Conjugating remotely sensed data assimilation and model-assisted estimation for efficient multivariate forest inventory

Remote sensing aims to provide precise information on forest ecosystems under climate and land use changes, much of which is in the form of parameters estimated for biotic and abiotic variables for various official reporting instruments. Model-assisted estimation (MA) that harnesses remote sensing has demonstrated a surpassing ability to balance the tradeoff between robustness and efficiency. However, (1) MA has to unfold in a way complying with rather than overriding a sampling design because modification to sample size and field protocol is usually not allowed for an established setup, thus impeding further increases to inventory precision; and (2) it is inefficient to predict multiple forest attributes with many individual models, producing inconsistencies in the estimates due to lack of preserving the correlations, and offsetting the gains in inventory precision with the cost spent on modeling. Consequently, within the statistical framework of MA, this study proposes a remotely sensed data assimilation procedure, DAMA, to support high-precision multivariate forest inventory. Based on populations in China and Burkina Faso, promising results indicate that (1) the DAMA estimator proposed is approximately design-unbiased with its variance affected by the sampling design, the prediction accuracy, and the type of remotely sensed auxiliaries involved in DA, in descending order; (2) with simple random sampling, DAMA estimator increases the inferential precision on average 14% and 7% for Horvitz-Thompson and MA counterparts; and (3) with two-stage sampling, remarkably, 180% and 57%. Overall, DAMA demonstrates considerable efficiency that would better serve natural resource observation and management.