Assessing burn severity and vegetation restoration in Alberta’s boreal forests following the 2016 Fort McMurray wildfire – a remote sensing time-series study
Forest fires play a crucial role in resetting boreal ecosystems and steering ecological succession dynamics. However, the escalating impacts of global climate change are anticipated to increase the frequency, intensity, and size of wildfires, leading to significant economic, ecological, and social c...
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ftdoajarticles:oai:doaj.org/article:2201524188af4f97b26ce3eb318678ae 2024-09-15T18:06:56+00:00 Assessing burn severity and vegetation restoration in Alberta’s boreal forests following the 2016 Fort McMurray wildfire – a remote sensing time-series study Eric Xu Mary Wei Tiffany Li Victor Lei Jennifer Gao Nathan Wang Yuhong He 2024-12-01T00:00:00Z https://doi.org/10.1080/27658511.2024.2361569 https://doaj.org/article/2201524188af4f97b26ce3eb318678ae EN eng Taylor & Francis Group https://www.tandfonline.com/doi/10.1080/27658511.2024.2361569 https://doaj.org/toc/2765-8511 doi:10.1080/27658511.2024.2361569 2765-8511 https://doaj.org/article/2201524188af4f97b26ce3eb318678ae Sustainable Environment, Vol 10, Iss 1 (2024) Remote sensing wildfire severity recovery Landsat NDVI Environmental sciences GE1-350 article 2024 ftdoajarticles https://doi.org/10.1080/27658511.2024.2361569 2024-08-05T17:49:12Z Forest fires play a crucial role in resetting boreal ecosystems and steering ecological succession dynamics. However, the escalating impacts of global climate change are anticipated to increase the frequency, intensity, and size of wildfires, leading to significant economic, ecological, and social consequences. To effectively address fire risk and optimize post-fire management strategies, close monitoring, assessment, and understanding of the spatial heterogeneity of wildfires and their impacts are essential. Remote sensing, with its extensive historical records, provides a cost-effective means to examine wildfires. This study focuses on a significant wildfire event that occurred in May 2016 that made a substantial impact on Fort McMurray, Alberta, Canada. Using the Google Earth Engine (GEE) Platform, Landsat images time series covering pre- and post-fire (2015 to 2023), and land cover maps, we delineated the fire’s extent and conducted a comprehensive assessment of variations in burn severity and subsequent vegetation recovery. The Differenced Normalized Burn Ratio (dNBR) was calculated from Landsat images to measure burn extent, burn severity, and burn spatial variability. The Normalized Difference Vegetation Index (NDVI) was used for post-fire vegetation recovery analysis. Our findings reveal that 53.5% of the burn area experienced fire damage. Swamps and forests experienced the most intense burns (dNBR of 0.55 for swamps and 0.41 for forests) due to denser vegetation and biomass. Grasslands had moderate burn severity (dNBR of 0.281). In contrast, bogs, marshes, and fens showed lower dNBR values (0.15, 0.12, and −0.003), indicating low to no burns, likely due to their wetter conditions acting as natural firebreaks. NDVI changes indicate varying rates of vegetation recovery post-wildfire across different land cover types. In fen and marsh areas, NDVI was initially at 0.66 and 0.65 in 2015, dropped slightly in 2016, but rebounded by 2017, showing resilience. Swamps’ NDVI declined from 0.69 in 2015 to 0.46 in ... Article in Journal/Newspaper Fort McMurray Directory of Open Access Journals: DOAJ Articles Sustainable Environment 10 1 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Remote sensing wildfire severity recovery Landsat NDVI Environmental sciences GE1-350 |
spellingShingle |
Remote sensing wildfire severity recovery Landsat NDVI Environmental sciences GE1-350 Eric Xu Mary Wei Tiffany Li Victor Lei Jennifer Gao Nathan Wang Yuhong He Assessing burn severity and vegetation restoration in Alberta’s boreal forests following the 2016 Fort McMurray wildfire – a remote sensing time-series study |
topic_facet |
