TY - JOUR
AU - Boyd, D. S.
AB - 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.
TI - Sharpened Mapping of Tropical Forest Biophysical Properties from Coarse Spatial Resolution Satellite Sensor Data
JF - Neural Computing and Applications
DO - 10.1007/s005210200017
DA - 2014-02-05
UR - https://www.deepdyve.com/lp/springer-journals/sharpened-mapping-of-tropical-forest-biophysical-properties-from-nT9iBHTL9C
SP - 62
EP - 70
VL - 11
IS - 1
DP - DeepDyve
ER -