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Special Issue "Remote Sensing of Wildfire"

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

Deadline for manuscript submissions: 25 July 2019

Special Issue Editors

Guest Editor
Dr. Quazi K. Hassan

Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr. N.W., Calgary, AB T2N 1N4, Canada
Website | E-Mail
Phone: +1-403-210-9494
Interests: optical/thermal remote sensing in: (i) forecasting and monitoring of natural hazards/disasters, such as forest fire, drought, and flooding; (ii) comprehending the dynamics of natural resources, such as forestry, agriculture, and water; and (iii) modelling issues related to boreal environment
Guest Editor
Dr. George P. Petropoulos

Department of Geography and Earth Sciences, University of Aberystwyth, Old College, King Street Llandinam Building, Room H4 Aberystwyth, Ceredigion SY23 3DB, UK
Website | E-Mail
Phone: +44-0-1970-621861
Interests: Earth Observation; GIS; multi- and hyper- spectral remote sensing; land use/cover mapping; change detection; natural hazards; fires; floods; land surface interactions; evapotranspiration; soil moisture; land surface temperature; land biosphere modelling; Soil Vegetation Atmosphere Transfer (SVAT) models; EO algorithms benchmarking; sensitivity analysis

Special Issue Information

Dear Colleagues,

Wildfires (which include forest fires, grass fires, brush fires, bush fires, and peat fires, among others) are an integral part of so many ecosystems across the world. In general, these fires are primarily viewed negatively despite their favorable contributions. Here, the purpose is to gather scientists/researchers related to this topic, aiming to highlight ongoing research investigations and new applications in the field. In this framework, the editors of this Special Issue would like to invite both applied and theoretical research contributions; submissions of original works furthering knowledge concerned with any aspect of the use of remote sensing in wildfires. Note that these manuscripts must be, not only unpublished, but also not under consideration for potential publication elsewhere. In addition, the manuscripts must employ one of the following remote sensing data types: Optical, thermal, hyperspectral, active and passive microwave acquired by either airborne or spaceborne remote sensing platforms, dealing with wildfires. The topics of interest include, but not limited to:

  • Comprehending of the pre-fire conditions,

  • Forecasting of wildfire danger/risk,

  • Modelling of wildfire behavior,

  • Fighting the wildfire,

  • Modelling prescribed burning,

  • Relation between vegetation phenology and fire,

  • Monitoring of the vegetation recovery following the fire events,

  • Mapping of burn area and ecological impacts,

  • Modelling of smoke propagation, and

  • Analyzing of historical fire regimes, among others.

Dr. Quazi K. Hassan
Dr. George P. Petropoulos
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 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 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 1800 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.

Published Papers (5 papers)

