Applications of Multimodal Remote Sensing Models in Ecological Environment Monitoring
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".
Deadline for manuscript submissions: 15 December 2025 | Viewed by 28
Special Issue Editors
Interests: landscape ecology; vegetation monitoring; temporal analysis; semi-arid landscapes; land cover conditions and trends
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing of vegetation structure; biodiversity and conservation; climate extremes and ecosystem health
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Advances in remote sensing highlight the limitations of single-sensor approaches in addressing the increasing complexity of ecological monitoring. Multimodal remote sensing—integrating data from multiple sources, including optical imagery, SAR, LiDAR, thermal sensors, etc.—offers a powerful alternative for capturing the spatial, spectral, and temporal diversity of environmental systems.
This Special Issue of Remote Sensing aims to present cutting-edge research that leverages multimodal data fusion techniques and machine learning algorithms to improve the monitoring, assessment, and management of ecological environments. The goal is to promote innovative approaches that advance both theoretical understanding and practical application of multimodal remote sensing in environmental science.
We invite contributions that explore methodological developments, case studies, and operational frameworks that push the boundaries of what is possible with integrated remote sensing technologies.
Topics of Interest:
We welcome original research articles, reviews, and technical notes in areas including, but not limited to, the following:
- Data fusion techniques for ecological monitoring;
- Multimodal approaches to vegetation health and biodiversity assessment;
- Land use/land cover classification using integrated sensor data;
- Water quality and hydrological monitoring;
- Detection of environmental change and degradation;
- Machine learning and AI in multimodal remote sensing;
- Integration of UAV, satellite, and ground-based data;
- Scalable and automated processing frameworks;
- Applications in conservation, forestry, agriculture, and urban ecology;
- Challenges in sensor interoperability and data harmonization.
Dr. R. Douglas Ramsey
Dr. Jin Wu
Dr. Jing Wang
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. 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.
Keywords
- multimodal remote sensing
- ecological monitoring
- data fusion
- vegetation assessment
- land cover classification
- environmental change detection
- machine learning
- AI
- geospatial analysis
- sustainable resource management
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