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26 pages, 11905 KiB  
Article
Evolution and Analysis of Water Yield under the Change of Land Use and Climate Change Based on the PLUS-InVEST Model: A Case Study of the Yellow River Basin in Henan Province
by Xiaoyu Ma, Shasha Liu, Lin Guo, Junzheng Zhang, Chen Feng, Mengyuan Feng and Yilun Li
Water 2024, 16(17), 2551; https://doi.org/10.3390/w16172551 - 9 Sep 2024
Viewed by 346
Abstract
Understanding the interrelationships between land use, climate change, and regional water yield is critical for effective water resource management and ecosystem protection. However, comprehensive insights into how water yield evolves under different land use scenarios and climate change remain elusive. This study employs [...] Read more.
Understanding the interrelationships between land use, climate change, and regional water yield is critical for effective water resource management and ecosystem protection. However, comprehensive insights into how water yield evolves under different land use scenarios and climate change remain elusive. This study employs the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models, Patch-generating Land Use Simulation (PLUS) model, and Geodetector within a unified framework to evaluate the dynamics of land use, water yield, and their relationships with various factors (meteorological, social, economic, etc.). To forecast the land use/cover change (LUCC) pattern of the Yellow River Basin by 2030, three scenarios were considered: economic development priority (Scenario 1), ecological development priority (Scenario 2), and cropland development priority (Scenario 3). Climate change scenarios were constructed using CMIP6 data, representing low-stress (SSP119), medium-stress (SSP245), and high-stress (SSP585) conditions. The results show the following: (1) from 2000 to 2020, cropland was predominant in the Yellow River Basin, Henan Province, with significant land conversion to impervious land (construction land) and forest land; (2) water yield changes during this period were primarily influenced by meteorological factors, with land use changes having negligible impact; (3) by 2030, the water yield of Scenario 1 is highest among different land use scenarios, marginally surpassing Scenario 2 by 1.60 × 108 m3; (4) climate scenarios reveal significant disparities, with SSP126 yielding 54.95 × 108 m3 higher water yield than SSP245, driven predominantly by precipitation; (5) Geodetector analysis identifies precipitation as the most influential single factor, with significant interactions among meteorological and socio-economic factors. These findings offer valuable insights for policymakers and researchers in formulating land use and water resource management strategies. Full article
(This article belongs to the Section Soil and Water)
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<p>The location of Yellow River Basin in Henan province and corresponding river systems.</p>
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<p>Presentation of the different data.</p>
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<p>Research framework of this study.</p>
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<p>The average water yield estimated in the Water Resources Bulletin was compared with the observed water yield (the blue dotted line is the fitting curve; the red dotted line is the 1:1 curve).</p>
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<p>The visualization of land use transfer matrix: (<b>a</b>) land use transformation, 2000–2010; (<b>b</b>) land use transformation, 2000–2010.</p>
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<p>Land use expansion: (<b>a</b>) 2000–2010; (<b>b</b>) 2010–2020.</p>
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<p>The contribution of driving factors to land use.</p>
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<p>Spatial distribution of land use predicted by PLUS model: (<b>a</b>) actual 2020’s land use; (<b>b</b>) predicted 2020’s land use; (<b>c</b>) predicted 2030’s land use; (<b>d</b>) predicted Scenario 1’s land use of 2030 (economic development priority scenario); (<b>e</b>) predicted Scenario 2’s land use of 2030 (ecological development priority scenario); (<b>f</b>) predicted Scenario 3’s land use of 2030 (cropland development priority scenario).</p>
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<p>Comparison of data distribution of water yield and precipitation in different years.</p>
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<p>Spatial distribution of historical water yield: (<b>a</b>) 2000; (<b>b</b>) 2010; (<b>c</b>) 2020; (<b>d</b>) changes from 2000 to 2010; (<b>e</b>) changes from 2010 to 2020; (<b>f</b>) changes from 2000 to 2020.</p>
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<p>Data distribution of water yield under different scenarios.</p>
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<p>The water yield spatial difference of different meteorological scenarios and different land use development scenarios in 2030: (<b>a</b>) ssp119’s water yield; (<b>b</b>) ssp245’s water yield; (<b>c</b>) ssp585’s water yield; (<b>d</b>) Scenario 1’s water yield; (<b>e</b>) Scenario 2’s water yield; (<b>f</b>) Scenario 3’s water yield; where (<b>d</b>–<b>f</b>) are local water yield maps, corresponding to the Luoyang urban area and Funiu Mountain district.</p>
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<p>Driving factors detection results: (<b>a</b>) the interactive detection results of each driving factor; (<b>b</b>) factor detection results of each driving factor.</p>
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23 pages, 4665 KiB  
Article
Natural Water Sources and Small-Scale Non-Artisanal Andesite Mining: Scenario Analysis of Post-Mining Land Interventions Using System Dynamics
by Mohamad Khusaini, Rita Parmawati, Corinthias P. M. Sianipar, Gatot Ciptadi and Satoshi Hoshino
Water 2024, 16(17), 2536; https://doi.org/10.3390/w16172536 - 7 Sep 2024
Viewed by 399
Abstract
Small-scale open-pit, non-artisanal mining of low-value ores is an understudied practice despite its widespread occurrence and potential impact on freshwater resources due to mining-induced land-use/cover changes (LUCCs). This research investigates the long-term impacts of andesite mining in Pasuruan, Indonesia, on the Umbulan Spring’s [...] Read more.
