Abstract

Remote sensing enables for cost-effective, timely efficient and multi-temporal monitoring of natural vegetation. Spectral reflectance pattern either in forms of row reflectance values or form of spectral vegetation indices (SVIs) could be used as estimators of plant biophysical and biochemical parameters through statistical models. The main objective of the current study is to correlate plant chlorophyll concentration with different (SVIs) and to identify the most sufficient index to discriminate among the twenty common natural vegetation species in Sinai Peninsula. Calculated values of five hyperspectral vegetation indices (normalized difference vegetation index (NDVI); Chlorophyll Index; Chlorophyll a,b; Simple ratio index (SRI); Modified chlorophyll absorption ratio index (MCARI) for the twenty observed vegetation species were used as spectral factors in the modeling process. The result showed that the relatively high chlorophyll content was found in broad leaves plants when needle-leaved plant species showed relatively low ones. Laboratory chlorophyll estimation indicated that Asclepias sinaica had the highest chlorophyll content (79 mg cm-2) when the same plant specious showed the highest chlorophyll index value. It was found that plants of family Zygophyllaceae have low chlorophyll content. Among observed SVIs, NDVI was the most correlated index with chlorophyll. At the same time, it was the optimal index to differentiate the different species.

Key words: Hyperspectral; Spectral indices; Chlorophyll content.