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    Author(s): Evan B. Brooks; John W. Coulston; Randolph H. Wynne; Valerie A. Thomas
    Date: 2016
    Source: Remote Sensing of Environment
    Publication Series: Scientific Journal (JRNL)
    Station: Southern Research Station
    PDF: Download Publication  (1.0 MB)


    The use of satellite-derived classification maps to improve post-stratified forest parameter estimates is well
    established.When reducing the variance of post-stratification estimates for forest change parameters such as forest
    growth, it is logical to use a change-related strata map. At the stand level, a time series of Landsat images is
    ideally suited for producing such a map. In this study, we generate strata maps based on trajectories of Landsat
    ThematicMapper-based normalized difference vegetation index values, with a focus on post-disturbance recovery
    and recent measurements. These trajectories, from1985 to 2010, are converted to harmonic regression coefficient
    estimates and classified according to a hierarchical clustering algorithm from a training sample. The
    resulting strata maps are then used in conjunction with measured plots to estimate forest status and change
    parameters in an Alabama, USA study area. These estimates and the variance of the estimates are then used to
    calculate the estimated relative efficiencies of the post-stratified estimates. Estimated relative efficiencies around
    or above 1.2 were observed for total growth, total mortality, and total removals, with different strata maps being
    more effective for each. Possible avenues for improvement of the approach include the following: (1) enlarging
    the study area and (2) using the Landsat images closest to the time ofmeasurement for each plot. Multitemporal
    satellite-derived strata maps show promise for improving the precision of change parameter estimates.

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    Brooks, Evan B.; Coulston, John W.; Wynne, Randolph H.; Thomas, Valerie A. 2016. Improving the precision of dynamic forest parameter estimates using Landsat. Remote Sensing of Environment, Vol. 179: 8 pages.: 162-169.  DOI:10.1016/j.rse.2016.03.017


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    Harmonic regression, Hierarchical cluster analysis, FIA, Site index, Post-stratification

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