Small-scale open-pit, non-artisanal mining of low-value ores is an understudied practice despite its widespread occurrence and potential impact on freshwater resources due to mining-induced land-use/cover changes (LUCCs). This research investigates the long-term impacts of andesite mining in Pasuruan, Indonesia, on the Umbulan Spring’s water discharge within its watershed. System Dynamics (SD) modeling captures the systemic and systematic impact of mining-induced LUCCs on discharge volumes and groundwater recharge. Agricultural and reservoir-based land reclamation scenarios then reveal post-mining temporal dynamics. The no-mining scenario sees the spring’s discharge consistently decrease until an inflection point in 2032. With mining expansion, reductions accelerate by ~1.44 million tons over two decades, or 65.31 thousand tons annually. LUCCs also decrease groundwater recharge by ~2.48 million tons via increased surface runoff. Proposed post-mining land interventions over reclaimed mining areas influence water volumes differently. Reservoirs on reclaimed land lead to ~822.14 million extra tons of discharge, 2.75 times higher than the agricultural scenario. Moreover, reservoirs can restore original recharge levels by 2039, while agriculture only reduces the mining impact by 28.64% on average. These findings reveal that small-scale non-artisanal andesite mining can disrupt regional hydrology despite modest operating scales. Thus, evidence-based guidelines are needed for permitting such mines based on environmental risk and site water budgets. Policy options include discharge or aquifer recharge caps tailored to small-scale andesite mines. The varied outputs of rehabilitation scenarios also highlight evaluating combined land and water management interventions. With agriculture alone proving insufficient, optimized mixes of revegetation and water harvesting require further exploration. Full article
(This article belongs to the Section Hydrogeology)
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<p>Systems scheme of the impact of small-scale mining on natural water discharge.</p>
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<p>Research design.</p>
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<p>Location of the mining area (<b>a</b>) in Pasuruan (<b>b</b>), East Java (<b>c</b>), Indonesia (<b>d</b>).</p>
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<p>Original conditions of the mining area.</p>
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<p>The Causal-Loop Diagram (CLD).</p>
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<p>The Stock-and-Flow Diagram (SFD).</p>
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<p>Projected water discharge of the Umbulan Spring without mining operations.</p>
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31 pages, 7572 KiB  
Review
Land-Based Carbon Effects and Human Well-Being Nexus
by Kexin Wang, Keren He, Xue-Chao Wang, Linglin Xie, Xiaobin Dong, Fan Lei, Changshuo Gong and Mengxue Liu
Land 2024, 13(9), 1419; https://doi.org/10.3390/land13091419 - 3 Sep 2024
Viewed by 458
Abstract
In light of international climate agreements and the Sustainable Development Goals (SDGs), there is a growing need to enhance the understanding of the linkages among land use/cover change (LUCC) and its carbon effects (CEs), as well as human well-being (HW). While existing studies [...] Read more.
In light of international climate agreements and the Sustainable Development Goals (SDGs), there is a growing need to enhance the understanding of the linkages among land use/cover change (LUCC) and its carbon effects (CEs), as well as human well-being (HW). While existing studies have primarily focused on the impacts of LUCC on CEs or ecosystem services, there remains a gap in systematically elucidating the complex relationships among LUCC, CEs, and HW. This paper presents a comprehensive review of the nexus between land-based CEs and HW, examining: (1) the correlation between LUCC and CEs, encompassing methodologies for investigating LUCC CEs; (2) the association between CEs and HW, introducing the concept of “low-carbon human well-being” and evaluation framework; and (3) the proposed framework of “LUCC-CEs-HW,” which delves into the intricate connections among three elements. The study identifies research gaps and outlines potential future directions, including assessments of LUCC CEs and low-carbon HW, exploration of the “LUCC-CEs-HW” nexus, and the development of standardized measurement approaches. Key opportunities for further investigation include establishing a unified evaluation index system and developing scalable methods. This paper elucidates the relationships among LUCC, CEs, and HW, offering insights for future works. Full article
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<p>The number of Sustainable Development Goals (SDGs) indicators related to carbon emissions, land use, and land cover change (LUCC) or human well-being (HW) (developed from [<a href="#B4-land-13-01419" class="html-bibr">4</a>]).</p>
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<p>An example of an observation and evaluation method system of a terrestrial ecosystem carbon sink (developed from Pu et al. [<a href="#B30-land-13-01419" class="html-bibr">30</a>]).</p>
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<p>Carbon effects of mutual transformation among various land use types.</p>
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<p>Relationship between carbon effects and HW.</p>
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<p>The framework of the LUCC-C-HW system. CCUS: carbon capture, utilization, and storage. LUCC: land use and land cover change. HW: human well-being.</p>
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24 pages, 25381 KiB  
Article
A Study on the Determination and Spatial Flow of Multi-Scale Watershed Water Resource Supply and Benefit Areas
by Xinping Ma, Jing Li and Yuyang Yu
Water 2024, 16(17), 2461; https://doi.org/10.3390/w16172461 - 30 Aug 2024
Cited by 1 | Viewed by 380
Abstract
Based on the principle of water supply and demand flow and the natural flow of water, this paper analyzes the flow direction and discharge of water resources in the study area. In order to provide scientific and systematic implementation suggestions for regional water [...] Read more.
Based on the principle of water supply and demand flow and the natural flow of water, this paper analyzes the flow direction and discharge of water resources in the study area. In order to provide scientific and systematic implementation suggestions for regional water resource protection management and ecological compensation, a SWAT (Soil and Water Assessment Tool) model was constructed to quantify the water resource supply of the upper Hanjiang River basin at three spatial scales: pixel, sub-basin, and administrative unit. The water demand at the three spatial scales was calculated using the LUCC (Land Use and Land Coverage) and water consumption index. The supply and benefit zones under different spatial and temporal scales were obtained. Simultaneously, this study uncovered the spatiotemporal dynamics inherent in water resource supply and demand, alongside elucidating the spatial extent and flow attributes of water supply. The ecological compensation scheme of water resource supply–demand was preliminarily determined. The findings indicate an initial increase followed by a decrease in both the water supply and demand in the upper reaches of the Han River, accompanied by spatial disparities in the water supply distribution. The direction of the water supply generally flows from branch to main stream. The final ecological compensation scheme should be combined with natural conditions and economic development to determine a reasonable financial compensation system. Full article
(This article belongs to the Section Water Resources Management, Policy and Governance)
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<p>Location of the research area.</p>
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<p>Distribution map of meteorological stations.</p>
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<p>Sub-basin division and spatial data. (<b>a</b> shows the scope of the study basin and the spatial distribution of DEM. <b>b</b> shows the spatial distribution of LUCC in the basin of the study area. There are 6 types of land use in the basin. <b>c</b> shows the soil type map of the study area.)</p>
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<p>Comparison of monthly runoff simulations and observations in terms of the periodic rate and validation period. (<b>top</b>) Rate periodic measured and simulated values; (<b>bottom</b>) The measured and simulated values in the validation period.</p>
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<p>Spatial and temporal distribution of the water supply in the upper reaches of the Hanjiang River. (<b>a</b>) Spatial distribution of water supply in the study area in 2000; (<b>b</b>) Spatial distribution of water supply in the study area in 2005; (<b>c</b>) Spatial distribution of water supply in the study area in 2010; (<b>d</b>) Spatial distribution of water supply in the study area in 2015.</p>
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<p>Spatial and temporal change characteristics of water production in the administrative regions in 2000, 2005, 2010, and 2015. (<b>a</b>) Total water production; (<b>b</b>) Spatial dispersion of water production in each county; (<b>c</b>) Variation range of water yield in each county.</p>
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<p>Changes in water supply over time and across different locations, considering various scales, reveal significant temporal and spatial variations.</p>
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<p>The spatiotemporal distribution of the water demand in the upper reaches of the Hanjiang River.</p>
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<p>Temporal and spatial variation in the water demand at different scales.</p>
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<p>Spatial distribution of water resource self-sufficiency rate in the upper reaches of the Hanjiang River.</p>
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<p>The spatial and temporal distribution of the water supply and demand at the pixel, sub-basin, and administrative unit scales.</p>
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<p>Spatiotemporal variation rate of the water supply in the upper Hanjiang River basin.</p>
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<p>Temporal and spatial changes in supply–benefit areas in the upper reaches of the Hanjiang River in Shaanxi Province at different scales.</p>
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<p>Flow statistics based on the DEM.</p>
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<p>Direction of water flow in the administrative units.</p>
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<p>Paths and spatial distribution of the flow of the water supply services based on the self-sufficiency rate.</p>
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23 pages, 15505 KiB  
Article
Coupling between Population and Construction Land Changes in the Beijing–Tianjin–Hebei (BTH) Region: Residential and Employment Perspectives
by Chen Chen
Systems 2024, 12(8), 308; https://doi.org/10.3390/systems12080308 - 19 Aug 2024
Viewed by 471
Abstract
To gain a deeper understanding of the human–land coupling relationship, this study analyzes the coupling relationships with the spatial distribution of construction land from two perspectives: the residential population and the employment population, exploring the similarities and differences in coupling relationships among different [...] Read more.
