Reliable species discrimination remains essential for the management of commercial forests. Therefore, this study sought to evaluate the utility of Partial Least Squares Linear Discriminant Analysis (PLS-LDA) and Partial Least Squares-Discriminant Analysis (PLS-DA) multivariate techniques for delineating forest species using Landsat 8 OLI. PLS-LDA produced a higher (88.9%) overall accuracy compared to the PLS-DA (79%). The high performance of PLS-LDA is associated with its ability to deal with correlation and variability between and within classes, hence offer great potential for the monitoring and management of commercial forest species.

可靠的物种歧视对于商品林的管理仍然至关重要。因此，本研究试图评估偏最小二乘线性判别分析（PLS-LDA）和偏最小二乘判别分析（PLS-DA）多元技术在使用Landsat 8 OLI描绘森林物种时的效用。与PLS-DA（79％）相比，PLS-LDA产生了更高的整体精度（88.9％）。PLS-LDA的高性能与其处理类别之间和类别内部的相关性和变异性的能力有关，因此为商品林物种的监测和管理提供了巨大的潜力。