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Remote Sensing
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Remote Sensing and Image Processing for Fire Science and Management
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Remote Sensing and Image Processing for Fire Science and Management

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

Deadline for manuscript submissions: closed (1 July 2020) | Viewed by 19610

Special Issue Editors

Prof. Dr. Alistair M. S. Smith

E-Mail Website
Guest Editor
Department of Forest, Rangeland and Fire Sciences, University of Idaho, Moscow, ID 83843, USA
Interests: wildfire; remote sensing; vegetation mortality and productivity
Special Issues, Collections and Topics in MDPI journals
Dr. Luigi Boschetti

E-Mail Website
Guest Editor
Department of Natural Resources and Society, University of Idaho, Moscow, ID 83844, USA
Interests: global biomass; burning remote sensing of fire; forest monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Grant Williamson

E-Mail Website
Guest Editor
School of Biological Sciences, University of Tasmania, Private Bag 55, Hobart, TAS 7001, Australia
Interests: air quality and smoke management; GIS; remote sensing; fire ecology; landscape ecology; fire modelling; smoke transport modelling; forests; climate change; emission factors
Special Issues, Collections and Topics in MDPI journals
Dr. Gareth Roberts

E-Mail Website
Guest Editor
Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
Interests: fuel consumption; fire radiative power; fire detection; air quality; biomass burning emissions; burned area

Special Issue Information

Dear Colleagues,

The large and often remote nature of wildfires has led to the widespread development and use of remote sensing products and image processing methods for assessing their impact. For decades, image processing, data integration, classification trees, mixture modelling, and other geospatial analysis approaches have been applied to assess the magnitude of fuels, fire behavior, area burned, and impacts of wildfires on the environment, broadly defined.

For example, thermal sensor imagery has been widely used to detect hot spots, quantify emissions, and evaluate fire impacts on the environment. Reflective sensor imagery has been widely applied to evaluate the area burned and severity of fires. Active sensors such as light detection and ranging have been applied to evaluate pre-fire fuel loading and resultant changes in aboveground biomass. Atmospheric sounders have been applied to evaluate emissions and magnitudes of smoke plumes. Furthermore, geographic information systems and modelling systems using remote sensing datasets have been widely applied for wildfire management to evaluate wildfire impacts and post-fire treatments.

Given the international attention raised by the recent wildfire seasons in South America, North America, Australia, Europe, and elsewhere, we seek articles that utilize remotely sensed data for fire science applications, including but not limited to:

  • Developing, assessing, or validating new algorithms related to active fire detection and characterization or burned area estimation
  • Applications of satellite, airborne, or field sensor data to assess fire behavior and fire impacts, including emissions
  • Laboratory and field studies on the reflective or thermal properties of fire and post-fire residues
  • Fuel consumption and fuel load estimation using remotely sensed observations (e.g., ground, UAV, airborne and spaceborne platforms)
  • Fire emissions estimation and air quality monitoring
  • Applications of remote sensing imagery in decision support systems related to fire management
  • Application of image processing techniques and machine learning to support fire management
  • Remote sensing of fires in any ecosystem

Dr. Alistair M. S. Smith
Dr. Luigi Boschetti
Dr. Grant Williamson
Dr. Gareth Roberts
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

  • fire
  • fire monitoring
  • fire mapping
  • burned area
  • emissions
  • fire management

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Published Papers (4 papers)