To gain a deeper understanding of the human–land coupling relationship, this study analyzes the coupling relationships with the spatial distribution of construction land from two perspectives: the residential population and the employment population, exploring the similarities and differences in coupling relationships among different subsystems. The Beijing–Tianjin–Hebei region of China is selected as the study area, covering the period from 2000 to 2020. An analytical framework is proposed, encompassing three approaches: coupling analysis based on county-level spatial units; mean center position analysis based on construction land grids; and regression fitting and residual analysis based on homogeneous grid units. The analysis results indicate: (1) the coupling between the employment population and construction land shows a significant advantage; (2) the coupling between the residential population and construction land has improved faster in recent years; (3) factors such as location, development level, and strategic opportunities have an important influence on the spatial and temporal changes in the coupling relationship. The study further discusses the trade-off relationship between different subsystems, key measures to enhance coupling degree, and the application pathways of this analytical framework at various stages of planning. Considering the limitations of industry sector differences, spatial unit precision, and construction land development intensity, this paper also outlines future research directions. Full article
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<p>Land use/cover classification and distribution of the residential and employment population density in the BTH region in 2020.</p>
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<p>Flowchart of analysis method.</p>
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<p>Construction land proportion at the county-level in 2000, 2010, and 2020.</p>
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<p>Per capita construction land area calculated based on the residential and the employment population in 2000, 2010, and 2020.</p>
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<p>The coupling degree C<sub>R</sub> and C<sub>E</sub> value in 2000, 2010, and 2020.</p>
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<p>The value obtained by subtracting C<sub>E</sub> from C<sub>R</sub> in 2000, 2010, and 2020.</p>
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<p>Residential and employment population density based on construction land in 2020.</p>
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<p>Mean center shift paths for construction land and population in the BTH region from 2000 to 2020.</p>
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<p>Mean center shift paths for construction land and population in each city from 2000 to 2020.</p>
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<p>Construction land area within each homogeneous grid in 2000, 2010, and 2020.</p>
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<p>Linear regression and coefficient of determination based on grids for the relationship between construction land area and the residential population count for different years.</p>
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<p>Linear regression and coefficient of determination based on grids for the relationship between construction land area and the employment population count for different years.</p>
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<p>OLS of construction and population in 2000, 2010, and 2020.</p>
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18 pages, 6976 KiB  
Article
Ecological and Environmental Risk Warning Framework of Land Use/Cover Change for the Belt and Road Initiative
by Yinjie He, Dafang Wu, Shuangcheng Li and Ping Zhou
Land 2024, 13(8), 1281; https://doi.org/10.3390/land13081281 - 14 Aug 2024
Viewed by 682
Abstract
Land use/cover change(LUCC) has a significant impact on the ecological environment. Within the Belt and Road Initiative (BRI), as the largest cross-spatial cooperation initiative in human history, one of the core issues is how to scientifically and effectively use and manage the land [...] Read more.
Land use/cover change(LUCC) has a significant impact on the ecological environment. Within the Belt and Road Initiative (BRI), as the largest cross-spatial cooperation initiative in human history, one of the core issues is how to scientifically and effectively use and manage the land in the region to prevent the destruction of important ecological and environmental resources. In order to reduce impact on the latter, in this study, we used the bivariate choropleth–multiple-criteria decision analysis (BC-MCDA) method based on the connotation of the sustainable development goals to construct an ecological and environmental risk warning framework. We found that in the study area, 10.51% of the land has high ecological and environmental risk and importance, corresponding to conflict zones, which require special attention. Conflict areas are mainly distributed in the Gangetic Plain in India, the plains in central and southern Cambodia, the Indonesian archipelago, and the southern coastal areas of China. Due to the uneven spatial distributions of population and important ecological and environmental resources, the pressure on this type of land use is very high. A share of 8.06% of the land has high risk–low importance, corresponding to economic development zones. Following years of human development, the ecological and environmental value of this type of land is low. A share of 58.75% of the land has low risk and importance, corresponding to wilderness areas. The natural climatic conditions of this type of land are relatively poor, often characterized by a cold climate or water scarcity, and the human interference index is low. A share of 22.68% of the land has low risk–high importance, corresponding to ecological conservation areas, which are the most important areas for ecological function services for humans at present. Finally, we proposed development suggestions for each type of land. Full article
(This article belongs to the Special Issue Ecological Restoration and Reusing Brownfield Sites)
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<p>Study area.</p>
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<p>Principle of bivariate choropleth mapping.</p>
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<p>Ecological and environmental risk layer.</p>
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<p>Ecological and environmental importance layer.</p>
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<p>Bivariate choropleth map.</p>
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<p>Ecological and environmental risk layer—taking China as an example.</p>
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<p>Layer of ecological and environmental importance—taking China as an example.</p>
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<p>Bivariate choropleth map—taking China as an example.</p>
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19 pages, 11946 KiB  
Article
Simulation of LUCC Scenarios and Analysis of the Driving Force of Carbon Stock Supply Changes in the North China Plain in the Context of Urbanization
by Dongling Ma, Qingji Huang, Qian Wang, Zhenxin Lin and Hailong Xu
Forests 2024, 15(8), 1414; https://doi.org/10.3390/f15081414 - 13 Aug 2024
Viewed by 453
Abstract
The North China Plain is the core region of China’s economic development, and exploring the impacts of its land use and cover change (LUCC) and different urbanization regional drivers on carbon stocks is conducive to promoting sustainable development and carbon balance within the [...] Read more.
