Evaluation of Xgboost and Lgbm Performance in Tree Species Classification with Sentinel-2 Data

Tree species classification with satellite data has become more and more popular since Sentinel-2 launch. We compared efficacy and effectiveness of Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LGBM) with widely used in remote sensing Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) algorithms. Analyses were performed over an area in Portugal with multi-temporal Sentinel-2 data registered in April, June, August and October 2018. The selected classes were: cork oak, holm oak, eucalyptus, other broadleaved, maritime pine, stone pine and other coniferous. Algorithm efficacy was measured through F1-score and accuracy while efficiency was measured through the median time needed for each fit. XGBoost and LGBM outperformed efficacy of other algorithms, which was already high (above 90% for the best variant of each algorithm). In terms of efficacy, LGBM overcame all algorithms, including XGBoost.