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Proceedings Paper

Multitemporal multispectral classification of global vegetation
Author(s): Richard K. Kiang
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Paper Abstract

Surface vegetation is an important link in the coupling between the atmosphere and the biosphere. Monitoring the condition of vegetation cover on the Earth surface is essential for detecting the changes in climate. Advanced Very-High Resolution Radiometer 10-day composite data in 1 X 1 degree resolution from NASA/GSFC and a global vegetation ground truth in the same resolution from the University of Maryland's Geography Department are used in this study. A fully connected multilayer neural network is used for supervised classification. The normalized difference vegetation index, which is also called the greenness index, is used along with the surface reflectance and brightness temperature as the input features. Trainings and classifications are performed for two spatial modes and three multitemporal modes.

Paper Details

Date Published: 9 June 1998
PDF: 6 pages
Proc. SPIE 3261, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing V, (9 June 1998); doi: 10.1117/12.310564
Show Author Affiliations
Richard K. Kiang, NASA Goddard Space Flight Ctr. (United States)


Published in SPIE Proceedings Vol. 3261:
Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing V
Thomas Taiwei Lu; Carol J. Cogswell; Jeremy M. Lerner; Jose-Angel Conchello; Jeremy M. Lerner; Thomas Taiwei Lu; Tony Wilson, Editor(s)

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