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: 20 June 2024 | Viewed by 2586

Special Issue Editor


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Guest Editor
Department of Informatics, Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba 265-8501, Japan
Interests: remote sensing; machine learning; ecology; plant communities
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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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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. Applied Sciences 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 2400 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.

Published Papers (1 paper)

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Research

15 pages, 6683 KiB  
Communication
Countrywide Mapping of Plant Ecological Communities with 101 Legends including Land Cover Types for the First Time at 10 m Resolution through Convolutional Learning of Satellite Images
by Ram C. Sharma
Appl. Sci. 2022, 12(14), 7125; https://doi.org/10.3390/app12147125 - 14 Jul 2022
Cited by 3 | Viewed by 1580
Abstract
This paper presents next-generation mapping of plant ecological communities including land cover and agricultural types at 10 m spatial resolution countrywide. This research introduces modelling and mapping of land cover and ecological communities separately in small regions-of-interest (prefecture level), and later integrating the [...] Read more.
This paper presents next-generation mapping of plant ecological communities including land cover and agricultural types at 10 m spatial resolution countrywide. This research introduces modelling and mapping of land cover and ecological communities separately in small regions-of-interest (prefecture level), and later integrating the outputs into a large scale (country level) for dealing with regional distribution characteristics of plant ecological communities effectively. The Sentinel-2 satellite images were processed for cloud masking and half-monthly median composite images consisting of ten multi-spectral bands and seven spectral indexes were generated. The reliable ground truth data were prepared from extant multi-source survey databases through the procedure of stratified sampling, cross-checking, and noisy-labels pruning. Deep convolutional learning of the time-series of the satellite data was employed for prefecture-wise classification and mapping of 29–62 classes. The classification accuracy computed with the 10-fold cross-validation method varied from 71.1–87.5% in terms of F1-score and 70.9–87.4% in terms of Kappa coefficient across 48 prefectural regions. This research produced seamless maps of 101 ecological communities including land cover and agricultural types for the first time at a country scale with an average accuracy of 80.5% F1-score. Full article
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