The North China Plain is the core region of China’s economic development, and exploring the impacts of its land use and cover change (LUCC) and different urbanization regional drivers on carbon stocks is conducive to promoting sustainable development and carbon balance within the region. In the study, the North China Plain was selected as the study area, and the Patch-Generating Land Use Simulation (PLUS) model and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were comprehensively applied to set up three land use policies, predict land use changes in 2030, and calculate carbon stock changes. Meanwhile, the Extreme Gradient Boosting (XGBoost) algorithm was used to analyze the degree of influence of different drivers on the supply of carbon stocks in different urbanization regions. Studies show that if the North China Plain prioritizes economic development, the area of farmland and forests will significantly decrease, leading to a substantial decline in carbon stocks. If ecological protection is the development focus, the reduction in farmland and forests will be less, and carbon stocks will remain relatively stable. If farmland protection is the development focus, the reduction in farmland will be minimal, but there will still be some impact on carbon stocks. The driving forces of carbon stock supply vary significantly across different regions. In underdeveloped regions, population density and vegetation cover have a greater impact on carbon stocks. In developing and urban–rural combined regions, vegetation cover and population migration have a greater impact on carbon stocks. In developed regions, the area of artificial land and gross domestic product (GDP) have a greater impact on carbon stocks. The study results provide scientific evidence for regional land use planning and policy formulation. Full article
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<p>The location of the study area.</p>
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<p>Flow chart of the study.</p>
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<p>Diagram of driving and limiting factors.</p>
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<p>LUCC in the North China Plain.</p>
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<p>Changes in different LUCC types (2010–2020 denotes from 2010 to 2020; 2020–2030 CPS denotes from 2020 to 2030 in the CPS; 2020–2030 EDS denotes from 2020 to 2030 in the EDS; 2020–2030 EPS denotes from 2020 to 2030 in the EPS).</p>
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<p>Distribution of carbon stocks in different administrative regions of the North China Plain.</p>
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<p>Extent of impact of different drivers on different urbanization areas.</p>
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21 pages, 3403 KiB  
Article
Evaluation and Prediction of Ecosystem Services Value in Urban Agglomerations Using Land Use/Cover Change Analysis: Case Study of Wuhan in China
by Qiaowen Lin, Hongyun Su, Peter Sammonds, Mengxin Xu, Chunxiao Yan and Zhe Zhu
Land 2024, 13(8), 1154; https://doi.org/10.3390/land13081154 - 27 Jul 2024
Viewed by 543
Abstract
The evaluation of ecosystem service value (ESV) is crucial for decision making in regional sustainable development. The close relationship between ecosystem services and land use/cover change (LUCC) is well acknowledged. However, the impact of the mutual transformation among different land use types on [...] Read more.
The evaluation of ecosystem service value (ESV) is crucial for decision making in regional sustainable development. The close relationship between ecosystem services and land use/cover change (LUCC) is well acknowledged. However, the impact of the mutual transformation among different land use types on the temporal and spatial differences in the ESV is still unclear. To fulfill this gap, this study evaluates the ESV in the Wuhan Urban Agglomerations based on LUCC, taking the spatiotemporal characteristics into consideration. The results show that (1) The land use structure in the Wuhan Urban Agglomerations has undergone great changes from 2012 to 2021, and the area of cultivated land converted to forest land is the largest, which may be related to policies such as returning farmland to forests. (2) The total amount of ESV shows a downward trend, and the spatial distribution of ESV is “low in the west and high in the central and eastern regions”, which may be related to the natural factors in study area. (3) The spatial distribution of ESV in the study area will remain unchanged in the future. However, the transformation among land use types may exacerbate the reduction in the total ESV, which will have an adverse impact on the ecological environment and sustainable development of the region. This study initiates a more comprehensive framework to better reflect the real scenario of ESV, which will hopefully provide a reference for regional sustainable development. Full article
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<p>Flowchart on the main steps of the study.</p>
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<p>Administrative zoning map of Wuhan Urban Agglomerations.</p>
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<p>Spatial distribution of land use types in Wuhan Urban Agglomerations from 2012 to 2021.</p>
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<p>Land use change in Wuhan Urban Agglomerations from 2012 to 2021. (Note: Numbers 1–6 denote arable land, forest land, grassland, waters, construction land, and unused land, respectively. In the legend, 12 represents the area where arable land is converted to forest land, 21 represents the area where forest land is converted to arable land, and so on).</p>
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<p>Amount and change rate of change in the ESVs of cities in Wuhan Urban Agglomerations from 2012 to 2021.</p>
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<p>Spatial distribution of ESV in Wuhan Urban Agglomerations from 2012 to 2021.</p>
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<p>Land use types in Wuhan Urban Agglomerations in 2035 and 2050.</p>
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<p>Prediction of ESV spatial distribution in Wuhan Urban Agglomerations in 2035 and 2050.</p>
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19 pages, 12382 KiB  
Article
Mapping the Functional Structure of Urban Agglomerations at the Block Level: A New Spatial Classification That Goes beyond Land Use
by Bin Ai, Zhenlin Lai and Shifa Ma
Land 2024, 13(8), 1148; https://doi.org/10.3390/land13081148 - 26 Jul 2024
Viewed by 395
Abstract
The functional structure of territorial space is an important factor for analyzing the interaction between humans and nature. However, the classification of remote sensing images struggles to distinguish between multiple functions provided by the same land use type. Therefore, we propose a framework [...] Read more.