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24 pages, 11678 KiB  
Article
Image Similarity Metrics Suitable for Infrared Video Stabilization during Active Wildfire Monitoring: A Comparative Analysis
by Mario M. Valero, Steven Verstockt, Christian Mata, Dan Jimenez, Lloyd Queen, Oriol Rios, Elsa Pastor and Eulàlia Planas
Remote Sens. 2020, 12(3), 540; https://doi.org/10.3390/rs12030540 - 6 Feb 2020
Cited by 6 | Viewed by 3733
Abstract
Aerial Thermal Infrared (TIR) imagery has demonstrated tremendous potential to monitor active forest fires and acquire detailed information about fire behavior. However, aerial video is usually unstable and requires inter-frame registration before further processing. Measurement of image misalignment is an essential operation for [...] Read more.
Aerial Thermal Infrared (TIR) imagery has demonstrated tremendous potential to monitor active forest fires and acquire detailed information about fire behavior. However, aerial video is usually unstable and requires inter-frame registration before further processing. Measurement of image misalignment is an essential operation for video stabilization. Misalignment can usually be estimated through image similarity, although image similarity metrics are also sensitive to other factors such as changes in the scene and lighting conditions. Therefore, this article presents a thorough analysis of image similarity measurement techniques useful for inter-frame registration in wildfire thermal video. Image similarity metrics most commonly and successfully employed in other fields were surveyed, adapted, benchmarked and compared. We investigated their response to different camera movement components as well as recording frequency and natural variations in fire, background and ambient conditions. The study was conducted in real video from six fire experimental scenarios, ranging from laboratory tests to large-scale controlled burns. Both Global and Local Sensitivity Analyses (GSA and LSA, respectively) were performed using state-of-the-art techniques. Based on the obtained results, two different similarity metrics are proposed to satisfy two different needs. A normalized version of Mutual Information is recommended as cost function during registration, whereas 2D correlation performed the best as quality control metric after registration. These results provide a sound basis for image alignment measurement and open the door to further developments in image registration, motion estimation and video stabilization for aerial monitoring of active wildland fires. Full article
(This article belongs to the Special Issue Remote Sensing and Image Processing for Fire Science and Management)
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13 pages, 2476 KiB  
Article
Wildfires on the Mongolian Plateau: Identifying Drivers and Spatial Distributions to Predict Wildfire Probability
by Wu Rihan, Jianjun Zhao, Hongyan Zhang, Xiaoyi Guo, Hong Ying, Guorong Deng and Hui Li
Remote Sens. 2019, 11(20), 2361; https://doi.org/10.3390/rs11202361 - 11 Oct 2019
Cited by 20 | Viewed by 7952
Abstract
With climate change, significant fluctuations in wildfires have been observed on the Mongolian Plateau. The ability to predict the distribution of wildfires in the context of climate change plays a critical role in wildfire management and ecosystem maintenance. In this paper, Ripley’s K [...] Read more.
With climate change, significant fluctuations in wildfires have been observed on the Mongolian Plateau. The ability to predict the distribution of wildfires in the context of climate change plays a critical role in wildfire management and ecosystem maintenance. In this paper, Ripley’s K function and a Random Forest (RF) model were applied to analyse the spatial patterns and main influencing factors affecting the occurrence of wildfire on the Mongolian Plateau. The results showed that the wildfires were mainly clustered in space due to the combination of influencing factors. The distance scale is less than 1/2 of the length of the Mongolian Plateau; that is, it does not experience boundary effects in the study area and it meets the requirements of Ripley’s K function. Among the driving factors, the fraction of vegetation coverage (FVC), land use degree (La), elevation, precipitation (pre), wet day frequency (wet), and maximum temperature (tmx) had the greatest influences, while the aspect had the lowest influence. The likelihood of fire was mainly concentrated in the northern, eastern, and southern parts of the Mongolian Plateau and in the border area between the Inner Mongolia Autonomous Region (Inner Mongolia) and Mongolian People’s Republic (Mongolia), and wildfires did not occur or occurred less frequently in the hinterland area. The fitting results of the RF model showed a prediction accuracy exceeding 90%, which indicates that the model has a high ability to predict wildfire occurrences on the Mongolian Plateau. This study can provide a reference for predictions and decision-making related to wildfires on the Mongolian Plateau. Full article
(This article belongs to the Special Issue Remote Sensing and Image Processing for Fire Science and Management)
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18 pages, 9745 KiB  
Article
Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation
by Long Wang, Xingwen Quan, Binbin He, Marta Yebra, Minfeng Xing and Xiangzhuo Liu
Remote Sens. 2019, 11(13), 1568; https://doi.org/10.3390/rs11131568 - 2 Jul 2019
Cited by 46 | Viewed by 4796 | Correction
Abstract
Fuel moisture content (FMC) is a crucial variable affecting fuel ignition and rate of fire spread. Much work so far has focused on the usage of remote sensing data from multiple sensors to derive FMC; however, little attention has been devoted to the [...] Read more.
Fuel moisture content (FMC) is a crucial variable affecting fuel ignition and rate of fire spread. Much work so far has focused on the usage of remote sensing data from multiple sensors to derive FMC; however, little attention has been devoted to the usage of the C-band Sentinel-1A data. In this study, we aimed to test the performance of C-band Sentinel-1A data for multi-temporal retrieval of forest FMC by coupling the bare soil backscatter linear model with the vegetation backscatter water cloud model (WCM). This coupled model that linked the observed backscatter directly to FMC, was firstly calibrated using field FMC measurements and corresponding synthetic aperture radar (SAR) backscatters (VV and VH), and then a look-up table (LUT) comprising of the modelled VH backscatter and FMC was built by running the calibrated model forwardly. The absolute difference (MAEr) of modelled and observed VH backscatters was selected as the cost function to search the optimal FMC from the LUT. The performance of the presented methodology was verified using the three-fold cross-validation method by dividing the whole samples into equal three parts. Two parts were used for the model calibration and the other one for the validation, and this was repeated three times. The results showed that the estimated and measured forest FMC were consistent across the three validation samples, with the root mean square error (RMSE) of 19.53% (Sample 1), 12.64% (Sample 2) and 15.45% (Sample 3). To further test the performance of the C-band Sentinel-1A data for forest FMC estimation, our results were compared to those obtained using the optical Landsat 8 Operational Land Imager (OLI) data and the empirical partial least squares regression (PLSR) method. The latter resulted in higher RMSE between estimated and measured forest FMC with 20.11% (Sample 1), 26.21% (Sample 2) and 26.73% (Sample 3) than the presented Sentinel-1A data-based method. Hence, this study demonstrated that the good capability of C-band Sentinel-1A data for forest FMC retrieval, opening the possibility of developing a new operational SAR data-based methodology for forest FMC estimation. Full article
(This article belongs to the Special Issue Remote Sensing and Image Processing for Fire Science and Management)
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2 pages, 678 KiB  
Correction
Correction: Wang, L., et al. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11(13), 1568
by Long Wang, Xingwen Quan, Binbin He, Marta Yebra, Minfeng Xing and Xiangzhuo Liu
Remote Sens. 2020, 12(2), 206; https://doi.org/10.3390/rs12020206 - 7 Jan 2020
Cited by 8 | Viewed by 2295
Abstract
The authors wish to make the following corrections to this paper [1]: 1 [...] Full article
(This article belongs to the Special Issue Remote Sensing and Image Processing for Fire Science and Management)
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