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Special Issue "New Trends in Forest Fire Research Incorporating Big Data and Climate Change Modeling"

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

Deadline for manuscript submissions: 31 December 2018

Special Issue Editors

Guest Editor
Prof. Ioannis Gitas

Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki, Greece
Website | E-Mail
Fax: +30 2310 992677
Interests: land use/land cover mapping; land cover change detection; pre-fire planning and post-fire assessment; soil erosion risk assessment/desertification; other environmental applications of remote sensing and GIS
Guest Editor
Dr. Vincent G. Ambrosia

NASA-Ames Research Center, Moffett Field, CA 94035, USA
Website | E-Mail
Phone: +1-650-604-6565
Interests: wildland fire; post-fire burn assessment; sensor systems; TIR; UAS platforms; decision support systems
Guest Editor
Dr. Chariton Kalaitzidis

Program in Geoinformation in Environmental Management, Mediterranean Agronomic Institute of Chania
Website | E-Mail
Interests: remote sensing for monitoring of vegetation and agricultural crops, primarily through field spectroscopy, unmanned airborne systems and satellite images

Special Issue Information

Dear Colleagues,

Satellite remote sensing systems and technologies have been long considered as basic components in fire research and management due to the extensive use of Earth Observation data over the past few decades, which has significantly contributed towards more integrated methodological schemes in fire-related studies and operational applications at local, regional and global scales. In recent years, new trends and shifts in fire research have been mainly driven by sensor availability, as well as data distribution policies, which have provided free access to large archives of satellite data.

Indeed, continuity and future missions of various satellite systems (e.g., SUOMI-VIIRS, Landsat-8, Sentinels, PROBA-V) ensure constant provision of coarse to high spatial resolution datasets, thus facilitating the systematic monitoring of fire disturbance at various spatio-temporal scales. In addition, the rapid progress in computer technology enables the processing and multi-sensor fusion of large volumes of datasets. Consequently, new approaches that have been proposed by the scientific community focus on the development of automated and semi-automated techniques, especially for active fire detection and mapping of burned areas.

The growing need for the extraction of valuable information from large volumes of spatio-temporal data regarding the long-term evolution of fire regimes resulted in a shift, in the last few years, from multi-temporal to hyper-temporal approaches. Exploitation of dense satellite time-series and efficient management of large data volumes, derived from multiple observation systems, are expected to contribute towards a more comprehensive understanding of the response of ecosystems and biomes under different fire frequency and severity scenarios.

Such scenarios are often triggered by climatic influences on fire regimes. Undoubtedly, the effects of climate change on fire occurrence are evident on a global scale, resulting in a substantial increase of extreme fire incidents, which, in turn, contribute to an increase in greenhouse gas emissions. Modeling tools and data assimilation schemes, exploiting available EO data, among others, are expected to significantly support future projections of the intensity of the climate change phenomenon, taking into account the contribution of forest fires.

The 11th EARSeL Forest Fire workshop will be held in Chania, Greece, 25–27 September, 2017, and will focus on “New Trends in Forest Fire Research Incorporating Big Data and Climate Change Modeling” (http://ffsig2017.maich.gr). The thematic sessions will include presentations and posters on the use of data delivered by the most recent satellite missions, employing big data and time-series for monitoring fire disturbance and post-fire vegetation trends, and modeling the effects of climate change on forests with regards to fire risk and post-fire vegetation development.

The EARSeL Special Interest Group on Forest Fires (FF-SIG) was created in 1995, following the initiative of several researchers studying fires in Mediterranean Europe. FF-SIG, which currently represents one of the most active groups within EARSeL, promotes the integration of advanced technologies and the production of satellite-derived products for the benefit of forest managers, researchers, local governments and global organizations. Previous workshops of the SIG have been held in Alcalá de Henares (1995), Luso (1998), Paris (2001), Ghent (2003), Zaragoza (2005), Thessaloniki (2007), Matera (2009), Stresa (2011) Coombe Abbey (2013) and Limassol (2015).

The 11th EARSeL Forest Fire workshop is co-organized by the School of Forestry and Natural Environment, Aristotle University of Thessaloniki, the Mediterranean Agronomic Institute of Chania, of the International Centre for Advanced Mediterranean Agronomic Studies, and the National Aeronautics and Space Administration. The workshop and proposed Special Issue will be focused on global systems for monitoring wildfires, as well as the missions providing data for this purpose, and the modeling endeavors with regards to climate change, considering the contribution of forest fires. We invite you to submit articles on the following topics:

(1) Studies on the impact of climate change on forest fires occurrence and severity;
(2) Contribution of the current and upcoming Sentinel missions on forest fire research;
(3) Exploitation of Big Data and dense satellite time-series for fire disturbance monitoring;
(4) Improved methods of modelling post-fire vegetation trends;
(5) Improved capabilities for sharing / understanding / modelling large-volume fire data sets;
(6) Methods of forest fire detection and monitoring on multiple scales;

Authors are required to check and follow specific Instructions to Authors, see https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf.

Prof. Ioannis Gitas
Dr. Vincent Ambrosia
Dr. Chariton Kalaitzidis
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 (1 paper)

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Research

Open AccessArticle Estimating Fire Background Temperature at a Geostationary Scale—An Evaluation of Contextual Methods for AHI-8
Remote Sens. 2018, 10(9), 1368; https://doi.org/10.3390/rs10091368
Received: 23 July 2018 / Revised: 22 August 2018 / Accepted: 27 August 2018 / Published: 28 August 2018
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Abstract
An integral part of any remotely sensed fire detection and attribution method is an estimation of the target pixel’s background temperature. This temperature cannot be measured directly independent of fire radiation, so indirect methods must be used to create an estimate of this
[...] Read more.
An integral part of any remotely sensed fire detection and attribution method is an estimation of the target pixel’s background temperature. This temperature cannot be measured directly independent of fire radiation, so indirect methods must be used to create an estimate of this background value. The most commonly used method of background temperature estimation is through derivation from the surrounding obscuration-free pixels available in the same image, in a contextual estimation process. This method of contextual estimation performs well in cloud-free conditions and in areas with homogeneous landscape characteristics, but increasingly complex sets of rules are required when contextual coverage is not optimal. The effects of alterations to the search radius and sample size on the accuracy of contextually derived brightness temperature are heretofore unexplored. This study makes use of imagery from the AHI-8 geostationary satellite to examine contextual estimators for deriving background temperature, at a range of contextual window sizes and percentages of valid contextual information. Results show that while contextual estimation provides accurate temperatures for pixels with no contextual obscuration, significant deterioration of results occurs when even a small portion of the target pixel’s surroundings are obscured. To maintain the temperature estimation accuracy, the use of no less than 65% of a target pixel’s total contextual coverage is recommended. The study also examines the use of expanding window sizes and their effect on temperature estimation. Results show that the accuracy of temperature estimation decreases significantly when expanding the examined window, with a 50% increase in temperature variability when using a larger window size than 5 × 5 pixels, whilst generally providing limited gains in the total number of temperature estimates (between 0.4%–4.4% of all pixels examined). The work also presents a number of case study regions taken from the AHI-8 disk in more depth, and examines the causes of excess temperature variation over a range of topographic and land cover conditions. Full article
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