Artificial Intelligence and Remote Sensing Applications in Natural Ecology 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 April 2026 | Viewed by 118
Special Issue Editor
Interests: environmental science; remote sensing; spatial ecology; biodiversity; GPS; GIS for natural resources; species distribution modeling; waveform lidar; hyperspectral imaging and spectroscopy; land use/land cover change monitoring; climate science
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Automated ecological analysis is now possible due to significant progress in machine learning, deep learning, and computational frameworks, as well as the broad implementation of satellite missions, UAVs, and sensor networks. These new methods of analysis have the ability to extract spatial patterns from multi-dimensional datasets. In the future, they could support real-time monitoring, predictive modeling, and early warning systems, which are needed to understand ecosystem dynamics.
The several AI approaches used for natural ecology monitoring with remote sensing data are thus examined in this Special Issue. Included research themes range from assessing biodiversity and species distribution to analysis of vegetation dynamics, identification of habitats, tracking ecological disturbances, and assessing the effects of climate change on natural resources.
Particularly, this Issue is interested in featuring AI-enhanced remote sensing systems in action, especially studies that address challenges in fusing data from different types of sensors, quantifying uncertainties, improving model interpretability, and scaling solutions across diverse ecological settings.
We welcome original research articles and reviews on the following topics:
- Machine learning and deep learning algorithms for extracting and classifying ecological parameters.
- Techniques for blending data from multiple sensors, including optical, thermal, radar, and LiDARobservations.
- Analyzing time-series data and detecting changes using AI for long-term ecological monitoring.
- Real-time monitoring systems integrating various sensors, satellite data, and AI processing.
- Species identification, distribution mapping, and biodiversity assessment using AI-enhanced remote sensing.
- Detecting ecological disturbances and recovery patterns, including natural and anthropogenic impacts.
- Monitoring and predicting ecological responses to extreme events like droughts, fires, and storms.
- Quantifying ecosystem services and their spatial-temporal variations using AI approaches.
- Improving the interpretability of AI-based ecological monitoring systems and quantifying their uncertainties.
- Using edge computing and distributed AI for on-site ecological monitoring in remote areas.
- Integrating citizen science and traditional ecological knowledge with AI platforms for community-led monitoring.
Dr. Eric Ariel L. Salas
Guest Editor
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.
Keywords
- artificial intelligence
- remote sensing
- natural ecology
- machine learning
- deep learning
- data fusion
- biodiversity monitoring
- ecosystem dynamics
- species distribution modeling
- climate change ecology
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