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    Author(s): T. Ryan McCarley; Crystal A. Kolden; Nicole M. VaillantAndrew T. Hudak; Alistair M. S. Smith; Brian M. Wing; Bryce S. Kellogg; Jason Kreitler
    Date: 2017
    Source: Remote Sensing of Environment. 191: 419-432.
    Publication Series: Scientific Journal (JRNL)
    Station: Pacific Northwest Research Station
    PDF: View PDF  (1.0 MB)

    Description

    Measuring post-fire effects at landscape scales is critical to an ecological understanding of wildfire effects. Predominantly this is accomplished with either multi-spectral remote sensing data or through ground-based field sampling plots.While these methods are important, field data is usually limited to opportunistic post-fire observations, and spectral data often lacks validation with specific variables of change. Additional uncertainty remains regarding how best to account for environmental variables influencing fire effects (e.g., weather) for which observational data cannot easily be acquired, and whether pre-fire agents of change such as bark beetle and timber harvest impact model accuracy. This study quantifies wildfire effects by correlating changes in forest structure derived from multi-temporal Light Detection and Ranging (LiDAR) acquisitions to multi-temporal spectral changes captured by the Landsat Thematic Mapper and Operational Land Imager for the 2012 Pole Creek Fire in central Oregon. Spatial regression modeling was assessed as a methodology to account for spatial autocorrelation, and model consistency was quantified across areas impacted by pre-fire mountain pine beetle and timber harvest. The strongest relationship (pseudo-r2=0.86, p<0.0001) was observed between the ratio of shortwave infrared and near infrared reflectance (d74) and LiDAR-derived estimate of canopy cover change. Relationships between percentage of LiDAR returns in forest strata and spectral indices generally increased in strength with strata height. Structural measurements made closer to the ground were not well correlated. The spatial regression approach improved all relationships, demonstrating its utility, but model performance declined across pre-fire agents of change, suggesting that such studies should stratify by pre-fire forest condition. This study establishes that spectral indices such as d74 and dNBR are most sensitive to wildfire-caused structural changes such as reduction in canopy cover and perform best when that structure has not been reduced pre-fire.

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    Citation

    McCarley, T. Ryan; Kolden, Crystal A.; Vaillant, Nicole M.; Hudak, Andrew T.; Smith, Alistair M. S.; Wing, Brian M.; Kellogg, Bryce S.; Kreitler, Jason. 2017. Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure. Remote Sensing of Environment. 191: 419-432.

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    Keywords

    wildfire, Eastern Cascades, dNBR, d74, canopy cover, simultaneous autoregression

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