Spatially-Explicit Prediction of Wildfire Burn Probability Using Remotely-Sensed and Ancillary Data
Wildfire is a critical process shaping the structure and composition of forest landscapes of western Canada. Spatially-explicit forest disturbance history and forest structure estimated using remotely-sensed data enables the characterization of burn probability, defined as the susceptibility of land...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-05-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2020.1788385 |
| _version_ | 1797661154929016832 |
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| author | Chen Shang Michael A. Wulder Nicholas C. Coops Joanne C. White Txomin Hermosilla |
| author_facet | Chen Shang Michael A. Wulder Nicholas C. Coops Joanne C. White Txomin Hermosilla |
| author_sort | Chen Shang |
| collection | DOAJ |
| description | Wildfire is a critical process shaping the structure and composition of forest landscapes of western Canada. Spatially-explicit forest disturbance history and forest structure estimated using remotely-sensed data enables the characterization of burn probability, defined as the susceptibility of landscapes to fire hazard over time. In this research, we leveraged the Landsat archive to determine the capacity of land cover, forest structure, and forest disturbance information, together with ancillary data, to estimate burn probability. We analyzed the interactions between a number of contributing factors and identified landscapes with high probability to burn across forested ecosystems of Saskatchewan, Canada. Overall, we found that forests composed of coniferous species, with shorter trees (<3 m), low canopy height variability, an open stand structure (<10% canopy cover), and low timber volumes (<50 m3/ha), had higher burn probabilities. A 2015 burn probability map indicated that forests that did burn in 2015 (determined using independently mapped wildfires) had a median predicted burn probability of 81%, while the median burn probability for unburned forest area was 19%. This paper demonstrates the potential to generate detailed and spatially-explicit burn probability maps from time series remote sensing data to inform wildland fire risk modeling, management, and mitigation. |
| first_indexed | 2024-03-11T18:40:43Z |
| format | Article |
| id | doaj.art-ee8e37abc1e045658c36ffe6b4238162 |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:40:43Z |
| publishDate | 2020-05-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-ee8e37abc1e045658c36ffe6b42381622023-10-12T13:36:23ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712020-05-0146331332910.1080/07038992.2020.17883851788385Spatially-Explicit Prediction of Wildfire Burn Probability Using Remotely-Sensed and Ancillary DataChen Shang0Michael A. Wulder1Nicholas C. Coops2Joanne C. White3Txomin Hermosilla4Department of Forest Resources Management, Faculty of Forestry, Integrated Remote Sensing Studio, University of British ColumbiaCanadian Forest Service (Pacific Forestry Centre), Natural Resources CanadaDepartment of Forest Resources Management, Faculty of Forestry, Integrated Remote Sensing Studio, University of British ColumbiaCanadian Forest Service (Pacific Forestry Centre), Natural Resources CanadaCanadian Forest Service (Pacific Forestry Centre), Natural Resources CanadaWildfire is a critical process shaping the structure and composition of forest landscapes of western Canada. Spatially-explicit forest disturbance history and forest structure estimated using remotely-sensed data enables the characterization of burn probability, defined as the susceptibility of landscapes to fire hazard over time. In this research, we leveraged the Landsat archive to determine the capacity of land cover, forest structure, and forest disturbance information, together with ancillary data, to estimate burn probability. We analyzed the interactions between a number of contributing factors and identified landscapes with high probability to burn across forested ecosystems of Saskatchewan, Canada. Overall, we found that forests composed of coniferous species, with shorter trees (<3 m), low canopy height variability, an open stand structure (<10% canopy cover), and low timber volumes (<50 m3/ha), had higher burn probabilities. A 2015 burn probability map indicated that forests that did burn in 2015 (determined using independently mapped wildfires) had a median predicted burn probability of 81%, while the median burn probability for unburned forest area was 19%. This paper demonstrates the potential to generate detailed and spatially-explicit burn probability maps from time series remote sensing data to inform wildland fire risk modeling, management, and mitigation.http://dx.doi.org/10.1080/07038992.2020.1788385 |
| spellingShingle | Chen Shang Michael A. Wulder Nicholas C. Coops Joanne C. White Txomin Hermosilla Spatially-Explicit Prediction of Wildfire Burn Probability Using Remotely-Sensed and Ancillary Data Canadian Journal of Remote Sensing |
| title | Spatially-Explicit Prediction of Wildfire Burn Probability Using Remotely-Sensed and Ancillary Data |
| title_full | Spatially-Explicit Prediction of Wildfire Burn Probability Using Remotely-Sensed and Ancillary Data |
| title_fullStr | Spatially-Explicit Prediction of Wildfire Burn Probability Using Remotely-Sensed and Ancillary Data |
| title_full_unstemmed | Spatially-Explicit Prediction of Wildfire Burn Probability Using Remotely-Sensed and Ancillary Data |
| title_short | Spatially-Explicit Prediction of Wildfire Burn Probability Using Remotely-Sensed and Ancillary Data |
| title_sort | spatially explicit prediction of wildfire burn probability using remotely sensed and ancillary data |
| url | http://dx.doi.org/10.1080/07038992.2020.1788385 |
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