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Sensors 2015, 15(9), 23589-23617; doi:10.3390/s150923589

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

Emeritus Scientist, U.S. Forest Service, Rocky Mountain Research Station, Fort Collins, CO 80521, USA
Academic Editor: Vittorio M. N. Passaro
Received: 2 April 2015 / Revised: 2 September 2015 / Accepted: 10 September 2015 / Published: 17 September 2015
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [603 KB, uploaded 17 September 2015]   |  

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. View Full-Text
Keywords: Landsat; MODIS; change detection; square root filter; big data; forest inventory and analysis program; FIA Landsat; MODIS; change detection; square root filter; big data; forest inventory and analysis program; FIA
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Czaplewski, R.L. Novel Kalman Filter Algorithm for Statistical Monitoring of Extensive Landscapes with Synoptic Sensor Data. Sensors 2015, 15, 23589-23617.

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