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Forests
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Forest Fires Prediction and Detection—Volume II
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Forest Fires Prediction and Detection—Volume II

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Natural Hazards and Risk Management".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2245

Special Issue Editors

Prof. Dr. Rafael Coll Delgado

E-Mail Website
Guest Editor
Center for Biological and Natural Sciences, Federal University of Acre, Rio Branco 69920-900, AC, Brazil
Interests: climate change; forest fires; forest soils; gross primary productivity; carbon emissions; deforestation; remote sensing and fire meteorology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rafael De Ávila Rodrigues

E-Mail Website
Guest Editor
Institute of Geography, Federal University of Catalão, Catalão 74704-020, GO, Brazil
Interests: climatology and meteorology; remote sensing; climate change

Special Issue Information

Dear Colleagues,

In recent years, the frequency and intensity of global fires has increased, significantly threatening the loss of biodiversity in forest areas. In addition to the loss of biodiversity in these regions, fires negatively affect the economic sector and increase the number of victims. With the most recurrent forest fires and the increase in air temperature and the presence of more intense weather phenomena, in addition to the great anthropic intervention in these regions, forests are, in turn, decreasing their fire resilience capacity and drastically reducing their areas. Understanding the relationships between meteorological elements, remote sensing, and statistical prediction models to associate the degree of fire hazard in these regions is important to understand the effects of climate change on these regions. This will also allow the development of strategic plans for growth and rational use of forest resources.

Submitted manuscripts must be original contributions, not previously published or submitted to other journals.

Prof. Dr. Rafael Coll Delgado
Prof. Dr. Rafael De Ávila Rodrigues
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
  • climate change
  • forest fires
  • fire models
  • fire monitoring

Published Papers (2 papers)

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Research

19 pages, 6939 KiB  
Article
A Forest Fire Prediction Method for Lightning Stroke Based on Remote Sensing Data
by Zhejia Zhang, Ye Tian, Guangyu Wang, Change Zheng and Fengjun Zhao
Forests 2024, 15(4), 647; https://doi.org/10.3390/f15040647 - 02 Apr 2024
Viewed by 649
Abstract
Forest fires ignited by lightning accounted for 68.28% of all forest fires in the Greater Khingan Mountains (GKM) region of northeast China. Forecasting the incidence of lightning-triggered forest fires in the region is imperative for mitigating deforestation, preserving biodiversity, and safeguarding distinctive natural [...] Read more.
Forest fires ignited by lightning accounted for 68.28% of all forest fires in the Greater Khingan Mountains (GKM) region of northeast China. Forecasting the incidence of lightning-triggered forest fires in the region is imperative for mitigating deforestation, preserving biodiversity, and safeguarding distinctive natural habitats and resources. Lightning monitoring data and vegetation moisture content have emerged as pivotal factors among the various influences on lightning-induced fires. This study employed innovative satellite remote sensing technology to swiftly acquire vegetation moisture content data across extensive forested regions. Firstly, the most suitable method to identify the lightning strikes that resulted in fires and two crucial lightning parameters correlated with fire occurrence are confirmed. Secondly, a logistic regression method is proposed for predicting the likelihood of fires triggered by lightning strikes. Finally, the method underwent verification using five years of fire data from the GKM area, resulting in an AUC value of 0.849 and identifying the primary factors contributing to lightning-induced fires in the region. Full article
(This article belongs to the Special Issue Forest Fires Prediction and Detection—Volume II)
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19 pages, 14298 KiB  
Article
The Consequences of Climate Change in the Brazilian Western Amazon: A New Proposal for a Fire Risk Model in Rio Branco, Acre
by Kennedy da Silva Melo, Rafael Coll Delgado, Marcos Gervasio Pereira and Givanildo Pereira Ortega
Forests 2024, 15(1), 211; https://doi.org/10.3390/f15010211 - 21 Jan 2024
Viewed by 1120
Abstract
The objective of this study was to verify the link between climate change, changes in land use, and the increasing frequency of forest fires in the state of Acre. Recognizing the importance of an accurate assessment of fire risk, we also proposed a [...] Read more.
The objective of this study was to verify the link between climate change, changes in land use, and the increasing frequency of forest fires in the state of Acre. Recognizing the importance of an accurate assessment of fire risk, we also proposed a new fire risk index for the capital Rio Branco, using meteorological data. Validated reanalysis data from 1961 to 2020 extracted for Rio Branco and different land uses were used. Data on fire foci, deforestation, and agricultural crops were also obtained. The new model was based on the Fire Risk Atlantic Forest (FIAF) Index, developed for the Atlantic Forest biome, and was subjected to multiple regression analysis. To validate the new model, projections were calculated using different scenarios from the Intergovernmental Panel on Climate Change (IPCC). The new model, entitled Rio Branco Fire Risk (FIRERBR), revealed an increase in fire risk, especially associated with agriculture, in future scenarios (SSP2-4.5 and SSP5-8.5) from 2023 onward. Rainfall and relative air humidity also showed a reduction in projections, indicating a higher degree of fire danger for the region. Full article
(This article belongs to the Special Issue Forest Fires Prediction and Detection—Volume II)
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