Random Forest Classification of three different species of trees in Delft, based on AHN point clouds

Abstract

Trees are an important aspect of the world around us, and play a sufficient role in our daily lives. They contribute to human health and well-being in various ways. Tree inventory and monitoring are of great interest for biomass estimations and changes in the purifying effect on the air. It is a very time consuming and cost inefficient way to check every tree in and around a city or town, therefore there is further research required in the use of AHN data. Together with the “tree information data set” formthemunicipality ofDelft, the location and the corresponding point cloud of tree different species of trees are selected. For the species of interest, Aesculus Hippocastanum, Acer Saccharinum and Platanus x Hispanica, different characteristics are determined. In this research six different characteristics are estimated; Height, Trunk Height, Normalized Trunk Height, Canopy Projected Area, Normalized Canopy Projected Area, Ratio of Diameters, Normalized Ratio of Diameter, Centre of Gravity and at least the Normalized Centre of Gravity. These characteristics are used as features for the Random Forest Classification, Consequently the Confusion Matrix is used as performance measurement. The results of a test of 30 pointclouds, per species of interest, show that the Random Forest Classification is able to classify individual trees. However, these three different species cannot by sufficiently classified using clustering.