Predicción de variables dasométricas del Inventario Forestal Nacional mediante datos LIDAR con técnicas de minera de datos

Abstract

The management of forest resources is essential for the development of our society. This requires a forest management planning based on innovative studies, according to new technologies and seeking to lower costs. In this project, a methodology has been developed for the extraction of predictive regression models to determine the main dasometric variables of the beech forest layer with over 70% of the forest cover density in Navarre. For this purpose, data mining techniques and Python as programming language have been used. The inputs of the work are: data from the plots of the national forest inventory (dependent variables) and statistics derived from the LIDAR-PNOA flight for these same plots (independent variables) obtained with the LasTools software. The output is the model that best fits the input data, determined by the methodology used.