The successful launch and operation of the Sentinel satellite platform has provided access to freely available remotely sensed data useful for commercial forest species discrimination. Sentinel – 1 (S1) with a Synthetic Aperture Radar (SAR) sensor and Sentinel – 2 (S2) multi-spectral sensor with additional and strategically positioned bands offer great potential for providing reliable information for discriminating and mapping commercial forest species. In this study, we sought to determine the value of S1 and S2 data characteristics in discriminating and mapping commercial forest species. Using linear discriminant analysis (LDA) algorithm, S2 multi-spectral imagery showed an overall classification accuracy of 84% (kappa = 0.81), with bands such as the red-edge (703.9-740.2 nm), narrow near infrared (835.1-864.8 nm), and short wave infrared (1613.7-2202.4 nm) particularly influential in discriminating individual forest species stands. When Sentinel 2’s spectral wavebands were fused with Sentinel 1’s (SAR) VV and VH polarimetric modes, overall classification accuracies improved to 87% (kappa = 0.83) and 88% (kappa = 0.85), respectively. These findings demonstrate the value of combining Sentinel’s multispectral and SAR structural information characteristics in improving commercial forest species discrimination. These, in addition to the sensors free availability, higher spatial resolution and larger swath width, offer unprecedented opportunities for improved local and large scale commercial forest species discrimination and mapping.

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.

The adoption of remotely sensed data in forest applications has grown significantly. Whereas high spatial resolution sensors have been successful in mapping and monitoring commercial forests, their cost, accessibility, and spatial coverage remain a critical challenge. Hence, it is was necessary to investigate the value of new and improved freely available sensors in forest species mapping using the Partial Least Square-Discriminant Analysis (PLS-DA). This study evaluated the performance of new freely available and improved raw and pan-sharpened Landsat 8 Operational Land Imager (OLI) imagery in discriminating seven key plantation forest species in KwaZulu-Natal, South Africa. Accuracies achieved using the Landsat (OLI) imagery were benchmarked against the WorldView-2 imagery. Results show that raw and pan-sharpened bands successfully delineated commercial forest species, with overall classification accuracies of 79% and 77.8%, respectively. Although these accuracies were lower than the 86.5% achieved from the higher resolution Worldview-2 image data, our findings demonstrate that the Landsat 8 OLI’s lower spatial resolution (30 m) generated a plausible performance in discriminating forest species. Hence, Landsat 8 OLI could be useful in providing existing and historical preliminary forestry assessment due to its free availability, wide spatial coverage as well as its rich archive dating back to the 1970s.