The mapping of southern yellow pines (loblolly, shortleaf, and Virginia pines) is important to supporting forest inventory and the management of forest resources. The overall aim of this study was to examine the integration of Landsat Operational Land Imager (OLI ) optical data with Sentinel-1 microwave C-band satellite data and vegetation indices in mapping the canopy cover of southern yellow pines. Specifically, this study assessed the overall mapping accuracies of the canopy cover classification of southern yellow pines derived using four data-integration scenarios: Landsat OLI alone; Landsat OLI and Sentinel-1; Landsat OLI with vegetation indices derived from satellite data—normalized difference vegetation index, soil-adjusted vegetation index, modified soil-adjusted vegetation index, transformed soil-adjusted vegetation index, and infrared percentage vegetation index; and 4) Landsat OLI with Sentinel-1 and vegetation indices. The results showed that the integration of Landsat OLI reflectance bands with Sentinel-1 backscattering coefficients and vegetation indices yielded the best overall classification accuracy, about 77%, and standalone Landsat OLI the weakest accuracy, approximately 67%. The findings in this study demonstrate that the addition of backscattering coefficients from Sentinel-1 and vegetation indices positively contributed to the mapping of southern yellow pines.
Akumu, C.E.; Amadi, E.O. "Examining the Integration of Landsat Operational Land Imager with Sentinel-1 and Vegetation Indices in Mapping Southern Yellow Pines (Loblolly, Shortleaf, and Virginia Pines)" Photogrammetric Engineering & Remote Sensing, Volume 88, Number 1, January 2022, pp. 29-38(10) https://doi.org/10.14358/PERS.21-00024R2