| Autor: |
Ying Zhang, Julie Lovitt, Maxim Fortin, Haoyu Fang, Sylvain G. Leblanc, Francis Canisius |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
International Journal of Applied Earth Observations and Geoinformation, Vol 133, Iss , Pp 104098- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1569-8432 |
| DOI: |
10.1016/j.jag.2024.104098 |
| Popis: |
Post-wildfire vegetation cover damage and loss can escalate the risks of secondary disasters such as flood, landslide, and water contamination, particularly in a major wildfire affected region where human settlements are situated. In assessments of the secondary disaster risks, the post-wildfire vegetation cover change plays a key role in influencing the distribution and intensity of the risks. In this work, a processing framework for mapping post-wildfire vegetation cover changes through information fusion has been generated and tested using Landsat8 and WorldView imagery data. The test site was the boreal forest region surrounding Fort McMurray, Alberta, Canada, affected by a massive wildfire in May 2016. The derived map results indicate that the fusion process in the framework is effective for generation of post-wildfire vegetation cover change information. The use of WorldView data revealed more variation details in distribution of the vegetation cover burn damages than use of Landsat data. Moreover, the uncertainty in vegetation burn severity using Landsat-based Differenced Normalized Burn Ratio (dNBR) index exists in the areas with low dNBR reading values due to the sub-pixel effect. The forest burn severity measured with dNBR index can be underestimated due to the quick herbaceous cover recovery after wildfire. These uncertainties in the post-wildfire vegetation cover mapping should be taken into consideration when the derived information is being used for risk assessments. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full Text from ScienceDirect