Foody, G.M. and Boyd, D.S.
Sharpened mapping of tropical forest biophysical properties from coarse spatial resolution satellite sensor data
Neural Computing and Applications, 11, (1), .
Full text not available from this repository.
Forest biophysical properties are typically estimated
and mapped from remotely sensed data through the
application of a vegetation index. This generally
does not make full use of the information content
of the remotely sensed data, using only the data
acquired in a limited number of spectral channels,
and may provide a relatively crude spatial representation
of the biophysical variable of interest. Using
imagery acquired by the NOAA AVHRR, it is shown
that a standard neural network may use all the
spectral channels available in a remotely sensed
data set to derive more accurate estimates of the
biophysical properties of tropical forests in Ghana
than a series of vegetation indices. Additionally, the
spatial representation derived can be refined by
fusion with finer spatial resolution imagery, achieved
with the application of a further neural network.
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