title: Novel Kalman Filter Algorithm for Statistical Monitoring of Extensive Landscapes with Synoptic Sensor Data.
abstract: Wall-to-wall remotely sensed data are increasingly available to monitor landscape dynamics over large geographic areas. However, statistical monitoring programs that use post-stratification cannot fully utilize those sensor data. The Kalman filter (KF) is an alternative statistical estimator. I develop a new KF algorithm that is numerically robust with large numbers of study variables and auxiliary sensor variables. A National Forest Inventory (NFI) illustrates application within an official statistics program. Practical recommendations regarding remote sensing and statistical issues are offered. This algorithm has the potential to increase the value of synoptic sensor data for statistical monitoring of large geographic areas.
authors.affiliation: Emeritus Scientist, U.S. Forest Service, Rocky Mountain Research Station, Fort Collins, CO 80521, USA. raymond_czaplewski@environmetrika.org.
authors.forename: Raymond L
authors.initials: RL
authors.lastname: Czaplewski
mesh_terms: D000465:Algorithms; D003120:Colorado; D065928:Forests; D005843:Geography; D058998:Remote Sensing Technology / Q000379:methods*; D013223:Statistics as Topic; D013997:Time Factors
keywords: FIA; Landsat; MODIS; big data; change detection; forest inventory and analysis program; square root filter