Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Remote Sensing of Burnt Area

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 52348

Share This Special Issue

Special Issue Editors

Prof. Dr. Eldar Kurbanov

E-Mail Website
Guest Editor

Professor of the Silviculture and Forest Inventory Chair and Head of the Sustainable Forest Management, Remote Sensing Center, Volga State University of Technology (Volgatech), 424000 Yoshkar-Ola, Russia
Interests: monitoring of forest ecosystems; remote sensing and GIS applications; geospatial data analysis; sustainable forest management; land use and land cover dynamic
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Alexander Alekseev

E-Mail Website
Guest Editor

Head of the Department of Forest Inventory, Management and GIS, Saint-Petersburg State Forest Technical University, 194021 Saint-Petersburg, Russia
Interests: remote sensing and GIS;forest inventory and management; national forest inventory; mathematical modeling of forest ecosystems.

Special Issue Information

Dear Colleagues,

During the last few decades ecosystems worldwide have been seriously affected by large wildfires, which significantly contribute to biogeochemical cycles and affect the composition and functioning of the global atmosphere. These severe catastrophic events have shown one more time the need to better understand their impact on ecosystems and LULC. Recently, various approaches and algorithms have been developed with the use of remote sensing data to estimate and monitor several factors and indicators like burnt areas, burn severity, and post-fire dynamics in the different ecosystems. Progress in computer technology, machine learning, big data processing, artificial intelligence, and availability of high resolution images provides new possibilities to support and improve monitoring of the burnt areas. The accuracy of such burnt area mapping is critical due to the potential of fire-affected areas to have important societal, ecological, and economic consequences. The Special Issue “Remote Sensing of Burnt Areas Monitoring” invites manuscripts focusing on research advances and innovative approaches in remote sensing in the field of burned area estimations and mapping in various ecosystems at different spatial and temporal scales. We invite you to submit research articles, reviews, perspectives, and case studies on topics including, but not limited to the following:

New methods and strategies for wildland fires prevention and monitoring
Big data for monitoring and mapping of burnt areas
Advances in remote sensing of burnt areas mapping
Data integration for ecosystems’ post fire management and mitigation
Mapping and monitoring of management practices on burnt lands
Post-fire vegetation regeneration
Time series for the monitoring

Prof. Dr. Eldar Kurbanov
Prof. Dr. Alexander Alekseev
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.

Keywords

Time series
monitoring of burnt areas
wildland fires, burn severity
normalized burn ratio
statistical modeling
burn index
fire ecology
landscape metrics
machine learning

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

26 pages, 12413 KiB

Open AccessArticle

Forest Fire Assessment Using Remote Sensing to Support the Development of an Action Plan Proposal in Ecuador

by Fernando Morante-Carballo, Lady Bravo-Montero, Paúl Carrión-Mero, Andrés Velastegui-Montoya and Edgar Berrezueta

Remote Sens. 2022, 14(8), 1783; https://doi.org/10.3390/rs14081783 - 7 Apr 2022

Cited by 18 | Viewed by 6190

Abstract

Worldwide, forest fires exert effects on natural ecosystems, contributing to economic/human losses, health effects, and climate change. Spectral indices are an essential tool for monitoring and analyzing forest fires. These indices make it possible to evaluate the affected areas and help mitigate possible future events and reduce damage. The case study addressed in this work corresponds to the Cerro of the Guadual community of La Carolina parish (Ibarra, Ecuador). This work aims to evaluate the degree of severity and the recovery of post-fire vegetation, employing the multitemporal analysis of spectral indices and correlating these with the climatological aspects of the region. The methodological process was based on (i) background information collection, (ii) remote sensing data, (iii) spectral index analysis, (iv) multivariate analysis, and (v) a forest fire action plan proposal. Landsat-8 OLI satellite images were used for multitemporal analysis (2014–2020). Using the dNDVI index, the fire’s severity was classified as unburned and very low severity in regard to the areas that did not regenerate post-fire, which represented 10,484.64 ha. In contrast, the areas classified as high and very high severity represented 5859.06 ha and 2966.98 ha, respectively. In addition, the dNBR was used to map the burned areas. The high enhanced regrowth zones represented an area of 8017.67 ha, whereas the moderate/high-severity to high-severity zones represented 3083.72 ha and 1233.49 ha, respectively. The areas with a high severity level corresponded to native forests, which are challenging to recover after fires. These fire severity models were validated with 31 in situ data from fire-starting points and they presented an accuracy of 99.1% in the high severity category. In addition, through the application of principal component analysis (PCA) with data from four meteorological stations in the region, a bimodal behavior was identified corresponding to the climatology of the area (dry season and rainy season), which is related to the presence of fires (in the dry season). It is essential to note that after the 2014 fire, locally, rainfall decreased and temperatures increased. Finally, the proposed action plan for forest fires made it possible to define a safe and effective evacuation route to reduce the number of victims during future events. Full article

