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Remote Sensing
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Remote Sensing of Wildfires under Climate Change
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Remote Sensing of Wildfires under Climate Change

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

Deadline for manuscript submissions: closed (10 March 2024) | Viewed by 4118

Special Issue Editors

Dr. Thomas Katagis

E-Mail Website
Guest Editor
Department of Forestry and Management of Environment and Natural Resources, Democritus University of Thrace, 68200 Orestiada, Greece
Interests: remote sensing; GIS; forest management; forest fires; time series analysis; land cover change
Special Issues, Collections and Topics in MDPI journals
Dr. Dimitris Stavrakoudis

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: fuzzy systems; machine learning; land use/land cover mapping; wildfires; remote sensing; GIS; image processing; burned area mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Ioannis Gitas

E-Mail Website
Guest Editor
Lab of Forest Management and Remote Sensing, Aristotle University of Thessaloniki, 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

Special Issue Information

Dear Colleagues,

Wildfire activity around the world in recent years exposes the impacts of a rapidly changing climate, with highly destructive and lethal wildfire events becoming more frequent or observed in regions considered less fire-prone until now. Prolonged drought periods and heatwaves due to climatic variations seemingly drive recent fire activity worldwide, without considering projections on global warming and weather conditions that could further aggravate future fire activity. At the same time, we cannot neglect human interventions or other bioclimatic factors (vegetation cover, fuel types, lightning, etc.) that affect fire ignition and occurrence, locally and regionally.

Therefore, detailed spatio-temporal information on the environmental impact of wildfires under climate change is of crucial importance for effective pre-fire planning, characterizing fire regimes, accurate reporting, and supporting fire modeling. Data provided by remotely sensed systems, such as Earth Observation and geostationary satellites or airborne and unmanned aerial vehicles have been successfully utilized during the past decades in all stages of the fire disturbance continuum, which includes pre-fire, active, and post-fire environments. In this Special Issue, we invite scientific contributions to the exploitation of new and/or advanced remote sensing and geospatial methods in fire-related research at local to global scale. The specific topics include:

  • The exploitation of Earth Observation continuity/synergy missions for fire monitoring: from MODIS to VIIRS and Sentinel-3 and from Landsat to Sentinel-2
  • New algorithms for burned area mapping, focusing on data fusion approaches
  • Monitoring and modeling vegetation recovery after fire disturbance
  • Characterizing fire behavior and fire regimes under different climatic projections
  • Investigation of interactions between climate, fire occurrence, and human interventions
  • Exploiting Big Data platforms and cloud computing for monitoring fire activity at a regional to global scale
  • Validation and evaluation of global fire products generated by international initiatives
  • Remote sensing products in support of wildfire mitigation policies’ designing and/or adaptation
  • New approaches in modeling risk and designing prevention measures in sensitive wildland-urban interface (WUI) areas

Dr. Thomas Katagis
Dr. Dimitris Stavrakoudis
Prof. Dr. Ioannis Z. Gitas
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

  • wildfires
  • climate change
  • remote sensing
  • burned areas
  • fire products
  • fire behavior
  • new wildfire strategies adoption
  • wildland-urban interface (WUI) fire risk

Published Papers (2 papers)

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Research

19 pages, 11195 KiB  
Article
A Novel Approach for Predicting Large Wildfires Using Machine Learning towards Environmental Justice via Environmental Remote Sensing and Atmospheric Reanalysis Data across the United States
by Nikita Agrawal, Peder V. Nelson and Russanne D. Low
Remote Sens. 2023, 15(23), 5501; https://doi.org/10.3390/rs15235501 - 25 Nov 2023
Viewed by 1503
Abstract
Large wildfires (>125 hectares) in the United States account for over 95% of the burned area each year. Predicting large wildfires is imperative; however, current wildfire predictive models are region-based and computationally intensive. Using a scalable model based on easily available environmental and [...] Read more.
Large wildfires (>125 hectares) in the United States account for over 95% of the burned area each year. Predicting large wildfires is imperative; however, current wildfire predictive models are region-based and computationally intensive. Using a scalable model based on easily available environmental and atmospheric data, this research aims to accurately predict whether large wildfires will develop across the United States. The data used in this study include 2109 wildfires over 20 years, representing 14 million hectares burned. Remote sensing environmental data (Normalized Difference Vegetation Index—NDVI; Enhanced Vegetation Index—EVI; Leaf Area Index—LAI; Fraction of Photosynthetically Active Radiation—FPAR; Land Surface Temperature during the Day—LST Day; and Land Surface Temperature during the Night—LST Night) consisting of 1.3 billion satellite observations was used. Atmospheric reanalysis data (u component of wind, v component of wind, relative humidity, temperature, and geopotential) at four pressure levels (300, 500, 700, and 850 Ha) were also factored in. Six machine learning classification models (Logistic Regression, Decision Tree, Random Forest, eXtreme Gradient Boosting, K-Nearest Neighbors, and Support Vector Machine) were created and tested on the resulting dataset to determine their accuracy in predicting large wildfires. Model validation tests and variable importance analysis were performed. The eXtreme Gradient Boosting (XGBoost) classification model performed best in predicting large wildfires, with 90.44% accuracy, a true positive rate of 0.92, and a true negative rate of 0.88. Furthermore, towards environmental justice, an analysis was performed to identify disadvantaged communities that are also vulnerable to wildfires. This model can be used by wildfire safety organizations to predict large wildfires with high accuracy and prioritize resource allocation to employ protective safeguards for impacted socioeconomically disadvantaged communities. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfires under Climate Change)
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25 pages, 9894 KiB  
Article
Evaluation of Multi-Spectral Band Efficacy for Mapping Wildland Fire Burn Severity from PlanetScope Imagery
by Dale Hamilton, William Gibson, Daniel Harris and Camden McGath
Remote Sens. 2023, 15(21), 5196; https://doi.org/10.3390/rs15215196 - 31 Oct 2023
Cited by 1 | Viewed by 1870
Abstract
Increased spatial resolution has been shown to be an important factor in enabling machine learning to map burn extent and severity with extremely high accuracy. Unfortunately, the acquisition of drone imagery is a labor-intensive endeavor, making the capture of drone imagery impractical for [...] Read more.
Increased spatial resolution has been shown to be an important factor in enabling machine learning to map burn extent and severity with extremely high accuracy. Unfortunately, the acquisition of drone imagery is a labor-intensive endeavor, making the capture of drone imagery impractical for large catastrophic fires, which account for the majority of the area burned each year in the western US. To overcome this difficulty, satellites, such as PlanetScope, are now available which can produce imagery with remarkably high spatial resolution (approximately three meters). In addition to having higher spatial resolution, PlanetScope imagery contains up to eight bands in the visible and near-infrared spectra. This study examines the efficacy of each of the eight bands observed in PlanetScope imagery using a variety of feature selection methods, then uses these bands to map the burn extent and biomass consumption of three wildland fires. Several classifications are produced and compared based on the available bands, resulting in highly accurate maps with slight improvements as additional bands are utilized. The near-infrared band proved contribute most to increased mapping accuracy, while the green 1 and yellow bands contributed the least. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfires under Climate Change)
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