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Identification of land cover/land use changes in the greater area of the Preveza peninsula in Greece using Landsat satellite data
Institution:1. CREAF, Campus de Bellaterra (UAB) Edifici C, Cerdanyola del Valles 08193, Spain;2. Comision Nacional Forestal, Periférico Poniente No. 5360, Zapopan 45019, Mexico;3. Universitat Autonoma Barcelona, Cerdanyola del Valles 08193, Spain;1. CONACYT - Instituto de Investigaciones sobre los Recursos Naturales, Universidad Michoacana de San Nicolás de Hidalgo, C.P. 58330 Morelia, Michoacán, Mexico;2. Laboratorio de Manejo Ambiental, Centro de Investigación en Alimentación y Desarrollo, A.C. - CIAD, AC. Sábalo-Cerritos s/n, 82100 Mazatlán, Sinaloa, Mexico;3. Instituto de Investigaciones sobre los Recursos Naturales, Universidad Michoacana de San Nicolás de Hidalgo, C.P. 58330 Morelia, Michoacán, Mexico;1. School of Political Science and Public Administration, Huaqiao University, Quanzhou, 362021, China;2. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China;3. College of Tourism, Huaqiao University, Quanzhou, 362021, China
Abstract:This study investigates the Land Use & Land Cover (LULC) changes in a coastal area of the southwest part of Epirus region, called Preveza, situated in North-western Greece. Remote sensing imagery coming from the Enhanced Thematic Mapper (ETM+) sensor on board at the Landsat 7 satellite platform is used for this purpose. More specifically, we identified LULC changes in this environmentally sensitive coastal area, using Landsat image scenes for the dates of June 19th, 2000 and July 22nd, 2009. During this period, there was an increasing tourist activity and a high growth in the construction sector of the study area. The land-use changes were identified, examining several vegetation indices and band combinations, along with the implementation of different well-known classification techniques. The Normalized Difference Vegetation Index (NDVI) and the Brightness Index (BI) have proved to be the most suitable indices to successfully identify discrete land surface classes for this study area. Regarding the classifiers, a series of traditional and modern algorithms were tested. The Artificial Neural Networks (ANNs) and the Support Vector Machines (SVMs) gave improved results in comparison to other more traditional classification techniques. The best overall accuracy for the study area was achieved with the SVM classifier and reached 96.25% and 97.15% on the dates of June 19th, 2000 and July 22nd, 2009 respectively. The classification results depicted notable urbanization, small deforestation and important LULC changes in the agriculture sector, indicating a rapid coastal environment change in the region of interest.
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