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Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis...
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Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis II

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

Deadline for manuscript submissions: 26 May 2024 | Viewed by 4655

Special Issue Editor

Dr. Víctor Fernández-García

E-Mail Website
Guest Editor
Institute of Geography and Environment, University of Lausanne, Lausanne, Switzerland
Interests: remote sensing; soil science; vegetation science; fire ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest fires are one of the most important disturbances around the world, producing negative impacts primarily in the provision and regulation of ecosystem services. Furthermore, during the last decade, the magnitude and extension of these fires have grown, making account management more difficult. In this context, remote sensing is a valuable tool to deal with the environmental challenges of fires and to drive solutions. Because of its versatility, the wealth of information it provides, and its rapid advancements in technology, techniques, and platforms, remote sensing is an essential tool for forest management, monitoring, damage analysis, and result reporting with the aim to facilitate post-fire management.

The previous Special Issue ‘Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis’ was a great success. This Special Issue invites studies covering new remote sensing technologies, sensors, data collections, and processing methodologies that can be successfully applied in post-fire damage mapping, ecosystem service recovery, and post-fire decision-making after large forest fires. We welcome submissions that cover but are not limited to:

  • predictive mapping of post-fire biodiversity patterns in forests using species distribution models and remote sensing data;
  • three-dimensional mapping by photogrammetry, LiDAR, and SAR in post-fire studies;
  • using unmanned aerial vehicles (UAV) in post-fire studies;
  • remote sensing methods to quantify the biophysical parameters of vegetation;
  • spectral unmixing models applied to the study of the post-fire recovery of vegetation;
  • hyperspectral imagery applied to the study of soil burn severity and the post-fire recovery of soils;
  • analysis of fire impacts in the wildland–urban interface (WUI);
  • estimation of carbon losses in soil and vegetation caused by fires;
  • methods to estimate forest canopy status and vegetation recovery after fire;
  • analysis of post-fire erosion, changes in water sediment loads, and water quality using remote sensing methods.

Dr. Víctor Fernández-García
Guest Editor

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

  • fire damage
  • post-fire
  • soil
  • vegetation
  • landsat
  • sentinel
  • MODIS
  • multispectral
  • LiDAR
  • radar

Published Papers (3 papers)

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13 pages, 39030 KiB  
Article
The Forest Fire Dynamic Change Influencing Factors and the Impacts on Gross Primary Productivity in China
by Lili Feng and Wenneng Zhou
Remote Sens. 2023, 15(5), 1364; https://doi.org/10.3390/rs15051364 - 28 Feb 2023
Cited by 3 | Viewed by 1358
Abstract
Forest fire as a common disturbance has an important role in the terrestrial ecosystem carbon cycling. However, the causes and impacts of longtime burned areas on carbon cycling need further exploration. In this study, we exploit Thematic Mapper (TM) and Moderate Resolution Imaging [...] Read more.
Forest fire as a common disturbance has an important role in the terrestrial ecosystem carbon cycling. However, the causes and impacts of longtime burned areas on carbon cycling need further exploration. In this study, we exploit Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to develop a quick and efficient method for large-scale forest fire dynamic monitoring in China. Band 2, band 4, band 6, and band 7 of MOD09A1 were selected as the most sensitive bands for calculating the Normalized Difference Fire Index (NDFI) to effectively estimate fire burned area. The Convergent Cross Mapping (CCM) algorithm was used to analyze the causes of the forest fire. A trend analysis was used to explore the impacts of forest fire on Gross Primary Productivity (GPP). The results show that the burned area has an increased tendency from 2009 to 2018. Forest fire is greatly influenced by natural factors compared with human factors in China. But only 30% of the forest fire causes GPP loss. The loss is mainly concentrated in the northeast forest region. The results of this study have important theoretical significance for vegetation restoration of the burned area. Full article
(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis II)
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36 pages, 15431 KiB  
Article
Up-Scaling Fuel Hazard Metrics Derived from Terrestrial Laser Scanning Using a Machine Learning Model
by Ritu Taneja, Luke Wallace, Samuel Hillman, Karin Reinke, James Hilton, Simon Jones and Bryan Hally
Remote Sens. 2023, 15(5), 1273; https://doi.org/10.3390/rs15051273 - 25 Feb 2023
Viewed by 1736
Abstract
The characterisation of fuel distribution across heterogeneous landscapes is important for wildfire mitigation, validating fuel models, and evaluating fuel treatment outcomes. However, efficient fuel mapping at a landscape scale is challenging. Fuel hazard metrics were obtained using Terrestrial Laser Scanning (TLS) and the [...] Read more.
The characterisation of fuel distribution across heterogeneous landscapes is important for wildfire mitigation, validating fuel models, and evaluating fuel treatment outcomes. However, efficient fuel mapping at a landscape scale is challenging. Fuel hazard metrics were obtained using Terrestrial Laser Scanning (TLS) and the current operational approach (visual fuel assessment) for seven sites across south-eastern Australia. These point-based metrics were then up-scaled to a continuous fuel map, an area relevant to fire management using random forest modelling, with predictor variables derived from Airborne Laser Scanning (ALS), Sentinel 2A images, and climate and soil data. The model trained and validated with TLS observations (R2 = 0.51 for near-surface fuel cover and 0.31 for elevated fuel cover) was found to have higher predictive power than the model trained with visual fuel assessments (R2 = −0.1 for the cover of both fuel layers). Models for height derived from TLS observations exhibited low-to-moderate performance for the near-surface (R2 = 0.23) and canopy layers (R2 = 0.25). The results from this study provide practical guidance for the selection of training data sources and can be utilised by fire managers to accurately generate fuel maps across an area relevant to operational fire management decisions. Full article
(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis II)
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19 pages, 25833 KiB  
Technical Note
Wildfire Burnt Area Severity Classification from UAV-Based RGB and Multispectral Imagery
by Tomás Simes, Luís Pádua and Alexandra Moutinho
Remote Sens. 2024, 16(1), 30; https://doi.org/10.3390/rs16010030 - 20 Dec 2023
Viewed by 945
Abstract
Wildfires present a significant threat to ecosystems and human life, requiring effective prevention and response strategies. Equally important is the study of post-fire damages, specifically burnt areas, which can provide valuable insights. This research focuses on the detection and classification of burnt areas [...] Read more.
Wildfires present a significant threat to ecosystems and human life, requiring effective prevention and response strategies. Equally important is the study of post-fire damages, specifically burnt areas, which can provide valuable insights. This research focuses on the detection and classification of burnt areas and their severity using RGB and multispectral aerial imagery captured by an unmanned aerial vehicle. Datasets containing features computed from multispectral and/or RGB imagery were generated and used to train and optimize support vector machine (SVM) and random forest (RF) models. Hyperparameter tuning was performed to identify the best parameters for a pixel-based classification. The findings demonstrate the superiority of multispectral data for burnt area and burn severity classification with both RF and SVM models. While the RF model achieved a 95.5% overall accuracy for the burnt area classification using RGB data, the RGB models encountered challenges in distinguishing between mildly and severely burnt classes in the burn severity classification. However, the RF model incorporating mixed data (RGB and multispectral) achieved the highest accuracy of 96.59%. The outcomes of this study contribute to the understanding and practical implementation of machine learning techniques for assessing and managing burnt areas. Full article
(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis II)
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