(This article belongs to the Special Issue Remote Sensing of Burnt Area)

► Show Figures

Figure 1

24 pages, 15471 KiB

Open AccessArticle

A New Application of the Disturbance Index for Fire Severity in Coastal Dunes

by Marcio D. DaSilva, David Bruce, Patrick A. Hesp and Graziela Miot da Silva

Remote Sens. 2021, 13(23), 4739; https://doi.org/10.3390/rs13234739 - 23 Nov 2021

Cited by 7 | Viewed by 2807

Abstract

Fires are a disturbance that can lead to short term dune destabilisation and have been suggested to be an initiation mechanism of a transgressive dune phase when paired with changing climatic conditions. Fire severity is one potential factor that could explain subsequent coastal dune destabilisations, but contemporary evidence of destabilisation following fire is lacking. In addition, the suitability of conventional satellite Earth Observation methods to detect the impacts of fire and the relative fire severity in coastal dune environments is in question. Widely applied satellite-derived burn indices (Normalised Burn Index and Normalised Difference Vegetation Index) have been suggested to underestimate the effects of fire in heterogenous landscapes or areas with sparse vegetation cover. This work assesses burn severity from high resolution aerial and Sentinel 2 satellite imagery following the 2019/2020 Black Summer fires on Kangaroo Island in South Australia, to assess the efficacy of commonly used satellite indices, and validate a new method for assessing fire severity in coastal dune systems. The results presented here show that the widely applied burn indices derived from NBR differentially assess vegetation loss and fire severity when compared in discrete soil groups across a landscape that experienced a very high severity fire. A new application of the Tasselled Cap Transformation (TCT) and Disturbance Index (DI) is presented. The differenced Disturbance Index (dDI) improves the estimation of burn severity, relative vegetation loss, and minimises the effects of differing soil conditions in the highly heterogenous landscape of Kangaroo Island. Results suggest that this new application of TCT is better suited to diverse environments like Mediterranean and semi-arid coastal regions than existing indices and can be used to better assess the effects of fire and potential remobilisation of coastal dune systems. Full article

(This article belongs to the Special Issue Remote Sensing of Burnt Area)

► Show Figures

Graphical abstract

16 pages, 5470 KiB

Open AccessArticle

Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity

by Max J. van Gerrevink and Sander Veraverbeke

Remote Sens. 2021, 13(22), 4611; https://doi.org/10.3390/rs13224611 - 16 Nov 2021

Cited by 14 | Viewed by 3330

Abstract

Fire severity represents fire-induced environmental changes and is an important variable for modeling fire emissions and planning post-fire rehabilitation. Remotely sensed fire severity is traditionally evaluated using the differenced normalized burn ratio (dNBR) derived from multispectral imagery. This spectral index is based on bi-temporal differenced reflectance changes caused by fires in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions. Our study aims to evaluate the spectral sensitivity of the dNBR using hyperspectral imagery by identifying the optimal bi-spectral NIR SWIR combination. This assessment made use of a rare opportunity arising from the pre- and post-fire airborne image acquisitions over the 2013 Rim and 2014 King fires in California with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. The 224 contiguous bands of this sensor allow for 5760 unique combinations of the dNBR at a high spatial resolution of approximately 15 m. The performance of the hyperspectral dNBR was assessed by comparison against field data and the spectral optimality statistic. The field data is composed of 83 in situ measurements of fire severity using the Geometrically structured Composite Burn Index (GeoCBI) protocol. The optimality statistic ranges between zero and one, with one denoting an optimal measurement of the fire-induced spectral change. We also combined the field and optimality assessments into a combined score. The hyperspectral dNBR combinations demonstrated strong relationships with GeoCBI field data. The best performance of the dNBR combination was derived from bands 63, centered at 0.962 µm, and 218, centered at 2.382 µm. This bi-spectral combination yielded a strong relationship with GeoCBI field data of R² = 0.70 based on a saturated growth model and a median spectral index optimality statistic of 0.31. Our hyperspectral sensitivity analysis revealed optimal NIR and SWIR bands for the composition of the dNBR that are outside the ranges of the NIR and SWIR bands of the Landsat 8 and Sentinel-2 sensors. With the launch of the Precursore Iperspettrale Della Missione Applicativa (PRISMA) in 2019 and several planned spaceborne hyperspectral missions, such as the Environmental Mapping and Analysis Program (EnMAP) and Surface Biology and Geology (SBG), our study provides a timely assessment of the potential and sensitivity of hyperspectral data for assessing fire severity. Full article