Remote sensing wildfire severity recovery Landsat NDVI Environmental sciences GE1-350 |
description |
Forest fires play a crucial role in resetting boreal ecosystems and steering ecological succession dynamics. However, the escalating impacts of global climate change are anticipated to increase the frequency, intensity, and size of wildfires, leading to significant economic, ecological, and social consequences. To effectively address fire risk and optimize post-fire management strategies, close monitoring, assessment, and understanding of the spatial heterogeneity of wildfires and their impacts are essential. Remote sensing, with its extensive historical records, provides a cost-effective means to examine wildfires. This study focuses on a significant wildfire event that occurred in May 2016 that made a substantial impact on Fort McMurray, Alberta, Canada. Using the Google Earth Engine (GEE) Platform, Landsat images time series covering pre- and post-fire (2015 to 2023), and land cover maps, we delineated the fire’s extent and conducted a comprehensive assessment of variations in burn severity and subsequent vegetation recovery. The Differenced Normalized Burn Ratio (dNBR) was calculated from Landsat images to measure burn extent, burn severity, and burn spatial variability. The Normalized Difference Vegetation Index (NDVI) was used for post-fire vegetation recovery analysis. Our findings reveal that 53.5% of the burn area experienced fire damage. Swamps and forests experienced the most intense burns (dNBR of 0.55 for swamps and 0.41 for forests) due to denser vegetation and biomass. Grasslands had moderate burn severity (dNBR of 0.281). In contrast, bogs, marshes, and fens showed lower dNBR values (0.15, 0.12, and −0.003), indicating low to no burns, likely due to their wetter conditions acting as natural firebreaks. NDVI changes indicate varying rates of vegetation recovery post-wildfire across different land cover types. In fen and marsh areas, NDVI was initially at 0.66 and 0.65 in 2015, dropped slightly in 2016, but rebounded by 2017, showing resilience. Swamps’ NDVI declined from 0.69 in 2015 to 0.46 in ... |
format |
Article in Journal/Newspaper |
author |
Eric Xu Mary Wei Tiffany Li Victor Lei Jennifer Gao Nathan Wang Yuhong He |
author_facet |
Eric Xu Mary Wei Tiffany Li Victor Lei Jennifer Gao Nathan Wang Yuhong He |
author_sort |
Eric Xu |
title |
Assessing burn severity and vegetation restoration in Alberta’s boreal forests following the 2016 Fort McMurray wildfire – a remote sensing time-series study |
title_short |
Assessing burn severity and vegetation restoration in Alberta’s boreal forests following the 2016 Fort McMurray wildfire – a remote sensing time-series study |
title_full |
Assessing burn severity and vegetation restoration in Alberta’s boreal forests following the 2016 Fort McMurray wildfire – a remote sensing time-series study |
title_fullStr |
Assessing burn severity and vegetation restoration in Alberta’s boreal forests following the 2016 Fort McMurray wildfire – a remote sensing time-series study |
title_full_unstemmed |
Assessing burn severity and vegetation restoration in Alberta’s boreal forests following the 2016 Fort McMurray wildfire – a remote sensing time-series study |
title_sort |
assessing burn severity and vegetation restoration in alberta’s boreal forests following the 2016 fort mcmurray wildfire – a remote sensing time-series study |
publisher |
Taylor & Francis Group |
publishDate |
2024 |
url |
https://doi.org/10.1080/27658511.2024.2361569 https://doaj.org/article/2201524188af4f97b26ce3eb318678ae |
genre |
Fort McMurray |
genre_facet |
Fort McMurray |
op_source |
Sustainable Environment, Vol 10, Iss 1 (2024) |
op_relation |
https://www.tandfonline.com/doi/10.1080/27658511.2024.2361569 https://doaj.org/toc/2765-8511 doi:10.1080/27658511.2024.2361569 2765-8511 https://doaj.org/article/2201524188af4f97b26ce3eb318678ae |
op_doi |
https://doi.org/10.1080/27658511.2024.2361569 |
container_title |
Sustainable Environment |
container_volume |
10 |
container_issue |
1 |
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1810444280467030016 |