Extensive expanses of forest often change at a slow pace. In this common situation, FIA produces informative estimates of current status with the Moving Average (MA) method and post-stratification with a remotely sensed map of forest-nonforest cover. However, MA "smoothes out" estimates over time, which confounds analyses of temporal trends; and post-stratification limits gains from remote sensing. Time-series estimators, like the Kalman Filter (KF), better detect and analyze unexpected or rapid changes in dynamic forests. KF is a recursive multivariate model-based estimator that separates complex time-series of panel estimates and multi-sensor remotely sensed data into a sequence of smaller and more manageable components. Population-level results are disaggregated into expansion factors that assure additivity and simplify small area and small domain estimation. Other statistics gauge fit of alternative models to annual FIA panel data, which permits quantitative rankings among alternative cause-effect hypotheses.