Forest fires under the lens: needleleaf index - a novel tool for satellite image analysis
Abstract High-resolution coniferous forest area datasets are needed to understand spatiotemporal variations in forest capacity1–3; however, separating coniferous forests from other vegetation covers remains challenging because of their similar spectral signatures4,5. Here, we propose a new spectral...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
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| Series: | npj Natural Hazards |
| Online Access: | https://doi.org/10.1038/s44304-025-00063-w |
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| Summary: | Abstract High-resolution coniferous forest area datasets are needed to understand spatiotemporal variations in forest capacity1–3; however, separating coniferous forests from other vegetation covers remains challenging because of their similar spectral signatures4,5. Here, we propose a new spectral index called the needleleaf index to extract coniferous forest areas in North American boreal forests based on Landsat imagery at a 30-m resolution by utilizing over 24,000 Landsat images. Our analysis revealed that 25% of the total area of coniferous forests burned over the past two decades was destroyed in the 2023 wildfires. Remotely sensed observations showed that the coniferous forest area in the 2018–2023 period increased by 5.62% compared with the 1984–1991 period and decreased by 4.85% since its peak in 1992–2001. While the needleleaf index holds potential for application in coniferous forests of the taiga biome across different continents, further validation is essential to assess its reliability. |
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| ISSN: | 2948-2100 |