Estimating Tree Volume of Dry Tropical Forest in the Brazilian Semi-Arid Region: A Comparison Between Regression and Artificial Neural Networks

The dry tropical forests of the Brazilian semi-arid region are a key component in the sustainable production of coal and firewood for power generation, although their estimates of volume and wood stock depend almost exclusively on equations adjusted from other semi-arid regions or form factor for data of managed species. Therefore, a systematic evaluation of new methodologies such as artificial neural networks and regression models is justifiable for the locale, since it aims to select a tool that reports reliable predictions of volume and that is low cost in the forest management of the region. Our main results show that less reliable estimates of trunk and branch volumes are obtained by simple input models and perceptron networks. The Schumacher-Hall linearized equation provides reliable estimates of volume, although the Multilayer-Perceptron neural networks indicate estimates, which are no less biased. Our results suggest that using volumetric equations to predict trunk and tree branch volume in the Brazilian semi-arid region is still more statistically advantageous, although the use of ANNs is not ruled out. This shows that there are obviously complex relationships between dependent and independent biological factors and that volumetric models are able to better explain such relationships.