Forest mapping:a comparison between hyperspectral and multispectral images and technologies

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).