论文信息 - Multitemporal multispectral classification of global vegetation

Multitemporal multispectral classification of global vegetation

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.

Richard K. Kiang

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