Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
New Remote Sensing Technologies in Forest Fire Analysis, Prevention and...
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

New Remote Sensing Technologies in Forest Fire Analysis, Prevention and Mitigation

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

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 9402

Special Issue Editors

Dr. Julia Armesto

E-Mail Website
Guest Editor
Forestry Engineering School, University of Vigo, University Campus A Xunqueira s/n, CP 36005 Pontevedra, Spain
Interests: LiDAR; remote sensing; automation; forestry; wildfire; natural resources
Dr. Juan Picos

E-Mail Website
Guest Editor
Forestry Engineering School, University of Vigo, University Campus A Xunqueira s/n, 36005 Pontevedra, Spain
Interests: environmental impact assessment; wildlife management; silviculture; planning; climate change; agroforestry; landscape ecology; sustainable forest management; forest industry; fire; remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andreas Nüchter

E-Mail Website
Guest Editor
Robotics and Telematics, Julius Maximilian University of Würzburg, Am Hubland, 97074 Würzburg, Germany
Interests: 3D robot vision (3D Point Cloud Processing); Robotics and automation; telematics/geomatics; sensing and perception; semantics, machine vision; cognition; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last years there has been intense research activity regarding the exploitation of remote sensing technologies in forest fire analysis. Forest fires have become a major concern worldwide given their impact in terms of victims, economic damage, affected ecosystems, desertification, air pollution and global warming. Climate change is affecting vegetation productivity, warm–dry cycles, or the occurrence of droughts, resulting in the higher vulnerability of regions to severe events. The seasonality, size and intensity of wildfires are also being altered. The risk of devastating megafires, like those that recently occurred in the Mediterranean area or on the American west coast, is increasing evidently. This scenario urges scientists and researchers to explore solutions that allow us to better understand the deep mechanisms of such complex events as forest fires, and gain a coherent science-based understanding to assess prevention and management strategies so that the occurrence of such disasters in the future can be prevented or mitigated.

Wildfire prevention, mitigation measures and the recovery of burned areas require accurate knowledge of several factors like land cover, forest fuels, population settlement pattern distribution, infrastructure networks, topographic conditions, and severity maps, among others. This knowledge is essential to support effective landscape management measures. Satellite, aerial and terrestrial technologies have been revealed to be suitable to effectively collect data on a large scale. However, the available technologies for land mapping have evolved significantly in the last years. Medium and high spatial resolution satellites with an acceptable spectral resolution, mobile systems providing extremely detailed information, autonomous systems or UAV-based solutions are some examples.

This Special Issue aims at studies covering new remote sensing technologies, data collections and processing methodologies that can be successfully applied in wildfire analysis, mitigation and prevention. We welcome submissions that cover, but are not limited to:

  • The mapping of the wildland–urban interface at different scales.
  • Data mining for the analysis of the factors influencing ecosystems in their vulnerability to forest fires.
  • Remote sensing techniques in the evaluation of the impact of climate change in forest fires.
  • The Copernicus Earth Observation Program in forest fire severity evaluation, land monitoring and recovery after forest fire occurrence.

Dr. Julia Armesto
Dr. Juan Juan Picos
Dr. Andreas Nüchter
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

  • Wildland–urban interface mapping
  • Robotic systems in forest fires analysis
  • Data mining in forest fire prevention
  • Climate change and forest fires
  • Remote sensing in wildfire severity evaluation and land monitoring

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 3514 KiB  
Article
Automatic Identification of Forest Disturbance Drivers Based on Their Geometric Pattern in Atlantic Forests
by Laura Alonso, Juan Picos and Julia Armesto
Remote Sens. 2022, 14(3), 697; https://doi.org/10.3390/rs14030697 - 01 Feb 2022
Cited by 2 | Viewed by 1900
Abstract
Monitoring forest disturbances has become essential towards the design and tracking of sustainable forest management. Multiple methodologies have been developed to detect these disturbances. However, few studies have focused on the automatic detection of disturbance drivers, an essential task as each disturbance has [...] Read more.
Monitoring forest disturbances has become essential towards the design and tracking of sustainable forest management. Multiple methodologies have been developed to detect these disturbances. However, few studies have focused on the automatic detection of disturbance drivers, an essential task as each disturbance has different implications for the functioning of the ecosystem and associated management actions. Wildfires and harvesting are two of the major drivers of forest disturbances across different ecosystems. In this study, an automated methodology is presented to automatically distinguish between the two once the disturbance is detected, using the properties of its geometry and shape. A cluster analysis was performed to automatically individualize each disturbance and afterwards calculate its geometric properties. Using these properties, a decision tree was built that allowed for the distinction between wildfires and harvesting with an overall accuracy of 91%. This methodology and further research relating to it could pose an essential aid to national and international agencies for incorporating forest-disturbance-driver-related information into forest-focused reports. Full article
(This article belongs to the Special Issue New Remote Sensing Technologies in Forest Fire Analysis, Prevention and Mitigation)
►▼ Show Figures

