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Remote Sensing
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Advances in the Remote Sensing of Forest Cover Change
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Advances in the Remote Sensing of Forest Cover Change

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 7668

Special Issue Editors

Prof. Dr. Ioannis Gitas

E-Mail Website
Guest Editor
Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, P.O. Box 248, 54124 Thessaloniki, Greece
Interests: forest fires; land use/land cover mapping; pre-fire planning and post-fire assessment; remote sensing; GIS; forest management
Special Issues, Collections and Topics in MDPI journals
Dr. Thomas Katagis

E-Mail Website
Guest Editor
Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, PO 248 GR, Thessaloniki, Greece
Interests: remote sensing; GIS; forest management; forest fires; time series analysis; land cover change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests are considered one of the Earth’s most diverse ecosystems, and as such the conservation of biodiversity from local to global scale is entirely dependent on the protection and management of these ecosystems. Forests provide valuable ecosystem services and a very wide variety of benefits related to climate and water regulation, food security, human health, and energy resources, to name just a few. At the same time, forest ecosystems are constantly exposed to a significant number of environmental, economic, and social threats and pressures. Climate-driven pressures (which are foreseen to increase), coupled with growing demands on natural resources, are challenging the health and resilience of these ecosystems.

Existing initiatives and established strategies by national and international organizations and agencies provide extensive reports on the current status of forests worldwide, and emphasize the continuation and improvement of monitoring approaches, among other practices. Remote sensing methods and Earth observation datasets are valuable tools for providing spatially explicit information on past and on-going forest cover changes and for assessing future risks. The availability of large archives of satellite imagery and the scheduled data continuity missions, as implemented from the Landsat mission and the Copernicus programme, are now fostering new approaches in the fields of spatio-temporal data analysis and forest management.

This Special Issue aims to gather the latest research related to the use of advanced remote-sensing-based methods and strategies for the detailed monitoring and assessment of changes in forest ecosystems. We therefore invite contributions that will provide further insight into the way forests respond to pressures at local, regional, and global scales. We welcome the submission of manuscripts covering topics including but not limited to the following:

  • Exploitation of Landsat archives and recent Sentinel-2 imagery;
  • Research on data fusion approaches (multispectral, hyperspectral and microwave data sources);
  • Detection and characterization of rapid and gradual changes in forest cover;
  • Development of operational solutions for mitigating degradation impacts on forests;
  • Advances in Big Data applications;
  • Time series methods for the detection of disturbances;
  • Evaluation of satellite products related to forest biophysical parameters.

Dr. Ioannis Gitas
Dr. Thomas Katagis
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

  • forest ecosystems
  • forest change monitoring
  • vegetation cover
  • remote sensing
  • satellite sensors
  • time series

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

25 pages, 4152 KiB  
Article
Forest Disturbance Detection with Seasonal and Trend Model Components and Machine Learning Algorithms
by Jonathan V. Solórzano and Yan Gao
Remote Sens. 2022, 14(3), 803; https://doi.org/10.3390/rs14030803 - 8 Feb 2022
Cited by 8 | Viewed by 3748
Abstract
Forest disturbances reduce the extent of natural habitats, biodiversity, and carbon sequestered in forests. With the implementation of the international framework Reduce Emissions from Deforestation and forest Degradation (REDD+), it is important to improve the accuracy in the estimation of the extent of [...] Read more.
Forest disturbances reduce the extent of natural habitats, biodiversity, and carbon sequestered in forests. With the implementation of the international framework Reduce Emissions from Deforestation and forest Degradation (REDD+), it is important to improve the accuracy in the estimation of the extent of forest disturbances. Time series analyses, such as Breaks for Additive Season and Trend (BFAST), have been frequently used to map tropical forest disturbances with promising results. Previous studies suggest that in addition to magnitude of change, disturbance accuracy could be enhanced by using other components of BFAST that describe additional aspects of the model, such as its goodness-of-fit, NDVI seasonal variation, temporal trend, historical length of observations and data quality, as well as by using separate thresholds for distinct forest types. The objective of this study is to determine if the BFAST algorithm can benefit from using these model components in a supervised scheme to improve the accuracy to detect forest disturbance. A random forests and support vector machines algorithms were trained and verified using 238 points in three different datasets: all-forest, tropical dry forest, and temperate forest. The results show that the highest accuracy was achieved by the support vector machines algorithm using the all-forest dataset. Although the increase in accuracy of the latter model vs. a magnitude threshold model is small, i.e., 0.14% for sample-based accuracy and 0.71% for area-weighted accuracy, the standard error of the estimated total disturbed forest area was 4352.59 ha smaller, while the annual disturbance rate was also smaller by 1262.2 ha year−1. The implemented approach can be useful to obtain more precise estimates in forest disturbance, as well as its associated carbon emissions. Full article
(This article belongs to the Special Issue Advances in the Remote Sensing of Forest Cover Change)
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23 pages, 2303 KiB  
Article
Crossing the Great Divide: Bridging the Researcher–Practitioner Gap to Maximize the Utility of Remote Sensing for Invasive Species Monitoring and Management
by Kelsey Parker, Arthur Elmes, Peter Boucher, Richard A. Hallett, John E. Thompson, Zachary Simek, Justin Bowers and Andrew B. Reinmann
Remote Sens. 2021, 13(20), 4142; https://doi.org/10.3390/rs13204142 - 16 Oct 2021
Cited by 5 | Viewed by 2901
Abstract
Invasive species are increasingly present in our ecosystems and pose a threat to the health of forest ecosystems. Practitioners are tasked with locating these invasive species and finding ways to mitigate their spread and impacts, often through costly field surveys. Meanwhile, researchers are [...] Read more.
Invasive species are increasingly present in our ecosystems and pose a threat to the health of forest ecosystems. Practitioners are tasked with locating these invasive species and finding ways to mitigate their spread and impacts, often through costly field surveys. Meanwhile, researchers are developing remote sensing products to detect the changes in vegetation health and structure that are caused by invasive species, which could aid in early detection and monitoring efforts. Although both groups are working towards similar goals and field data are essential for validating RS products, these groups often work independently. In this paper, we, a group of researchers and practitioners, discuss the challenges to bridging the gap between researchers and practitioners and summarize the literature on this topic. We also draw from our experiences collaborating with each other to advance detection, monitoring, and management of the Hemlock Woolly Adelgid (Adelges tsugae; HWA), an invasive forest pest in the eastern U.S. We conclude by (1) highlighting the synergies and symbiotic mutualism of researcher–practitioner collaborations and (2) providing a framework for facilitating researcher–practitioner collaborations that advance fundamental science while maximizing the capacity of RS technologies in monitoring and management of complex drivers of forest health decline such as invasive species. Full article
(This article belongs to the Special Issue Advances in the Remote Sensing of Forest Cover Change)
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