Predictive modelling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data
Age is a powerful variable that can be of significant value when modelling the health of forest-dominated ecosystem. Traditional investigations have attempted to extract age information from remotely sensed data by regressing the spectral values with in situ derived age data. Traditional statistical approaches assume (a) normally distributed remote sensing and in situ data, (b) no collinearity among variables, and (c) linear data relationships. Artificial neural networks (ANNs) are not bound by such assumptions and may yield improved predictive modelling of forest stand biophysical parameters if properly utilized. This study investigated traditional statistical and ANN approaches to perform the predictive modelling of the age of loblolly pine (Pinus taeda) for large stands in southern Brazil using Thematic Mapper (TM) data. An extensive comparison of pattern associator and back-propagation ANNs versus both linear and nonlinear regression analysis was conducted. Various neural network architectures were investigated to determine the optimal configuration for this particular dataset. Certain back-propagation ANNs modelled stand age significantly better than traditional statistical approaches because of their ability to take into account nonlinear, nonnormally distributed data. The results suggest that ANN analysis may be of significant value when using remote sensing data to model certain forest variables.