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

Abstract

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-source data to predict stand volume of a Eucalyptus plantation in South Africa. The SGB ensemble, random forest (RF), and stepwise multiple-linear regression (SMLR) were used to predict Eucalyptus stand volume and other related tree-structural attributes such as mean tree height and mean diameter at breast height (DBH). Multi-source data consisted of SPOT-5 raw spectral features (four bands), 14 spectral vegetation indices, rainfall data, and stand age. When all variables were used, the SGB algorithm showed that stand volume can be accurately estimated (R2 = 0.78 and RMSE = 33.16 m3 ha−1 (23.01% of the mean)). The competing RF ensemble produced an R2 value of 0.76 and a RMSE value of 37.28 m3 ha−1 (38.28% of the mean). SMLR on the other hand, produced an R2 value of 0.65 and an RMSE value of 42.50 m3 ha−1 (42.50% of the mean). Our study further showed that Eucalyptus mean tree height (R2 = 0.83 and RMSE = 1.63 m (9.08% of the mean)) and mean diameter at breast height (R2 = 0.74 and RMSE = 1.06 (7.89% of the mean)) can also be reasonably predicted using SGB and multi-source data. Furthermore, when the most important SGB model-selected variables were used for prediction, the predictive accuracies improved significantly for mean DBH (R2 = 0.81 and RMSE = 1.21 cm (6.12% of the mean)), mean tree height (R2 = 0.86 and RMSE = 1.39 m (7.02% of the mean)), and stand volume (R2 = 0.83 and RMSE = 29.58 m3 ha−1 (17.63% of the mean)). These results underscore the importance of integrating multi-source data with remotely sensed data for predicting Eucalyptus stand volume and related tree-structural attributes.