Novel Kalman filter algorithm for statistical monitoring of extensive landscapes with synoptic sensor data

Raymond L. Czaplewski

doi:10.3390/s150923589

Treesearch

Novel Kalman filter algorithm for statistical monitoring of extensive landscapes with synoptic sensor data

Formally Refereed

Authors:

Raymond L. Czaplewski

Year:

2015

Type:

Scientific Journal

Station:

Rocky Mountain Research Station

DOI:

https://doi.org/10.3390/s150923589

Source:

Sensors. 15: 23589-23617.

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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.

Keywords

Landsat, MODIS, change detection, square root filter, big data, forest inventory and analysis program, FIA

Citation

Czaplewski, Raymond L. 2015. Novel Kalman filter algorithm for statistical monitoring of extensive landscapes with synoptic sensor data. Sensors. 15: 23589-23617.