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Forests
Special Issues
Remote Sensing of Forest Disturbance and Recovery
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Remote Sensing of Forest Disturbance and Recovery

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 6472

Special Issue Editors

Dr. Carlos Alberto Silva

E-Mail Website
Guest Editor
1. Department of Geographical Sciences, University of Maryland, College Park, MD, USA
2. School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA
Interests: LiDAR and hyperspectral remote sensing; tropical forest structure and ecology; industrial forest plantations; algorithms and tools development; data integration and change detection
Special Issues, Collections and Topics in MDPI journals
Dr. Eben N. Broadbent

E-Mail Website
Guest Editor
Spatial Ecology and Conservation Lab, School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32603, USA
Interests: remote sensing; forest ecology; conservation biology; airborne sensors; GatorEye; landscape simulation models; ecosystem and canopy ecology
Special Issues, Collections and Topics in MDPI journals
Dr. Rubén Valbuena

E-Mail Website
Guest Editor
School of Natural Sciences, Bangor University, Bangor LL57 2PZ, UK
Interests: forest ecology; remote sensing; LiDAR; forest inventory; tree size scaling theories; forest structure; competition and dominance; modelling; data fusion
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Carine Klauberg

E-Mail Website
Guest Editor
Federal University of São João Del Rei – UFSJ, Sete Lagoas, MG 35701-970, Brazil
Interests: forests and nontimber forest products; tropical forest ecology; remote sensing; LiDAR; forest inventory; wildfire; data integration; change detection; fire ecology and fire behavior modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Adrián Cardil

E-Mail Website
Guest Editor
Professor, Department of Vegetal Production and Forestry Science, University of Lleida, Plaça de Víctor Siurana, 1, 25003 Lleida, Spain
Dr. Danilo Roberti Alves de Almeida

E-Mail Website
Guest Editor
Department of Forest Sciences, University of São Paulo, “Luiz de Queiroz” College of Agriculture (USP/ESALQ), Piracicaba, SP, Brazil
Interests: tropical forest; forest restoration; forest ecology; forest management; remote sensing; Lidar and hyperspectral remote sensing; unmanned aerial vehicles and forest inventory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest disturbances, such as those that occur due to fire or logging, and subsequent recovery play an important role in environmental processes, including the global carbon and water cycles and energy balance, and are closely related to changes in fauna and flora biodiversity and human factors, influencing long-term sustainability. Remote sensing technologies, such as passive (e.g., Landsat 8) and active sensors (LiDAR), can be efficiently used for capturing forest disturbance and recovery across spatial and temporal scales, from a smallholder farm in the Amazon to fires crossing the boreal forests of Russia.

The purpose of this Special Issue is to gather state-of-the-art remote sensing technologies for forest disturbance and recovery detection and monitoring. Review papers and research contributions are encouraged. In particular, contributions covering the following subtopics are welcome:

  • Forest disturbance and recovery detection using passive and active remote sensing data, such as Landsat 8 OLI, Sentinel 2A, and LiDAR.
  • Time series analysis for forest disturbance and recovery detection on multiple scales.
  • Machine learning and deep learning approaches for detecting, modelling, and mapping forest disturbance and recovery.
  • Developments of new algorithms and tools for forest disturbance and recovery analysis.
  • Fusion approaches and synergies among platforms (airborne, terrestrial, and spaceborne) for forest disturbance and recovery mapping.
  • Application of GEDI and ICESat-2 and future NISAR missions for forest disturbance and recovery assessment.

Dr. Carlos Alberto Silva
Dr. Eben North Broadbent
Dr. Rubén Valbuena
Prof. Dr. Carine Klauberg
Dr. Adrián Cardil
Dr. Danilo Roberti Alves de Almeida
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. Forests is an international peer-reviewed open access monthly 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 2600 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
  • mapping
  • LiDAR
  • disturbance
  • deforestation
  • degradation
  • REDD+
  • Fire
  • time series analysis
  • tools

Published Papers (1 paper)

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Research

12 pages, 3869 KiB  
Article
Detecting Vegetation Recovery after Fire in A Fire-Frequented Habitat Using Normalized Difference Vegetation Index (NDVI)
by Danielle L. Lacouture, Eben N. Broadbent and Raelene M. Crandall
Forests 2020, 11(7), 749; https://doi.org/10.3390/f11070749 - 10 Jul 2020
Cited by 15 | Viewed by 5381
Abstract
Research Highlights: Fire-frequented savannas are dominated by plant species that regrow quickly following fires that mainly burn through the understory. To detect post-fire vegetation recovery in these ecosystems, particularly during warm, rainy seasons, data are needed on a small, temporal scale. In the [...] Read more.
Research Highlights: Fire-frequented savannas are dominated by plant species that regrow quickly following fires that mainly burn through the understory. To detect post-fire vegetation recovery in these ecosystems, particularly during warm, rainy seasons, data are needed on a small, temporal scale. In the past, the measurement of vegetation regrowth in fire-frequented systems has been labor-intensive, but with the availability of daily satellite imagery, it should be possible to easily determine vegetation recovery on a small timescale using Normalized Difference Vegetation Index (NDVI) in ecosystems with a sparse overstory. Background and Objectives: We explore whether it is possible to use NDVI calculated from satellite imagery to detect time-to-vegetation recovery. Additionally, we determine the time-to-vegetation recovery after fires in different seasons. This represents one of very few studies that have used satellite imagery to examine vegetation recovery after fire in southeastern U.S.A. pine savannas. We test the efficacy of using this method by examining whether there are detectable differences between time-to-vegetation recovery in subtropical savannas burned during different seasons. Materials and Methods: NDVI was calculated from satellite imagery approximately monthly over two years in a subtropical savanna with units burned during dry, dormant and wet, growing seasons. Results: Despite the availability of daily satellite images, we were unable to precisely determine when vegetation recovered, because clouds frequently obscured our range of interest. We found that, in general, vegetation recovered in less time after fire during the wet, growing, as compared to dry, dormant, season, albeit there were some discrepancies in our results. Although these general patterns were clear, variation in fire heterogeneity and canopy type and cover skewed NDVI in some units. Conclusions: Although there are some challenges to using satellite-derived NDVI, the availability of satellite imagery continues to improve on both temporal and spatial scales, which should allow us to continue finding new and efficient ways to monitor and model forests in the future. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Disturbance and Recovery)
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