Data-Driven Geospatial Methods for Land Use and Land Cover Change Monitoring
A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Use, Impact Assessment and Sustainability".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 48
Special Issue Editors
Interests: spatial analysis; spatial sciences for agricultural production and water resources management; mapping; satellite image analysis; satellite image processing; remote sensing; geospatial science; data science
Interests: agriculture, land, and farm management; sustainable cities and communities; the application of remote sensing to areas such as agriculture (abiotic stress and precision irrigation), invasive species (detection, mapping, and management), water bodies (evapotranspiration and water balance), and environmental stress (biomass, dieback, recovery, and the effects of disturbances), among others
Special Issue Information
Dear Colleagues,
Informed agricultural planning and sustainable land management require land-use and land-cover monitoring. Accelerated climate change, urban expansion, and agricultural transformation make accurate, timely, and scalable LULC data much needed. Remote sensing and Geographic Information Systems (GISs) are used to monitor spatial and temporal patterns of land use and land cover. New developments in machine learning have opened new realms of possibility for precise classification, robust change detection, and intelligent data fusion across diverse landscapes.
Machine learning and deep learning techniques automate and refine land-use and land-cover mapping using remote sensing imagery. Adaptability to complex environmental conditions and the integration of heterogeneous data sources, including satellite imagery and field observations, make these techniques promising. In addition, these techniques enable resilient farming practices, precision agriculture, and informed management and policy development through robust, data-driven insights.
The goal of this Special Issue is to collect papers (original research articles and review papers) to give insights into the application of machine learning integrated with geospatial techniques for land use and land cover mapping and change detection. This Special Issue will highlight cutting-edge methodologies, practical applications, and interdisciplinary approaches that advance LULC mapping in agricultural and climatic contexts. This topic aligns with the scope of Land, emphasising land systems, land-use dynamics, and sustainable development, while focusing on machine-learning-enhanced techniques that contribute to the journal’s mission of fostering innovative research.
This Special Issue will welcome manuscripts that link the following themes:
- Machine learning and deep learning techniques for land use and land cover (LULC) classification and change detection;
- Integration of remote sensing data with ground-based observations;
- AI-driven approaches for agricultural land use and land cover mapping, monitoring, and management;
- Validation, calibration, and uncertainty assessment of LULC models and outputs;
- Visualization, interpretation, and communication of LULC information for decision-making and policy support;
- Case studies highlighting the role of geospatial intelligence in sustainable land management and policy development.
We look forward to receiving your original research articles and reviews.
Dr. Sabah Sabaghy
Dr. Deepak Gautam
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 250 words) can be sent to the Editorial Office for assessment.
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
- land use and land cover (LULC)
- machine learning
- deep learning
- remote sensing
- GIS
- change detection
- data integration
- spatial analysis
- precision agriculture
- environmental monitoring
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