Titlebook: Artificial Neuronal Networks; Application to Ecolo Sovan Lek,Jean-Fran?ois Guégan Book 2000 Springer-Verlag Berlin Heidelberg 2000 Tempo.al

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Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Usingbiosphere/atmosphere interactions, and carbon dynamics (Asrar and Dozier 1994; Hall et al. 1995). The success of efforts to extract vegetation variables such as these from remotely sensed data and available ancillary data will determine the degree and scope of vegetation-related science performed using EOS data.

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Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Usingharton and Myers 1997). The satellite digital data sets and ancillary data products will require the development of efficient algorithms that can incorporate and functionally utilize disparate data types. Numerous vegetation variables, e.g. leaf area, height, canopy roughness, land cover, stomatal r

CREEK

Soft Mapping of Coastal Vegetation from Remotely Sensed Imagery with a Feed-Forward Neuronal Networkoor quality (Williams 1994; DeFries and Townshend 1994). Often the only practicable means of acquiring data on vegetation distribution at appropriate spatial and temporal resolutions is through remote sensing (Townshend et al. 1991; Skole 1994). The considerable potential of remote sensing for mappi

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