Examining the effectiveness of Sentinel-1 and 2 imagery for commercial forest species mapping

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.

Automated summary

The combination of Sentinel's multispectral and SAR structural information characteristics has shown significant value in improving the discrimination and mapping of commercial forest species, offering unprecedented opportunities for enhanced local and large scale applications.