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Spatiotemporal Data Analytics and Modeling of Land Systems: Shaping Sustainable Landscape

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A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".

Deadline for manuscript submissions: closed (28 October 2024) | Viewed by 16069

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Special Issue Editors

Dr. Wenwu Tang

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Guest Editor

Department of Geography, University of North Carolina at Charlotte, Charlotte, NC 28223-0001, USA
Interests: geographic information science; spatial cyberinfrastructure; agent-based modeling; land use and land cover change; complex adaptive spatial systems
Special Issues, Collections and Topics in MDPI journals

Dr. Jianxin Yang

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Guest Editor

Department of Land Resource Management, School of Public Administration, China University of Geosciences, Wuhan 430074, China
Interests: spatial analysis; environment; environmental impact assessment; land use planning; natural resource management; mapping; spatial statistics; sustainability; geoinformation; geographical analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Minrui Zheng

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Guest Editor

School of Public Administration and Policy, Renmin University of China, Beijing 100872, China
Interests: big data for spatial governance; GIScience; spatial analysis and modeling; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Jingye Li

E-Mail Website
Guest Editor

Land Resource Management, School of Public Administration, Hohai University, No. 8 West Focheng Road, Jiangning, Nanjing, China
Interests: land use change; land use planning; spatial analysis; remote sensing and gis; ecosystem services; landscape ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to introduce our forthcoming special issue of the Land journal, entitled "Spatiotemporal Data Analytics and Modeling of Land Systems: Shaping Sustainable Landscape". This special issue aims to explore complex dynamics of land systems (urban, rural or coupled) and their sustainability through the lens of spatiotemporal data analytics and modeling. We invite contributions that delve into the multifaceted aspects of land use, land cover, spatio-temporal analysis of landscape patterns and land resources management, emphasizing the critical role of spatiotemporal data analytics and modeling methods as well as geocomputing capabilities from advanced cyberinfrastructure, artificial intelligence, and high-performance or cloud computing.

The goal of this Special Issue is to collect papers (original research articles and review papers) to give insights into spatiotemporal analysis and modeling in face of a number of challenges when applied to the study of land systems, including, but not limited by, handling large datasets, problems with data quality, spatiotemporal scale and complexity, and the necessity for specific computing methods represented by cyberinfrastructure and artificial intelligence technologies. Geography, urban studies, landscape ecology, environmental science, sustainability science, archaeology and anthropology, and earth science are just a few examples of the research fields that spatial-temporal data analytics and modeling are applied. We encourage researchers to examine the intricate interplay of natural and human factors shaping landscapes, with a particular focus on fostering sustainability, resilience, and adaptive management within spatiotemporal context. Submissions may encompass a wide range of topics, including land change dynamics, spatial modeling (including simulation, optimization, and statistics), remote sensing (including close range, e.g., using unmanned vehicles), geospatial and cyberinfrastructure technologies, artificial intelligence, digital twins, and policy interventions that contribute to our understanding of land system for sustainable and resilient landscape. By bringing together cutting-edge research, this special issue aspires to provide insights and strategies for better-informed decision-making, ultimately fostering the sustainable development and management of our precious landscapes.

This Special Issue welcomes manuscripts that link the following themes:

Land change dynamics;
Spatio-temporal analysis of landscape pattern;
Spatial simulation, spatial optimization, and spatial statistics;
Shaping sustainable landscapes;
Land development;
Land resources management;
Geocomputing and cyberinfrastructure technologies;
Modern artificial intelligence applications in the study of land systems;

We look forward to receiving your original research articles and reviews. You may choose our Joint Special Issue in Land.

Dr. Wenwu Tang
Dr. Jianxin Yang
Dr. Minrui Zheng
Dr. Jingye Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

spatiotemporal modeling
spatial-temporal landscape patterns and processes
landscape sustainability and resilience
geospatial technologies
land use/land cover
land change modeling
landscape assessment methods
landscape dynamic evolution

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Related Special Issue

Spatiotemporal Data Analytics and Modeling of Land Systems: Shaping Sustainable Landscape, Second Edition in Land

