An Historically Consistent and Broadly Applicable MRV System Based on Lidar Sampling and Landsat Time-series

Warren B. Cohen, USDA Forest Service, warren.cohen@oregonstate.edu (Presenter)
Hans Erik Andersen, USDA Forest Service, handersen@fs.fed.us
Sean P Healey, USDA Forest Service, seanhealey@fs.fed.us
Gretchen Moisen, US Forest Service, gmoisen@fs.fed.us
Christopher W. Woodall, USDA Forest Service, cwoodall@fs.fed.us
Grant M Domke, USDA Forest Service, gmdomke@fs.fed.us
Zhiqiang Yang, Oregon State University, zhiqiang.yang@oregonstate.edu
Stephen Stehman, SUNY College of Environ Sci & Forestry, svstehma@syr.edu
Robert E Kennedy, Oregon State University, rkennedy@coas.oregonstate.edu
Curtis Woodcock, Boston University, curtis@bu.edu
Zhe Zhu, Boston University, zhuzhe@bu.edu
Todd A. Schroeder, US Forest Service, tschroeder@fs.fed.us
James E. Vogelmann, USGS Sioux Falls, vogel@usgs.gov
Daniel Steinwand, U.S. Geological Survey/EROS, steinwand@usgs.gov
Chengquan Huang, University of Maryland, cqhuang@umd.edu

We are developing a REDD MRV system that tests different biomass estimation frameworks and components. Design-based inference from a costly field plot network is 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 datasets in a design-based inference framework is examined. We are also testing estimators that rely primarily on Landsat within a model-based inference framework. Contributions from Landsat are current (e.g., 2014) 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 US, while not a REDD country, is party to the UNFCCC and has a need for similar NGHGI baseline information. The various components of our MRV system will be tested in the US, where the best data are available for parsing the uncertainty contributions of the several system components we are testing.

Associated Project(s): 

• Cohen (CMS 2013)