Statistical methods are needed to consistently monitor forests at the global scale. Many nations have monitoring programs, but results are not compatible across multiple nations. Other nations have no monitoring program. A global system to produce compatible data is necessary to assess conditions across diverse multinational regions, and evaluate outcomes of international policies. Research will accommodate unequal probability sampling and multivariate approaches, including solutions to numerical problems with large vectors.
Models can predict current measurements for field plots and sample trees measured in the past.
This allows use of past data to improve accuracy of current estimates. Research will cover estimators of covariance matrices for model prediction errors, and smoothing techniques to improve accuracy in estimates of change between plot re-measurements. This research will accommodate estimates of annual growth and mortality rates using plots re-measured over different time intervals. Model predictions from past measurements will be compared with current measurements. The residual differences will be used to detect biases and incorrect assumptions in models, which are primary risks in model-based estimation.