Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub
    Author(s): Zhengyang Hou; Qing Xu; Ronald E. McRoberts; Jonathan A. Greenberg; Jinxiu Liu; Janne Heiskanen; Sari Pitkänen; Petteri Packalen
    Date: 2017
    Source: Remote Sensing of Environment
    Publication Series: Scientific Journal (JRNL)
    Station: Northern Research Station
    PDF: View PDF  (3.0 MB)

    Description

    One of the benefits of model-based inference relative to design-based inference is that probability samples are not required which means that models can be constructed using data external to the area of interest. Although "external" usually means spatially or geographically external, it could also be used in the temporal sense that the model is constructed using data whose dates are temporally external to the dates of the data to which the model is applied. This study focuses on assessing the effects of such temporally external application data on model-based inference using remotely sensed auxiliary information. The study area was in Burkina Faso, and the variable of interest was firewood volume (m3/ha). A sample of 160 field plots was selected from the population and measured, and auxiliary datasets from Landsat 8 were acquired. Models were fit using weighted least squares; the population mean, μ, was estimated; and the variance of the population mean, Var(μˆ), was estimated using both an analytical variance estimator, (μˆ) an, and an empirical bootstrap estimator, V(μˆ)boot. The estimates, μˆ and Var(μˆ), were compared for models constructed using calibration and application data of the same date and models constructed using calibration and application data whose dates differed. The primary results were twofold. First, for cases for which the dates of the model calibration and application data were the same, μˆ, Vˆ(μˆ)an, V(μˆ)boot and Bias(μˆ) were similar across datasets. These results suggest that the particular date of the dataset from which the calibration and application data are obtained may be mostly arbitrary assuming the relation between the dependent and independent variables does not change over time. Second, for a model for which the calibration and application data were obtained from temporally different datasets, (μˆ)an, V(μˆ)boot, and Bias(μˆ) were all greater than when the calibration and application data were not temporally different. Further, the criterion for screening candidatemodelsmust be based on estimation of μˆ and Var(μˆ) rather than the model prediction accuracy or goodness of fit. The adverse effects of differing dates for the calibration and application datawere exacerbated as the difference in dates increased. Finally, because the temporal differences also affected the analytical variance calculation, the bootstrapping procedure is recommended.

    Publication Notes

    • Check the Northern Research Station web site to request a printed copy of this publication.
    • Our on-line publications are scanned and captured using Adobe Acrobat.
    • During the capture process some typographical errors may occur.
    • Please contact Sharon Hobrla, shobrla@fs.fed.us if you notice any errors which make this publication unusable.
    • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

    Citation

    Hou, Zhengyang; Xu, Qing; McRoberts, Ronald E.; Greenberg, Jonathan A.; Liu, Jinxiu; Heiskanen, Janne; Pitkänen, Sari; Packalen, Petteri. 2017. Effects of temporally external auxiliary data on model-based inference. Remote Sensing of Environment. 198: 150-159. https://doi.org/10.1016/j.rse.2017.06.013.

    Cited

    Google Scholar

    Keywords

    Natural resource inventory, Model-based inference, Sampling, Uncertainty, Bootstrapping, Covariance matrix estimator

    Related Search


    XML: View XML
Show More
Show Fewer
Jump to Top of Page
https://www.fs.usda.gov/treesearch/pubs/56386