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    Author(s): Sabrina B. Turner; David P. TurnerAndrew N. Gray; Will Fellers
    Date: 2019
    Source: International Journal of Remote Sensing. 40(7): 2558-2575.
    Publication Series: Scientific Journal (JRNL)
    Station: Pacific Northwest Research Station
    PDF: View PDF  (3.0 MB)

    Description

    Forests represent a significant opportunity for carbon sequestration, but quantifying biomass change at the landscape scale and larger remains a challenge. Here we develop an approach based on repeated tree-level analysis using high-resolution airborne lidar (around 8 pulses/m2). The study area was 53 km2 of actively managed coniferous forestland in the Coast Range Mountains in western Oregon. The study interval was 2006–2012. Tree heights and crown areas were determined from the lidar data using point cloud clustering. Biomass per tree was estimated with allometry. Tree-level data (N = 14,709) from local USDA Forest Service Forest Inventory and Analysis plots provided the basis for the allometry. Estimated biomass change over the 6-year interval averaged −1.3 kg m−2 year−1, with the average gain in undisturbed areas of 1.0 kg m−2 year−1. Full harvest occurred on 3% of the area per year. For surviving trees, the mean change in height was 0.5 m year−1 (SD = 0.3) and the mean change in biomass was 45.3 kg year−1 (SD = 6.7). The maximum bin-average increase in biomass per tree (57.3 kg year−1) was observed in trees of intermediate height (35–40 m). In addition to high spatially resolved tracking of forest biomass change, potential applications of repeated tree-level surveys include analysis of mortality. In this relatively productive forest landscape, an interval of 6 years between lidar acquisitions was adequate to resolve significant changes in tree height and area-wide biomass.

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    Citation

    Turner, Sabrina B.; Turner, David P.; Gray, Andrew N.; Fellers, Will. 2019. An approach to estimating forest biomass change over a coniferous forest landscape based on tree-level analysis from repeated lidar surveys. International Journal of Remote Sensing. 40(7): 2558-2575. https://doi.org/10.1080/01431161.2018.1528401.

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    Keywords

    Lidar, tree biomass, growth.

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https://www.fs.usda.gov/treesearch/pubs/59062