The functional structure of territorial space is an important factor for analyzing the interaction between humans and nature. However, the classification of remote sensing images struggles to distinguish between multiple functions provided by the same land use type. Therefore, we propose a framework to combine multi-source data for the recognition of dominant functions at the block level. Taking the Guangdong–Hong Kong–Macau Greater Bay Area (GBA) as a case study, its block-level ‘production–living–ecology’ functions were interpreted. The whole GBA was first divided into different blocks and its total, average, and proportional functional intensities were then calculated. Each block was labeled as a functional type considering the attributes of human activity and social information. The results show that the combination of land use/cover data, point of interest identification, and open street maps can efficiently separate the multiple and mixed functions of the same land use types. There is a great difference in the dominant functions of the cities in the GBA, and the spatial heterogeneity of their mixed functions is closely related to the development of their land resources and socio-economy. This provides a new perspective for recognizing the spatial structure of territorial space and can give important data for regulating and optimizing landscape patterns during sustainable development. Full article
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<p>Technical flowchart for identifying and analyzing PLE functions at the block level.</p>
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<p>Scope of the study area, land use in 2020, and divided blocks: (<b>a</b>) the river is not separated to maintain its integrity; (<b>b</b>) blocks containing natural elements; and (<b>c</b>) blocks within urban areas.</p>
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<p>Total, average, and proportional function intensity of different PLE function units.</p>
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<p>Identification principle for determining the function type of blocks in the GBA.</p>
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<p>Distribution pattern of different dominant function types in the GBA, where (<b>a</b>–<b>h</b>) are the local spatial distribution in Zhaoqing, western Guangdong, southern Guangzhou, Foshan, Shenzhen, Jiangmen, Zhongshan, Hong Kong.</p>
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<p>Accuracy validation of PLE identification by comparison of high-resolution images and street view map, where ①–④ are examples of PLE identification results in this study.</p>
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<p>Quantity structure of dominant function types in different cities of the GBA.</p>
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<p>Proportions of POIs for different PLE functions (Logo icons demonstrate representative annotation points for different types of POIs).</p>
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<p>Typical cases of identified PLE blocks overlaid with high-resolution imagery, including residential communities (<b>A</b>,<b>C</b>,<b>E</b>) an ecological space (<b>B</b>) and a production space (<b>D</b>).</p>
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<p>Comparison between PLE identification result and classification product. (<b>a</b>) Examples of PLE identification results in this study. (<b>b</b>) ESA land cover product at 10 m resolution. (<b>c</b>) Google Earth images. ①–④ are the examples for the comparison.</p>
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21 pages, 4180 KiB  
Article
Responses of Ecosystem Services to Land Use/Cover Changes in Rapidly Urbanizing Areas: A Case Study of the Shandong Peninsula Urban Agglomeration
by Yongwei Liu and Yao Zhang
Sustainability 2024, 16(14), 6100; https://doi.org/10.3390/su16146100 - 17 Jul 2024
Viewed by 789
Abstract
The rapid expansion of built-up land, a hallmark of accelerated urbanization, has emerged as a pivotal factor contributing to regional climate change and the degradation of ecosystem functions. The decline in ecosystem service value (ESV) has consequently garnered significant attention in global sustainable [...] Read more.
The rapid expansion of built-up land, a hallmark of accelerated urbanization, has emerged as a pivotal factor contributing to regional climate change and the degradation of ecosystem functions. The decline in ecosystem service value (ESV) has consequently garnered significant attention in global sustainable development research. The Shandong Peninsula urban agglomeration is crucial for promoting the construction of the Yellow River Economic Belt in China, with its ecological status increasingly gaining prominence. This study investigated the ESV response to land use/cover change (LUCC) through the elasticity coefficient in order to analyze the degree of disturbance caused by land use activities on ecosystem functions in the Shandong Peninsula urban agglomeration. This analysis was based on the examination of LUCC characteristics and ESV from 1990 to 2020. The findings reveal that (1) the Shandong Peninsula urban agglomeration experienced a continuous increase in the proportion of built-up land from 1990 to 2020, alongside a highly complex transfer between different land use types, characterized by diverse transfer trajectories. The most prominent features were noted to be the rapid expansion of built-up land and the simultaneous decline in agricultural land. (2) The analysis of four landscape pattern indices, encompassing Shannon’s diversity index, indicates that the continuous development of urbanization has led to increased fragmentation in land use and decreased connectivity. However, obvious spatial distribution differences exist among different districts and counties. (3) The ESV was revised using the normalized difference vegetation index, revealing a slight decrease in the total ESV of the Shandong Peninsula urban agglomeration. However, significant differences were observed among districts and counties. The number of counties and districts exhibiting low and high ESVs continuously increased, whereas those with intermediate levels generally remained unchanged. (4) The analysis of the elasticity coefficient reveals that LUCC exerts a substantial disturbance and influence on ecosystem services, with the strongest disturbance ability occurring from 2000 to 2010. The elasticity coefficient exhibits obvious spatial heterogeneity across both the entire urban agglomeration and within individual cities. Notably, Qingdao and Jinan, the dual cores of the Shandong Peninsula urban agglomeration, exhibit markedly distinct characteristics. These disparities are closely related to their development foundations in 1990 and their evolution over the past 30 years. The ESV response to LUCC displays significant variation across different time periods and spatial locations. Consequently, it is imperative to formulate dynamic management policies on the basis of regional characteristics. Such policies aim to balance social and economic development while ensuring ecological protection, thereby promoting the social and economic advancement and ecological environment preservation of the Shandong Peninsula urban agglomeration. Full article
(This article belongs to the Special Issue Farmers’ Adaptation to Climate Change and Sustainable Development)
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<p>Study area map.</p>
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<p>Spatial distribution of land use/cover in the Shandong Peninsula urban agglomeration from 1990 to 2020.</p>
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<p>Chord chart of land use transitions for various land use types from 1990 to 2020 (unit: km<sup>2</sup>).</p>
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<p>Spatial distribution of the PARA_MN index.</p>
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<p>Spatial distribution of the PD index.</p>
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<p>Spatial distribution of the SHDI.</p>
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<p>Spatial distribution of the AI index.</p>
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<p>Spatial distribution of ESV.</p>
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<p>Spatial distribution of elasticity coefficients from 1990 to 2020.</p>
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20 pages, 5319 KiB  
Article
Predicting Land Use Changes under Shared Socioeconomic Pathway–Representative Concentration Pathway Scenarios to Support Sustainable Planning in High-Density Urban Areas: A Case Study of Hangzhou, Southeastern China
by Song Yao, Yonghua Li, Hezhou Jiang, Xiaohan Wang, Qinchuan Ran, Xinyi Ding, Huarong Wang and Anqi Ding
Buildings 2024, 14(7), 2165; https://doi.org/10.3390/buildings14072165 - 14 Jul 2024
Viewed by 604
Abstract
Amidst the challenges posed by global climate change and accelerated urbanization, the structure and distribution of land use are shifting dramatically, exacerbating ecological and land-use conflicts, particularly in China. Effective land resource management requires accurate forecasts of land use and cover change (LUCC). [...] Read more.
