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 68
Special Issue Editors
Interests: remote sensing; photogrammetry; land management
Special Issues, Collections and Topics in MDPI journals
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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