Forest stand parameter estimation by using neural networks

Abstract

Wood is a vital material used everywhere. And it is very important to monitor the state of forests. One of the most common ways to estimate different forest parameters is to make the model based on the data obtained by airborne light detection and ranging (LiDAR). But, for building an accurate model on large areas, it is necessary to use hundreds of expensive and time-consuming field sample measurements. Besides that, species-specific estimations require a deeper study of the issue and additional measurements. All these data forms poorly structured sets with a high correlation between independent variables. That makes a problem to build a model with an acceptable level of error for using it in practice.

Recent studies in the machine learning area illustrate that Artificial Neural Networks(ANN) are good at modelling complex relationships between data. This thesis provides an analysis of the possible use of neural network algorithms for estimating forest stand parameters on Finnish forest sites for both general and species-specific analysis.