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    Author(s): W. Cohen; H. Andersen; S. HealeyG. MoisenT. Schroeder; C. Woodall; G. Domke; Z. Yang; S. Stehman; R. Kennedy; C. Woodcock; Z. Zhu; J. Vogelmann; D. Steinwand; C. Huang
    Date: 2014
    Source: The International Forestry Review. 16(5): 405.
    Publication Series: Abstract
    Station: Rocky Mountain Research Station
    PDF: Download Publication  (85.68 KB)


    The authors are developing a REDD+ MRV system that tests different biomass estimation frameworks and components. Design-based inference from a costly fi eld plot network was compared to sampling with LiDAR strips and a smaller set of plots in combination with Landsat for disturbance monitoring. Biomass estimation uncertainties associated with these different data sets in a design-based inference framework was examined. The authors are also testing estimators that rely primarily on Landsat within a model-based inference framework. Contributions from Landsat are current (e.g., 2013) spectral response and metrics describing disturbance history derived from a time-series leading up to the current date. An advantage of the model-based framework is its extension back in time (e.g., to 1990) using a consistent approach based on disturbance history as an indicator of biomass density. This requires use of the older, MSS archive to be fully effective in estimating biomass for the 1990 baseline. The United States, though not a REDD country, is party to the UNFCCC and has a need for similar NGHGI baseline information. The various components of the authors’ MRV system will be tested in the United States, where sufficient data are available for parsing the uncertainty contributions of the several system components being tested.

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    Cohen, W.; Andersen, H.; Healey, S.; Moisen, G.; Schroeder, T.; Woodall, C.; Domke, G.; Yang, Z.; Stehman, S.; Kennedy, R.; Woodcock, C.; Zhu, Z.; Vogelmann, J.; Steinwand, D.; Huang, C. 2014. An historically consistent and broadly applicable MRV system based on LiDAR sampling and Landsat time-series. future. The International Forestry Review. 16(5): 405.


    MRV, LiDAR, sampling, biomass estimation

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