Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2021/r/gumushane/13334
  Temporal monitoring of land use/land cover change in Kahramanmaraş city center

  Aliyazicioglu, Kubra; Beker, Fatmanur; Topaloglu, Raziye Hale; Bilgilioglu, Burhan Baha; Comert, Resul

  Irregular urbanization causes problems such as decrease in fertile agricultural areas, irregular industrialization and urbanization. To provide a healthier life opportunity for future generations without disturbing nature, it is essential to determine the temporal changes in land use and to make land management plans accordingly. In this study, land use/land cover (LULC) change occurred in Kahramanmaras province within 30 years was investigated by remote sensing and integration of Geographic Information Systems. Landsat satellite images of 1988-1998-2008-2018 were obtained for the production of LULC maps. Each image was classified according to supervised classification approach using the support vector machines (SVMs) algorithms. The confusion matrix was created for each year to examine the accuracy of the LULC maps. The overall accuracy of the thematic maps was obtained as 91.76%, 93.56%, 86.89% and 88.29%, respectively. Also, Kappa values of thematic maps for each year were obtained as 0.88, 0.91, 0.81 and 0.84, respectively. When the results were examined, the development of industry in the city area and the construction of the airport contributed to the development of the social and economic structure of the city. The increase in the number of housing in the related regions has led to an increase in the amount of urban areas and a decrease in agricultural areas.

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• BildiriYalnız künye2019/r/gumushane/15040
  Object-based classification of Izmir Metropolitan City by using sentinel-2 images

  Ozlem Yilmaz, Elif; Varol, Beril; Hale Topaloglu, Raziye; Sertel, Elif

  This study aims to create Land Cover Land Use (LCLU)map of a part of the Izmir metropolitan city in Turkey, based on an enhanced Urban Atlas nomenclature and object based classification approach. Multi-temporal Sentinel-2 images from different seasons are used and rule-based object oriented classification techniques are applied on the images. Totally 20 LCLU classes are identified in the study area with different accuracy values. Thematic open source data were also integrated into classification to better identify some land use classes and to increase the total classification accuracy. Our results show that 10-m bands of Sentinel-2 images are capable to produce thematically detailed LCLU map with an overall accuracy of 86% and 0.852 Kappa values.

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• MakaleYalnız künye2019/r/gumushane/13490
  Mapping Istanbul quarry mining region using Landsat 8 OLI&TIRS data

  Balcik, Filiz Bektas; Topaloglu, Raziye Hale; Goksel, Cigdem

  Open mining operations have brought number of environmental challenges, which may result in soil erosion, dust, noise, water pollution, and severe impacts on biodiversity. Determination of the mining activities and its surrounding environment is a major issue in sustainable resource management. Many remote sensing indices have been developed and widely applied as an effective tool to determine different land surface types. The main objective of this study was to determine quarry mines and its surroundings land use and land cover (LULC) categories in the selected study area of Istanbul using different remote sensing indices including Tasseled Cap Transformation (Brightness, Greenness and Wetness) and Normalized Difference Bareness Index (NDBals). In this study, an open mining area in the northern Black Sea coastal part of Istanbul was selected as study area. Freely available Landsat 8 OLI & TIRs image was used to determine quarries and land cover/ land use categories in the concerned area. By using original bands of Landsat 8 OLI, TCT components and NDBal, three different data sets were created. Each image was then separately subjected to support vector machine classification and processed to identify and quantify LULC categories. The accuracy of classification results were evaluated in a comparitive manner using error matrix. Data set C had the highest overall accuracy and Kappa statistics that were calculated as 83.00 and 0.80, respectively.

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• MakaleAçık Erişim2018/r/gumushane/13427
  Comparison of landscape metrics for three different level land cover/land use maps

  Sertel, Elif; Topaloğlu, Raziye Hale; Şallı, Betül; Algan, Irmak Yay; Aksu, Gül Aslı

  This research aims to investigate how different landscape metrics are affected by the enhancement of the thematic classes in land cover/land use (LC/LU) maps. For this aim, three different LC/LU maps based on three different levels of CORINE (Coordination of Information on The Environment) nomenclature were created for the selected study area using GEOBIA (Geographic Object Based Image Analysis) techniques. First, second and third level LC/LU maps of the study area have five, thirteen and twenty-seven hierarchical thematic classes, respectively. High-resolution Spot 7 images with 1.5 m spatial resolution were used as the main Earth Observation data to create LC/LU maps. Additional geospatial data from open sources (OpenStreetMap and Wikimapia) were also integrated to the classification in order to identify some of the 2nd and 3rd level LC/LU classes. Classification procedure was initially conducted for Level 3 classes in which we developed decision trees to be used in object-based classification. Afterwards, Level 3 classes were merged to create Level 2 LC/LU map and then Level 2 classes were merged to create the Level 1 LC/LU map according to CORINE nomenclature. The accuracy of Level 1, Level 2, Level 3 maps are calculated as; 93.50%, 89.00%, 85.50% respectively. At the last stage, several landscape metrics such as Number of Patch (NP), Edge Density (ED), Largest Patch Index (LPI), Euclidean Nearest Neighbor Distance (ENN), Splitting Index (SPLIT) and Aggregation Index (AI) metrics and others were calculated for different level LC/LU maps and landscape metrics values were compared to analyze the impact of changing thematic details on landscape metrics. Our results show that, increasing the thematic detail allows landscape characteristics to be defined more precisely and ensure comprehensive assessment of cause and effect relationships between classes.

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