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Special Issue "Fire Remote Sensing: Capabilities, Innovations, Opportunities and Challenges"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 10836

Special Issue Editor

Dr. Daniel Feldman
E-Mail Website
Guest Editor
Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 50A4037, Berkeley, CA 94720, USA
Interests: remote sensing; radiative forcing; climate model diagnostics

Special Issue Information

Dear Colleagues,

The recent, dramatic increase in the destructiveness of fires motivates the need for more scientific research on this multi-faceted topic.  Both focused, topical research and higher-level research, which may even be interdisciplinary, are needed to bend the curve of fire effects so that direct impacts on safety, wildlife, and ecology, and indirect impacts on air quality and even landscape stability are better understood.  These are complex topics, covering spatial scales ranging from the microscopic to the global, and time-scales of seconds to decades.  Remote sensing has played and will continue to play a significant role both in advancing fire science and in societal applications and response to fires.  Recent technological advancements in data coverage from ground, aerial (including manned aircrafts and drones), and satellite (both low-earth orbit, including rapid revisit constellations, and geosynchronous orbit) can speak directly to an array of fire research needs, but these data are highly underutilized at present. We therefore solicit manuscripts that advance the understanding of the role of remote sensing in advancing the scientific understanding of processes related to fire as well as its role in practical applications such as the evaluation of management practices, response, and recovery. Manuscripts can, but are not necessarily limited to responding to the following topics:

(1) Real-time monitoring of fire progression from public or private satellites or aircraft or drone operations.

(2) The strengths and weaknesses of existing or planned remote sensing assets to assess fire risk prior to ignition, monitor fire progression, or diagnose fire emissions/burned area.

(3) Conceptual and/or technical barriers to greater utilization of remote sensing for fire mitigation, and how they differ for real-time monitoring versus long-term risk abatement in the face of climate change.

(4) Remote sensing of weather conditions before, during, and after a fire, and implications for understanding of fire risk, fire behavior, and the effects of smoke.

(5) Remote sensing requirements to improve scientific understanding in fire risk and emissions in the short term and in the face of climate change.

(6) Remote sensing of fuels and their moisture content, including what instrument specifications are most valuable or needed.

(7) Remote sensing requirements needed to make substantial advances in fire behavior modeling. Manuscripts that show new capabilities from the new technologies, innovative new ways to use existing and/or new technologies, community-wide needs for fire remote sensing, and fundamental challenges that past, current, or future remote sensing systems face are particularly welcome.

Dr. Daniel Feldman
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 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

  • Fire behavior 
  • Fuel characterization 
  • Fire risk 
  • Fire and climate 
  • Forest management 
  • Smoke detection 
  • Machine learning

Published Papers (4 papers)

