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    Author(s): John Hogland; David L. R. Affleck; Nathaniel Anderson; Carl Seielstad; Solomon Dobrowski; Jon Graham; Robert Smith
    Date: 2020
    Source: Forests. 11: 426.
    Publication Series: Scientific Journal (JRNL)
    Station: Rocky Mountain Research Station
    PDF: Download Publication  (3.0 MB)


    Effective forest management is predicated on accurate information pertaining to the characteristics and condition of forests. Unfortunately, ground-based information that accurately describes the complex spatial and contextual nature of forests across broad landscapes is cost prohibitive to collect. In this case study we address technical challenges associated with estimating forest characteristics from remotely sensed data by incorporating field plot layouts specifically designed for calibrating models from such data, applying new image normalization procedures to bring images of varying spatial resolutions to a common radiometric scale, and implementing an ensemble generalized additive modeling technique. Image normalization and ensemble models provided accurate estimates of forest types, presence/absence of longleaf pine (Pinus palustris), and tree basal areas and tree densities over a large segment of the panhandle of Florida, USA. This study overcomes several of the major barriers associated with linking remotely sensed imagery with plot data to estimate key forest characteristics over large areas.

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    Hogland, John; Affleck, David L. R.; Anderson, Nathaniel; Seielstad, Carl; Dobrowski, Solomon; Graham, Jon; Smith, Robert. 2020. Estimating forest characteristics for longleaf pine restoration using normalized remotely sensed imagery in Florida USA. Forests. 11: 426.


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    restoration, longleaf, relative normalization, ensemble generalized additive models, forests

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