Multitemporal multispectral classification of global vegetation

Richard K. Kiang

doi:10.1117/12.310564

9 June 1998 Multitemporal multispectral classification of global vegetation

Richard K. Kiang

Proceedings Volume 3261, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing V; (1998) https://doi.org/10.1117/12.310564
Event: BiOS '98 International Biomedical Optics Symposium, 1998, San Jose, CA, United States

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.

Citation Download Citation

Richard K. Kiang "Multitemporal multispectral classification of global vegetation", Proc. SPIE 3261, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing V, (9 June 1998); https://doi.org/10.1117/12.310564

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