Geospatial Technology for Land Cover Analysis

The Izmir region in Turkey is extremely valuable for the environment while being used immensely for tourism, agriculture and recreation. The geographic area of western Turkey is known for its unique physical and environmental settings. Due to its location in the Mediterranean climate, the region has certain typical features and is influenced by the local and regional climatic setting. The geography of the region combines diverse landforms, various geomorphological features, natural landscapes, species and vegetation richness: mountainous and hilly landscapes, caves, islands in the coastal area and lakes. The geology of the area of Western Anatolia is characterized by the neotectonic active faulting and high seismicity, which leads to the geomorphic instabilities and landslide formation in the research area. The vegetation in the study area is dominated by typical Mediterranean flora. The data used in the article consists of the following types: Landsat TM Image from the EarthSat producer, WRS-2, Path 180, Row 033, acquired on 1987.06.05, distributed by GLCF in GeoTIFF format. Landsat ETM+ image from the USGS producer, WRS-2, Path 180, Row 033 acquired on 2000.06.16, distributed by GLCF in GeoTIFF format. Spatial analysis Development of spectral signatures for land cover classes The classification of Landsat TM imagery for the same study area taken at different years allowed the recording of gradual land degradation. The last one is mainly caused by intensive construction of the second house summer cottages, and tourist activities. Recent changes in land use / land cover types in selected regions of Turkey have been proved by Evrendilek et al., (2011) who performed historical land use change detection using measurements of carbon that indirectly indicate the extent of peatlands within the landscapes. The use of the Landsat TM images was applied to assess changes in the land cover types over time. The thematic mapping of the land cover types in the selected area has been done using methods of supervised classification.