Novel Kalman Filter Algorithm for Statistical Monitoring of Extensive Landscapes with Synoptic Sensor Data

Sensors

Raymond L. Czaplewski¹ 

[1] Emeritus Scientist, U.S. Forest Service, Rocky Mountain Research Station, Fort Collins, CO 80521, USA; E-Mail

关键词: Landsat;    MODIS;    change detection;    square root filter;    big data;    forest inventory and analysis program;    FIA;   

DOI  :  10.3390/s150923589

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.