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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #260685
Title: Making spatial predictions of rangeland ecosystem attributes using regression kriging

Author
item Karl, Jason

Submitted to: Society for Range Management Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 11/1/2009
Publication Date: 2/7/2010
Citation: Karl, J.W. 2010. Making spatial predictions of rangeland ecosystem attributes using regression kriging [abstract]. 63rd Society for Range Management Annual Meeting, February 7-11, 2010, Denver, Colorado. 0-186.

Interpretive Summary:

Technical Abstract: Sound rangeland management requires accurate information on condition over large landscapes. Typical approaches to making spatial predictions rangeland condition attributes (e.g., shrub or bare ground cover) are via regression between field and remotely-sensed data. This works well in some situations but has limited utility when correlations between field and image data are low and does not take advantage of all information contained in the field data. I compared, for three rangeland attributes (percent cover of shrubs, bare ground, and cheatgrass [Bromus tectorum L.]) in a southern Idaho study area, spatial predictions from generalized least-squares (GLS) regression to a geostatistical interpolator, regression kriging (RK) that 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 TM 5 images into polygons (i.e., objects) because previous research has shown that OBIA yields higher image-to-field data correlations. Spatial dependence, the decrease in autocorrelation with increasing distance, was strongest for bare ground (samples autocorrelated up to a distance [i.e., range] of 12,646m), but present in all three variables (range of 3,653m and 768m for shrub and cheatgrass cover, respectively). As a result, RK produced more accurate results than GLS regression alone for all three variables measured by cross-validated root mean-squared error. Additionally, the ability to map how prediction confidence changes with distance from field samples is a significant benefit of regression kriging and makes this approach suitable for landscape-level assessments and management planning.