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    Author(s): Mark D. NelsonSean HealeyW. Keith Moser; J.G. Masek; Warren Cohen
    Date: 2011
    Source: Mathematical and Computational Forestry and Natural-Resource Sciences. 3(2): 102-113
    Publication Series: Scientific Journal (JRNL)
    Station: Northern Research Station
    PDF: View PDF  (390.19 KB)

    Description

    We assessed the consistency across space and time of spatially explicit models of forest presence and biomass in southern Missouri, USA, for adjacent, partially overlapping satellite image Path/Rows, and for coincident satellite images from the same Path/Row acquired in different years. Such consistency in satellite image-based classification and estimation is critical to national and continental monitoring programs that depend upon processed satellite imagery, such as the North American Forest Dynamics Program. We tested the interchangeability of particular image acquisitions across time and space in the context of modeling forest biomass and forest presence with a non-parametric Random Forests-based approach. Validation at independent USA national forest inventory plots suggested statistically consistent model accuracy, even when the images used to apply the models were acquired in different years or in different image frames from the images used to build the models. For mapping projects using near-anniversary date imagery and employing careful radiometric correction, advantages of image interchangeability include the ability to build models with more ground data by combining adjacent image frames and the ability to apply models of assessed accuracy to early satellite images for which no corresponding field data may be available.

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    Citation

    Nelson, M.D.; Healey, S.P.; Moser, W.K.; Masek, J.G.; Cohen, W.B. 2011. Consistency of forest presence and biomass predictions modeled across overlapping spatial and temporal extents. Mathematical and Computational Forestry and Natural-Resource Sciences. 3(2): 102-113.

    Keywords

    Consistency analyses, North American Forest Dynamics, NAFD, Landsat, Random Forests, forest inventory, FIA

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