Evaluating the potential of sentinel-2, landsat-8, and irs satellite images in tree species classification of hyrcanian forest of iran using random forest

IRS-Pansharpened satellite imagery outperformed Landsat-8 and Sentinel-2 data in classifying tree species within Iran's Hyrcanian forests, achieving the highest accuracy (84.9% Overall Accuracy) using Random Forest algorithms. The study identified the near-infrared spectral band as most critical for distinguishing among Pinus taeda, Alnus spp., and Populus spp. These findings offer a robust tool for forest managers and conservationists to monitor tree species composition, supporting sustainable management of this UNESCO-listed ecosystem.

Soleimannejad, L., Ullah, S., Abedi, R., Dees, M., & Koch, B. (2019). Evaluating the potential of sentinel-2, landsat-8, and irs satellite images in tree species classification of hyrcanian forest of iran using random forest. Journal of Sustainable Forestry, 38(7), 615–628. https://doi.org/10.1080/10549811.2019.1598443