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Article
Near Real-Time Automated Early Mapping of the Perimeter of Large Forest Fires from the Aggregation of VIIRS and MODIS Active Fires in Mexico
Remote Sens. 2020, 12(12), 2061; https://doi.org/10.3390/rs12122061 - 26 Jun 2020
Cited by 10 | Viewed by 2103
Abstract
In contrast with current operational products of burned area, which are generally available one month after the fire, active fires are readily available, with potential application for early evaluation of approximate fire perimeters to support fire management decision making in near real time. [...] Read more.
In contrast with current operational products of burned area, which are generally available one month after the fire, active fires are readily available, with potential application for early evaluation of approximate fire perimeters to support fire management decision making in near real time. While previous coarse-scale studies have focused on relating the number of active fires to a burned area, some local-scale studies have proposed the spatial aggregation of active fires to directly obtain early estimate perimeters from active fires. Nevertheless, further analysis of this latter technique, including the definition of aggregation distance and large-scale testing, is still required. There is a need for studies that evaluate the potential of active fire aggregation for rapid initial fire perimeter delineation, particularly taking advantage of the improved spatial resolution of the Visible Infrared Imaging Radiometer (VIIRS) 375 m, over large areas and long periods of study. The current study tested the use of convex hull algorithms for deriving coarse-scale perimeters from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire detections, compared against the mapped perimeter of the MODIS collection 6 (MCD64A1) burned area. We analyzed the effect of aggregation distance (750, 1000, 1125 and 1500 m) on the relationships of active fire perimeters with MCD64A1, for both individual fire perimeter prediction and total burned area estimation, for the period 2012–2108 in Mexico. The aggregation of active fire detections from MODIS and VIIRS demonstrated a potential to offer coarse-scale early estimates of the perimeters of large fires, which can be available to support fire monitoring and management in near real time. Total burned area predicted from aggregated active fires followed the same temporal behavior as the standard MCD64A1 burned area, with potential to also account for the role of smaller fires detected by the thermal anomalies. The proposed methodology, based on easily available algorithms of point aggregation, is susceptible to be utilized both for near real-time and historical fire perimeter evaluation elsewhere. Future studies might test active fires aggregation between regions or biomes with contrasting fuel characteristics and human activity patterns against medium resolution (e.g., Landsat and Sentinel) fire perimeters. Furthermore, coarse-scale active fire perimeters might be utilized to locate areas where such higher-resolution imagery can be downloaded to improve the evaluation of fire extent and impact. Full article
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Article
The Effect of Snow Depth on Spring Wildfires on the Hulunbuir from 2001–2018 Based on MODIS
Remote Sens. 2019, 11(3), 321; https://doi.org/10.3390/rs11030321 - 06 Feb 2019
Cited by 8 | Viewed by 1619
Abstract
Wildfires are one of the important disturbance factors in natural ecosystems and occur frequently around the world. Detailed research on the impact of wildfires is crucial not only for the development of livestock husbandry but also for the sustainable use of natural resources. [...] Read more.
Wildfires are one of the important disturbance factors in natural ecosystems and occur frequently around the world. Detailed research on the impact of wildfires is crucial not only for the development of livestock husbandry but also for the sustainable use of natural resources. In this study, based on the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product MC464A1 and site snow depth measurements, the kernel density estimation method (KDE), unary linear regression analysis, Sen + Mann-Kendall trend analysis, correlation analysis, and R/S analysis were used to evaluate the relationship between snow and spring wildfires (SWFs) in Hulunbuir. Our results indicated that SWFs decreased during the period of 2001–2018, were mainly distributed in the eastern portion of the study area, and that the highest SWF density was 7 events/km2. In contrast, the maximum snow depth increased during the period of 2001–2018 and the snow depth was deeper in the middle but shallower in the east and west. The SWFs and snow depth have significant negative correlations over space and time. The snow depth mainly affects the occurrence of SWFs indirectly by affecting the land surface temperature (LST) and Land Surface Water Index (LSWI) in spring. The snow depth was positively correlated with the LSWI in most of Hulunbuir and strongly negatively correlated with the LST, and this correlation was stronger in the eastern and western regions of Hulunbuir. The results of the Hurst exponent indicated that in the future, the snow depth trend will be opposite that of the current state, meaning that the trend of decreasing snow depth will increase dramatically in most of the study area, and SWFs may become more prominent. According to the validation results, the Hurst exponent is a reliable method for predicting the snow depth tendency. This research can be based on the snow conditions of the previous year to identify areas where fires are most likely to occur, enabling an improved and more targeted preparation for spring fire prevention. Additionally, the present study expands the theory and methods of wildfire occurrence research and promotes research on disasters and disaster chains. Full article
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Letter
Potential Underestimation of Satellite Fire Radiative Power Retrievals over Gas Flares and Wildland Fires
Remote Sens. 2020, 12(2), 238; https://doi.org/10.3390/rs12020238 - 10 Jan 2020
Cited by 8 | Viewed by 1872
Abstract
Fire Radiative Power (FRP) is related to fire combustion rates and is used to quantify the atmospheric emissions of greenhouse gases and aerosols. FRP over gas flares and wildfires can be retrieved remotely using satellites that observe in shortwave infrared (SWIR) to middle [...] Read more.
Fire Radiative Power (FRP) is related to fire combustion rates and is used to quantify the atmospheric emissions of greenhouse gases and aerosols. FRP over gas flares and wildfires can be retrieved remotely using satellites that observe in shortwave infrared (SWIR) to middle infrared (MIR) wavelengths. Heritage techniques to retrieve FRP developed for wildland fires using the MIR 4 μm radiances have been adapted for the hotter burning gas flares using the SWIR 2 μm observations. Effects of atmosphere, including smoke and aerosols, are assumed to be minimal in these algorithms because of the use of longer than visual wavelengths. Here we use Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS) and Landsat 8 observations acquired before and during emergency oil and gas flaring in eastern Saudi Arabia to show that dark, sooty smoke affects both 4 μm and 2 μm observations. While the 2 μm observations used to retrieve gas FRP may be reliable during clear atmospheric conditions, performance is severely impacted by dark smoke. Global remote sensing-based inventories of wildfire and gas flaring need to consider the possibility that soot and dark smoke can potentially lead to an underestimation of FRP over fires. Full article
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Letter
Preliminary Results from a Wildfire Detection System Using Deep Learning on Remote Camera Images
Remote Sens. 2020, 12(1), 166; https://doi.org/10.3390/rs12010166 - 02 Jan 2020
Cited by 26 | Viewed by 4443
Abstract
Pioneering networks of cameras that can search for wildland fire signatures have been in development for some years (High Performance Wireless Research & Education Network—HPWREN cameras and the ALERT Wildfire camera). While these cameras have proven their worth in monitoring fires reported by [...] Read more.
Pioneering networks of cameras that can search for wildland fire signatures have been in development for some years (High Performance Wireless Research & Education Network—HPWREN cameras and the ALERT Wildfire camera). While these cameras have proven their worth in monitoring fires reported by other means, we have developed a functioning prototype system that can detect smoke from fires usually within 15 min of ignition, while averaging less than one false positive per day per camera. This smoke detection system relies on machine learning-based image recognition software and a cloud-based work-flow capable of scanning hundreds of cameras every minute. The system is operating around the clock in Southern California and has already detected some fires earlier than the current best methods—people calling emergency agencies or satellite detection from the Geostationary Operational Environmental Satellite (GOES) satellites. This system is already better than some commercial systems and there are still many unexplored methods to further improve accuracy. Ground-based cameras are not going to be able to detect every wildfire, and so we are building a system that combines the best of terrestrial camera-based detection with the best approaches to satellite-based detection. Full article
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