Unrecorded Tundra Fires in Canada, 1986–2022. Hethcoat, M. G., Jain, P., Parisien, M., Skakun, R., Rogic, L., & Whitman, E. Remote Sensing, 16(2):230, January, 2024. Number: 2 Publisher: Multidisciplinary Digital Publishing Institute![Unrecorded Tundra Fires in Canada, 1986–2022 [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Climate-driven changes in fire regimes are expected across the pan-Arctic region. Trends in arctic fires are thought to be generally increasing; however, fire mapping across the region is far from comprehensive or systematic. We developed a new detection workflow and built a dataset of unrecorded tundra fires in Canada using Landsat data. We built a reference dataset of spectral indices from previously mapped fires in northern Canada to train a Random Forest model for detecting new fires between 1986 and 2022. In addition, we used time series information for each pixel to reduce false positives and narrow the large search space down to a finite set of regions that had experienced changes. We found 209 previously undetected fires in the Arctic and sub-Arctic regions, increasing the mapped burned area by approximately 30%. The median fire size was small, with roughly 3/4 of the fires being \textless100 ha in size. The majority of newly detected fires (69%) did not have satellite-derived hotspots associated with them. The dataset presented here is commission error-free and can be viewed as a reference dataset for future analyses. Moreover, future improvements and updates will leverage these data to improve the detection workflow outlined here, particularly for small and low-severity fires. These data can facilitate broader analyses that examine trends and environmental drivers of fire across the Arctic region. Such analyses could begin to untangle the mechanisms driving heterogeneous fire responses to climate observed across regions of the Circumpolar North.
@article{hethcoat_unrecorded_2024,
title = {Unrecorded {Tundra} {Fires} in {Canada}, 1986–2022},
volume = {16},
copyright = {http://creativecommons.org/licenses/by/3.0/},
issn = {2072-4292},
url = {https://www.mdpi.com/2072-4292/16/2/230},
doi = {10.3390/rs16020230},
abstract = {Climate-driven changes in fire regimes are expected across the pan-Arctic region. Trends in arctic fires are thought to be generally increasing; however, fire mapping across the region is far from comprehensive or systematic. We developed a new detection workflow and built a dataset of unrecorded tundra fires in Canada using Landsat data. We built a reference dataset of spectral indices from previously mapped fires in northern Canada to train a Random Forest model for detecting new fires between 1986 and 2022. In addition, we used time series information for each pixel to reduce false positives and narrow the large search space down to a finite set of regions that had experienced changes. We found 209 previously undetected fires in the Arctic and sub-Arctic regions, increasing the mapped burned area by approximately 30\%. The median fire size was small, with roughly 3/4 of the fires being {\textless}100 ha in size. The majority of newly detected fires (69\%) did not have satellite-derived hotspots associated with them. The dataset presented here is commission error-free and can be viewed as a reference dataset for future analyses. Moreover, future improvements and updates will leverage these data to improve the detection workflow outlined here, particularly for small and low-severity fires. These data can facilitate broader analyses that examine trends and environmental drivers of fire across the Arctic region. Such analyses could begin to untangle the mechanisms driving heterogeneous fire responses to climate observed across regions of the Circumpolar North.},
language = {en},
number = {2},
urldate = {2024-03-20},
journal = {Remote Sensing},
author = {Hethcoat, Matthew G. and Jain, Piyush and Parisien, Marc-André and Skakun, Rob and Rogic, Luka and Whitman, Ellen},
month = jan,
year = {2024},
note = {Number: 2
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {NALCMS},
pages = {230},
}
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We built a reference dataset of spectral indices from previously mapped fires in northern Canada to train a Random Forest model for detecting new fires between 1986 and 2022. In addition, we used time series information for each pixel to reduce false positives and narrow the large search space down to a finite set of regions that had experienced changes. We found 209 previously undetected fires in the Arctic and sub-Arctic regions, increasing the mapped burned area by approximately 30%. The median fire size was small, with roughly 3/4 of the fires being \\textless100 ha in size. The majority of newly detected fires (69%) did not have satellite-derived hotspots associated with them. The dataset presented here is commission error-free and can be viewed as a reference dataset for future analyses. Moreover, future improvements and updates will leverage these data to improve the detection workflow outlined here, particularly for small and low-severity fires. 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