Alfredo Huete

Alfredo HueteProfessor Alfredo Huete is a professor in the School of Life Sciences, University of Technology, Sydney. He is a world renowned geospatial ecologist who uses advanced remote sensing tools to assess broad scale ecosystem functioning, vegetation phenolgy and health. He uses satellite and field observations to assess land surface interactions and ecosystem resilience with climate, land use activities and major disturbance and extreme events. Professor Huete has twenty-five years experience in vegetation remote sensing for NASA mission teams. He is a founding and continuing member of the NASA-EOS MODIS Science Team. In recognition of his pioneering work in the design of vegetation satellite products used by the remote sensing community to assess vegetation biophysical states and processes of global ecosystems, he earned a NASA Service Achievement Award for NASA MODIS Product Development and a NASA Group Achievement Award for the Suomi NPP Mission Development Team. The satellite products he developed are among the most widely used by the scientific community and natural resource and agriculture stakeholders. He has published several high impact papers in journals such as Nature, Science, and the Proceedings of the National Academy of Sciences. Read More Read Less

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Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation43 %
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Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation38 %
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Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation43 %
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Biophysical and Biochemical Characterization and Plant Species Studies48 %
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Hyperspectral Indices and Image Classifications for Agriculture and Vegetation43 %
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Fundamentals of Satellite Remote Sensing54 %
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01 Dec 2009
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Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for VegetationNR
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Hyperspectral Indices and Image Classifications for Agriculture and VegetationNR
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Biophysical and Biochemical Characterization and Plant Species StudiesNR
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Vegetation Dynamics18 % NR
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Hyperspectral Remote Sensing of Vegetation28 %
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