LAUSR

Mapping forests is an important process in managing natural resources. At present, due to spectral resolution limitations, multispectral images do not give a complete separation between different forest species. In contrast, advances in remote sensing technologies have provided hyperspectral tools and images as a solution for the determination of species. In this study, spectral signatures for stone pine (Pinus pinea L.) forests were collected using an advanced spectroradiometer “ASD FieldSpec 4 Hi-Res” with an accuracy of 1 nm. These spectral signatures are used to compare between different multispectral and hyperspectral satellite images. The comparison is based on processing satellite images: hyperspectral Hyperion, hyperspectral CHRIS-Proba, Advanced Land Imager (ALI), and Landsat 8. Enhancement and classification methods for hyperspectral and multispectral images are investigated and analyzed. In addition, a well-known hyperspectral image classification algorithm, spectral angle mapper (SAM), has been improved to perform the classification process efficiently based on collected spectral signatures. The results show that the modified SAM is 9% more accurate than the conventional SAM. In addition, experiments indicate that the CHRIS-Proba image is more accurate than Landsat 8 (overall accuracy 82%, precision 93%, and Kappa coefficient 0.43 compared to 60, 67%, and 0.035, respectively). Similarly, Hyperion is better than ALI in mapping stone pine (overall accuracy 92%, precision 97%, and Kappa coefficient 0.74 compared to 52, 56%, and − 0.032, respectively).