Ecological Applications of Remote Sensing and Machine/Deep Learning Techniques
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Ecology Science and Engineering".
Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 3290
Special Issue Editor
Interests: remote sensing; machine learning; ecology; plant communities
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
I am pleased to invite you to contribute to the Special Issue on “Ecological Applications of Remote Sensing and Machine/Deep Learning Techniques” which is focused on the applications of remote sensing and machine/deep learning techniques to ecological issues.
Today, we face a number of severe environmental problems, such as deforestation, habitat fragmentation, overgrazing, land use changes and degradation and urbanization. These changes have resulted in unintended ecosystem disturbance, including changes in ecosystem structure and pattern, distribution of species, and loss of ecosystem productivity and resilience.
This Special Issue aims to gather together articles dealing with quantitative remote sensing approaches that apply multi-spectral, hyper-spectral, multi-angular, synthetic-aperture radar (SAR), or light detection and ranging (Lidar) sensor data from satellite, aerial, or terrestrial platforms to a variety of ecological problems. The issue hopes to cover a wide range of machine/deep learning methods, with the goal of enhancing ecological applications research with data-driven studies.
I would like to invite original contributions from concerned researchers, managers, and graduate students with a strong methodological basis for explicitly addressing key ecological questions and offering insights to a wide international audience.
In this Special Issue, original research articles, letters, and reviews are welcome. Research areas include a broad spectrum of ecological applications from local to global scale including, but not limited to, the following:
- Spectral analysis of plant communities and functional traits;
- Improved land cover and vegetation mapping;
- Feature engineering and fusion of optical, SAR, and Lidar sensors;
- Explainable machine learning;
- Convolution, recurrent, and attention learning;
- Semantic segmentation of land cover and vegetation types;
- Distribution modeling and projection of plant communities;
- Land use change modeling and projection;
- Estimating biomass, productivity, and carbon sequestration;
- Post-disaster land use monitoring and management.
I thank you in advance for you cooperation; and look forward to hearing from you soon.
Best wishes,
Dr. Ram C. Sharma
Guest Editor
Manuscript Submission Information
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