A Comparative Study between Two Regression Methods on LiDAR Data: A Case Study

Abstract

Airborne LiDAR (Light Detection and Ranging) has become an excellent tool for accurately assessing vegetation characteristics in forest environments. Previous studies showed empirical relationships between LiDAR and field-measured biophysical variables. Multiple linear regression (MLR) with stepwise feature selection is the most common method for building estimation models. Although this technique has provided very interesting results, many other data mining techniques may be applied. The overall goal of this study is to compare different methodologies for assessing biomass fractions at stand level using airborne Li- DAR data in forest settings. In order to choose the best methodology, a comparison between two different feature selection techniques (stepwise selection vs. genetic-based selection) is presented. In addition, classical MLR is also compared with regression trees (M5P). The results when each methodology is applied to estimate stand biomass fractions from an area of northern Spain show that genetically-selected M5P obtains the best results.