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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: closed (31 December 2018) | Viewed by 25040

Special Issue Editors

Lab of Forest Management and Remote Sensing, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: forest fires; land-use/land-cover mapping; pre-fire planning and post-fire assessment; remote sensing; GIS; forest management
Special Issues, Collections and Topics in MDPI journals
NASA-Ames Research Center, Moffett Field, CA 94035, USA
Interests: wildland fire; post-fire burn assessment; sensor systems; TIR; UAS platforms; decision support systems
Special Issues, Collections and Topics in MDPI journals
Program in Geoinformation in Environmental Management, Mediterranean Agronomic Institute of Chania, Hania, Greece
Interests: remote sensing for monitoring of vegetation and agricultural crops, primarily through field spectroscopy, unmanned airborne systems and satellite images
Special Issues, Collections and Topics in MDPI journals

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 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.

Published Papers (3 papers)

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Research

30 pages, 22248 KiB  
Article
30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine
Remote Sens. 2019, 11(5), 489; https://doi.org/10.3390/rs11050489 - 27 Feb 2019
Cited by 136 | Viewed by 13805
Abstract
Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In [...] Read more.
Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively. Full article
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28 pages, 11016 KiB  
Article
Detection and Validation of Tropical Peatland Flaming and Smouldering Using Landsat-8 SWIR and TIRS Bands
Remote Sens. 2019, 11(4), 465; https://doi.org/10.3390/rs11040465 - 24 Feb 2019
Cited by 21 | Viewed by 6031 | Correction
Abstract
A Tropical Peatland Combustion Algorithm (ToPeCAl) was first established from Landsat-8 images acquired in 2015, which were used to detect peatland combustion in flaming and smouldering stages. Detection of smouldering combustion from space remains a challenge due to its low temperature and generally [...] Read more.
A Tropical Peatland Combustion Algorithm (ToPeCAl) was first established from Landsat-8 images acquired in 2015, which were used to detect peatland combustion in flaming and smouldering stages. Detection of smouldering combustion from space remains a challenge due to its low temperature and generally small spatial extent. The ToPeCAl consists of the Shortwave Infrared Combustion Index based on reflectance (SICIρ), and Top of Atmosphere (TOA) reflectance in Shortwave Infrared band-7 (SWIR-2), TOA brightness temperature of Thermal Infrared band-10 (TIR-1), and TOA reflectance of band-1, the Landsat-8 aerosol band. The implementation of ToPeCAl was then validated using terrestrial and aerial images (helicopter and drone) collected during fieldwork in Central Kalimantan, Indonesia in the 2018 fire season, on the same day as Landsat-8 overpasses. The overall accuracy of ToPeCAl was found to be 82% with omission errors in a small area (less than 30 m × 30 m) from mixtures of smouldering and vegetation pixels, and commission errors (with minimum area of 30 m x 30 m) on high reflective building rooftops in urban areas. These errors were further reduced by masking and removing urban areas prior to analysis using landuse Geographic Information System (GIS) data; improving the overall mapping accuracy to 93%. For comparison, the day and night-time VIIRS (375 m) active fire product (VNP14IMG) was utilised, obtaining a lower probability of fire detection of 71% compared to ground truth, and 57–72% agreement in a buffer distance of 375 m to 1500 m when compared to the Landsat-8 ToPeCAl results. The night-time data of VNP14IMG was found to have a better correspondence with ToPeCAl results from Landsat 8 than day-time data. This finding could lead to a potential merger of ToPeCAl with VNP14IMG to fill the temporal gaps of peatland fire information when using Landsat. However, the VNP14IMG product exhibited overestimation compared with the results of ToPeCAl applied to Landsat-8. Full article
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29 pages, 5625 KiB  
Article
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 - 28 Aug 2018
Cited by 9 | Viewed by 3767
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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