Comparing rotation forests and extreme gradient boosting for monitoring drought damage on KwaZulu-Natal commercial forests

This study explored the utilization of rotation forests (RTF) and extreme gradient boosting (XGBoost) machine learning algorithms (MLAs) to classify drought damage in commercial forests in KwaZulu-Natal (KZN). These algorithms were trained using information obtained from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation and conditional drought indices. The results demonstrated that both algorithms were capable of accurately detecting trees that exhibit drought damage and those that did not, this was more apparent when classifying based on information derived from conditional drought indices that yielded an overall accuracy of 82% and 76% for XGBoost and RTF, respectively. However, the accuracy decreased when using vegetation indices data, resulting in an accuracy of 69% and 72% for XGBoost and RTF, respectively. Overall, the results demonstrated that MLAs could be utilized for classifying drought damage on forest vegetation. Additionally, the study showed that MODIS imagery could be used for MLA classification.

Automated summary

This study explored the utilization of rotation forests and extreme gradient boosting machine learning algorithms to classify drought damage in commercial forests in KwaZulu-Natal. The results demonstrate that both algorithms are capable of accurately detecting trees with drought damage and those without, especially when using conditional drought indices.