Special Issue "Remote Sensing of Forest Fire: Data, Science and Operational Applications"

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

Deadline for manuscript submissions: 15 December 2020.

Special Issue Editor

Special Issue Information

Dear Colleagues,

Remote sensing technologies have long been considered as a key tool for fire data, science, modelling, management, and monitoring. The most recent developments in computer technology, data processing, artificial intelligence (AI), deep learning approaches, and geospatial data mining techniques, enable advanced dynamic modelling, tools, data integration, and assimilation schemes, and are expected to significantly support and improve fire science and operational applications. Recently, the availability of new sensors from satellite, aerial, drone, and ground, along with the free access to large archives of data, has opened new perspectives for both fire science and applications. We invite you to submit articles on topics including, but not limited to, the following:

  • Earth observation (optical, SAR, UAV, and LiDAR) as a tool for data science and operational applications
  • Advanced geospatial data mining techniques
  • Integration of satellite, aerial/drone, and in situ observation in the Copernicus Era
  • Fire disturbance monitoring at multiple spatio-temporal scales
  • Deep learning approaches for fire science and applications
  • Advances in remote sensing of forest fire fuel mapping
  • Data integration for fire and post fire geo-hazards risk mitigation and management
  • Earth big data for monitoring and mapping fire and post-fire induced risk
  • Fusion and integration of data and information from multiple sources
  • Integration of RS with climate and meteorological data and forecasting
  • New tools and methods for fire monitoring and mitigation strategies

Dr. Rosa Lasaponara
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 papers will be 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 2200 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 
  • Earth observation 
  • Copernicus
  • Data integration

Published Papers (5 papers)

