Digital Technologies and Ecological Modelling for Land Risk Monitoring and Prediction

Dear Colleagues,

Land ecological risks—such as habitat fragmentation, land degradation, soil erosion, biodiversity loss, and declines in ecosystem services—are rising under the combined pressures of rapid land use and cover change (LUCC), climate extremes, and intensifying human activities. These risks can emerge quickly, spread across regions, and interact in complex ways, making them difficult to detect early using traditional field surveys alone. Land managers and policymakers increasingly need fast, consistent, and transparent monitoring, reporting, and verification (MRV) systems that can identify hotspots, track trends, and provide credible forecasts to support timely intervention.

Meanwhile, digital technologies are transforming how land risks can be monitored and predicted, while cloud-based geospatial platforms enable large scale processing and rapid updates. AI-enabled modelling systems—including machine learning, geospatial foundation models, and hybrid model data approaches—are improving the speed and accuracy of risk mapping and forecasting across diverse landscapes. Emerging digital twin concepts further strengthen this direction by coupling real-time observations with ecological models to test scenarios and evaluate management options. Together, these advances create a timely opportunity to move from retrospective assessment toward operational, accessible, and decision-ready land ecological risk monitoring and prediction.

The goal of this Special Issue is to collect papers (original research articles and review papers) that give insight into how AI-enabled modelling systems, machine learning, and remote sensing/GIS can be integrated with ecological modelling to deliver fast, accurate, and accessible forecasts for the monitoring, reporting, and verification (MRV) of land ecological risks and to support risk management, early warning, and decision-making under land use and climate change.

This Special Issue will welcome manuscripts that link the following themes:

• AI + remote sensing/GIS for land ecological risk mapping;
• Machine learning forecasts and early warning of ecological risks;
• Scenario simulation for land use change, climate extremes, and risk prevention;
• Ecological/process-based models to explain risk drivers and mechanisms;
• Hybrid modelling (ML + ecological models) and model–data integration;
• Multi-source data fusion for risk monitoring and hotspot detection;
• MRV frameworks: indicators, reporting methods, and verification workflows.

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

Dr. Li Tan
Prof. Dr. Antonio Miguel Martínez-Graña
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-anonymized 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.