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    Author(s): Rachel RiemannBarry Tyler WilsonAndrew Lister; Sarah Parks
    Date: 2010
    Source: Remote Sensing of Environment. 114: 2337-2352.
    Publication Series: Scientific Journal (JRNL)
    Station: Northern Research Station
    PDF: Download Publication  (1.06 MB)

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    Geospatial datasets of forest characteristics are modeled representations of real populations on the ground. The continuous spatial character of such datasets provides an incredible source of information at the landscape level for ecosystem research, policy analysis, and planning applications, all of which are critical for addressing current challenges related to climate change, urbanization pressures, and data requirements for monitoring carbon sequestration. However, the effectiveness of these applications is dependent upon the accuracy of the geospatial input datasets. A comprehensive set of robust measures is necessary to provide sufficient information to effectively assess the accuracy of these modeled geospatial datasets being produced. Yet challenges in the availability of reference data, in the appropriateness of assessment methods to dataset use, and in the completeness of assessment methods available have continued to hamper the timely and consistent application of map assessments. In this study we present a suite of assessments that can be used to characterize the accuracy of geospatial datasets of modeled continuous variable--an increasingly common format for modeling such attributes as proportion or probability of forestland as well as more traditionally continuous attributes such as leaf area index and forest biomass.

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    Riemann, Rachel; Wilson, Barry Tyler; Lister, Andrew; Parks, Sarah. 2010. An effective assessment protocol for continuous geospatial datasets of forest characteristics using USFS Forest Inventory and Analysis (FIA) data. Remote Sensing of Environment. 114: 2337-2352.


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    accuracy, biomass, uncertainty, geospatial datasets, FIA, comparative assessment, forest characteristics, Minnesota, New York, moderate resolution datasets, measures of agreement

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