Machine learning-based classification of Hyrcanian forests using PRISMA data

Forest type mapping is crucial for sustainable forest management, but field-based methods are costly and time-consuming. PRISMA hyperspectral imageryis promising for forest type classification. This study evaluates PRISMA data for mapping mixed Hyrcanian forests in northern Iran using classical supervised machine learning (ML) algorithms. Four classifiers, i.e. Random Forests (RF), Support Vector Machine (SVM), Neural Network (NN) and Linear Discriminant Analysis (LDA), were employed to analyze five datasets: (1) original PRISMA bands; (2) Minimum Noise Fraction (MNF)-transformed bands; (3) original bands combined with vegetation indices; (4) bands most correlated with selected components and (5) principal component–based bands within near-infrared and shortwave infrared regions. Results showed that the RF classifier with MNF-transformed data achieved the highest accuracy based on 5-fold cross validation, with an overall accuracy of 88.65% ± 1.02% and a kappa coefficient of 0.83, outperforming the original spectral bands (OA = 72.16% ± 2.22%, KC = 0.59). Among the other classifiers, SVM and LDA achieved their highest accuracies using Dataset 3 (OA = 73.58% ± 2.98%, KC = 0.61 and OA = 72.57% ± 1.76%, KC = 0.61, respectively), while NN showed a lower performance (OA = 66.72% ± 4.09%, KC = 0.52). The classification performed well for pure beech and mixed forest types, highlighting the potential of PRISMA hyperspectral data, particularly after MNF preprocessing, for forest type mapping in Hyrcanian forests.