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Forests
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Using Remote Sensing to Monitor Forest Fire Behavior and Forest...
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Using Remote Sensing to Monitor Forest Fire Behavior and Forest Landscape

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 13291

Special Issue Editors

Dr. Olga Viedma

E-Mail Website
Guest Editor
Department of Environmental Sciences, University of Castilla-La Mancha, Avda. Carlos III, 45071 Toledo, Spain
Interests: forest fires; land use–land cover changes; wildfire modelling; fire severity; regeneration; remote sensing; LiDAR; GIS
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chunying Ren

E-Mail Website
Guest Editor
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Interests: post-fire disturbance; vegetation change; forest aboveground biomass estimation; forest carbon sequestration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fire behavior is a product of fuel properties, weather conditions and topography during the fire advance. The new generation of fires we are facing requires a deeper knowledge about where, and under which conditions, fires occur, focusing on fuel properties and climate hazards (e.g., drought, heat waves). Remote sensing systems (i.e., LiDAR and satellite images) have proven to be highly accurate in estimating critical fuel continuities and moisture, helping to identify areas with high potential for extreme wildfire events. Additionally, fuel data derived from remote sensing systems, when included in fire behavior simulators, can predict fire spread and intensity adequately. On the other hand, UAV-borne NIR and thermal IR cameras have collected consistent time series of fire rate of spread (RoS) and direction in complex fires, which has improved knowledge about fire–environment interactions. In addition, historical fire scars and fire severity maps derived from remote sensing systems are needed to estimate the accuracy of fire behavior simulators. Automated methods based on change detection algorithms, artificial intelligence and deep learning approaches have been widely used for fire mapping. The integration of optical remotely sensed imagery and LIDAR data provides improved opportunities to understand fire behavior.

This Special Issue of Forests is focused on experimental studies, monitoring and modeling approaches on fire behavior, including characterizing pre-fire vegetation conditions, assessing fire severities and evaluate the post-fire recovery. Research articles may focus on any aspect of remote sensing techniques and/or products applied to modeling fire behavior and methods for fuel characterization and monitoring.

Dr. Olga Viedma
Prof. Dr. Chunying Ren
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

  • fire behavior modeling
  • fuel characterization
  • fire mapping
  • fire severity
  • postfire regeneration
  • time series
  • unmannaged aerial vehicles
  • remote sensing systems

Published Papers (5 papers)