Amidst the challenges posed by global climate change and accelerated urbanization, the structure and distribution of land use are shifting dramatically, exacerbating ecological and land-use conflicts, particularly in China. Effective land resource management requires accurate forecasts of land use and cover change (LUCC). However, the future trajectory of LUCC, influenced by climate change and urbanization, remains uncertain. This study developed an integrated multi-scenario framework by combining system dynamics and patch-generating land use simulation models to predict future LUCC in high-density urban regions under various Shared Socioeconomic Pathway (SSP)–Representative Concentration Pathway (RCP) scenarios. The results showed the following: (1) From 2020 to 2050, cultivated land, unused land, and water are projected to decrease, while construction land is expected to increase. (2) Future land use patterns exhibit significant spatial heterogeneity across three scenarios. Construction land will expand in all districts of Hangzhou, particularly in the main urban areas. Under the SSP585 scenario, the expansion of construction land is most significant, while it is the least under the SSP126 scenario. (3) Distinct factors drive the expansion of different land use types. The digital elevation model is the predominant factor for the expansion of forest and grassland, contributing 19.25% and 30.76%, respectively. Night light contributes the most to cultivated land and construction land, at 13.94% and 20.35%, respectively. (4) The average land use intensity (LUI) in central urban districts markedly surpasses that in the surrounding suburban areas, with Xiacheng having the highest LUI and Chun’an the lowest. Under the SSP126 scenario, the area with increased LUI is significantly smaller than under the SSP245 and SSP585 scenarios. These findings offer valuable guidance for sustainable planning and built environment management in Hangzhou and similarly situated urban centers worldwide. Full article
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<p>Study area. (<b>a</b>) Hangzhou’s position in China and (<b>b</b>) digital elevation model (DEM) in Hangzhou.</p>
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<p>Research framework.</p>
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<p>SD model causal feedback chart in Hangzhou.</p>
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<p>Projections of land use demand in Hangzhou.</p>
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<p>Future land use distribution patterns in Hangzhou.</p>
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<p>Local land use distribution in 2050.</p>
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<p>Spatial distribution of development potential of each land use type in Hangzhou.</p>
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<p>Contributions of driving factors to the expansion of each land use type.</p>
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<p>Spatial distribution of future LUI in Hangzhou.</p>
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<p>Spatial distribution in future LUI changes in Hangzhou.</p>
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21 pages, 13223 KiB  
Article
The Past, Present and Future of Land Use and Land Cover Changes: A Case Study of Lower Liaohe River Plain, China
by Rina Wu, Ruinan Wang, Leting Lv and Junchao Jiang
Sustainability 2024, 16(14), 5976; https://doi.org/10.3390/su16145976 - 12 Jul 2024
Viewed by 637
Abstract
Understanding and managing land use/cover changes (LUCC) is crucial for ensuring the sustainability of the region. With the support of remote sensing technology, intensity analysis, the geodetic detector model, and the Mixed-Cell Cellular Automata (MCCA) model, this paper constructs an integrated framework linking [...] Read more.
Understanding and managing land use/cover changes (LUCC) is crucial for ensuring the sustainability of the region. With the support of remote sensing technology, intensity analysis, the geodetic detector model, and the Mixed-Cell Cellular Automata (MCCA) model, this paper constructs an integrated framework linking historical evolutionary pattern-driving mechanisms for future simulation for LUCC in the Lower Liaohe Plain. From 1980 to 2018, the increasing trends were in built-up land and water bodies, and the decreasing trends were in grassland, cropland, forest land, unused land, and swamps. Overall, the changes in cropland, forest land, and built-up land are more active, while the changes in water bodies are more stable; the sources and directions of land use conversion are more fixed. Land use changes in the Lower Liaohe Plain are mainly influenced by socio-economic factors, of which population density, primary industry output value, and Gross Domestic Product (GDP) have a higher explanatory power. The interactive influence of each factor is greater than any single factor. The results of the MCCA model showed high accuracy, with an overall accuracy of 0.8242, relative entropy (RE) of 0.1846, and mixed-cell figure of merit (mcFoM) of 0.1204. By 2035, the built-up land and water bodies will increase, while the rest of the land use categories will decrease. The decrease is more pronounced in the central part of the plains. The findings of the study provide a scientific basis for strategically allocating regional land resources, which has significant implications for land use research in similar regions. Full article
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<p>Location of the study area.</p>
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<p>The overall research framework.</p>
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<p>Confusion table of land transition patterns.</p>
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<p>Time Interval Level Intensity Analysis.</p>
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<p>Category Level Intensity Analysis.</p>
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<p>Cross-contingency table of land use change patterns at transfer levels.</p>
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<p>Interactive detection results of the driving factors.</p>
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<p>Prediction of land use structure types.</p>
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<p>Transformation rule.</p>
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<p>Distribution map of land use structure from 2025 to 2035.</p>
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<p>Simulation results of land use.</p>
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<p>LUCC in Lower Liaohe River Plain between 1980 and 2018.</p>
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21 pages, 35247 KiB  
Article
Dynamic Spatio-Temporal Simulation of Land Use and Ecosystem Service Value Assessment in Agro-Pastoral Ecotone, China
by Longlong Liu, Shengwang Bao, Maochun Han, Hongmei Li, Yingshuang Hu and Lixue Zhang
Sustainability 2024, 16(14), 5922; https://doi.org/10.3390/su16145922 - 11 Jul 2024
Cited by 1 | Viewed by 669
Abstract
In the past, during development processes, major ecological and environmental problems have occurred in the agro-pastoral ecotone of China, which have had a strong impact on regional sustainable development. As such, analyzing the evolution of the regional ecosystem service value (ESV) and predicting [...] Read more.