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Research

26 pages, 4376 KiB

Open AccessArticle

Machine Learning for Criteria Weighting in GIS-Based Multi-Criteria Evaluation: A Case Study of Urban Suitability Analysis

by Lan Qing Zhao, Alysha van Duynhoven and Suzana Dragićević

Land 2024, 13(8), 1288; https://doi.org/10.3390/land13081288 - 15 Aug 2024

Cited by 1 | Viewed by 1269

Abstract

Geographic Information System-based Multi-Criteria Evaluation (GIS-MCE) methods are designed to assist in various spatial decision-making problems using spatial data. Deriving criteria weights is an important component of GIS-MCE, typically relying on stakeholders’ opinions or mathematical methods. These approaches can be costly, time-consuming, and prone to subjectivity or bias. Therefore, the main objective of this study is to investigate the use of Machine Learning (ML) techniques to support criteria weight derivation within GIS-MCE. The proposed ML-MCE method is explored in a case study of urban development suitability analysis of the City of Kelowna, Canada. Feature importance values drawn from three ML techniques–Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM)–are used to derive criteria weights. The suitability scores obtained using the ML-MCE methodology are compared with Equal-Weights (EW) and the Analytical Hierarchy Process (AHP) approach for criteria weighting. The results indicate that ML-derived criteria weights can be used in GIS-MCE, where RF and XGB techniques provide more similar values for criteria weights than those derived from SVM. The similarities and differences are confirmed with Kappa indices obtained from comparing pairs of suitability maps. The proposed new ML-MCE methodology can support various decision-making processes in urban land-use planning. Full article

(This article belongs to the Special Issue Spatiotemporal Data Analytics and Modeling of Land Systems: Shaping Sustainable Landscape)

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11 pages, 7000 KiB

Open AccessArticle

Analyzing the Losses and Gains of a Land Category: Insights from the Total Operating Characteristic

by Thomas Mumuni Bilintoh, Robert Gilmore Pontius, Jr. and Zhen Liu

Land 2024, 13(8), 1177; https://doi.org/10.3390/land13081177 - 31 Jul 2024

Viewed by 734

Abstract

This manuscript provides guidance concerning how to use the Total Operating Characteristic (TOC) when (1) analyzing change through time, (2) ranking a categorical independent variable, and (3) constraining the extent for a gaining category. The illustrative variable is the marsh land-cover category in the Plum Island Ecosystems of northeastern Massachusetts, USA. The data are an elevation map and maps showing the land categories of water, marsh, and upland in 1938, 1971, and 2013. There were losses and gains near the edge of the marsh between 1938 and 1972 and between 1972 and 2013. The TOC curves show that marsh gained most intensively at intermediate elevations during the first time interval and then had a weaker association with elevation during the second time interval. Marsh gains more intensively from water than from upland during both time intervals. The TOC curves also demonstrate that the marsh gains occurred where marsh was previously lost, a phenomenon called Alternation. Furthermore, eliminating far distances and extreme elevations from the spatial extent decreased the area under the curve (AUC) for distance and increased the AUC for elevation. We invite scientists to use the TOC because the TOC is easier to interpret and shows more information than the Relative Operative Characteristic. Full article

(This article belongs to the Special Issue Spatiotemporal Data Analytics and Modeling of Land Systems: Shaping Sustainable Landscape)

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22 pages, 7309 KiB

Open AccessArticle

Simulation of Urban Growth Boundary under the Guidance of Stock Development: A Case Study of Wuhan City

by Yang Zhang, Xiaojiang Xia, Jiandong Li, Luge Xing, Chengchao Yang, Haofeng Wang, Xiaoai Dai and Jue Wang

Land 2024, 13(8), 1174; https://doi.org/10.3390/land13081174 - 30 Jul 2024

Viewed by 930

Abstract

The implementation of an urban growth boundary (UGB) can effectively control urban sprawl and promote efficient land use, which is crucial for future urban development. However, most of existing studies overlook the reuse of existing idle and inefficient land within the city in the delineation of UGBs. With China’s urban construction shifting from incremental development to stock development, this study focuses on Wuhan and presents a set of technical approaches for delineating UGBs with a stock development orientation. First, a built-up area composite index (POI&ISA) is constructed based on point of interest (POI) kernel density analysis and impervious surface index extraction to evaluate constructive levels in 2010 and 2020 and identify the urban vitality zone. Then, we combine the current land use status and control policies to divide the urban spatial development potential into five categories: urban vitality land, urban non-vitality land, other vitality land, other non-vitality land, and restricted development land. Finally, the PLUS model is applied in the analysis of the driving forces of land use change in Wuhan, simulating the UGBs in three stages of incremental development (2020–2030), incremental and stock development (2030–2040), and stock development (2040–2050). Finally, the PLUS model simulation projects the UGB areas to be 436.436 km², 474.617 km², and 520.396 km² for the years 2030, 2040, and 2050, respectively. The predicted timespan of urban development extends up to 30 years, serving as a reliable reference for Wuhan’s long-term and near-term planning. Full article

