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 classification scheme achieves better results, in terms of overall accuracy, than the training on Initial classification scheme.

Flood and as an impact of inundation is the natural event. It mainly occurs when the river catchment, (that is the area of land that feeds water into the river and the streams that flow into the main river) receives greater than usual amounts of water (for example through rainfall or melting snow). The river can not cope and this extra water causes the level of the water in the river to rise and a flood to take place. This flooding may take place at any point along the river course and not necessarily at the place where the extra water has entered. The Kura river is the largest river of Azerbaijan. It stretches for 1,515 kilometers and covers an area of 188 thousand sq. km. The Kura originates from the Hel River in Turkey, passes through Georgia and Azerbaijan and flows into the Caspian Sea in southeastern part of the country. Achievements in information systems, satellites imaging systems and improved software technologies have led to opportunities for a new level of information products from remote sensed data. The integration of these new products into existing response systems can provide a wide range of analysis tools and information products on the base of developed geographical information system (GIS). Using the higher resolution of space imagery and change detection analysis natural disaster awareness and damage assessment can be conducted rapidly and accurately.On the base of the developed database using the remote sensing methods and GIS technology there is resources and opportunities of prediction, reduction of natural risk due to the timely implementation of appropriate engineering and technological activities.The results of the carried out project "Application of Remote Sensing and GIS Technology to Reduce Flood Risk" is concerned to the high technology application. It is important to note that the space technology, project development approach used for the project implementation are very useful issues which definitely will be find out a place in our future activities for the similar problem solving. For this project implementation purposes multichannel ALOS space image was used with spatial resolution of 10m. With the rapid development of tools such as Remote Sensing methods and Geographic Information Systems (GIS) technologies bridging gaps between data gathering, modeling, and flood prediction is becoming more feasible. Conventional modeling approaches using Remote Sensing and GIS technologies have been of limited value in the floodplains of the Kura River basin. Digital Elevation Models (DEMs) are either not availableor too coarse to adequately capture subtle variations in flooding topography important in characterizing the spatial variability of the hydro period regime. Consequently, DEM-based models have been developed to represent floodplain geomorphology in this particular region. Additionally, analyses using Remotely Sensed imagery have been also scarce for the area.In spite of the limitations regarding data quality and availability, this analysis evaluates the sensitivity of a DEM-based su...

On November 4, 2003, the Board of the International Finance Corporation (IFC), the private sector arm of the World Bank Group, approved lending to the Baku-Tbilisi-Ceyhan (BTC) oil pipeline and theAzeri-Chirag-Deepwater Gunashli (ACG) Phase 1 oil field. The BTC pipeline is a dedicated crude oil pipeline system, which extending from the ACG field through Azerbaijan and Georgia to a terminal at Ceyhan on the Mediterranean coast of Turkey. The pipeline transports up to 1 million barrels per day, and at 1760 kilometers is one of the longest of its kind in the world. The BTC pipeline complements oil transport from two existing pipelines -the Northern Route pipeline to Novorossiysk, Russia and the Western Route pipeline which ends in Supsa, Georgia.Presently very sharply there is a significant safety problem of oil and gas transportation in regions of Azerbaijan/Georgia/Turkey after successful construction of the oil pipeline to the Baku -Tbilisi -Ceyhan as well as gas pipeline Baku -Tbilisi -Erzurum. Construction of the railway of Baku -Tbilisi -Akhalkalaki -Kars has received the legal status after signing an Agreement between country representatives of the foregoing railway.The use of advance technologies make available to enhance planning, project management and design, operation and maintenance of the pipeline infrastructure. Aero and satellite remote sensing data integrated into the geographic information system (GIS) creates an opportunity to assist pipeline risk assessment, environmental issues, safety of pipeline infrastructure as well social impacts. Modern capacity of data processing techniques is opening a new technological opportunities to develop capability to accomplish the pipeline mapping with a variety of futures of infrastructure and safety needs of the industry where social aspects undertaken. These technologies when combined with GIS have significant and unique potential for application to a number of cross cutting system security elements [1].

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