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.
Highlights
An official statistics program, such as a National Forest Inventory (NFI), is a large and complex interdisciplinary enterprise
The related objective is to demonstrate to the sample survey statistician that multivariate model-assisted regression estimators are feasible in an official statistics program, which involves numerous study variables and numerous remotely sensed auxiliary variables
This statistical approach uses the Kalman filter (KF), which has been applied in navigation and control systems engineering for over 50 years
Summary
An official statistics program, such as a National Forest Inventory (NFI), is a large and complex interdisciplinary enterprise. The related objective is to demonstrate to the sample survey statistician that multivariate model-assisted regression estimators are feasible in an official statistics program, which involves numerous study variables and numerous remotely sensed auxiliary variables. This statistical approach uses the Kalman filter (KF), which has been applied in navigation. The KF constrains sample survey estimates for the auxiliary variables to exactly equal their census constants, which are known exactly from GIS enumeration of remotely sensed and other geospatial data for each and every pixel and polygon in the target population These constraints improve statistical efficiency and reduce uncertainty for every study variable that is well correlated with any auxiliary sensor variable
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