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): Paul L. Patterson; Sean P. Healey; Goran Stahl; Svetlana Saarela; Soren Holm; Hans-Erik Andersen; Ralph O. Dubayah; Laura Duncanson; Steven Hancock; John Armston; James R. Kellner; Warren B. Cohen; Zhiqiang Yang
    Date: 2019
    Source: Environmental Research Letters. 14: 065007.
    Publication Series: Scientific Journal (JRNL)
    Station: Rocky Mountain Research Station
    PDF: Download Publication  (1.0 MB)


    NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission will collect waveform lidar data at a dense sample of ~25 m footprints along ground tracks paralleling the orbit of the International Space Station (ISS). GEDI’s primary science deliverable will be a 1 km grid of estimated mean aboveground biomass density (Mg ha-1), covering the latitudes overflown by ISS (51.6 ° S to 51.6 ° N). One option for using the sample of waveforms contained within an individual grid cell to produce an estimate for that cell is hybrid inference, which explicitly incorporates both sampling design and model parameter covariance into estimates of variance around the population mean. We explored statistical properties of hybrid estimators applied in the context of GEDI, using simulations calibrated with lidar and field data from six diverse sites across the United States. We found hybrid estimators of mean biomass to be unbiased and the corresponding estimators of variance appeared to be asymptotically unbiased, with under-estimation of variance by approximately 20% when data from only two clusters (footprint tracks) were available. In our study areas, sampling error contributed more to overall estimates of variance than variability due to the model, and it was the design-based component of the variance that was the source of the variance estimator bias at small sample sizes. These results highlight the importance of maximizing GEDI’s sample size in making precise biomass estimates. Given a set of assumptions discussed here, hybrid inference provides a viable framework for estimating biomass at the scale of a 1 km grid cell while formally accounting for both variability due to the model and sampling error.

    Publication Notes

    • You may send email to to request a hard copy of this publication.
    • (Please specify exactly which publication you are requesting and your mailing address.)
    • 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.


    Patterson, Paul L.; Healey, Sean P.; Stahl, Goran; Saarela, Svetlana; Holm, Soren; Andersen, Hans-Erik; Dubayah, Ralph O.; Duncanson, Laura; Hancock, Steven; Armston, John; Kellner, James R.; Cohen, Warren B.; Yang, Zhiqiang. 2019. Statistical properties of hybrid estimators proposed for GEDI - NASA’s global ecosystem dynamics investigation. Environmental Research Letters. 14: 065007.


    Google Scholar


    carbon monitoring, lidar, forest biomass

    Related Search

    XML: View XML
Show More
Show Fewer
Jump to Top of Page