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Remote Sensing and Time-Series Analysis to Track Ecosystem Transitions

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: 30 August 2025 | Viewed by 651

Special Issue Editors


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Guest Editor
Department of Natural Resources and Environment Engineering, University of Vigo|UVIGO, Vigo, Spain
Interests: remote sensing application; forestry; water security; water governance; new technologies; satellite images

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Guest Editor
Agroforestry Research Group, University of Vigo, 36005 Pontevedra, Spain
Interests: remote sensing application; ecology; water science and technology; computers in earth sciences; forestry; satellite images
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ecosystems are constantly transforming due to natural and anthropogenic factors, including climate change, land-use changes, and pressure on water and forest resources. The combination of remote sensing, machine learning, and time-series analysis has become an essential tool for detecting, characterizing, and predicting ecosystem transitions at different spatial and temporal scales.

We are pleased to invite you to submit your contributions to this Special Issue on "Remote Sensing and Time-Series Analysis to Track Ecosystem Transitions". This Special Issue will cover studies that utilize remote sensing technologies (satellite imagery, LiDAR, UAVs), advanced data analysis techniques, and predictive models to monitor ecosystem transitions. Contributions are welcome that address both green (forests, grasslands, agroecosystems) and blue areas (rivers, lakes, wetlands, coastal zones). Relevant topics include but are not limited to ecosystem dynamics, landscape fragmentation, extreme events, adaptive management strategies, and the impact of human activities and climate change on ecosystems.

This Special Issue aims to discuss innovative research on the use of remote sensing, machine learning, and time-series analysis to monitor changes in the structure, function, and dynamics of ecosystems. The focus is on ecological connectivity, environmental degradation, ecological restoration, and ecosystem resilience in the face of global change. Contributions are welcome that address both green areas (forests, grasslands, agroecosystems) and blue areas (rivers, lakes, wetlands, coastal zones).

Research articles and reviews will be considered. Research areas may include (but are not limited to) the following:

  • Ecosystem dynamics;
  • Landscape fragmentation;
  • Extreme events;
  • Adaptive management strategies;
  • Monitoring ecosystem transitions;
  • Impact of climate change and human activities;
  • Ecological restoration and resilience;
  • Remote sensing methodologies (satellite imagery, LiDAR, UAVs);
  • Predictive modeling and machine learning.

We look forward to receiving your contributions.

Prof. Dr. Carolina Acuña-Alonso
Prof. Dr. Xana Álvarez Bermúdez
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

  • remote sensing
  • ecosystem transitions
  • time-series analysis
  • machine learning
  • ecological connectivity
  • environmental degradation
  • ecological restoration
  • predictive models
  • LiDAR
  • UAVs

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Published Papers (1 paper)

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Research

13 pages, 7745 KiB  
Article
Classification of Forest Stratification and Evaluation of Forest Stratification Changes over Two Periods Using UAV-LiDAR
by Hideyuki Niwa
Remote Sens. 2025, 17(10), 1682; https://doi.org/10.3390/rs17101682 - 10 May 2025
Viewed by 476
Abstract
The demand for spatially explicit and comprehensive forest attribute data has continued to increase. Light detection and ranging (LiDAR) remote sensing, which can measure three-dimensional (3D) forest attributes, plays a significant role. However, only a few studies have used uncrewed aerial vehicle (UAV)-LiDAR [...] Read more.
The demand for spatially explicit and comprehensive forest attribute data has continued to increase. Light detection and ranging (LiDAR) remote sensing, which can measure three-dimensional (3D) forest attributes, plays a significant role. However, only a few studies have used uncrewed aerial vehicle (UAV)-LiDAR to extract the characteristics of the 3D structure of the forest understory. Therefore, this study proposes a method for classifying and mapping forest stratification and evaluating forest stratification changes using multitemporal UAV-LiDAR data. The study area is a forest of approximately 25 ha on the west side of the Expo Commemorative Park (Suita City, Osaka Prefecture, Japan). Three-dimensional point cloud models from two measurement periods during the leaf-fall season were used. Forest stratification was classified using time-series clustering of 2024 data. The classification of forest stratification and its spatial distribution effectively reflected the actual site conditions. By applying time-series clustering, the forest stratification was successfully classified using only UAV-LiDAR data. Changes in forest stratification were evaluated using data from 2022 to 2024. In areas where changes in forest stratification were evaluated as significant, evidence of tree felling was confirmed. In addition, changes in forest stratification were quantitatively evaluated. The proposed method uses only UAV-LiDAR, which is highly versatile; thus, it is expected to apply to various forests. The results of this study are expected to deepen our ecological understanding of forests and contribute to forest monitoring and management. Full article
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