U.S. flag

An official website of the United States government

Dot gov

Official websites use .gov
A .gov website belongs to an official government organization in the United States.


Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.


Main content area

A double-sampling approach to deriving training and validation data for remotely-sensed vegetation products

Jason W. Karl, Jason Taylor, Matt Bobo
International journal of remote sensing 2014 v.35 no.5 pp. 1936-1955
canopy, data collection, grasses, image analysis, regression analysis, remote sensing, shrubs, vegetation cover, Colorado
The need for large sample sizes to train, calibrate, and validate remote-sensing products has driven an emphasis towards 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 qualitative (i.e. ocular estimation) field protocol with the quantitative field protocol used by the Bureau of Land Management’s Assessment, Inventory and Monitoring (AIM) programme to determine whether double-sampling could be used to adjust the qualitative estimates to improve the relationship between rapidly collected field data and high-resolution satellite imagery. We used beta regression to establish the relationship between the quantitative and qualitative 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 qualitative estimates and compared the results, along with the original measurements, to 5 m-resolution RapidEye satellite imagery. We found good correlation between quantitative and ocular estimates for dominant site components such as 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). Using the beta-regression models to adjust the qualitative estimates with the quantitative 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.