(This article belongs to the Special Issue Spatiotemporal Data Analytics and Modeling of Land Systems: Shaping Sustainable Landscape)

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15 pages, 20542 KiB

Open AccessArticle

Long-Term Ecological and Environmental Quality Assessment Using an Improved Remote-Sensing Ecological Index (IRSEI): A Case Study of Hangzhou City, China

by Cheng Cai, Jingye Li and Zhanqi Wang

Land 2024, 13(8), 1152; https://doi.org/10.3390/land13081152 - 27 Jul 2024

Viewed by 777

Abstract

The integrity and resilience of our environment are confronted with unprecedented challenges, stemming from the escalating pressures of urban expansion and the need for ecological preservation. This study proposes an Improved Remote Sensing Ecological Index (IRSEI), which employs humidity (WET), the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), a standardized Building–Bare Soil Index (NDBSI), aerosol optical depth (AOD), and the comprehensive salinity index (CSI). The IRSEI model was utilized to assess the ecological quality of Hangzhou over the period from 2003 to 2023. Additionally, the random forest model was employed to analyze the factors driving ecological quality. Furthermore, the gradient effect in the horizontal direction away from the urban center was examined using the buffer zone method. Our analysis reveals the following: (1) approximately 95% of the alterations in ecological quality observed from 2003 to 2023 exhibited marginal improvements, declines, or were negligible; (2) the transformations in IRSEI during this period, including variations in surface temperature and transportation networks, exhibited strong correlations (0.85) with human activities. Moreover, the influence of AOD and the comprehensive salinity index on IRSEI demonstrated distinct spatial disparities; (3) the IRSEI remained generally stable up to 30 km outside the city center, indicating a trend of agglomeration in the center and significant areas in the surroundings. The IRSEI serves as a robust framework for bolstering the assessment of regional ecological health, facilitating ecological preservation and rejuvenation efforts, and fostering coordinated sustainable regional development. Full article

(This article belongs to the Special Issue Spatiotemporal Data Analytics and Modeling of Land Systems: Shaping Sustainable Landscape)

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33 pages, 41920 KiB

Open AccessArticle

Exploring the Spatiotemporal Evolution Patterns and Determinants of Construction Land in Mianning County on the Eastern Edge of the Qinghai–Tibet Plateau

by Yinbing Zhao, Zhongyun Ni, Yang Zhang, Peng Wan, Chuntao Geng, Wenhuan Yu, Yongjun Li and Zhenrui Long

Land 2024, 13(7), 993; https://doi.org/10.3390/land13070993 - 5 Jul 2024

Viewed by 872

Abstract

Studying the spatiotemporal evolution and driving forces behind construction land amidst the intricate ecological and geological setting on the eastern edge of the Qinghai–Tibet Plateau offers invaluable insights for local sustainable development in a landscape transition zone and ecologically fragile area. Using construction land data from four phases, spanning 1990 to 2020, in Mianning County, this study employs methodologies like the Landscape Expansion Index (LEI) and land use transfer matrix to delineate the spatiotemporal evolution characteristics of construction land. A comprehensive set of 12 influencing factors across five categories—geomorphology, geological activity, climate, river and vegetation environment, and social economy—were examined. The Geographically Weighted Regression (GWR) model was then employed to decipher the spatial distribution pattern of construction land in 1990 and 2020, shedding light on the driving mechanisms behind its changes over the three decades. The research reveals distinct patterns of construction land distribution and evolution in Mianning County, shaped by the ecological and geological landscape. Notably, the Anning River wide valley exhibits a concentrated and contiguous development mode, while the Yalong River deep valley showcases a decentralized development pattern, and the Dadu River basin manifests an aggregation development mode centered around high mountain lakes. Over the study period, all three river basins witnessed varying degrees of construction land expansion, transitioning from quantitative expansion to qualitative enhancement. Edge expansion predominantly characterizes the expansion mode, complemented by leapfrog and infilling modes, accompanied by conversions from cropland and forest land to construction land. An analysis of the spatial pattern and drivers of construction land change highlights human-induced factors dominating the Anning River Basin, contrasting with natural factors prevailing in the Yalong River Basin and the Dadu River Basin. Future efforts should prioritize climate change considerations and environmental capacity, aiming for an ecologically resilient spatial pattern of construction land. Full article

