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Carbon Monitoring System (CMS)

June, 2018


Flowchart showing the creation of biomass and disturbance estimates
Using LandSat stacks and multiple algorithms to create ensemble biomass estimates.

We are developing a pilot Monitoring, Reporting, and Verification (MRV) accounting system that could be used by developing countries within the context of the United Nations (UN) REDD (Reducing Emissions from Deforestation and Forest Degradation) Programme. Because one system will not fit all needs, we consider different biomass estimation frameworks and different components for inclusion in the system. Design-based inference is commonly applied to a sample field plot network. But field plot networks are expensive to install and maintain. Sampling with lidar strips, supported by a smaller set of plots may be an attractive alternative that is highly relevant to many REDD countries, as is the use of Landsat for disturbance monitoring. Biomass estimation uncertainties associated with use of these different datasets in a design-based inference framework are being examined. We are also developing and testing estimators that rely primarily on Landsat data within a model-based inference framework. The contributions from Landsat are the current (e.g., 2013) spectral response and metrics that describe disturbance history derived from a time series leading up to the current date. In this context, either plot data or lidar data can be used to parameterize the model and we are contrasting the uncertainty effects of these datasets.

Three objectives are being addressed that test various components of the MRV system at six study sites (map of sites) in the US:

Components of the proposed MRV System. Limited field inventory data are used to translate lidar strip samples into live biomass density, then lidar and LandSat time series and disturbance history metrics are used to derive both maps and biomass estimates
Components of the proposed MRV System

  1. Create current aboveground live biomass maps and estimates at each site that integrate a selection of spatially-and temporally-coincident FIA plots, lidar samples, and Landsat imagery, with Landsat-derived disturbance history metrics. This full complement of datasets facilitates examination of estimation uncertainty in both model-based and design-based inference frameworks.
  2. Map and estimate historic live biomass at each site for each year from the current period back to 1990. Uncertainty is being assessed in the model-based inference framework, as a design-based framework is not feasible in this context.
  3. Improve the existing US NGHGI approach to estimate live biomass consistently through time at each site for each year from the current period back to 1990. Uncertainty is being assessed in the design‐based inference framework currently in use.

Project Contact: 

Principal Investigators:
Warren B. Cohen - USFS Pacific Northwest Research Station
Zhiqiang Yang - Oregon State University

Zhe Zhu - Boston University
Dan Steinwand - USGS EROS
Jim Vogelmann - USGS EROS
Hans-Erik Anderson - USFS Pacific Northwest Research Station
Robert E Kennedy - Oregon State University
Todd Schroeder - USFS Southern Research Station
Chris Woodall - USFS Northern Research Station
Grant Domke - USFS Northern Research Station
Steve Stehman - SUNY ESF
Chengquan Huang - University of Maryland
Curtis Woodcock - Boston University