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A double-sampling approach to deriving training and validation data for remotely-sensed vegetation products
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The need for large sample sizes to train, calibrate, and validate remote-sensing products has driven an emphasis toward rapid, and in many cases, qualitative field methods. Double-sampling is an option for calibrating less-precise field measurements with data from a more precise method collected at a subset of sampling locations. While applicable to the creation of training and validation datasets for remote-sensing products, double-sampling has rarely been used in this context. Our objective was to compare vegetation indicators developed from a rapid ocular-estimation field protocol with the quantitative field protocol used by the Bureau of Land Management’s Assessment, Inventory and Monitoring (AIM) program to determine if double-sampling could improve the relationship between field data and high-resolution satellite imagery. We used beta-regression to establish the relationship between AIM- and ocular-protocol estimates of vegetation cover from 50 field sites in the Piceance Basin of northwestern Colorado, USA. Using the defined regression models for eight vegetation indicators we adjusted the ocular-protocol estimates and compared the results, along with the original measurements, to 5m-resolution RapidEye satellite imagery. We found good correlation between AIM- and ocular-protocol estimates for dominant site components like shrub cover and bare ground, but low correlations for minor site components (e.g., annual grass cover) or indicators where observers were required to estimate over multiple life forms (e.g., total canopy cover). Correcting the ocular-protocol estimates with the AIM-protocol data significantly improved correlation with the RapidEye imagery for most indicators. As a means of improving training data for remote sensing projects, double-sampling should be used where a strong relationship exists between quantitative and qualitative field techniques. Accordingly, ocular techniques should be used only when they can generate reliable estimates of vegetation cover.
Jason W. Karl
USDA Scientist Submission
International Journal of Remote Sensing 2014 3 v.35 no.5
Journal Articles, USDA Authors, Peer-Reviewed
Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.
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