Geospatial Technology for Land Cover Analysis
Creators: name: Lemenkova, Polina
Publisher: name: figshare
Year: 2018
Subjects: subject: 60302 Biogeography and Phylogeography
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Geography
subject: 160401 Economic Geography
subject: FOS: Social and economic geography
subject: FOS: Social and economic geography
subject: 160499 Human Geography not elsewhere classified
subject: 40699 Physical Geography and Environmental Geoscience not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: FOS: Earth and related environmental sciences
subject: Physical Geography
subject: 160403 Social and Cultural Geography
subject: 90901 Cartography
subject: FOS: Environmental engineering
subject: FOS: Environmental engineering

description: Published in : Middle East &amp; Africa Geospatial Digest November 2013. <br>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 traditional methods of classification can roughly be divided into two approaches: unsupervised and supervised. The current study was done using supervised methods, since it better corresponds to the target task. The unsupervised approach categorises pixels into spectral groups that may be mixed with other lands cover types, or do not clearly specify as to which class group do they belong to. Supervised classification enables much more user control on the process. During supervised classification, the pixel classification process is carefully supervised by the user, via creation of training polygons and then assigning of pixels into these groups by the specific algorithm approach. There are different methods of the supervised classification, which depend on the mathematic approaches of the computer GIS program that uses a specific algorithm procedure for the image classification. Training sites have been created for the respective land cover types that are typical for the study area in Izmir surroundings (Fig.6). The representative sample sites of known 12 cover types, were recognized on the image and manually digitised. There areas are used as a digital interpretation key that indicated the 'ideal' spectral signatures for the respective land cover classes, according to their individual characteristics. Subsequently, the whole image was classified using Erdas Imagine. It was done using automatic comparison of each pixel within the image to each 'ideal' land category in the training sites, and then assigning all pixels to the classes to which they best corresponded. The difference between the MD and ML methods consists in the mathematical approaches of this comparison and the method of the class categorisation. The logical idea of the MD classification approach is in calculation of the distance in Euclidean coordinate systems from the values of every pixel to the value of mean vector. Mathematically, the calculation is based on the Pythagorean Theorem. The ML classifier has more statistical than geometrical character. It estimates the greatest probability of each pixel to approach the model group, which is represented by the normally distributed Gaussian Curve, i.e. core training areas. The quality control and validation of the results has been performed using accuracy assessment operations in Erdas Imagine.