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    • Rangeland Ecology & Management, Volume 63 (2010)
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    Spatial Predictions of Cover Attributes of Rangeland Ecosystems Using Regression Kriging and Remote Sensing

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    Author
    Karl, Jason W.
    Issue Date
    2010-05-01
    Keywords
    Bromus tectorum
    geostatistics
    Idaho
    landscape-scale assessment
    shrub cover
    statistical modeling
    
    Metadata
    Show full item record
    Citation
    Karl, J. W. (2010). Spatial predictions of cover attributes of rangeland ecosystems using regression kriging and remote sensing. Rangeland Ecology & Management, 63(3), 335-349.
    Publisher
    Society for Range Management
    Journal
    Rangeland Ecology & Management
    URI
    http://hdl.handle.net/10150/642794
    DOI
    10.2111/REM-D-09-00074.1
    Additional Links
    https://rangelands.org/
    Abstract
    Sound rangeland management requires accurate information on rangeland condition over large landscapes. A commonly applied approach to making spatial predictions of attributes related to rangeland condition (e.g., shrub or bare ground cover) from remote sensing is via regression between field and remotely sensed data. This has worked well in some situations but has limited utility when correlations between field and image data are low and it does not take advantage of all information contained in the field data. I compared spatial predictions from generalized least-squares (GLS) regression to a geostatistical interpolator, regression kriging (RK), for three rangeland attributes (percent cover of shrubs, bare ground, and cheatgrass [Bromus tectorum L.]) in a southern Idaho study area. The RK technique combines GLS regression with spatial interpolation of the residuals to improve predictions of rangeland condition attributes over large landscapes. I employed a remote-sensing technique, object-based image analysis (OBIA), to segment Landsat 5 Thematic Mapper images into polygons (i.e., objects) because previous research has shown that OBIA yields higher image-to-field data correlations and can be used to select appropriate scales for analysis. Spatial dependence, the decrease in autocorrelation with increasing distance, was strongest for percent shrub cover (samples autocorrelated up to a distance [i.e., range] of 19 098 m) but present in all three variables (range of 12 646 m and 768 m for bare ground and cheatgrass cover, respectively). As a result, RK produced more accurate results than GLS regression alone for all three attributes when predicted versus observed values of each attribute were measured by leave- one-out cross validation. The results of RK could be used in assessments of rangeland conditions over large landscapes. The ability to create maps quantifying how prediction confidence changes with distance from field samples is a significant benefit of regression kriging and makes this approach suitable for landscape-level management planning. 
    Type
    text
    Article
    Language
    en
    ISSN
    0022-409X
    ae974a485f413a2113503eed53cd6c53
    10.2111/REM-D-09-00074.1
    Scopus Count
    Collections
    Rangeland Ecology & Management, Volume 63, Number 3 (May 2010)

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