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Research

13 pages, 3867 KiB  
Article
Forest Fire Detection of FY-3D Using Genetic Algorithm and Brightness Temperature Change
by Zhangyu Dong, Jinqiu Yu, Sen An, Jin Zhang, Jinhui Li and Daoli Xu
Forests 2022, 13(6), 963; https://0-doi-org.brum.beds.ac.uk/10.3390/f13060963 - 20 Jun 2022
Cited by 5 | Viewed by 2037
Abstract
As one of China’s new generation polar-orbiting meteorological satellites, FengYun-3D (FY-3D) provides critical data for forest fire detection. Most of the existing related methods identify fire points by comparing the spatial features and setting thresholds empirically. However, they ignore temporal features that are [...] Read more.
As one of China’s new generation polar-orbiting meteorological satellites, FengYun-3D (FY-3D) provides critical data for forest fire detection. Most of the existing related methods identify fire points by comparing the spatial features and setting thresholds empirically. However, they ignore temporal features that are associated with forest fires. Besides, they are difficult to generalize to multiple areas with different environmental characteristics. A novel method based on FY-3D combining the genetic algorithm and brightness temperature change detection is proposed in this work to improve these problems. After analyzing the spatial features of the FY-3D data, it adaptively detects potential fire points based on these features using the genetic algorithm, then filters the points with contextual information. To address the false alarms resulting from the confusing spectral characteristics between fire pixels and conventional hotspots, temporal information is introduced and the “MIR change rate” based on the multitemporal brightness temperature change is further proposed. In order to evaluate the performance of the proposed algorithm, several fire events occurring in different areas are used for testing. The Moderate-Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire products (MYD14) is chosen as the validation data to assess the accuracy of the proposed algorithm. A comparison of results demonstrates that the algorithm can identify fire points effectively and obtain a higher accuracy than the previous FY-3D algorithm. Full article
(This article belongs to the Special Issue Using Remote Sensing to Monitor Forest Fire Behavior and Forest Landscape)
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10 pages, 8713 KiB  
Communication
Estimation of Postfire Reforestation with SAR Polarimetry and NDVI Time Series
by Valery Bondur, Tumen Chimitdorzhiev, Irina Kirbizhekova and Aleksey Dmitriev
Forests 2022, 13(5), 814; https://0-doi-org.brum.beds.ac.uk/10.3390/f13050814 - 23 May 2022
Cited by 8 | Viewed by 1856
Abstract
This communication is devoted to the methodology of remote complex analysis of forest restoration after strong wildfires. It is proposed to quantify the projective leaf/needles area index by multispectral optical images. The increase in dimensions of trunks and branches commensurate with a radar [...] Read more.
This communication is devoted to the methodology of remote complex analysis of forest restoration after strong wildfires. It is proposed to quantify the projective leaf/needles area index by multispectral optical images. The increase in dimensions of trunks and branches commensurate with a radar wavelength of 24 cm is estimated using radar polarimetric data. It is shown that the growth’s potential of aboveground biomass in different spots of test site ranges from 35 to 70% in the case under consideration. Such a new approach will make it possible to further consider more accurately the role of boreal forests as one of the largest carbon stocks. Full article
(This article belongs to the Special Issue Using Remote Sensing to Monitor Forest Fire Behavior and Forest Landscape)
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13 pages, 11992 KiB  
Article
Biomass Assessment and Carbon Sequestration in Post-Fire Shrublands by Means of Sentinel-2 and Gaussian Processes
by David Vinué-Visús, Ricardo Ruiz-Peinado, David Fuente, Jose-Vicente Oliver-Villanueva, Eloína Coll-Aliaga and Victoria Lerma-Arce
Forests 2022, 13(5), 771; https://0-doi-org.brum.beds.ac.uk/10.3390/f13050771 - 17 May 2022
Cited by 2 | Viewed by 1615
Abstract
In this contribution, we assessed the biomass and carbon stock of a post-fire area covered by a young oak coppice of Quercus pyrenaica Willd. associated with shrubs, mainly of Cistus laurifolius L. This area was burned during the fire event of Chequilla (Guadalajara, [...] Read more.
In this contribution, we assessed the biomass and carbon stock of a post-fire area covered by a young oak coppice of Quercus pyrenaica Willd. associated with shrubs, mainly of Cistus laurifolius L. This area was burned during the fire event of Chequilla (Guadalajara, Spain) in 2012. Sentinel-2 imagery was used together with our own forest inventories in 2020 and machine learning methods to assess the total biomass of the area. The inventory includes plots of total dry weight ranging between 6 and 14 Mg·ha−1 with individuals up to 8 years old. Nonlinear, nonparametric Gaussian process regression methods were applied to link reflectance values from Sentinel-2 imagery with total shrub biomass. With a reduced inventory of only 32 plots covering 136 ha, the total biomass could be assessed with a root-mean-square error of 1.36 Mg·ha−1 and a bias of −0.04 Mg·ha−1, getting a relative error between 9.8% and 20.4% for the gathered biomass. This is a rather good estimation considering the little effort and time invested; thus, the suggested methodology is very suitable for forest monitoring and management. Full article
(This article belongs to the Special Issue Using Remote Sensing to Monitor Forest Fire Behavior and Forest Landscape)
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20 pages, 5546 KiB  
Article
Characterizing Global Fire Regimes from Satellite-Derived Products
by Mariano García, M. Lucrecia Pettinari, Emilio Chuvieco, Javier Salas, Florent Mouillot, Wentao Chen and Inmaculada Aguado
Forests 2022, 13(5), 699; https://0-doi-org.brum.beds.ac.uk/10.3390/f13050699 - 29 Apr 2022
Cited by 6 | Viewed by 2055
Abstract
We identified four global fire regimes based on a k-means algorithm using five variables covering the spatial, temporal and magnitude dimensions of fires, derived from 19-year long satellite burned area and active fire products. Additionally, we assessed the relation of fire regimes to [...] Read more.
We identified four global fire regimes based on a k-means algorithm using five variables covering the spatial, temporal and magnitude dimensions of fires, derived from 19-year long satellite burned area and active fire products. Additionally, we assessed the relation of fire regimes to forest fuels distribution. The most extensive fire regime (35% of cells having fire activity) was characterized by a long fire season, medium size fire events, small burned area, high intensity and medium variability. The next most extensive fire regime (25.6%) presented a long fire season, large fire events and the highest mean burned area, yet it showed the lowest intensity and the least variability. The third group (22.07%) presented a short fire season, the lowest burned area, with medium-low intensity, the smallest fire patches and large variability. The fourth group (17.3%) showed the largest burned area with large fire patches of moderate intensity and low variability. Fire regimes and fuel types showed a statistically significant relation (CC = 0.58 and CC’ = 0.67, p < 0.001), with most fuel types sustaining all fire regimes, although a clear prevalence was observed in some fuel types. Further efforts should be directed towards the standardization of the variables in order to facilitate comparison, analysis and monitoring of fire regimes and evaluate whether fire regimes are effectively changing and the possible drivers. Full article
(This article belongs to the Special Issue Using Remote Sensing to Monitor Forest Fire Behavior and Forest Landscape)
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17 pages, 4125 KiB  
Article
Burned Area Detection Using Multi-Sensor SAR, Optical, and Thermal Data in Mediterranean Pine Forest
by Saygin Abdikan, Caglar Bayik, Aliihsan Sekertekin, Filiz Bektas Balcik, Sadra Karimzadeh, Masashi Matsuoka and Fusun Balik Sanli
Forests 2022, 13(2), 347; https://0-doi-org.brum.beds.ac.uk/10.3390/f13020347 - 18 Feb 2022
Cited by 17 | Viewed by 4374
Abstract
Burned area (BA) mapping of a forest after a fire is required for its management and the determination of the impacts on ecosystems. Different remote sensing sensors and their combinations have been used due to their individual limitations for accurate BA mapping. This [...] Read more.
Burned area (BA) mapping of a forest after a fire is required for its management and the determination of the impacts on ecosystems. Different remote sensing sensors and their combinations have been used due to their individual limitations for accurate BA mapping. This study analyzes the contribution of different features derived from optical, thermal, and Synthetic Aperture Radar (SAR) images to extract BA information from the Turkish red pine (Pinus brutia Ten.) forest in a Mediterranean ecosystem. In addition to reflectance values of the optical images, Normalized Burn Ratio (NBR) and Land Surface Temperature (LST) data are produced from both Sentinel-2 and Landsat-8 data. The backscatter of C-band Sentinel-1 and L-band ALOS-2 SAR images and the coherence feature derived from the Interferometric SAR technique were also used. The pixel-based random forest image classification method is applied to classify the BA detection in 24 scenarios created using these features. The results show that the L-band data provided a better contribution than C-band data and the combination of features created from Landsat LST, NBR, and coherence of L-band ALOS-2 achieved the highest accuracy, with an overall accuracy of 96% and a Kappa coefficient of 92.62%. Full article
(This article belongs to the Special Issue Using Remote Sensing to Monitor Forest Fire Behavior and Forest Landscape)
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