International Geoinformatics Research and Development Journal Rare vegetation classification of remotely sensed images, Gobustan National Park, Azerbaijan Yelena M. G.1, Adil Y. G.2, Rustam B. R.3, Maral H. Z.4 1 R.I.S.K. Company, Baku, Azerbaijan E-mail:
[email protected] 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 Abstract This study concentrated to develop a remote sensing and GIS method to estimate rare vegetation in Gobustan, Azerbaijan. For the study SPOT 5 of May 2007 image was procured. The paper analyses the supervised classification method Maximum Likelihood Classification algorithm. A number of steps were involved in the process of the study, including Geographical Data Base design, creation of Specialized GIS Environment and classification. Study reveals that optical remote sensing techniques could effectively be used to assess rare vegetation and map them accordingly with a reliable accuracy of 74.2%. Keywords: Remote sensing, vegetation, classification, SPOT5 Introduction Satellite remote sensing has become a common tool of investigation, prediction and forecast of environmental change and scenarios through the development of GIS-based models and decision- support instruments that have further enhanced and considerably supported decision making [1], [2], [3]. With the advent of new high spatial and spectral resolution satellite, new applications for precision mapping and accurate monitoring have become feasible. Remote sensing is expected to provide us an efficient tool for vegetation monitoring. In particular, as vegetation is characterized by a mixture of vegetation, soil and water in mixed conditions, remote sensing is expected to delineate the relation between them.