(This article belongs to the Special Issue Remote Sensing of Burnt Area)

► Show Figures

Figure 1

18 pages, 1516 KiB

Open AccessArticle

Immunized Token-Based Approach for Autonomous Deployment of Multiple Mobile Robots in Burnt Area

by Sulemana Nantogma, Weizhi Ran, Pengfei Liu, Zhang Yu and Yang Xu

Remote Sens. 2021, 13(20), 4135; https://doi.org/10.3390/rs13204135 - 15 Oct 2021

Cited by 1 | Viewed by 1856

Abstract

Collaborative exploration, sensing and communication in previously unknown environments with high network latency, such as outer space, battlefields and disaster hit areas are promising in multi-agent applications. When disasters such as large fires or natural disasters occur, previously established networks might be destroyed or incapacitated. In these cases, multiple autonomous mobile robots (AMR) or autonomous unmanned ground vehicles carrying wireless devices and/or thermal sensors can be deployed to create an end-to-end communication and sensing coverage to support rescue efforts or access the severity of damage. However, a fundamental problem is how to rapidly deploy these mobile agents in such complex and dynamic environments. The uncertainties introduced by the operational environment and wide range of scheduling problem have made solving them as a whole challenging. In this paper, we present an efficient decentralized approach for practical mobile agents deployment in unknown, burnt or disaster hit areas. Specifically, we propose an approach that combines methods from Artificial Immune System (AIS) with special token messages passing for a team of interconnected AMR to decide who, when and how to act during deployment process. A distributed scheme is adopted, where each AMR makes its movement decisions based on its local observation and a special token it receives from its neighbors. Empirical evidence of robustness and effectiveness of the proposed approach is demonstrated through simulation. Full article

(This article belongs to the Special Issue Remote Sensing of Burnt Area)

► Show Figures

Graphical abstract

23 pages, 8142 KiB

Open AccessArticle

Mapping Forest Burn Extent from Hyperspatial Imagery Using Machine Learning

by Dale Hamilton, Kamden Brothers, Cole McCall, Bryn Gautier and Tyler Shea

Remote Sens. 2021, 13(19), 3843; https://doi.org/10.3390/rs13193843 - 25 Sep 2021

Cited by 14 | Viewed by 2934

Abstract

Support vector machines are shown to be highly effective in mapping burn extent from hyperspatial imagery in grasslands. Unfortunately, this pixel-based method is hampered in forested environments that have experienced low-intensity fires because unburned tree crowns obstruct the view of the surface vegetation. This obstruction causes surface fires to be misclassified as unburned. To account for misclassifying areas under tree crowns, trees surrounded by surface burn can be assumed to have been burned underneath. This effort used a mask region-based convolutional neural network (MR-CNN) and support vector machine (SVM) to determine trees and burned pixels in a post-fire forest. The output classifications of the MR-CNN and SVM were used to identify tree crowns in the image surrounded by burned surface vegetation pixels. These classifications were also used to label the pixels under the tree as being within the fire’s extent. This approach results in higher burn extent mapping accuracy by eliminating burn extent false negatives from surface burns obscured by unburned tree crowns, achieving a nine percentage point increase in burn extent mapping accuracy. Full article

(This article belongs to the Special Issue Remote Sensing of Burnt Area)