(This article belongs to the Special Issue Spatiotemporal Data Analytics and Modeling of Land Systems: Shaping Sustainable Landscape)

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22 pages, 17182 KiB

Open AccessFeature PaperArticle

Towards a Comprehensive Framework for Regional Transportation Land Demand Forecasting: Empirical Study from Yangtze River Economic Belt, China

by Ke Wang, Li Wang and Jianjun Zhang

Land 2024, 13(6), 847; https://doi.org/10.3390/land13060847 - 13 Jun 2024

Cited by 1 | Viewed by 912

Abstract

China is currently experiencing rapid expansion in its transportation land. To promote sustainable land use, accurately estimating transportation land demand is crucial. This study aims to develop a comprehensive framework for urban transportation land forecasting within the Yangtze River Economic Belt (YREB), providing support for optimizing regional land allocation. Employing methods such as meta-analysis, statistical analysis, and BP neural network analysis, this study forecasts the transportation land demand of 127 cities in the YREB. The study findings indicate that cities with high transportation land demand are mainly distributed in the middle and upper reaches of the Yangtze River. Moreover, the growth rate of transportation land in the upper reaches significantly outstrips that in the middle and lower reaches, suggesting a focus shift in transportation infrastructure construction toward the upper regions. Additionally, some cities within the YREB face a mismatch between the supply and demand of transportation land, necessitating proactive adjustments to their land supply plans to achieve a balance between supply and demand. The main contribution of this study is the development of a comprehensive and adaptable framework that guides the development of future strategies for optimal land allocation by forecasting transportation land demand at a regional level. Full article

(This article belongs to the Special Issue Spatiotemporal Data Analytics and Modeling of Land Systems: Shaping Sustainable Landscape)

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16 pages, 2887 KiB

Open AccessArticle

Potential and Influencing Factors of Urban Spatial Development under Natural Constraints: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area

by Yukui Zhang, Tao Lin, Junmao Zhang, Meixia Lin, Yuan Chen, Yicheng Zheng, Xiaotong Wang, Yuqin Liu, Hong Ye and Guoqin Zhang

Land 2024, 13(6), 783; https://doi.org/10.3390/land13060783 - 1 Jun 2024

Viewed by 766

Abstract

As urbanization in China progresses, urban spatial development is transitioning from rapid expansion to more intensive and compact growth. This study examined the role of physical geography and environmental factors in shaping the urban spatial development in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Based on the current natural conditions, we selected evaluation indices from topography, hydrogeology, climatic conditions, and natural disasters. These indices were used to create a carrying capacity and suitability evaluation system for development land under natural constraints. Finally, the spatial development potential of the city was finalized by taking into account the current state of the built-up area of the city. Meanwhile, we employed the Optimal Parameters-based Geographical Detector and assessed the impact of 14 natural factors on the spatial development of urban built-up areas. In 2020, the GBA had 52,168.77 km² of land suitable for construction, of which 34,241.13 km² was highly suitable (61.29%) and 17,927.64 km² was moderately suitable (32.09%). At the Bay Area level, 90.15% of the development potential remains untapped; at the city level, Zhaoqing City has the highest potential at 99.56%, while Macao has the lowest at 26.83%. Key factors influencing urban development include silty sand content, annual average relative humidity, and cumulative temperature above 0 °C, with varying impacts across different urban scales. At the Bay Area level, the silty sand content, annual average relative humidity, and cumulative temperature above 0 °C are the main influencing factors on the spatial development of urban built-up areas; at the city level, the main factors are annual average relative humidity and cumulative active temperature above 0 °C. This study reveals the important influence of natural environmental factors on urban spatial development, which is conducive to promoting sustainable development of land resources in GBA. Full article

(This article belongs to the Special Issue Spatiotemporal Data Analytics and Modeling of Land Systems: Shaping Sustainable Landscape)