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Research

Open AccessArticle
Fire Detection and Fire Radiative Power in Forests and Low-Biomass Lands in Northeast Asia: MODIS versus VIIRS Fire Products
Remote Sens. 2020, 12(18), 2870; https://doi.org/10.3390/rs12182870 - 04 Sep 2020
Abstract
Fire omission and commission errors, and the accuracy of fire radiative power (FRP) from satellite moderate-resolution impede the studies on fire regimes and FRP-based fire emissions estimation. In this study, we compared the accuracy between the extensively used 1-km fire product of MYD14 [...] Read more.
Fire omission and commission errors, and the accuracy of fire radiative power (FRP) from satellite moderate-resolution impede the studies on fire regimes and FRP-based fire emissions estimation. In this study, we compared the accuracy between the extensively used 1-km fire product of MYD14 from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the 375-m fire product of VNP14IMG from the Visible Infrared Imaging Radiometer Suite (VIIRS) in Northeastern Asia using data from 2012–2017. We extracted almost simultaneous observation of fire detection and FRP from MODIS-VIIRS overlapping orbits from the two fire products, and identified and removed duplicate fire detections and corresponding FRP in each fire product. We then compared the performance of the two products between forests and low-biomass lands (croplands, grasslands, and herbaceous vegetation). Among fire pixels detected by VIIRS, 65% and 83% were missed by MODIS in forests and low-biomass lands, respectively; whereas associated omission rates by VIIRS for MODIS fire pixels were 35% and 53%, respectively. Commission errors of the two fire products, based on the annual mean measurements of burned area by Landsat, decreased with increasing FRP per fire pixel, and were higher in low-biomass lands than those in forests. Monthly total FRP from MODIS was considerably lower than that from VIIRS due to more fire omission by MODIS, particularly in low-biomass lands. However, for fires concurrently detected by both sensors, total FRP was lower with VIIRS than with MODIS. This study contributes to a better understanding of fire detection and FRP retrieval performance between MODIS and its successor VIIRS, providing valuable information for using those data in the study of fire regimes and FRP-based fire emission estimation. Full article
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Open AccessArticle
An Automatic Processing Chain for Near Real-Time Mapping of Burned Forest Areas Using Sentinel-2 Data
Remote Sens. 2020, 12(4), 674; https://doi.org/10.3390/rs12040674 - 18 Feb 2020
Cited by 5
Abstract
A fully automated processing chain for near real-time mapping of burned forest areas using Sentinel-2 multispectral data is presented. The acronym AUTOBAM (AUTOmatic Burned Areas Mapper) is used to denote it. AUTOBAM is conceived to work daily at a national scale for the [...] Read more.
A fully automated processing chain for near real-time mapping of burned forest areas using Sentinel-2 multispectral data is presented. The acronym AUTOBAM (AUTOmatic Burned Areas Mapper) is used to denote it. AUTOBAM is conceived to work daily at a national scale for the Italian territory to support the Italian Civil Protection Department in the management of one of the major natural hazards, which affects the territory. The processing chain includes a Sentinel-2 data procurement component, an image processing algorithm, and the delivery of the map to the end-user. The data procurement component searches every day for the most updated products into different archives. The image processing part represents the core of AUTOBAM and implements an algorithm for burned forest areas mapping that uses, as fundamental parameters, the relativized form of the delta normalized burn ratio and the normalized difference vegetation index. The minimum mapping unit is 1 ha. The algorithm implemented in the image processing block is validated off-line using maps of burned areas produced by the Copernicus Emergency Management Service. The results of the validation shows an overall accuracy (considering the classes of burned and unburned areas) larger than 95% and a kappa coefficient larger than 80%. For what concerns the class of burned areas, the commission error is around 1%−3%, except for one case where it reaches 25%, while the omission error ranges between 6% and 25%. Full article
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Open AccessArticle
Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa
Remote Sens. 2020, 12(2), 334; https://doi.org/10.3390/rs12020334 - 20 Jan 2020
Cited by 2
Abstract
This study provides a comparative analysis of two Sentinel-1 and one Sentinel-2 burned area (BA) detection and mapping algorithms over 10 test sites (100 × 100 km) in tropical and sub-tropical Africa. Depending on the site, the burned area was mapped at different [...] Read more.
This study provides a comparative analysis of two Sentinel-1 and one Sentinel-2 burned area (BA) detection and mapping algorithms over 10 test sites (100 × 100 km) in tropical and sub-tropical Africa. Depending on the site, the burned area was mapped at different time points during the 2015–2016 fire seasons. The algorithms relied on diverse burned area (BA) mapping strategies regarding the data used (i.e., surface reflectance, backscatter coefficient, interferometric coherence) and the detection method. Algorithm performance was compared by evaluating the detected BA agreement with reference fire perimeters independently derived from medium resolution optical imagery (i.e., Landsat 8, Sentinel-2). The commission (CE) and omission errors (OE), as well as the Dice coefficient (DC) for burned pixels, were compared. The mean OE and CE were 33% and 31% for the optical-based Sentinel-2 time-series algorithm and increased to 66% and 36%, respectively, for the radar backscatter coefficient-based algorithm. For the coherence based radar algorithm, OE and CE reached 72% and 57%, respectively. When considering all tiles, the optical-based algorithm provided a significant increase in agreement over the Synthetic Aperture Radar (SAR) based algorithms that might have been boosted by the use of optical datasets when generating the reference fire perimeters. The analysis suggested that optical-based algorithms provide for a significant increase in accuracy over the radar-based algorithms. However, in regions with persistent cloud cover, the radar sensors may provide a complementary data source for wall to wall BA detection. Full article
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Open AccessArticle
Exploring the Potential of C-Band SAR in Contributing to Burn Severity Mapping in Tropical Savanna
Remote Sens. 2020, 12(1), 49; https://doi.org/10.3390/rs12010049 - 20 Dec 2019
Cited by 2
Abstract
The ability to map burn severity and to understand how it varies as a function of time of year and return frequency is an important tool for landscape management and carbon accounting in tropical savannas. Different indices based on optical satellite imagery are [...] Read more.
The ability to map burn severity and to understand how it varies as a function of time of year and return frequency is an important tool for landscape management and carbon accounting in tropical savannas. Different indices based on optical satellite imagery are typically used for mapping fire scars and for estimating burn severity. However, cloud cover is a major limitation for analyses using optical data over tropical landscapes. To address this pitfall, we explored the suitability of C-band Synthetic Aperture Radar (SAR) data for detecting vegetation response to fire, using experimental fires in northern Australia. Pre- and post-fire results from Sentinel-1 C-band backscatter intensity data were compared to those of optical satellite imagery and were corroborated against structural changes on the ground that we documented through terrestrial laser scanning (TLS). Sentinel-1 C-band backscatter (VH) proved sensitive to the structural changes imparted by fire and was correlated with the Normalised Burn Ratio (NBR) derived from Sentinel-2 optical data. Our results suggest that C-band SAR holds potential to inform the mapping of burn severity in savannas, but further research is required over larger spatial scales and across a broader spectrum of fire regime conditions before automated products can be developed. Combining both Sentinel-1 SAR and Sentinel-2 multi-spectral data will likely yield the best results for mapping burn severity under a range of weather conditions. Full article
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Open AccessArticle
Temporal Decorrelation of C-Band Backscatter Coefficient in Mediterranean Burned Areas
Remote Sens. 2019, 11(22), 2661; https://doi.org/10.3390/rs11222661 - 14 Nov 2019
Cited by 2
Abstract
Burned area algorithms from radar images are often based on temporal differences between pre- and post-fire backscatter values. However, such differences may occur long past the fire event, an effect known as temporal decorrelation. Improvements in radar-based burned areas monitoring depend on a [...] Read more.
Burned area algorithms from radar images are often based on temporal differences between pre- and post-fire backscatter values. However, such differences may occur long past the fire event, an effect known as temporal decorrelation. Improvements in radar-based burned areas monitoring depend on a better understanding of the temporal decorrelation effects as well as its sources. This paper analyses the temporal decorrelation of the Sentinel-1 C-band backscatter coefficient over burned areas in Mediterranean ecosystems. Several environmental variables influenced the radar scattering such as fire severity, post-fire vegetation recovery, water content, soil moisture, and local slope and aspect were analyzed. The ensemble learning method random forests was employed to estimate the importance of these variables to the decorrelation process by land cover classes. Temporal decorrelation was observed for over 32% of the burned pixels located within the study area. Fire severity, vegetation water content, and soil moisture were the main drivers behind temporal decorrelation processes and are of the utmost importance for areas detected as burned immediately after fire events. When burned areas were detected long after fire (decorrelated areas), due to reduced backscatter coefficient variations between pre- to post-fire acquisitions, water content (soil and vegetation) was the main driver behind the backscatter coefficient changes. Therefore, for efficient synthetic aperture radar (SAR)-based monitoring of burned areas, detection, and mapping algorithms need to account for the interaction between fire impact and soil and vegetation water content. Full article
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