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Special Issue "Remote Sensing Applications in Wildfire Research and Management"

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

Deadline for manuscript submissions: 31 March 2023 | Viewed by 1109

Special Issue Editors

Dr. Dong Chen
E-Mail Website
Guest Editor
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Interests: remote sensing; wildfires; high latitude ecosystems
Dr. Maria Zubkova
E-Mail Website
Guest Editor
Department of Geographical Sciences, College Park, University of Maryland, College Park, MD 20742, USA
Interests: wildfire; remote sensing; fire regimes; climate change; savanna fires
Dr. Joanne Hall
E-Mail Website
Guest Editor
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Interests: agricultural burning; active fires; burned area; validation; geostationary
Dr. Michael Humber
E-Mail Website
Guest Editor
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Interests: wildfire detection; burned area

Special Issue Information

Dear Colleagues,

Wildfires are a major disturbance agent in many ecosystems and are capable of exerting substantial climatic, ecological, and societal impacts. Alongside ongoing climate change, they are expected to be more influential in the near future in many parts of the world. Due to their strong impacts, wildfires are being monitored and managed by land management agencies worldwide and have been the focus of numerous studies of various spatial scales carried out by the global scientific community. Because of its wide and consistent spatial coverage, remote sensing has been a key tool in the study of wildfires’ various impacts. Additionally, thanks to the low latency of many remotely sensed products, satellite imagery plays a crucial role in fire management and relief efforts.

Today, many regional and global fire-related, remotely sensed data products are being systematically produced. The depth of our understanding of the monitoring and impacts of fire is increasing rapidly as the continual launching of moderate-resolution sensors (e.g., Sentinel-2A/B’s MSI and Landsat-8/9’s OLI/TIRS) as well as fleets of high- and very-high-resolution sensors spearheaded by the private sector (Planet, Maxar, and others) enable us to observe every terrestrial location on the Earth’s surface with decreasing revisit times. Additionally, with ever-increasing computing power and constant updates being made to machine learning algorithms, there has been an abundance of novel applications of remote sensing in fire research and management efforts. This Special Issue aims to collect some of these recent accomplishments with the hope to inspire further development of fire-related remote sensing methodologies. These works can include developments related to remote-sensing-derived fire products as well as developments in the estimation and understanding of how fire interacts with other variables at the landscape scale, such as fuel build-up, fuel post-fire succession, fire regimes, and vegetation type.

Dr. Dong Chen
Dr. Maria Zubkova
Dr. Joanne Hall
Dr. Michael Humber
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 2500 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

  • wildfire
  • remote sensing
  • GIS
  • ecology
  • fire management
  • forest
  • grassland
  • climate change
  • natural hazard
  • disturbance

Published Papers (1 paper)

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Research

Article
A Mixed Methods Approach for Fuel Characterisation in Gorse (Ulex europaeus L.) Scrub from High-Density UAV Laser Scanning Point Clouds and Semantic Segmentation of UAV Imagery
Remote Sens. 2022, 14(19), 4775; https://doi.org/10.3390/rs14194775 - 24 Sep 2022
Viewed by 475
Abstract
The classification and quantification of fuel is traditionally a labour-intensive, costly and often subjective operation, especially in hazardous vegetation types, such as gorse (Ulex europaeus L.) scrub. In this study, unmanned aerial vehicle (UAV) technologies were assessed as an alternative to traditional [...] Read more.
The classification and quantification of fuel is traditionally a labour-intensive, costly and often subjective operation, especially in hazardous vegetation types, such as gorse (Ulex europaeus L.) scrub. In this study, unmanned aerial vehicle (UAV) technologies were assessed as an alternative to traditional field methodologies for fuel characterisation. UAV laser scanning (ULS) point clouds were captured, and a variety of spatial and intensity metrics were extracted from these data. These data were used as predictor variables in models describing destructively and non-destructively sampled field measurements of total above ground biomass (TAGB) and above ground available fuel (AGAF). Multiple regression of the structural predictor variables yielded correlations of R2 = 0.89 and 0.87 for destructively sampled measurements of TAGB and AGAF, respectively, with relative root mean square error (RMSE) values of 18.6% and 11.3%, respectively. The best metrics for non-destructive field-measurements yielded correlations of R2 = 0.50 and 0.49, with RMSE values of 40% and 30.8%, for predicting TAGB and AGAF, respectively, indicating that ULS-derived structural metrics offer higher levels of precision. UAV-derived versions of the field metrics (overstory height and cover) predicted TAGB and AGAF with R2 = 0.44 and 0.41, respectively, and RMSE values of 34.5% and 21.7%, demonstrating that even simple metrics from a UAV can still generate moderate correlations. In further analyses, UAV photogrammetric data were captured and automatically processed using deep learning in order to classify vegetation into different fuel categories. The results yielded overall high levels of precision, recall and F1 score (0.83 for each), with minimum and maximum levels per class of F1 = 0.70 and 0.91. In conclusion, these ULS-derived metrics can be used to precisely estimate fuel type components and fuel load at fine spatial resolutions over moderate-sized areas, which will be useful for research, wildfire risk assessment and fuel management operations. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Wildfire Research and Management)
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