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20 pages, 8025 KiB

Open AccessArticle

Impact of Urban Expansion on Carbon Emissions in the Urban Agglomerations of Yellow River Basin, China

by Zhenwei Wang, Yi Zeng, Xiaochun Wang, Tianci Gu and Wanxu Chen

Land 2024, 13(5), 651; https://doi.org/10.3390/land13050651 - 10 May 2024

Cited by 3 | Viewed by 1454

Abstract

Continued urban expansion (UE) has long been regarded as a huge challenge for climate change mitigation. However, much less is known about how UE affects carbon emissions (CEs), especially in the urban agglomerations of the Yellow River Basin (UAYRB), China. In this regard, this study introduced kernel density analysis, the Gini coefficient, and Markov chains to reveal the UE patterns and carbon emissions intensity (CEI) in the UAYRB at the county level, and explored the spatial heterogeneity of the impact of UE on CEI with the geographically and temporally weighted regression model. The results show that both CEI and UE in the UAYRB showed a steady growing trend during the study period. The kernel density of CEI and UE revealed that CEI in the UAYRB was weakening, while the UE rate continuously slowed down. The Gini coefficients of both CEI and UE in the UAYRB region were at high levels, indicating obvious spatial imbalance. The Markov transfer probability matrix for CEI with a time span of five years showed that CEI growth will still occur over the next five years, while that of UE was more obvious. Meanwhile, counties with a regression coefficient of UE on CEI higher than 0 covered the majority, and the distribution pattern remained quite stable. The regression coefficients of different urban landscape metrics on CEI in the UAYRB varied greatly; except for the landscape shape index, the regression coefficients of the aggregation index, interspersion and juxtaposition index, and patch density overall remained positive. These findings can advance the policy enlightenment of the high-quality development of the Yellow River Basin. Full article

(This article belongs to the Special Issue Spatiotemporal Data Analytics and Modeling of Land Systems: Shaping Sustainable Landscape)

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21 pages, 5755 KiB

Open AccessArticle

Spatial and Temporal Changes in Ecological Resilience in the Shanxi–Shaanxi–Inner Mongolia Energy Zone with Multi-Scenario Simulation

by Xinmeng Cai, Yongyong Song, Dongqian Xue, Beibei Ma, Xianfeng Liu and Liwei Zhang

Land 2024, 13(4), 425; https://doi.org/10.3390/land13040425 - 27 Mar 2024

Cited by 1 | Viewed by 1247

Abstract

The energy-driven expansion of artificial surfaces has resulted in severe ecological problems. Scientific evaluation of regional ecological resilience under different scenarios is crucial for promoting ecological restoration. This study chose the Shanxi–Shaanxi–Inner Mongolia Energy Zone (SEZ) and modeled an ecological resilience evaluation based on resistance, adaptability, and recovery. Land-use change and ecological resilience from 1980 to 2020 were then analyzed. Moreover, the SEZ land-use patterns and ecological resilience in 2030 were simulated under business as usual (BAU), energy and mineral development (EMD), and ecological conservation and restoration (ECR) scenarios. The results showed that (1) the SEZ was dominated by cultivated land, grassland, and unused land. (2) Ecological resilience showed a changing trend of decreasing and then increasing, with high ecological resilience areas mainly located in the Yellow River Basin, whereas low ecological resilience areas spread outward from the central urban areas. (3) The ecological resilience level was the lowest under the EMD scenario and the highest under the ECR scenario. This study not only expands the analysis framework of ecological resilience research but also provides scientific support for ecological conservation in ecologically fragile areas with intensive human activity worldwide. Full article

(This article belongs to the Special Issue Spatiotemporal Data Analytics and Modeling of Land Systems: Shaping Sustainable Landscape)

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23 pages, 23364 KiB

Open AccessArticle

Exploring Integrative Development of Urban Agglomeration from the Perspective of Urban Symbiosis and Production–Living–Ecological Function

