A boreal wildfire and harvesting database with ensemble confidence attributes for Ontario (1972–2021+)
We present a repeatable workflow that produced a comprehensive wildfire and timber harvesting database for Ontario (1972–2021) that accommodates annual updates after each new fire season. Training sites for classification are identified at the individual scene-level to avoid spectral variations intr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223000213 |
| _version_ | 1811163480195072000 |
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| author | Tarmo K. Remmel Marc Ouellette Wesley J. Wu |
| author_facet | Tarmo K. Remmel Marc Ouellette Wesley J. Wu |
| author_sort | Tarmo K. Remmel |
| collection | DOAJ |
| description | We present a repeatable workflow that produced a comprehensive wildfire and timber harvesting database for Ontario (1972–2021) that accommodates annual updates after each new fire season. Training sites for classification are identified at the individual scene-level to avoid spectral variations introduced by time and distance. ISODATA classification on the training data produces clusters that are modelled by a smooth polynomial function to identify a local minimum point along the classification clusters that distinguishes disturbances from non-disturbances. This threshold is then applied to map disturbances on independent scenes of Landsat MSS, TM, ETM+, or OLI imagery. Results are aggregated to 1.44 ha cells and converted to points for dissemination; we do not map explicit boundaries to avoid issues of context- and scale-dependence, or the realities of transitional boundaries. Disturbance points are cross-referenced through time, ensuring that the earliest date for each disturbance is recorded. Disturbance points are intersected with other harvesting and fire databases to assess their ensemble confidence which is attached to each mapped location. We present summaries of Ontario’s boreal disturbance mapping with respect to levels of assessed confidence. |
| first_indexed | 2024-04-10T15:06:13Z |
| format | Article |
| id | doaj.art-3facfea932c8471cb9ed88aa6e7794c5 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-10T15:06:13Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-3facfea932c8471cb9ed88aa6e7794c52023-02-15T04:27:30ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-03-01117103199A boreal wildfire and harvesting database with ensemble confidence attributes for Ontario (1972–2021+)Tarmo K. Remmel0Marc Ouellette1Wesley J. Wu2Faculty of Environmental and Urban Change, York University, N430 Ross Building, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada; Corresponding author.Ontario Forest Research Institute, Ministry of Natural Resources and Forestry (MNRF), 1235 Queen Street East, Sault Ste. Marie P6A 2E5, CanadaFaculty of Environmental and Urban Change, York University, N430 Ross Building, 4700 Keele Street, Toronto, Ontario M3J 1P3, CanadaWe present a repeatable workflow that produced a comprehensive wildfire and timber harvesting database for Ontario (1972–2021) that accommodates annual updates after each new fire season. Training sites for classification are identified at the individual scene-level to avoid spectral variations introduced by time and distance. ISODATA classification on the training data produces clusters that are modelled by a smooth polynomial function to identify a local minimum point along the classification clusters that distinguishes disturbances from non-disturbances. This threshold is then applied to map disturbances on independent scenes of Landsat MSS, TM, ETM+, or OLI imagery. Results are aggregated to 1.44 ha cells and converted to points for dissemination; we do not map explicit boundaries to avoid issues of context- and scale-dependence, or the realities of transitional boundaries. Disturbance points are cross-referenced through time, ensuring that the earliest date for each disturbance is recorded. Disturbance points are intersected with other harvesting and fire databases to assess their ensemble confidence which is attached to each mapped location. We present summaries of Ontario’s boreal disturbance mapping with respect to levels of assessed confidence.http://www.sciencedirect.com/science/article/pii/S1569843223000213WildfireHarvestingLandsatClassificationConfidencePoints |
| spellingShingle | Tarmo K. Remmel Marc Ouellette Wesley J. Wu A boreal wildfire and harvesting database with ensemble confidence attributes for Ontario (1972–2021+) International Journal of Applied Earth Observations and Geoinformation Wildfire Harvesting Landsat Classification Confidence Points |
| title | A boreal wildfire and harvesting database with ensemble confidence attributes for Ontario (1972–2021+) |
| title_full | A boreal wildfire and harvesting database with ensemble confidence attributes for Ontario (1972–2021+) |
| title_fullStr | A boreal wildfire and harvesting database with ensemble confidence attributes for Ontario (1972–2021+) |
| title_full_unstemmed | A boreal wildfire and harvesting database with ensemble confidence attributes for Ontario (1972–2021+) |
| title_short | A boreal wildfire and harvesting database with ensemble confidence attributes for Ontario (1972–2021+) |
| title_sort | boreal wildfire and harvesting database with ensemble confidence attributes for ontario 1972 2021 |
| topic | Wildfire Harvesting Landsat Classification Confidence Points |
| url | http://www.sciencedirect.com/science/article/pii/S1569843223000213 |
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