Land Use and Land Cover Change Impact on Soil Erosion

Dear Colleagues,

Land use and land cover change are one of the most significant drivers of accelerated soil erosion worldwide, affecting agricultural productivity, ecosystem services, water quality and long-term land sustainability. Rapid urbanization, deforestation, agricultural intensification and climate variability have altered surface processes, increasing soil loss and landscape vulnerability across diverse environments. Understanding the complex interactions between land use and land cover dynamics and soil erosion is therefore critical for informed land management and policy formulation. Quantifying and monitoring soil erosion driven by land use and land cover change provides critical indicators for assessing land degradation trends and progress toward achieving Sustainable Development Goals, particularly Sustainable Development Goals15, which aims to protect, restore and promote the sustainable use of terrestrial ecosystems. Geospatially enabled erosion assessments, therefore, play a key role in supporting evidence-based strategies for achieving Sustainable Development Goals15 at local, regional and global scales.

This Special Issue aims to advance scientific knowledge by integrating geospatial technologies, including Geographic Information Systems, remote sensing and spatial modelling, with advanced analytical approaches such as machine learning and process-based erosion models. Emphasis is placed on innovative applications of these methods to quantify, monitor and predict soil erosion under changing land-use scenarios. The issue also seeks contributions that explore scenario analysis, uncertainty assessment and decision-support tools for sustainable land and watershed management.

This Special Issue welcomes original research articles and review papers that address, but are not limited to, the following themes:

• Land use and land cover–erosion linkages across spatial and temporal scales.
• Machine learning and artificial intelligence in soil erosion prediction.
• Climate change–land use and land cover interactions and future soil erosion scenarios.
• Geospatial workflows, soil erosion risk modelling and practical mitigation strategies.
• Decision-support tools for sustainable land and watershed management.
• Evidence-based solutions to soil erosion challenges in a changing landscape.

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

Dr. Kwanele Phinzi
Dr. Quoc Bao Pham
Dr. Sudhir K. Singh
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.

• land use and land cover change
• soil erosion
• climate change
• geographic information system
• remote sensing
• machine learning
• spatio-temporal analysis
• sustainable land management