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Research

Open AccessFeature PaperArticle An Advanced Forest Fire Danger Forecasting System: Integration of Remote Sensing and Historical Sources of Ignition Data
Remote Sens. 2018, 10(6), 923; https://doi.org/10.3390/rs10060923
Received: 20 May 2018 / Revised: 2 June 2018 / Accepted: 11 June 2018 / Published: 12 June 2018
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Abstract
Forest fire is one of the major natural hazards/disasters in Canada and many ecosystems across the world. Here, our objective was to enhance the performance of an existing solely remote sensing-based forest fire danger forecasting system (FFDFS), and its implementation over the northern
[...] Read more.
Forest fire is one of the major natural hazards/disasters in Canada and many ecosystems across the world. Here, our objective was to enhance the performance of an existing solely remote sensing-based forest fire danger forecasting system (FFDFS), and its implementation over the northern region of the Canadian province of Alberta. The modified FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived daily surface temperature (Ts) and precipitable water (PW), and 8-day composite of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), where we assumed that cloud-contaminant pixels would reduce the risk of fire occurrences. In addition, we generated ignition cause-specific static fire danger (SFD) maps derived using the historical human- and lightning-caused fires during the period 1961–2014. Upon incorporating different combinations of the generated SFD maps with the modified FFDFS, we evaluated their performances against actual fire spots during the 2009–2011 fire seasons. Our findings revealed that our proposed modifications were quite effective and the modified FFDFS captured almost the same amount of fires as the original FFDFS, i.e., about 77% of the detected fires on an average in the top three fire danger classes of extremely high, very high, and high categories, where about 50% of the study area fell under low and moderate danger classes. Additionally, we observed that the combination of modified FFDFS and human-caused SFD map (road buffer) demonstrated the most effective results in fire detection, i.e., 82% of detected fires on an average in the top three fire danger classes, where about 46% of the study area fell under the moderate and low danger categories. We believe that our developments would be helpful to manage the forest fire in order to reduce its overall impact. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire)
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Open AccessArticle Evaluation of a Bayesian Algorithm to Detect Burned Areas in the Canary Islands’ Dry Woodlands and Forests Ecoregion Using MODIS Data
Remote Sens. 2018, 10(5), 789; https://doi.org/10.3390/rs10050789
Received: 11 April 2018 / Revised: 14 May 2018 / Accepted: 17 May 2018 / Published: 19 May 2018
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Abstract
Burned Area (BA) is deemed as a primary variable to understand the Earth’s climate system. Satellite remote sensing data have allowed for the development of various burned area detection algorithms that have been globally applied to and assessed in diverse ecosystems, ranging from
[...] Read more.
Burned Area (BA) is deemed as a primary variable to understand the Earth’s climate system. Satellite remote sensing data have allowed for the development of various burned area detection algorithms that have been globally applied to and assessed in diverse ecosystems, ranging from tropical to boreal. In this paper, we present a Bayesian algorithm (BY-MODIS) that detects burned areas in a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2012 of the Canary Islands’ dry woodlands and forests ecoregion (Spain). Based on daily image products MODIS, MOD09GQ (250 m), and MOD11A1 (1 km), the surface spectral reflectance and the land surface temperature, respectively, 10 day composites were built using the maximum temperature criterion. Variables used in BY-MODIS were the Global Environment Monitoring Index (GEMI) and Burn Boreal Forest Index (BBFI), alongside the NIR spectral band, all of which refer to the previous year and the year the fire took place in. Reference polygons for the 14 fires exceeding 100 hectares and identified within the period under analysis were developed using both post-fire LANDSAT images and official information from the forest fires national database by the Ministry of Agriculture and Fisheries, Food and Environment of Spain (MAPAMA). The results obtained by BY-MODIS can be compared to those by official burned area products, MCD45A1 and MCD64A1. Despite that the best overall results correspond to MCD64A1, BY-MODIS proved to be an alternative for burned area mapping in the Canary Islands, a region with a great topographic complexity and diverse types of ecosystems. The total burned area detected by the BY-MODIS classifier was 64.9% of the MAPAMA reference data, and 78.6% according to data obtained from the LANDSAT images, with the lowest average commission error (11%) out of the three products and a correlation (R2) of 0.82. The Bayesian algorithm—originally developed to detect burned areas in North American boreal forests using AVHRR archival data Long-Term Data Record—can be successfully applied to a lower latitude forest ecosystem totally different from the boreal ecosystem and using daily time series of satellite images from MODIS with a 250 m spatial resolution, as long as a set of training areas adequately characterising the dynamics of the forest canopy affected by the fire is defined. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire)
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Open AccessArticle Geostationary Sensor Based Forest Fire Detection and Monitoring: An Improved Version of the SFIDE Algorithm
Remote Sens. 2018, 10(5), 741; https://doi.org/10.3390/rs10050741
Received: 18 April 2018 / Revised: 2 May 2018 / Accepted: 7 May 2018 / Published: 11 May 2018
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Abstract
The paper aims to present the results obtained in the development of a system allowing for the detection and monitoring of forest fires and the continuous comparison of their intensity when several events occur simultaneously—a common occurrence in European Mediterranean countries during the
[...] Read more.
