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Remote Sensing of Burnt Area II
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Remote Sensing of Burnt Area II

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 (31 January 2024) | Viewed by 10434

Special Issue Editor

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

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 once more forced us to face our need to better understand their impact on ecosystems and land use/land cover (LULC). Recently, various approaches and algorithms have been developed with the use of remote sensing data to estimate and monitor several factors and indicators, such as burnt areas, burn severity, and post-fire dynamics in 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 area. 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 on “Remote Sensing of Burnt Area” 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 fire prevention and monitoring;
  • Big data for monitoring and mapping of burnt areas;
  • Advances in remote sensing of burnt area 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 monitoring.

Prof. Dr. Eldar Kurbanov
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 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

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Published Papers (2 papers)

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Research

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19 pages, 13534 KiB  
Article
Evaluating the Abilities of Satellite-Derived Burned Area Products to Detect Forest Burning in China
by Xueyan Wang, Zhenhua Di and Jianguo Liu
Remote Sens. 2023, 15(13), 3260; https://doi.org/10.3390/rs15133260 - 25 Jun 2023
Viewed by 1492
Abstract
Fire plays a prominent role in the construction and destruction of ecosystems, and the accurate estimation of the burned area (BA) after a fire occurrence is of great significance to protect ecosystems and save people’s lives and property. This study evaluated the performances [...] Read more.
Fire plays a prominent role in the construction and destruction of ecosystems, and the accurate estimation of the burned area (BA) after a fire occurrence is of great significance to protect ecosystems and save people’s lives and property. This study evaluated the performances of three publicly available BA satellite products (GFED4, MCD64CMQ, and FireCCI5.1) in detecting Chinese forest fire burning from 2001 to 2016 across different time scales (yearly, monthly, and seasonally) and spatial scales (regional and provincial). The reference data were derived from the monthly China Forestry Statistical Yearbook (CFSY), and they were mainly used to evaluate the detection ability of each of the three BA products in the three major forest fire areas of China consisting of the Northeast (NE), Southwest (SW), and Southeast (SE) regions. The main results are as follows: (1) A significant declining BA trend was demonstrated in the whole study area and in the NE and SE subregions. Specifically, the slopes for the whole area ranged from −3821.1 ha/year for MCD64CMQ to −33,218 ha/year for the CFSY, the slopes for the NE region ranged from −3821.1 ha/year for MCD64CMQ to −33,218 ha/year for the CFSY, and the slopes for the SE region ranged from −594.24 ha/year for GFED4 to −3162.1 ha/year for the CFSY. The BA in China was mainly dominated by forest fires in the NE region, especially in 2003 and 2006 when this region accounted for 90% and 87% of occurrences, respectively. (2) Compared with the CFSY, GFED4 had the best performance at the yearly scale with an RMSE of 23.9 × 104 ha/year and CC of 0.83. Similarly, at the monthly scale, GFED4 also had the best performance for the three regions, with the lowest RMSE ranging from 0.33 × 104 to 5.4 × 104 ha/month—far lower than that of FireCC5.1 which ranged from 1.16 × 104 to 8.56 × 104 ha/month (except for the SE region where it was slightly worse than MCD64CMQ). At the seasonal scale, GFFD4 had the best performance in spring and winter. It was also noted that the fewer BAs in summer made the differences among the products insignificant. (3) Spatially, GFED4 had the best performance in RMSEs for all the provinces of the three regions, in CCs for the provinces of the SW and SE regions, and in MEs for the provinces of the SE region. (4) All three products had stronger detection abilities for severe and disaster fires than for common fires. Additionally, GFED4 had a more consistent number of months with the CFSY than the other products in the NE region. Moreover, the conclusion that GFED4 had the best performance in the China region was also proved using other validated BA datasets. These results will help us to understand the BA detection abilities of the satellite products in China and promote the further development of multi-source satellite fire data fusion. Full article
(This article belongs to the Special Issue Remote Sensing of Burnt Area II)
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Review

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35 pages, 3875 KiB  
Review
Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
by Eldar Kurbanov, Oleg Vorobev, Sergey Lezhnin, Jinming Sha, Jinliang Wang, Xiaomei Li, Janine Cole, Denis Dergunov and Yibo Wang
Remote Sens. 2022, 14(19), 4714; https://doi.org/10.3390/rs14194714 - 21 Sep 2022
Cited by 30 | Viewed by 7739
Abstract
Wildland fires dramatically affect forest ecosystems, altering the loss of their biodiversity and their sustainability. In addition, they have a strong impact on the global carbon balance and, ultimately, on climate change. This review attempts to provide a comprehensive meta-analysis of studies on [...] Read more.
Wildland fires dramatically affect forest ecosystems, altering the loss of their biodiversity and their sustainability. In addition, they have a strong impact on the global carbon balance and, ultimately, on climate change. This review attempts to provide a comprehensive meta-analysis of studies on remotely sensed methods and data used for estimation of forest burnt area, burn severity, post-fire effects, and forest recovery patterns at the global level by using the PRISMA framework. In the study, we discuss the results of the analysis based on 329 selected papers on the main aspects of the study area published in 48 journals within the past two decades (2000–2020). In the first part of this review, we analyse characteristics of the papers, including journals, spatial extent, geographic distribution, types of remote sensing sensors, ecological zoning, tree species, spectral indices, and accuracy metrics used in the studies. The second part of this review discusses the main tendencies, challenges, and increasing added value of different remote sensing techniques in forest burnt area, burn severity, and post-fire recovery assessments. Finally, it identifies potential opportunities for future research with the use of the new generation of remote sensing systems, classification and cloud performing techniques, and emerging processes platforms for regional and large-scale applications in the field of study. Full article
(This article belongs to the Special Issue Remote Sensing of Burnt Area II)
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