Zhang

Land 2025, 14(3), 633; https://doi.org/10.3390/land14030633 - 17 Mar 2025

Viewed by 369

Abstract

It is of great significance to study the impact of China’s digital economy on green innovation under present conditions. In this work, panel data were used, and research tools such as the entropy method, the Markov chain with a spatial Markov probability transition [...] Read more.

It is of great significance to study the impact of China’s digital economy on green innovation under present conditions. In this work, panel data were used, and research tools such as the entropy method, the Markov chain with a spatial Markov probability transition matrix, and a spatial Durbin model were applied to analyze the temporal and spatial evolution of the digital economy and green innovation in 287 Chinese cities from 2011 to 2021, exploring the influence of the digital economy on green innovation. The results show that the digital economy and green innovation in Chinese cities exhibited an upward trend. There was a basic spatial pattern consisting of “high levels in the east and low levels in the west” regarding the digital economy and green innovation, with the aggregation types primarily being “HH” and “LH”. Moreover, the types of digital economy and green innovation in Chinese cities are relatively stable, with neighboring areas influencing local changes. The digital economy has a significant promotional effect on green innovation, as well as spatial spillover effects; the differing influences over time can be used to categorize the cities into four groups, with most falling within the first two categories. Based on these findings, relevant countermeasures are proposed, seeking to further enhance the role of the digital economy in promoting green innovation. This work provides a research basis and policy suggestions to contribute to continuous improvements in China’s digital economy and green innovation, leveraging the promotional effects of the former on the latter. Full article

(This article belongs to the Special Issue New Urbanization: Urban-Rural Coordination and Sustainable Development)

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21 pages, 7096 KiB

Open AccessArticle

Analyzing Dispersion Characteristics of Fine Particulate Matter in High-Density Urban Areas: A Study Using CFD Simulation and Machine Learning

by Daeun Lee, Caryl Anne M. Barquilla and Jeongwoo Lee

Land 2025, 14(3), 632; https://doi.org/10.3390/land14030632 - 17 Mar 2025

Viewed by 534

Abstract

This study examines how urban morphology, road configurations, and meteorological factors shape fine particulate matter (PM_2.5) dispersion in high-density urban environments, addressing a gap in block-level air quality analysis. While previous research has focused on individual street canyons, this study highlights [...] Read more.

This study examines how urban morphology, road configurations, and meteorological factors shape fine particulate matter (PM_2.5) dispersion in high-density urban environments, addressing a gap in block-level air quality analysis. While previous research has focused on individual street canyons, this study highlights the broader influence of building arrangement and height. Integrating computational fluid dynamics (CFD) simulations with interpretable machine learning (ML) models quantifies PM_2.5 concentrations across various urban configurations. CFD simulations were conducted on different road layouts, block height configurations, and aspect ratio (AR) levels. The resulting dataset trained five ML models with Extreme Gradient Boosting (XGBoost), achieving the highest accuracy (91–95%). Findings show that road-specific mitigation strategies must be tailored. In loop-road networks, centrally elevated buildings enhance ventilation, while in grid-road networks, taller perimeter buildings shield inner blocks from arterial emissions. Additionally, this study identifies a threshold effect of AR, where values exceeding 2.5 improve PM_2.5 dispersion under high wind velocity. This underscores the need for wind-sensitive designs, including optimized wind corridors and building alignments, particularly in high-density areas. The integration of ML with CFD enhances predictive accuracy, supporting data-driven urban planning strategies to optimize road layouts, zoning regulations, and aerodynamic interventions for improved air quality. Full article

(This article belongs to the Special Issue Local and Regional Planning for Sustainable Development)

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Land, Volume 14, Issue 3 (March 2025) – 224 articles

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