► Show Figures

Figure 1

20 pages, 89773 KiB

Open AccessArticle

Evaluating the Differenced Normalized Burn Ratio for Assessing Fire Severity Using Sentinel-2 Imagery in Northeast Siberian Larch Forests

by Clement J. F. Delcourt, Alisha Combee, Brian Izbicki, Michelle C. Mack, Trofim Maximov, Roman Petrov, Brendan M. Rogers, Rebecca C. Scholten, Tatiana A. Shestakova, Dave van Wees and Sander Veraverbeke

Remote Sens. 2021, 13(12), 2311; https://doi.org/10.3390/rs13122311 - 12 Jun 2021

Cited by 36 | Viewed by 6824

Abstract

Fire severity is a key fire regime characteristic with high ecological and carbon cycle relevance. Prior studies on boreal forest fires primarily focused on mapping severity in North American boreal forests. However, the dominant tree species and their impacts on fire regimes are different between North American and Siberian boreal forests. Here, we used Sentinel-2 satellite imagery to test the potential for using the most common spectral index for assessing fire severity, the differenced Normalized Burn Ratio (dNBR), over two fire scars and 37 field plots in Northeast Siberian larch-dominated (Larix cajanderi) forests. These field plots were sampled into two different forest types: (1) dense young stands and (2) open mature stands. For this evaluation, the dNBR was compared to field measurements of the Geometrically structured Composite Burn Index (GeoCBI) and burn depth. We found a linear relationship between dNBR and GeoCBI using data from all forest types (R² = 0.42, p < 0.001). The dNBR performed better to predict GeoCBI in open mature larch plots (R² = 0.56, p < 0.001). The GeoCBI provides a holistic field assessment of fire severity yet is dominated by the effect of fire on vegetation. No significant relationships were found between GeoCBI components (overall and substrate stratum) and burn depth within our fires (p > 0.05 in all cases). However, the dNBR showed some potential as a predictor for burn depth, especially in the dense larch forests (R² = 0.63, p < 0.001). In line with previous studies in boreal North America, the dNBR correlated reasonably well with field data of aboveground fire severity and showed some skills as a predictor of burn depth. More research is needed to refine spaceborne fire severity assessments in the larch forests of Northeast Siberia, including assessments of additional fire scars and integration of dNBR with other geospatial proxies of fire severity. Full article

(This article belongs to the Special Issue Remote Sensing of Burnt Area)

► Show Figures

Graphical abstract

18 pages, 22622 KiB

Open AccessArticle

A Practical Method for High-Resolution Burned Area Monitoring Using Sentinel-2 and VIIRS

by Miguel M. Pinto, Ricardo M. Trigo, Isabel F. Trigo and Carlos C. DaCamara

Remote Sens. 2021, 13(9), 1608; https://doi.org/10.3390/rs13091608 - 21 Apr 2021

Cited by 20 | Viewed by 10896

Abstract

Mapping burned areas using satellite imagery has become a subject of extensive research over the past decades. The availability of high-resolution satellite data allows burned area maps to be produced with great detail. However, their increasing spatial resolution is usually not matched by a similar increase in the temporal domain. Moreover, high-resolution data can be a computational challenge. Existing methods usually require downloading and processing massive volumes of data in order to produce the resulting maps. In this work we propose a method to make this procedure fast and yet accurate by leveraging the use of a coarse resolution burned area product, the computation capabilities of Google Earth Engine to pre-process and download Sentinel-2 10-m resolution data, and a deep learning model trained to map the multispectral satellite data into the burned area maps. For a 1500 ha fire our method can generate a 10-m resolution map in about 5 min, using a computer with an 8-core processor and 8 GB of RAM. An analysis of six important case studies located in Portugal, southern France and Greece shows the detailed computation time for each process and how the resulting maps compare to the input satellite data as well as to independent reference maps produced by Copernicus Emergency Management System. We also analyze the feature importance of each input band to the final burned area map, giving further insight about the differences among these events. Full article

(This article belongs to the Special Issue Remote Sensing of Burnt Area)

