Forest health assessment using hyper spectral image and multi-criteria analysis: A case study: Ramsar Forest, North of Iran

Abstract

The hyperspectral images have so far been widely used to monitor and detect environmental changes in vast areas. The analysis of hyperspectral images provides the spatial distribution (maps) of terrain physical and ecological characteristics. In this study, fuzzy set theory integrated with a decision-making algorithm in a Geographic Information Systems (GIS) was used to map Ramsar forest health. In the fuzzy set theory, all classes must have a certain boundary or grouping. (i.e., fuzzy) and consist of a rule base, membership functions, and an inference procedure. For forest health assessment, NDWI, CRI1, PSRI, PRI, and NDVI indices were used to infer the causative factors of forest health. Spectral indices can provide different methods for identifying vegetation coverings. The results of this study are quite useful in identifying potential forest health, 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 introduce the potentiality of multi-criteria analysis integrated with GIS as an effective tool in assessing the fire-prone areas of forests.