In the past, during development processes, major ecological and environmental problems have occurred in the agro-pastoral ecotone of China, which have had a strong impact on regional sustainable development. As such, analyzing the evolution of the regional ecosystem service value (ESV) and predicting the futural spatio-temporal evolution under different development scenarios will provide a scientific basis for further sustainable development. This research analyzed the regional land use and land cover change (LUCC) from 2000 to 2020, adopted the Mark-PLUS model to construct different scenarios (prioritizing grassland development, PDG; prioritizing cropland development, PCD; business as usual, BAU), and simulated the future LUCC. The driving factors influencing each land use type were revealed using the PLUS model. Based on the LUCC data, the spatio-temporal distribution of the regional ESV was calculated via the ESV equivalent factor method, including four primary services (supply service, adjustment service, support service, and cultural service) and eleven secondary services (water resource supply, maintaining nutrient circulation, raw material production, aesthetic landscape, food production, environmental purification, soil conservation, maintaining biodiversity, gas regulation, climate regulation, and hydrologic regulation). The results showed that the total ESV increased first and then declined from 2000 to 2020, reaching the highest value of CNY 8207.99 million in 2005. In the different future scenarios, the ESV shows a trend of PGD (CNY 8338.79 million) > BAU (CNY 8194.82 million) > PCD (CNY 8131.10 million). The global Moran index also follows this distribution. Additionally, precipitation (18%), NDVI (16%), and DEM (16%) are the most important factors in the regional LUCC. The spatial agglomeration characteristics of ESV were revealed using the global Moran’s index and local indicators of spatial auto-correlation, which show a high coordination degree between the high–high cluster areas and water areas. These results point out the key points in the next step of ecological restoration projects and help with achieving the sustainable development goals more effectively. Full article
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<p>Study area.</p>
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<p>Flowchart of the methodology in this study.</p>
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<p>The selected driving factors of LUCC in agro-pastoral ecotone.</p>
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<p>Spatial and temporal evolution of LUCC from 2000 to 2020.</p>
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<p>Simulation validation of LUCC in 2020. (<b>a</b>): Real LUCC Remote Sensing Map in 2020; (<b>b</b>): Simulated LUCC Remote Sensing Map in 2020.</p>
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<p>LUCC simulation under different scenarios.</p>
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<p>ESV evolution trend from 2000 to 2020.</p>
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<p>Each ecosystem service value assessment in the agro-pastoral ecotone. I: water resource supply, II: maintaining nutrient circulation, III: raw material production, IV: aesthetic landscape, V: food production, VI: environmental purification, VII: soil conservation, VIII: maintaining biodiversity, XI: gas regulation, X: climate regulation, and XI: hydrologic regulation.</p>
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<p>Spatio-temporal evolution of ESV under different scenarios in the agro-pastoral ecotone.</p>
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<p>Each secondary ecosystem service value under different scenarios. I: water resource supply, II: maintaining nutrient circulation, III: raw material production, IV: aesthetic landscape, V: food production, VI: environmental purification, VII: soil conservation, VIII: maintaining biodiversity, XI: gas regulation, X: climate regulation, XI: hydrologic regulation.</p>
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<p>Simulation validation and environmental factors’ contribution.</p>
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<p>Overall agglomeration characteristics of ESV. (<b>a</b>): Global Moran’s I index; (<b>b</b>) Z-score; (<b>c</b>): P-test.</p>
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<p>Spatial agglomeration distribution of ESV in the agro-pastoral ecotone.</p>
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<p>Spatial agglomeration distribution analysis at the pixel level.</p>
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21 pages, 6121 KiB  
Article
Spatio-Temporal Variations in Soil Erosion and Its Driving Forces in the Loess Plateau from 2000 to 2050 Based on the RUSLE Model
by Jie Min, Xiaohuang Liu, Hongyu Li, Ran Wang and Xinping Luo
Appl. Sci. 2024, 14(13), 5945; https://doi.org/10.3390/app14135945 - 8 Jul 2024
Cited by 1 | Viewed by 640
Abstract
Assessing the spatio-temporal variability and driving forces of soil erosion on the Loess Plateau is critical for ecological and environmental management. In this paper, the Revised Universal Soil Loss Equation (RUSLE) model, the patch-generating land use simulation, and the Geographical Detector are used [...] Read more.
Assessing the spatio-temporal variability and driving forces of soil erosion on the Loess Plateau is critical for ecological and environmental management. In this paper, the Revised Universal Soil Loss Equation (RUSLE) model, the patch-generating land use simulation, and the Geographical Detector are used to investigate the spatio-temporal variations of the Loess Plateau’s soil erosion from 2000 to 2050. The results showed that: (1) The primary categories of soil erosion from 2000 to 2020 were moderate, mild, and slight, and the average level of soil erosion exhibited a decreasing and then an increasing tendency during the last 20 years. (2) Soil erosion was directly impacted by changes in land use, with cropland and forest being the primary land use and land cover changes in the study region. Cropland and construction land being turned into woodland between 2000 and 2020 resulted in a significant decrease in the severity of soil erosion. Projected soil erosion is expected to increase significantly between 2020 and 2050 due to arable land being converted into construction land. (3) The key variables impacting the spatial distribution of soil erosion were LUCC (Land-Use and Land-Cover Change), NDVI (Normalized Difference Vegetation Index), and slope, and the interplay of these variables may increase their ability to explain soil erosion. Grasslands with an NDVI ranging from 0.9 to 1, rain ranging from 0.805 to 0.854 m, a slope above 35°, and a terrain elevation ranging from 1595 to 2559 m were identified as having a high risk of soil erosion. Soil erosion prevention and management efforts should focus on the ecological restoration of upland areas in the future. Full article
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<p>Location map of the warm temperate forest-farming subregion in the northeast of Loess Plateau, China.</p>
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<p>Process and research approaches. RUSLE, or the revised universal soil loss equation, is a digital elevation model. The degree of soil erosion is a factor, vegetation cover factor, or C factor. The R component stands for rainfall erosivity; LS for topography; K for soil erodibility; and P for support practice.</p>
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<p>Temporal and spatial distribution of rainfall erosivity factors in the research region from 2000 to 2020.</p>
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<p>Distribution of soil erosion severity across space in the research region in (<b>a</b>) 2000; (<b>b</b>) 2005; (<b>c</b>) 2010; (<b>d</b>) 2015; (<b>e</b>) 2020.</p>
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<p>Soil erosion area share-transfer matrix chord diagram for the study area from 2000 to 2020: (<b>a</b>) 2000 to 2005; (<b>b</b>) from 2005 to 2010; (<b>c</b>) from 2010 to 2015; (<b>d</b>) from 2015 to 2020.</p>
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<p>Different land use spatial distributions on the forest farming subregion of Loess Plateau from 2000 to 2020. (<b>a</b>) The year 2000; (<b>b</b>) the year 2005; (<b>c</b>) the year 2010; (<b>d</b>) the year 2015; (<b>e</b>) the year 2020.</p>
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<p>Transfer matrix for land use types in the research area from 2020 to 2050: (<b>a</b>) 2020 to 2025; (<b>b</b>) 2025 to 2030; (<b>c</b>) 2030 to 2050.</p>
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<p>Spatial distribution of soil erosion degree in the forest farming sub-region of Loess Plateau from 2025 to 2050: (<b>a</b>) 2025; (<b>b</b>) 2030; (<b>c</b>) 2050.</p>
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<p>Explaining potential of the two-factor interaction depending on (<b>a</b>) the different forms of land usage and (<b>b</b>) soil erosion.</p>
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<p>Results of the risk detector. The non-high-risk areas are those where: (<b>a</b>) the terrain elevation is between 1595 and 2559 m; (<b>b</b>) the rainfall is between 808 and 854 mm; (<b>c</b>) the LUCC is grassland; (<b>d</b>) the slope is greater than 35 degrees; and (<b>e</b>) the NDVI ranges between 0.9 and 1.</p>
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18 pages, 7378 KiB  
Article
Assessment of Soil Wind Erosion and Population Exposure Risk in Central Asia’s Terminal Lake Basins
by Wei Yu, Xiaofei Ma, Wei Yan and Yonghui Wang
Water 2024, 16(13), 1911; https://doi.org/10.3390/w16131911 - 4 Jul 2024
Viewed by 974
Abstract
In the face of climate change and human activities, Central Asia’s (CA) terminal lake basins (TLBs) are shrinking, leading to deteriorating natural environments and serious soil wind erosion (SWE), which threatens regional socio-economic development, human health, and safety. Limited research on SWE and [...] Read more.
