Rare Vegetation Conservation In Gobustan National Park, Azerbaijan | 12105
Journal of Earth Science & Climatic Change
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he Gobustan State National Park (GSNP) is a nationally important desert/semi-desert located west and south-west of Baku,
Azerbaijan. In 2007, Gobustan was declared a UNESCO World Heritage Site considered being of "outstanding universal
value" for the quality and density of its rock art engravings. The Study Area at Gobustan contains a wealth of historical and
archaeological sites and is also known for its rare vegetation.
The desert communities in GSNP represent the most ecologically important habitat. The great age of many of the desert
communities and their slow growth rate further enhance their botanic significance. Climate change and various anthropogenic
activities are causing large losses to natural habitats in this area. The importance of this habitat type is one of the reasons that
the Gobustan desert has been proposed as the State National Park, so that some level of protection is offered to this desert. Plant
communities such as these, which develop very slowly are particularly susceptible to this disturbance and are easily lost, taking
many years to recover (at least 10-12 years).
Remote sensing technology in combination with Geographic Information System (GIS) can render reliable information on
rare vegetation cover. The analysis of the spatial extent and temporal change of rare vegetation cover using remotely sensed data
is of critical importance to rare vegetation monitoring. Change detection as defined by authors is temporal effects as Variation in
spectral response involves situations where the spectral characteristics of the vegetation or other cover type in a given location
change over time. The authors describe change detection as a process that observes the differences of an object or phenomenon
at different times.
Change detection and monitoring involve the use of multi-date images to evaluate differences in vegetation distribution due
to environmental conditions and human actions between the acquisition dates of images. In this study, The Vegetation Index
techniques of change detection were applied
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