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Remote Sensing
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Fires on Forest Environments
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Fires on Forest Environments

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 7105

Special Issue Editors

Prof. Dr. Arturo Sanchez-Azofeifa

E-Mail Website
Guest Editor
Earth and Atmospheric Sciences Department, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: tropical dry forests; UAVs; carbon fluxes and phenology; hyperspectral remote sensing; ecological succession
Special Issues, Collections and Topics in MDPI journals
Dr. Joanne Nightingale

E-Mail Website
Guest Editor
Earth Observation, Climate and Optical Group, National Physical Laboratory, Teddington TW11 0LW, UK
Interests: assessing the quality of information about forests derived from in situ measurement devices and Earth Observation satellites; improving global satellite-derived biophysical product validation strategies; and contributing to good practice guidance for the evaluation of ECV data records
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increase frequency and damage that fires cause in natural ecosystem is becoming a global fingerprint of climate change. Recent fires in California, Europe and the Amazon basin are a clear example of the need for accurate monitoring and assessment of conditions of fires and the conditions that can cause then, respectively. In this special issue, we would like to explore the role of fires and its monitoring and assessment via remote sensing technologies. We invite those researchers interested on the topics above to submit their contributions. As climate change becomes more predominant, and ecosystems become more responsive to natural and human induced fires, there is a pressing need to build consensus and knowledge base on fire remote-sensing topics. Therefore, regular, methods and synthesis papers are invited.

Prof. Arturo Sanchez-Azofeifa
Dr. Joanne Nightingale
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

  • Thermal remote sensing
  • Fire monitoring
  • Fire mapping
  • Mapping of fire scars
  • Fires and remote sensing

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Published Papers (1 paper)

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Research

24 pages, 5954 KiB  
Article
Intercomparison of Burned Area Products and Its Implication for Carbon Emission Estimations in the Amazon
by Ana Carolina M. Pessôa, Liana O. Anderson, Nathália S. Carvalho, Wesley A. Campanharo, Celso H. L. Silva Junior, Thais M. Rosan, João B. C. Reis, Francisca R. S. Pereira, Mauro Assis, Aline D. Jacon, Jean P. Ometto, Yosio E. Shimabukuro, Camila V. J. Silva, Aline Pontes-Lopes, Thiago F. Morello and Luiz E. O. C. Aragão
Remote Sens. 2020, 12(23), 3864; https://doi.org/10.3390/rs12233864 - 25 Nov 2020
Cited by 33 | Viewed by 6457
Abstract
Carbon (C) emissions from forest fires in the Amazon during extreme droughts may correspond to more than half of the global emissions resulting from land cover changes. Despite their relevant contribution, forest fire-related C emissions are not directly accounted for within national-level inventories [...] Read more.
Carbon (C) emissions from forest fires in the Amazon during extreme droughts may correspond to more than half of the global emissions resulting from land cover changes. Despite their relevant contribution, forest fire-related C emissions are not directly accounted for within national-level inventories or carbon budgets. A fundamental condition for quantifying these emissions is to have a reliable estimation of the extent and location of land cover types affected by fires. Here, we evaluated the relative performance of four burned area products (TREES, MCD64A1 c6, GABAM, and Fire_cci v5.0), contrasting their estimates of total burned area, and their influence on the fire-related C emissions in the Amazon biome for the year 2015. In addition, we distinguished the burned areas occurring in forests from non-forest areas. The four products presented great divergence in the total burned area and, consequently, total related C emissions. Globally, the TREES product detected the largest amount of burned area (35,559 km2), and consequently it presented the largest estimate of committed carbon emission (45 Tg), followed by MCD64A1, with only 3% less burned area detected, GABAM (28,193 km2) and Fire_cci (14,924 km2). The use of Fire_cci may result in an underestimation of 29.54 ± 3.36 Tg of C emissions in relation to the TREES product. The same pattern was found for non-forest areas. Considering only forest burned areas, GABAM was the product that detected the largest area (8994 km2), followed by TREES (7985 km2), MCD64A1 (7181 km2) and Fire_cci (1745 km2). Regionally, Fire_cci detected 98% less burned area in Acre state in southwest Amazonia than TREES, and approximately 160 times less burned area in forests than GABAM. Thus, we show that global products used interchangeably on a regional scale could significantly underestimate the impacts caused by fire and, consequently, their related carbon emissions. Full article
(This article belongs to the Special Issue Fires on Forest Environments)
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