At. Cialella et al., PREDICTING SOIL DRAINAGE CLASS USING REMOTELY-SENSED AND DIGITAL ELEVATION DATA, Photogrammetric engineering and remote sensing, 63(2), 1997, pp. 171-178
Soil drainage strongly affects the patterns and processes of ecosystem
s, including biomass production, vegetation community distribution, so
il development, aeration, hydrologic processes, and trace gas fluxes.
To obviate the need for extensive field surveys, we present a techniqu
e to use a remotely sensed optical image and digital elevation data to
predict soil drainage class at a 6- by 4-km research site in a mixed
conifer forest in Howland, Maine. Elevation, detrended elevation, slop
e, aspect, and flow accumulation were determined from a 10-m resolutio
n digital elevation model (DEM) of the site. Normalized Difference Veg
etation Index (NDVI) data derived from the Advanced Visible and Infrar
ed Imaging Spectrometer (AVIRIS) were used to represent differences in
vegetation cover. Classification tree analysis predicted soil drainag
e class with an average of 78 percent accuracy.