Graphical abstract

11 pages, 1643 KiB  
Communication
Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms
by Luis A. Pérez-Rodríguez, Carmen Quintano, Elena Marcos, Susana Suarez-Seoane, Leonor Calvo and Alfonso Fernández-Manso
Remote Sens. 2020, 12(8), 1295; https://doi.org/10.3390/rs12081295 - 20 Apr 2020
Cited by 16 | Viewed by 3235
Abstract
Prescribed fires have been applied in many countries as a useful management tool to prevent large forest fires. Knowledge on burn severity is of great interest for predicting post-fire evolution in such burned areas and, therefore, for evaluating the efficacy of this type [...] Read more.
Prescribed fires have been applied in many countries as a useful management tool to prevent large forest fires. Knowledge on burn severity is of great interest for predicting post-fire evolution in such burned areas and, therefore, for evaluating the efficacy of this type of action. In this research work, the severity of two prescribed fires that occurred in “La Sierra de Uría” (Asturias, Spain) in October 2017, was evaluated. An Unmanned Aerial Vehicle (UAV) with a Parrot SEQUOIA multispectral camera on board was used to obtain post-fire surface reflectance images on the green (550 nm), red (660 nm), red edge (735 nm), and near-infrared (790 nm) bands at high spatial resolution (GSD 20 cm). Additionally, 153 field plots were established to estimate soil and vegetation burn severity. Severity patterns were explored using Probabilistic Neural Networks algorithms (PNN) based on field data and UAV image-derived products. PNN classified 84.3% of vegetation and 77.8% of soil burn severity levels (overall accuracy) correctly. Future research needs to be carried out to validate the efficacy of this type of action in other ecosystems under different climatic conditions and fire regimes. Full article
(This article belongs to the Special Issue New Remote Sensing Technologies in Forest Fire Analysis, Prevention and Mitigation)
►▼ Show Figures

Graphical abstract

18 pages, 9491 KiB  
Article
Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network
by João E. Pereira-Pires, Valentine Aubard, Rita A. Ribeiro, José M. Fonseca, João M. N. Silva and André Mora
Remote Sens. 2020, 12(6), 909; https://doi.org/10.3390/rs12060909 - 12 Mar 2020
Cited by 18 | Viewed by 3533
Abstract
The difficult job of fighting fires and the nearly impossible task to stop a wildfire without great casualties requires an imperative implementation of proactive strategies. These strategies must decrease the number of fires, the burnt area and create better conditions for the firefighting. [...] Read more.
The difficult job of fighting fires and the nearly impossible task to stop a wildfire without great casualties requires an imperative implementation of proactive strategies. These strategies must decrease the number of fires, the burnt area and create better conditions for the firefighting. In this line of action, the Portuguese Institute of Nature and Forest Conservation defined a fire break network (FBN), which helps controlling wildfires. However, these fire breaks are efficient only if they are correctly maintained, which should be ensured by the local authorities and requires verification from the national authorities. This is a fastidious task since they have a large network of thousands of hectares to monitor over a full year. With the increasing quality and frequency of the Earth Observation Satellite imagery with Sentinel-2 and the definition of the FBN, a semi-automatic remote sensing methodology is proposed in this article for the detection of maintenance operations in a fire break. The proposed methodology is based on a time-series analysis, an object-based classification and a change detection process. The change detection is ensured by an artificial neural network, with reflectance bands and spectral indices as features. Additionally, an analysis of several bands and spectral indices is presented to show the behaviour of the data during a full year and in the presence of a maintenance operation. The proposed methodology achieved a relative error lower than 4% and a recall higher than 75% on the detection of maintenance operations. Full article
(This article belongs to the Special Issue New Remote Sensing Technologies in Forest Fire Analysis, Prevention and Mitigation)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop