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Land
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Advancements in Geospatial Techniques for Land Change Analysis and Management
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Advancements in Geospatial Techniques for Land Change Analysis and Management

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: 30 September 2025 | Viewed by 892

Special Issue Editors

Prof. Dr. Jūratė Sužiedelytė-Visockienė

E-Mail Website1 Website2
Guest Editor
Department of Geodesy and Cadastre, Vilniaus Gedimino Technikos Universitetas, Vilnius, Lithuania
Interests: remote sensing; photogrammetry; land management
Special Issues, Collections and Topics in MDPI journals
Dr. Eglė Tumelienė

E-Mail Website
Guest Editor
Department of Geodesy and Cadastre, Vilnius Gediminas Technical University, Sauletekio av. 11, LT-10223 Vilnius, Lithuania
Interests: geodesy; land management; environmental engineering science

Special Issue Information

Dear Colleagues,

This Special Issue discusses the rapid advancements in geospatial data analysis methods. New technologies are emerging that combine geospatial data with other complementary data, machine learning algorithms, and artificial intelligence. Once processed, data can be analyzed anew with greater accuracy through innovations. The global dynamics of land use and air pollution are influenced by factors such as population growth, urbanization, sustainable agricultural development, and climate change mitigation in the interests of environmental protection. Monitoring and managing these changes require sophisticated tools and new data-accuracy methods. Geospatial technologies, including remote sensing, geographic information systems (GIS), spatial modeling, and machine learning, are now essential for understanding these processes and enabling more informed decisions. This Special Issue brings together a body of ground-breaking research highlighting how new technologies (combining diverse data) are being combined and applied to address the complex challenges of land change and air pollution at local, regional, and global scales.

The issue provides a forward-looking perspective on the critical role those geospatial technologies will continue to play in addressing the complex challenges of land change and air pollutants in the years to come.

Key topics include, but are not limited to, the following:

  • The use of remote sensing technologies for land change detection;
  • Air pollution monitoring using remote sensing technologies and climate data;
  • Geospatial big data for forecasting land use changes and assessing their impacts;
  • Machine learning and AI in land change and air pollution detection;
  • Policy and governance for effective land management and climate change;
  • How land management can be optimized to mitigate climate-related impacts;
  • Climate change mitigation in the interest of environmental protection.

We look forward to receiving your original research articles and reviews.

Kind regards

Prof. Dr. Jūratė Sužiedelytė-Visockienė
Dr. Eglė Tumelienė
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

  • geospatial data analysis
  • remote sensing technologies
  • air pollution
  • climate change mitigation
  • land management
  • land use changes
  • machine learning and AI

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

29 pages, 22994 KiB  
Article
Simulating Land Use and Evaluating Spatial Patterns in Wuhan Under Multiple Climate Scenarios: An Integrated SD-PLUS-FD Modeling Approach
by Hao Yuan, Xinyu Li, Meichen Ding, Guoqiang Shen and Mengyuan Xu
Land 2025, 14(7), 1412; https://doi.org/10.3390/land14071412 - 4 Jul 2025
Viewed by 362
Abstract
Amid intensifying global climate anomalies and accelerating urban expansion, land use systems have become increasingly dynamic, complex, and uncertain. Accurately predicting and scientifically evaluating the evolution of land use patterns is essential to advancing territorial spatial governance and achieving ecological security goals. However, [...] Read more.
Amid intensifying global climate anomalies and accelerating urban expansion, land use systems have become increasingly dynamic, complex, and uncertain. Accurately predicting and scientifically evaluating the evolution of land use patterns is essential to advancing territorial spatial governance and achieving ecological security goals. However, most existing land use models emphasize quantity forecasting and spatial allocation, while overlooking the third critical dimension—structural complexity, which is essential for understanding the nonlinear, fragmented evolution of urban systems, thus limiting their ability to fully capture the evolutionary characteristics of urban land systems. To address this gap, this study proposes an integrated SD-PLUS-FD model, which combines System Dynamics, Patch-based Land Use Simulation, and Fractal Dimension analysis to construct a comprehensive three-dimensional framework for simulating and evaluating land use patterns in terms of quantity, spatial distribution, and structural complexity. Wuhan is selected as the case study area, with simulations conducted under three IPCC-aligned climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to project land use changes by 2030. The SD model demonstrates robust predictive performance, with an overall error of less than ±5%, while the PLUS model achieves high spatial accuracy (average Kappa >0.7996; average overall accuracy >0.8856). Fractal dimension analysis further reveals that since 2000, the spatial boundary complexity of all land use types—except forest land—has generally shown an upward trend across multiple scenarios, highlighting the increasingly nonlinear and fragmented nature of urban expansion. The FD values for construction land and cultivated land declined to their historical low in 2005, then gradually increased, reaching their peak under the SSP1-2.6 scenario. Notably, the increase in FD for construction land was significantly greater than that for cultivated land, indicating a stronger dynamic response in spatial structural evolution. In contrast, forest land exhibited pronounced scenario-dependent variations in FD. Its structural complexity remained generally stable under all scenarios except SSP5-8.5, reflecting higher structural resilience and boundary adaptability under diverse socioclimatic conditions. The SD-PLUS-FD model effectively reveals how land systems respond to different socioclimatic drivers in both spatial and structural dimensions. This three-dimensional framework reveals how land systems respond to socioclimatic drivers across temporal, spatial, and structural scales, offering strategic insights for climate-resilient planning and optimized land resource management in rapidly urbanizing regions. Full article
(This article belongs to the Special Issue Advancements in Geospatial Techniques for Land Change Analysis and Management)
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24 pages, 12568 KiB  
Article
Geospatial Explainable AI Uncovers Eco-Environmental Effects and Its Driving Mechanisms—Evidence from the Poyang Lake Region, China
by Mingfei Li, Zehong Zhu, Junye Deng, Jiaxin Zhang and Yunqin Li
Land 2025, 14(7), 1361; https://doi.org/10.3390/land14071361 - 27 Jun 2025
Viewed by 353
Abstract
Intensified human activities and changes in land-use patterns have led to numerous eco-environmental challenges. A comprehensive understanding of the eco-environmental effects of land-use transitions and their driving mechanisms is essential for developing scientifically sound and sustainable environmental management strategies. However, existing studies often [...] Read more.
Intensified human activities and changes in land-use patterns have led to numerous eco-environmental challenges. A comprehensive understanding of the eco-environmental effects of land-use transitions and their driving mechanisms is essential for developing scientifically sound and sustainable environmental management strategies. However, existing studies often lack a comprehensive analysis of these mechanisms due to methodological limitations. This study investigates the eco-environmental effects of land-use transitions in the Poyang Lake Region over the past 30 years from the perspective of the production-living-ecological space (PLES) framework. Additionally, a geographically explainable artificial intelligence (GeoXAI) framework is introduced to further explore the mechanisms underlying these eco-environmental effects. The GeoXAI framework effectively addresses the challenges of integrating nonlinear relationships and spatial effects, which are often not adequately captured by traditional models. The results indicate that (1) the conversion of agricultural space to forest and lake spaces is the primary factor contributing to eco-environmental improvement. Conversely, the occupation of forest and lake spaces by agricultural and residential uses constitutes the main driver of eco-environmental degradation. (2) The GeoXAI demonstrated excellent performance by incorporating geographic variables to address the absence of spatial causality in traditional machine learning. (3) High-altitude and protected water areas are more sensitive to human activities. In contrast, geographic factors have a greater impact on densely populated urban areas. The results and methodology presented here can serve as a reference for eco-environmental assessment and decision-making in other areas facing similar land-use transformation challenges. Full article
(This article belongs to the Special Issue Advancements in Geospatial Techniques for Land Change Analysis and Management)
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