The paper aims to present the results obtained in the development of a system allowing for the detection and monitoring of forest fires and the continuous comparison of their intensity when several events occur simultaneously—a common occurrence in European Mediterranean countries during the summer season. The system, called SFIDE (Satellite FIre DEtection), exploits a geostationary satellite sensor (SEVIRI, Spinning Enhanced Visible and InfraRed Imager, on board of MSG, Meteosat Second Generation, satellite series). The algorithm was developed several years ago in the framework of a project (SIGRI) funded by the Italian Space Agency (ASI). This algorithm has been completely reviewed in order to enhance its efficiency by reducing false alarms rate preserving a high sensitivity. Due to the very low spatial resolution of SEVIRI images (4 × 4 km2 at Mediterranean latitude) the sensitivity of the algorithm should be very high to detect even small fires. The improvement of the algorithm has been obtained by: introducing the sun elevation angle in the computation of the preliminary thresholds to identify potential thermal anomalies (hot spots), introducing a contextual analysis in the detection of clouds and in the detection of night-time fires. The results of the algorithm have been validated in the Sardinia region by using ground true data provided by the regional Corpo Forestale e di Vigilanza Ambientale (CFVA). A significant reduction of the commission error (less than 10%) has been obtained with respect to the previous version of the algorithm and also with respect to fire-detection algorithms based on low earth orbit satellites. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire)
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Open AccessArticle Burned Area Mapping of an Escaped Fire into Tropical Dry Forest in Western Madagascar Using Multi-Season Landsat OLI Data
Remote Sens. 2018, 10(3), 371; https://doi.org/10.3390/rs10030371
Received: 27 December 2017 / Revised: 12 February 2018 / Accepted: 20 February 2018 / Published: 27 February 2018
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Abstract
A human-induced fire cleared a large area of tropical dry forest near the Ankoatsifaka Research Station at Kirindy Mitea National Park in western Madagascar over several weeks in 2013. Fire is a major factor in the disturbance and loss of global tropical dry
[...] Read more.
A human-induced fire cleared a large area of tropical dry forest near the Ankoatsifaka Research Station at Kirindy Mitea National Park in western Madagascar over several weeks in 2013. Fire is a major factor in the disturbance and loss of global tropical dry forests, yet remotely sensed mapping studies of fire-impacted tropical dry forests lag behind fire research of other forest types. Methods used to map burns in temperature forests may not perform as well in tropical dry forests where it can be difficult to distinguish between multiple-age burn scars and between bare soil and burns. In this study, the extent of forest lost to stand-replacing fire in Kirindy Mitea National Park was quantified using both spectral and textural information derived from multi-date satellite imagery. The total area of the burn was 18,034 ha. It is estimated that 6% (4761 ha) of the Park’s primary tropical dry forest burned over the period 23 June to 27 September. Half of the forest burned (2333 ha) was lost in the large conflagration adjacent to the Research Station. The best model for burn scar mapping in this highly-seasonal tropical forest and pastoral landscape included the differenced Normalized Burn Ratio (dNBR) and both uni- and multi-temporal measures of greenness. Lessons for burn mapping of tropical dry forest are much the same as for tropical dry forest mapping—highly seasonal vegetation combined with a mixture of background spectral information make for a complicated analysis and may require multi-temporal imagery and site specific techniques. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire)
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Open AccessArticle A Simple Normalized Difference Approach to Burnt Area Mapping Using Multi-Polarisation C-Band SAR
Remote Sens. 2017, 9(8), 764; https://doi.org/10.3390/rs9080764
Received: 12 June 2017 / Revised: 7 July 2017 / Accepted: 19 July 2017 / Published: 31 July 2017
Cited by 1 | PDF Full-text (9532 KB) | HTML Full-text | XML Full-text
Abstract
In fire-prone ecosystems, periodic fires are vital for ecosystem functioning. Fire managers seek to promote the optimal fire regime by managing fire season and frequency requiring detailed information on the extent and date of previous burns. This paper investigates a Normalised Difference α-Angle
[...] Read more.
In fire-prone ecosystems, periodic fires are vital for ecosystem functioning. Fire managers seek to promote the optimal fire regime by managing fire season and frequency requiring detailed information on the extent and date of previous burns. This paper investigates a Normalised Difference α-Angle (NDαI) approach to burn-scar mapping using C-band data. Polarimetric decompositions are used to derive α-angles from pre-burn and post-burn scenes and NDαI is calculated to identify decreases in vegetation between the scenes. The technique was tested in an area affected by a wildfire in January 2016 in the Western Cape, South Africa. The quad-pol H-A-α decomposition was applied to RADARSAT-2 data and the dual-pol H-α decomposition was applied to Sentinel-1A data. The NDαI results were compared to a burn scar extracted from Sentinel-2A data. High overall accuracies of 97.4% (Kappa = 0.72) and 94.8% (Kappa = 0.57) were obtained for RADARSAT-2 and Sentinel-1A, respectively. However, large omission errors were found and correlated strongly with areas of high local incidence angle for both datasets. The combined use of data from different orbits will likely reduce these errors. Furthermore, commission errors were observed, most notably on Sentinel-1A results. These errors may be due to the inability of the dual-pol H-α decomposition to effectively distinguish between scattering mechanisms. Despite these errors, the results revealed that burnt areas could be extracted and were in good agreement with the results from Sentinel-2A. Therefore, the approach can be considered in areas where persistent cloud cover or smoke prevents the extraction of burnt area information using conventional multispectral approaches. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire)
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