Journal of Geographic Information System, 2010, 2, 93-99 doi:10.4236/jgis.2010.22014 Published Online April 2010 (http://www.SciRP.org/journal/jgis) Copyright © 2010 SciRes. JGIS Remote Sensing and GIS as an Advance Space Technologies for Rare Vegetation Monitoring in Gobustan State National Park, Azerbaijan Yelena M. Gambarova 1 , Adil Y. Gambarov 2 , Rustam B. Rustamov 3 , Maral H. Zeynalova 4 1 R.I.S.K. Company, Baku, Azerbaijan 2 SAHIL IT Company, Baku, Azerbaijan 3 Institute of Physics of the National Academy of Sciences, Baku, Azerbaijan 4 Institute of Botany of the National Academy of Sciences, Baku, Azerbaijan E-mail: elenag@risk.az, AGambarov@sahil.info Abstract This paper describes remote sensing methodologies for monitoring rare vegetation with special emphasis on the Image Statistic Analysis for set of training samples and classification. At first 5 types of Rare Vegetation communities were defined and the Initial classification scheme was designed on that base. After preliminary Statistic Analysis for training samples, a modification algorithm of the classification scheme was defined: one led us to creating a 4 class’s scheme (Final classification scheme). The different methods analysis such as signature statistics, signature separability and scatter plots are used. According to the results, the average separability (Transformed Divergence) is 1951.14, minimum is 1732.44 and maximum is 2000 which shows an acceptable level of accuracy. Contingency Matrix computed on the results of the training on Final classi- fication scheme achieves better results, in terms of overall accuracy, than the training on Initial classification scheme. Keywords: Remote Sensing, GIS, Seperability, Classification 1. Introduction The vegetation is one of the key and best instrument and indicator for monitoring of identification of impacts of the natural processes, environmental and ecological is- sues. As changes in vegetation are rapid and serious due to various human activities, it is urgent to monitor vege- tation and their surrounding environment from physical, biological or social viewpoints. Remote sensing is ex- pected to provide us an efficient tool for monitoring vege- tation environment. In particular, as considering vegeta- tion is often characterized by a mixture of different vege- tations, soil and water, remote sensing is expected to delineate the relation between them. This paper describes Remote Sensing and GIS as an advance Space Technology for Rare Vegetation moni- toring in Gobustan State National Park with special em- phasis on Image Statistic Analysis for set of training samples and classification. Determination of the ‘best’ bands combinations in the context of Image statistical analysis is very important. The best band combinations will be used in accurate classification. Methods used to select the optimum bands combination are known as feature selection techniques. A number of criteria can be used to categorize feature selection techniques. As they can be classified on the basis of whether they are graphical or statistical in nature [1], they can also be classified into two categories based on whether or not they use classification algorithms to evaluate the performance of subsets. Techniques that use the former approach are called ‘wrapper techniques’; techniques using the latter approach are known as ‘filter techniques’ [2]. A filter is defined as a feature selection algorithm using a performance metric based entirely on the training data, without reference to the classifier for which the features are to be selected. The most widely used filter methods are based on class separability indices. Use of this ap- proach in the context of Image statistical analysis was investigated in this study. Class Separability indices were employed to determine the best band combination of SPOT 5 image datasets.