چکیده (انگلیسی):

Global vegetation distribution is the result of environmental conditions and the genetic make-up of plants. In view of the intensive discussions on climate change and its effect on living organisms on earth, it has become necessary to develop new methods and strategies to monitor and follow the changes in global plant distribution.In this research, we focused on a vegetation analysis using spectral reflectance profiles linked to biochemical concentrations. We performed the spectroscopic measurements of Pistacia species at canopy and leaf levels. Concomitant chemical analyses of leaf and bark materials enabled the development of remote sensing (RS) indices of structure-related biochemicals and pigments. We developed RS indices for cellulose, lignin, chlorophyll, carotenoid, anthocyanin and wax. Since the wax was the least studied in this context and due to the fact that it is the first layer of the plant surface that interacts with the incident light, it deserved special attention and was assumed to affect the spectral reflectance and, in combination with the other biochemicals, to contribute to better species identification.In the modeling process, we showed that apart from the major energy absorption spectral bands related to a given biochemical, there were supplementary bands that had a significant effect on the accuracy of the biochemical content estimation. Another factor affecting the accuracy of the biochemical estimation was the season. Thus, we divided the biochemical content estimation models into seasonal groups of spring, summer and fall. The RS indices developed in this work, together with literature-reported RS indices, were used for Pistacia classification. The accuracy (69%) of species classification was significantly higher in spring at an early vegetation stage than in summer (50%), and fall (37%). As a proof of concept, the same sets of RS indices were also used for classifying the genera and families of various plants in the Mediterranean forest using EO1 Hyperion images. The accuracy of the classification maps was 79%, when the full set of RS indices developed in this work was used