Predicting Eucalyptus spp. stand volume in Zululand, South Africa: ann analysis using a stochastic gradient boosting regression ensemble withn multi-source data sets

摘要

Accurate, reliable, and up-to-date forest stand volume information is a prerequisite for a detailed evaluation of commercial forest resources and their sustainable management. Commercial forest responses to global climate change remain uncertain, and hence the mapping of stand volume as carbon sinks is fundamentally important in understanding the role of forests in stabilizing climate change effects. The aim of this study was to examine the utility of stochastic gradient boosting (SGB) and multi