by Sijia Lin, Chun Li, Yanbo Li and Liding Chen

Land 2024, 13(2), 258; https://doi.org/10.3390/land13020258 - 19 Feb 2024

Cited by 1 | Viewed by 1675

Abstract

Integrative development is an effective way to enhance urban potential and implement resource-optimal relocation, especially in urban agglomeration regions. Conventionally, the evaluation of urban integration is usually studied from one aspect of urban interaction intensity or urban functional similarity, but considering both together can better reflect the integrative condition of urban agglomeration. This paper introduces the symbiosis theory into the exploration of urban integration. The production–living–ecological function is taken to analyze urban function, and the improved radiation model is adopted to measure urban interaction. Under the framework of symbiosis theory, we integrate urban function and urban interaction to indicate the integrative condition of urban agglomeration from a production–living–ecological aspect. Urban agglomeration in the Central Yunnan Urban Agglomeration is taken as the study area. The results show that (1) spatial variations occur in high-value areas with distinct functions. The east emphasizes production and living, while the west leans towards ecology. (2) Urban agglomeration is in its early developmental stages without stable symbiosis. Interactions among counties mostly show sporadic point symbiosis, lacking stability. It mainly radiates outward from the central area, with more stable interactions in high-value areas, often causing inter-city competition. (3) Urban agglomeration integration is generally low, with distinct high-value production and ecological areas. The central, eastern, and southern regions exhibit strong production and living interactions, while the west benefits from ecological interactions. These findings can offer some insights for informing relevant policies and fostering the integrated development of urban agglomerations. Full article

(This article belongs to the Special Issue Spatiotemporal Data Analytics and Modeling of Land Systems: Shaping Sustainable Landscape)

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19 pages, 8414 KiB

Open AccessArticle

Spatiotemporal Analysis of the Impacts of Land Use Change on Ecosystem Service Value: A Case from Guiyang, China

by Qinglan Li, Liu Yang, Hongzan Jiao and Qing He

Land 2024, 13(2), 211; https://doi.org/10.3390/land13020211 - 8 Feb 2024

Cited by 2 | Viewed by 1479

Abstract

The significance of ecosystem services and land use for human well-being and sustainable development cannot be understated. Scientifically assessing the ecosystem service value (ESV) and studying the relationship between land use change and the ESV can provide a theoretical groundwork for land use planning and ecological administration in Guiyang. In this study, gradient analysis was utilized to explore the changes of ESV at district level of Guiyang. Then, the synergistic relationship and the strength of the interaction between land use intensity (LUI) and ESV were explored by using a coupled coordination model and spatial autocorrelation analysis. Furthermore, polynomial fitting was carried out for the LUI index and its linked coordination index in relation to the ESV. The results showed that (1) the areas of farmland, forest, grassland, and unused land in Guiyang decreased from 2000 to 2020, while the areas of construction land and water body increased conversely. (2) The expansion of the construction land and water body was the main cause of the ESV change pattern in Guiyang, which first moved downward and then upward. (3) The ESV and LUI had a low overall coupling coordination degree (CCD). Spatial autocorrelation studies showed that low–to–low aggregation and high–to–high aggregation dominated the spatial patterns of essential regions. (4) The LUI and CCD indexes exhibited an inverted U-shaped curve correlation. Full article

(This article belongs to the Special Issue Spatiotemporal Data Analytics and Modeling of Land Systems: Shaping Sustainable Landscape)

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15 pages, 11349 KiB

Open AccessArticle

Spatial Optimization and Temporal Changes in the Ecological Network: A Case Study of Wanning City, China

by Shisi Zou, Rong Fan and Jian Gong

Land 2024, 13(1), 122; https://doi.org/10.3390/land13010122 - 22 Jan 2024

Cited by 2 | Viewed by 1568

Abstract

Ecological networks serve as vital tools for safeguarding biodiversity and ensuring regional ecological stability. This study, conducted in Wanning City, employs minimum-area threshold analysis to pinpoint crucial ecological sources while extracting potential ecological corridors using the minimum cumulative resistance model. Our investigation delves into the ecological network’s elements and structural transformations within Wanning City, spanning the period from 2000 to 2020, and assesses the priorities for ecological network preservation. The findings of our research reveal noteworthy spatial disparities in the distribution of ecological sources across Wanning City. Furthermore, the ecological corridors display sparse patterns in the north and denser patterns in the south. Over the two decades from 2000 to 2020, Wanning’s ecological resources exhibited a discernible trend of contraction and fragmentation, accompanied by an uneven spatial distribution. The average path length of the ecological corridors has increased, indicative of reduced biological flow efficiency. Correspondingly, the structural accessibility of the ecological network has decreased, signifying a decline in landscape connectivity. Based on our analysis, we propose an ecological protection and restoration framework denoted as “One Belt, Four Sources, Eight districts, multiple corridors, and multiple points”. Therefore, with the Shangxi–Jianling, Liulianling, Nanlin, and Jiexin nature reserves as the core area, and Houan Town, Damao Town, Changfeng Town, and Liji Town as the key restoration areas, we have proposed an ecological protection and restoration pattern. Full article

(This article belongs to the Special Issue Spatiotemporal Data Analytics and Modeling of Land Systems: Shaping Sustainable Landscape)

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