Sürdürülebilir Kalkınma Amaçları

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Yazarlar

• Abdulmuttalib, H.M.(1)
• Akpinar, B.(1)
• Bayram, B.(1)
• Bozkurt, S.(1)
• Catal Reis, H.(1)
• Comert, Resul(1)
• Di, K.(1)
• Erdem, F.(1)
• Faruque, F.S.(1)
• Honkavaara, E.(1)
• Ince, A.K.(1)
• Isikdag, U.(1)
• Jiang, J.(1)
• Kaplan, Onur(1)
• Karas, I.R.(1)
• Komp, K.(1)
• Li, R.(1)
• Liang, X.(1)
• Maden, Nafiz(1)
• Osmanoglu, B.(1)
• Peled, A.(1)
• Rahman, A.A.(1)
• Scaioni, M.(1)
• Seker, D.Z.(1)
• Shaker, A.(1)
• Soergel, U.(1)
• Stilla, U.(1)
• Tanzi, J.J.(1)
• Wu, L.(1)
• Yoshimura, M.(1)
• Zhang, H.(1)
• Zhang, J.(1)

• MakaleAçık Erişim2020/r/gumushane/14701
  Historical aurora borealis catalog for Anatolia and Constantinople (hABcAC) during the Eastern Roman Empire period: Implications for past solar activity

  Maden, Nafiz

  Herein, Anatolian aurorae are reviewed based on the existing catalogs to establish a relationship between the aurora observations and past solar activity during the Medieval period. For this purpose, historical aurora catalogs for Constantinople and Anatolia are compiled based on the existing catalogs and compared with those in the Middle East region. The available catalogs in the literature are mostly related to the records observed in Europe, Japan, China, Russia, and the Middle East. There is no study dealing only with the historical aurora observations recorded in Anatolia and Constantinople. The data of the catalog show that there is a considerable relationship between the aurora activity and past strong solar activity. High auroral activity around the extreme solar particle storm in 774/775 and the Medieval grand maximum in the 1100s in Anatolia and the Middle East is quite consistent with the past solar variability reported in other scientific literature.

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• BildiriAçık Erişim2018/r/gumushane/13086
  OBJECT BASED BUILDING EXTRACTION and BUILDING PERIOD ESTIMATION from UNMANNED AERIAL VEHICLE DATA

  Comert, Resul; Kaplan, Onur; Honkavaara, E.; Komp, K.; Stilla, U.; Jiang, J.; Zhang, H.; Peled, A.; Soergel, U.; Scaioni, M.

  The aim of this study is to examine whether it is possible to estimate the building periods with respect to the building heights in the urban scale seismic performance assessment studies by using the building height retrieved from the unmanned aerial vehicle (UAV) data. For this purpose, a small area, which includes eight residential reinforced concrete buildings, was selected in Eskisehir (Turkey) city center. In this paper, the possibilities of obtaining the building heights that are used in the estimation of building periods from UAV based data, have been investigated. The investigations were carried out in 3 stages; (i) Building boundary extraction with Object Based Image Analysis (OBIA), (ii) height calculation for buildings of interest from nDSM and accuracy assessment with the terrestrial survey. (iii) Estimation of building period using height information. The average difference between the periods estimated according to the heights obtained from field measurements and from the UAV data is 2.86 % and the maximum difference is 13.2 %. Results of this study have shown that the building heights retrieved from the UAV data can be used in the building period estimation in the urban scale vulnerability assessments.

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• BildiriAçık Erişim2017/r/gumushane/15165
  THE EFFICIENCY of RANDOM FOREST METHOD for SHORELINE EXTRACTION from LANDSAT-8 and GOKTURK-2 IMAGERIES

  Bayram, B.; Erdem, F.; Akpinar, B.; Ince, A.K.; Bozkurt, S.; Catal Reis, H.; Seker, D.Z.; Karas, I.R.; Rahman, A.A.; Isikdag, U.

  Coastal monitoring plays a vital role in environmental planning and hazard management related issues. Since shorelines are fundamental data for environment management, disaster management, coastal erosion studies, modelling of sediment transport and coastal morphodynamics, various techniques have been developed to extract shorelines. Random Forest is one of these techniques which is used in this study for shoreline extraction.. This algorithm is a machine learning method based on decision trees. Decision trees analyse classes of training data creates rules for classification. In this study, Terkos region has been chosen for the proposed method within the scope of "TUBITAK Project (Project No: 115Y718) titled "Integration of Unmanned Aerial Vehicles for Sustainable Coastal Zone Monitoring Model – Three-Dimensional Automatic Coastline Extraction and Analysis: Istanbul-Terkos Example". Random Forest algorithm has been implemented to extract the shoreline of the Black Sea where near the lake from LANDSAT-8 and GOKTURK-2 satellite imageries taken in 2015. The MATLAB environment was used for classification. To obtain land and water-body classes, the Random Forest method has been applied to NIR bands of LANDSAT-8 (5th band) and GOKTURK-2 (4th band) imageries. Each image has been digitized manually and shorelines obtained for accuracy assessment. According to accuracy assessment results, Random Forest method is efficient for both medium and high resolution images for shoreline extraction studies.

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• Dergi/r/gumushane/14702
  Annales Geophysicae
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• Dergi/r/gumushane/13087
  ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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