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Author Czaplewski, Raymond L.
Source World Health Organization (WHO)-Global Index Medicus
Content type Text
Publisher Multidisciplinary Digital Publishing Institute
File Format HTM / HTML
Language English
Difficulty Level Medium
Subject Domain (in DDC) Natural sciences & mathematics ♦ Mathematics ♦ Life sciences; biology ♦ Physiology & related subjects ♦ Technology ♦ Medicine & health ♦ Human physiology ♦ Diseases ♦ Manufacture for specific uses ♦ Precision instruments & other devices
Subject Domain (in MeSH) Diagnosis ♦ Investigative Techniques ♦ Analytical, Diagnostic and Therapeutic Techniques and Equipment ♦ Physical Phenomena ♦ Biological Phenomena ♦ Mathematical Concepts ♦ Biological Sciences ♦ Natural Science Disciplines ♦ Physical Sciences ♦ Geographic Locations ♦ Geographic Locations
Subject Keyword Discipline Biotechnology ♦ Algorithms ♦ Forests ♦ Remote Sensing Technology ♦ Methods ♦ Statistics As Topic ♦ Colorado ♦ Geography ♦ Time Factors ♦ Journal Article
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.
Spatial Coverage Colorado
Description Country affiliation: United States
Author Affiliation: Czaplewski RL ( Emeritus Scientist, U.S. Forest Service, Rocky Mountain Research Station, Fort Collins, CO 80521, USA. raymond_czaplewski@environmetrika.org.)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Reading ♦ Research ♦ Self Learning
Interactivity Type Expositive
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-09-17
Publisher Place Switzerland
e-ISSN 14248220
Journal Sensors
Volume Number 15
Issue Number 9


Source: WHO-Global Index Medicus