A comparison of regression tree ensembles: Predicting Sirex noctilio induced water stress in Pinus patula forests of KwaZulu-Natal, South Africa

In this study we compared the performance of regression tree ensembles using hyperspectral data. More specifically, we compared the performance of bagging, boosting and random forest to predict Sirex noctilio induced water stress in Pinus patula trees using nine spectral parameters derived from hyperspectral data. Results from the study show that the random forest ensemble achieved the best overall performance (R2 = 0.73) and that the predictive accuracy of the ensemble was statistically different (p < 0.001) from bagging and boosting. Additionally, by using random forest as a wrapper we simplified the modeling process and identified the minimum number (n = 2) of spectral parameters that offered the best overall predictive accuracy (R2 = 0.76). The water index and Ratio975 had the best ability to assay the water status of S. noctilio infested trees thus making it possible to remotely predict and quantify the severity of damage caused by the wasp.