► Show Figures

Graphical abstract

28 pages, 94494 KiB

Open AccessEditor’s ChoiceArticle

Landsat and Sentinel-2 Based Burned Area Mapping Tools in Google Earth Engine

by Ekhi Roteta, Aitor Bastarrika, Magí Franquesa and Emilio Chuvieco

Remote Sens. 2021, 13(4), 816; https://doi.org/10.3390/rs13040816 - 23 Feb 2021

Cited by 47 | Viewed by 10558

Abstract

Four burned area tools were implemented in Google Earth Engine (GEE), to obtain regular processes related to burned area (BA) mapping, using medium spatial resolution sensors (Landsat and Sentinel-2). The four tools are (i) the BA Cartography tool for supervised burned area over the user-selected extent and period, (ii) two tools implementing a BA stratified random sampling to select the scenes and dates for validation, and (iii) the BA Reference Perimeter tool to obtain highly accurate BA maps that focus on validating coarser BA products. Burned Area Mapping Tools (BAMTs) go beyond the previously implemented Burned Area Mapping Software (BAMS) because of GEE parallel processing capabilities and preloaded geospatial datasets. BAMT also allows temporal image composites to be exploited in order to obtain BA maps over a larger extent and longer temporal periods. The tools consist of four scripts executable from the GEE Code Editor. The tools’ performance was discussed in two case studies: in the 2019/2020 fire season in Southeast Australia, where the BA cartography detected more than 50,000 km², using Landsat data with commission and omission errors below 12% when compared to Sentinel-2 imagery; and in the 2018 summer wildfires in Canada, where it was found that around 16,000 km² had burned. Full article

(This article belongs to the Special Issue Remote Sensing of Burnt Area)

► Show Figures

Graphical abstract

19 pages, 6265 KiB

Open AccessArticle

Evaluating the Near and Mid Infrared Bi-Spectral Space for Assessing Fire Severity and Comparison with the Differenced Normalized Burn Ratio

by Max J. van Gerrevink and Sander Veraverbeke

Remote Sens. 2021, 13(4), 695; https://doi.org/10.3390/rs13040695 - 14 Feb 2021

Cited by 11 | Viewed by 3801

Abstract

Fire severity, defined as the degree of environmental change caused by a fire, is a critical fire regime attribute of interest to fire emissions modelling and post-fire rehabilitation planning. Remotely sensed fire severity is traditionally assessed by the differenced normalized burn ratio (dNBR). This spectral index captures fire-induced reflectance changes in the near infrared (NIR) and short-wave infrared (SWIR) spectral regions. This study evaluates a spectral index based on a band combination including the NIR and mid infrared (MIR) spectral regions, the differenced normalized difference vegetation index with mid infrared (dNDVI_MID), to assess fire severity. This evaluation capitalized upon the unique opportunity stemming from the pre- and post-fire airborne acquisitions over the Rim (2013) and King (2014) fires in California with the MODIS/ASTER Airborne Simulator (MASTER) instrument. The field data consist of 85 Geometrically structured Composite Burn Index (GeoCBI) plots. In addition, six different index combinations, respectively three with a NIR–SWIR combination and three with a NIR–MIR combination, were evaluated based on the optimality of fire-induced spectral displacements. The optimality statistic ranges between zero and one, with values of one representing pixel displacements that are unaffected by noise. The results show that the dNBR demonstrated a stronger relationship with GeoCBI field data when field measurements over the two fire scars were combined than the dNDVI_MID approaches. The results yielded an R² of 0.68 based on a saturated growth model for the best performing dNBR index, whereas the performance of the dNDVI_MID indices was lower with an R² = 0.61 for the best performing dNDVI_MID index. The dNBR also outperformed the dNDVI_MID in terms of spectral optimality across both fires. The best performing dNBR index yielded median optimality statistics of 0.56 over the Rim and 0.60 over the King fire. The best performing dNDVI_MID index recorded optimality values of 0.49 over the Rim and 0.46 over the King fire. We also found that the dNBR approach led to considerable differences in the form of the relationship with the GeoCBI between the two fires, whereas the dNDVI_MID approach yielded comparable relationships with the GeoCBI over the two fires. This suggests that the dNDVI_MID approach, despite its slightly lower performance than the dNBR, may be a more robust method for estimating and comparing fire severity over large regions. This premise needs additional verification when more air- or spaceborne imagery with NIR and MIR bands will become available with a spatial resolution that allows ground truthing of fire severity. Full article

(This article belongs to the Special Issue Remote Sensing of Burnt Area)

► Show Figures

Journal Menu

Journal Browser

Remote Sensing of Burnt Area

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (9 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI