Advances in Forest Degradation and Deforestation Monitoring with AI and Multi-Source Remote Sensing Data

Dear Colleagues,

Forest degradation and deforestation caused by different drivers (e.g., fire, logging, urbanization, plant diseases/pests, and agriculture expansion) are critical environmental issues that have profound implications for biodiversity, climate change, and ecosystem services across the globe. The integration of Artificial Intelligence (AI) and multi-source remote sensing data has emerged as a transformative approach to monitor and understand these processes with unprecedented accuracy and efficiency. Remote sensing technologies, including optical, radar, and LiDAR systems, provide vast amounts of spatial and temporal data, while AI techniques, such as traditional machine learning, deep learning, and recent remote sensing multimodal large models, enable the extraction of meaningful insights from these complex datasets. This convergence of technologies offers a powerful toolkit for detecting, quantifying, and predicting these forest changes, thereby supporting informed decision-making and sustainable forest management. Given the urgency of addressing global degradation, deforestation, and their associated environmental impacts, this research area has gained significant scientific and societal importance, making it a focal point for interdisciplinary collaboration and innovation. 

This Special Issue, ‘Advances in Forest Degradation and Deforestation Monitoring with AI and Multi-Source Remote Sensing Data’, aims to showcase cutting-edge research that leverages AI and remote sensing to advance our understanding of forest degradation and deforestation. This Special Issue seeks to highlight novel methodologies, tools, and applications that address the challenges of monitoring forest degradation and deforestation across diverse ecosystems and scales. By bringing together contributions from experts in remote sensing, AI, ecology, and environmental science, this Special Issue will provide a comprehensive platform for disseminating innovative solutions and fostering interdisciplinary dialogue. The topic aligns closely with the scope of Remote Sensing, which emphasizes the development and application of remote sensing technologies to address pressing environmental issues. This Special Issue will not only advance the scientific community’s knowledge, but also contribute to global efforts in forest conservation and climate change mitigation. 

We invite submissions that explore a wide range of themes, including, but not limited to, the following: the development of AI-driven algorithms for remote sensing-based forest change detection; the integration of multi-source remote sensing data for enhanced monitoring accuracy of forest disturbances; the application of deep learning techniques in forest degradation and deforestation monitoring; and the use of remote sensing for assessing the impacts of deforestation on biodiversity and carbon stocks. Both original research articles and review papers are encouraged, as well as case studies that demonstrate the practical application of these technologies in real-world scenarios. Additionally, contributions that address the challenges of data availability, scalability, and interpretability in AI-based remote sensing approaches are particularly welcome. By encompassing these diverse themes, the Special Issue aims to provide a holistic perspective on the current state and future directions of forest degradation and deforestation monitoring, ultimately driving progress in this critical field.

Prof. Dr. Ran Meng
Prof. Dr. Huaguo Huang
Prof. Dr. Huabing Huang
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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.