Integrating Vegetation Indices and PRISMA Hyperspectral Imagery for Forest Risk Assessment in Northern Iran

In recent years, the forests of northern Iran have experienced increasing ecological stress due to climate change and human activities, highlighting the need for effective forest health monitoring. This study evaluates the applicability of PRISMA hyperspectral imagery for assessing forest risk in this region during 2024. Leveraging the sensor’s high spectral resolution, several vegetation indices were extracted and grouped into three categories: greenness, pigment, and canopy water/light use efficiency. These indices were combined and classified into five risk levels using multi-index integration and weighting techniques in a GIS environment. Validation of the classification results revealed a high level of accuracy, with an overall accuracy of 93.73% and a Kappa coefficient of 0.9157. The combined indices outperformed individual indices in identifying vegetation stress patterns. Results indicated that the central and western forest areas are in healthy condition, while the eastern, northeastern, and southeastern regions exhibit notable stress, likely linked to water deficit or pest and disease pressures. The findings underscore the effectiveness of integrating hyperspectral data and vegetation indices for forest risk assessment and offer valuable insights for improving spatiotemporal forest health monitoring strategies.