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Remote Sensing of Forest Fire Severity and Vegetation Recovery
Abstract
Burned forested areas have patterns of varying burn severity as a consequence of various topographic, vegetation, and meteorological factors. These patterns are detected and mapped using satellite data. Other ecological information can be abstracted from satellite data regarding rates of recovery of vegetation foliage and variation of burn severity on different vegetation types. Middle infrared wavelengths are useful for burn severity mapping because the land cover changes associated with burning increase reflectance in this part of the electromagnetic spectrum. Simple stratification of Landsat Thematic Mapper data define varying classes of burn severity because of changes in canopy cover, biomass removal, and soil chemical composition. Reasonable maps of burn severity are produced when the class limits of burn severity reflectance are applied to the entire satellite data. Changes in satellite reflectance over multiple years reveal the dynamics of vegetation and fire severity as low burn areas have lower changes in reflectance relative to high burn areas. This results as a consequence of how much the site was altered due to the burn and how much space is available for vegetation recovery. Analysis of change in reflectance across steppe, riparian, and forested vegetation types indicate that fires potentially increase biomass in steppe areas, while riparian and forested areas are slower to regrow to pre-fire conditions. This satellite-based technology is useful for mapping severely burned areas by exploring the ecological manifestations before and after fire.
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Citations
Cited by
A Project for Monitoring Trends in Burn Severity
Eidenshink, J., Schwind, B., Brewer, K., Zhu, Z.-L., Quayle, B., & Howard, S. (2007) · Fire Ecology · doi:10.4996/fireecology.0301003 · Cited by 1,423
Stand- and landscape-level effects of prescribed burning on two Arizona wildfires
Finney, M. A., McHugh, C. W., & Grenfell, I. C. (2005) · Canadian Journal of Forest Research · doi:10.1139/x05-090 · Cited by 230
Meng, R., Dennison, P. E., D’Antonio, C. M., & Moritz, M. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0110637 · Cited by 65
Metadata
- Authors
- White, J., Ryan, K., Key, C., & Running, S.
- Venue
- International Journal of Wildland Fire
- Publisher
- CSIRO Publishing
- Published
- 1996-09-01
- Volume
- 6
- Issue
- 3
- Pages
- 125-136
- ISSN
- 1049-8001 (ISSN-L)
- 1448-5516
- OpenAlex
- W2144606028
- Cited by
- 355
- References
- 0
- FWCI
- 1.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Remote Sensing of Forest Fire Severity and Vegetation Recovery. https://app.cochat.ai/papers/10.1071/wf9960125
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- White, J., Ryan, K., Key, C., & Running, S. (1996). Remote Sensing of Forest Fire Severity and Vegetation Recovery. International Journal of Wildland Fire. https://doi.org/10.1071/wf9960125
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.