Artificial neural network inversion of tree canopy parameters in the presence of diversity

Artificial neural networks have shown themselves to be able to invert many parameters of interest from multi-frequency and multi-polarization radar data when the tree canopies exhibit low variability in their parameters for a given age (Pierce, et. al., 1992). However, when this same model is applied to a forest stand whose parameters are slightly off the modeled age curve, then the inversion fails. This paper describes the authors' efforts to develop an inversion technique for Loblolly pine stands with the natural diversity of their canopy parameters accounted for. Given a set of Loblolly pine stands from the Duke forest a set of parameters that sufficiently model the stands using MIMICS (Ulaby, et al., 1988) were developed. Next, several important parameters were chosen to be varied over a specified interval with a Gaussian distribution to allow for natural variation. This generated a large data set of 1000 different Loblolly pine canopies. MIMICS was then employed to generate the expected radar backscatter from this set at P, L, and C bands. This data, was then used to train two artificial neural networks: one to invert for the average trunk diameter and subsequently another to invert for the large branch density. This cascaded approach worked well giving errors of only a few percent for trunk diameters and 5% for branch density, when using data with trunk diameters varying between 1 cm and 10 cm. Beyond 10 cm the radar signal begins to saturate due to the high biomass, and was not invertible.<
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