In the face of climate change and human activities, Central Asia’s (CA) terminal lake basins (TLBs) are shrinking, leading to deteriorating natural environments and serious soil wind erosion (SWE), which threatens regional socio-economic development, human health, and safety. Limited research on SWE and population exposure risk (PER) in these areas prompted this study, which applied the RWEQ and a PER model to assess the spatiotemporal changes in SWE and PER in TLBs in CA, including the Ili River Basin (IRB), Tarim River Basin (TRB), Syr Darya River Basin (SRB), and Amu Darya River Basin (ARB), from 2000 to 2020. We analyzed the driving factors of SWE and used the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to simulate dust event trajectories. The findings from 2000 to 2020 show a spatial reduction trend in SWE and PER, with primary SWE areas in the Taklamakan Desert, Aral Sea Basin, and Lake Balkhash. Significant PER was observed along the Tarim River, near Lake Balkhash, and in the middle and lower reaches of the ARB and SRB. Over the past 21 years, temporal trends in SWE have occurred across basins, decreasing in the IRB, but increasing in the TRB, SRB, and ARB. Dust movement trajectories indicate that dust from the lower reaches of the SRB and ARB could affect Europe, while dust from the TRB could impact northern China and Japan. Correlations between SWE, NDVI, temperature, and precipitation revealed a negative correlation between precipitation and NDVI, suggesting an inhibitory impact of precipitation and vegetation cover on SWE. SWE also varied significantly under different LUCCs, with increases in cropland, forestland, and desert land, and decreases in grassland and wetland. These insights are vital for understanding SWE and PER in TLBs and offer theoretical support for emergency mitigation in arid regions. Full article
(This article belongs to the Special Issue Soil Erosion and Soil and Water Conservation)
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<p>Overview map of the TLBs in CA. IRB represents the Ili River Basin, TRB represents the Tarim River Basin, SRB represents the Syr Darya River Basin, and ARB represents the Amu Darya River Basin.</p>
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<p>Spatial distribution and trend changes in SWE in the TLBs in CA: (<b>a</b>–<b>c</b>) represent the spatial distribution maps of SWE in the TLBs in CA for 2000, 2010, and 2020, respectively; (<b>d</b>) shows the spatial trend changes in SWE in the TLBs in CA from 2000 to 2020. WEM represents the wind erosion modulus.</p>
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<p>Temporal changes in SWE in the TLBs in CA from 2000 to 2020: (<b>a</b>–<b>d</b>) represent the temporal changes in SWE in the IRB, TRB, SRB, and ARB, respectively, from 2000 to 2020. IRB represents the Ili River Basin, TRB represents the Tarim River Basin, SRB represents the Syr Darya River Basin, and ARB represents the Amu Darya River Basin. WEM represents the wind erosion modulus.</p>
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<p>Simulation map of the forward trajectory of dust following two typical dust events in the TLBs in CA. Specifically, (<b>a</b>) represents the 72 h dust movement trajectory at altitudes of 100 m (red), 500 m (blue), and 1000 m (green) above the SRB and ARB at 0:00 Coordinated Universal Time (UTC) on 24 March 2020; (<b>b</b>) represents the 72 h dust movement trajectory at altitudes of 100 m (red), 500 m (blue), and 1000 m (green) above the Tarim River Basin (TRB) at 0:00 UTC on 2 April 2018. Based on two typical dust events, the spatial dynamic movement trajectory of the SWE in the study area can be more clearly understood.</p>
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<p>Spatial distribution and trend map of PER in the TLBs in CA: (<b>a</b>–<b>c</b>) represent the spatial distribution maps of PER in the TLBs in CA for 2000, 2010, and 2020, respectively; (<b>d</b>) represents the spatial trend changes in the PER in the TLBs in CA from 2000 to 2020.</p>
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<p>Spatial correlation between SWE and PM2.5 in the TLBs in CA. Basin abbreviations: IRB (Ili River Basin), TRB (Tarim River Basin), SRB (Syr Darya River Basin), and ARB (Amu Darya River Basin). WEM represents the wind erosion modulus. The brackets indicate the percentage of the area within each basin that showed a positive correlation.</p>
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<p>Chord diagram of changes in different LUCCs in the TLBs in CA from 2000 to 2020. K is the letter form of the measurement unit, which represents a thousand units.</p>
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<p>Spatial correlation maps of SWE with temperature (<b>a</b>), precipitation (<b>b</b>), and NDVI (<b>c</b>) in the TLBs in CA. IRB, Ili River Basin; TRB, Tarim River Basin; SRB, Syr Darya Basin; ARB, Amu Darya Basin. The brackets indicate the percentage of the area within each basin that showed a positive correlation.</p>
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