Abstract
The hyperspectral images have so far been widely utilized in monitoring and detecting the changes in a broad range of environmentally related matters. The hyperspectral image analysis yields maps that show spatial dispersion of physical and ecological characteristics of the terrain. Within the scope of the current study, an integrated Fuzzy-MCDM in a Geographic Information Systems (GIS) platform was used to map the health condition of Ramsar forest. Spectral indices can provide different methods for identifying vegetation coverings. For forest health analysis, spectral indices such as NDWI, CRI1, PSRI, PRI, and NDVI were used to infer the causative factors of forest health. The findings highlight the suitability of the used methodology in identifying potential forest statuses, where forest health protection measures can be taken in advance. The results also suggest that the southern and the western aspects of the study area are of “very low” to “low” forest health. Furthermore, the results indicate a high potentiality for applying the spatial MCDM techniques as an effective tool for the forest health investigation.
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