Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2023/r/gumushane/12781
  3D data integration for geo-located cave mapping based on unmanned aerial vehicle and terrestrial laser scanner data

  Çömert, Resul; Özdemir, Samed; Bilgilioglu, Burhan Baha; Alemdag, Selçuk; Zeybek, Halil Ibrahim

  The Akçakale cave is a significant natural and cultural heritage site in the Black Sea region of eastern Turkey. The complex geometry and difficult-to-access areas of the cave have made the use of traditional mapping methods challenging. To overcome these limitations, this study utilized TLS and UAV technology to produce highly accurate 2D and 3D data for cave management and risk assessment purposes. The TLS system was used to create a detailed 3D point cloud of the cave interior, while the UAV system generated a 3D model of the surface topography outside the cave. The two sets of data were combined in the GIS environment using a geodetic network established in the study area, providing a common geodetic reference system for both TLS and UAV data. The study found that the cave area is 13,750 m2, which is smaller than the area of 18,000 m2 that was previously estimated using conventional measurement methods. The volume and ceiling heights of the cave were calculated using the elevation models generated from TLS point cloud data. The 3D point cloud data were also used to map dripstone locations on the floor and ceiling of the cave, and the boundaries of rock blocks on the ground were precisely determined. The study identified potential risks associated with the cave, particularly the risk of rockfall in the source rock areas around the cave entrance and the southern part of the cave. The nearest building to the cave is approximately 35 meters away, and all the buildings in the area are less than 300 meters from the cave. In the event of the cave collapse, the buildings in the southern part of the cave are at risk of rockfall. This study demonstrates the effectiveness of combining data from TLS and UAV systems to generate broad and sensitive cave mapping and risk assessment data, which are critical for cave management and safety. The collected data can be used for cave stability investigations and rockfall risk assessments. This study provides a foundation for future explorations of the Akcakale cave and highlights the potential for modern surveying techniques to enhance our understanding of complex geological structures such as caves.

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• MakaleYayıncıda açık erişim2022/r/gumushane/12787
  Analyzing and Predicting Spatiotemporal Urban Sprawl in Eskişehir Using Remote Sensing Data

  Küçük Matcı, Dilek; Çömert, Resul; Avdan, Uğur

  Urban areas of major cities in developing countries are expanding rapidly due to rapid population growth and industrialization. Determining the reasons for rapid urbanization and the amount of urban area that will be needed in the future is important for the planned growth of these cities. In this study, the LU/LC change of Eskisehir city center that has a rapid rate of urbanization and industrialization in Turkey between 1984 and 2020 was determined. Then, the area needed for urban and industrial areas in 2030 was investigated using the Cullullar Automata–Markov Chain (CA–MC) hybrid model simulation. Each year's LU/LC map was produced with over 80% kappa accuracies performing the pixel-based Random Forest (RF) algorithm for change analysis. In the change analysis made between 1984 and 2020, it was determined that there was a change of 117% in the urban area and 977% in the industrial area. The prediction was made for the validation of the CA–MC model from the period 2000–2010 to 2020. When compared with the 2020 LU/LC map, the model success was obtained as 0.84, 0.87, and 0.87 in the Kstandard, Klocation, and Kno Kappa metrics, respectively. By using the 2010–2020 periods in the estimation of 2030, it has been observed that the urban and industrial area will increase by 22.95%; therefore, there will be decreases in agriculture and other natural areas as in previous years.

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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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• BildiriAçık Erişim2021/r/gumushane/13332
  GIS-BASED OPTIMUM SITE SELECTION for SOLAR ELECTRIC VEHICLE CHARGING STATION: ANKARA-ISTANBUL HIGHWAY CASE

  Hisoglu, Seyit; Comert, R.

  Energy sources are divided into renewable and non-renewable sources. It can be seen that non-renewable energy resources are not adequately meeting the increased demands of worldwide technological developments, increasing population, and global consumption. Therefore, the demand for renewable resources is increasing day by day. When it comes to the use of non-renewable carbon-based fossil fuels, one of the first areas that come to mind is undoubtedly the Automotive sector. Today, it is realized that one of the main reasons for the lack of electric motor cars compared to petroleum fuelled cars, is the scarcity of electric vehicle charging stations and the difficulty of their accessibility. In this study; an analysis of solar-powered electric charging stations site selection was carried out for electric vehicles. The Ankara-Istanbul highway, which has a high traffic density, was chosen as the sample route for the study. Within the scope of the study, the areas where stations can be installed on the highway were carried out using the Multi-Criteria Decision Making Method with the help of Geographic Information System. Solar radiation, slope, aspect, land use/land cover, traffic volume and proximity to the road, criteria of the route, and site selection analysis were determined as input data. The maps of the determined criteria were arranged according to the study area and prepared for analysis. The criteria maps obtained were reclassified according to the above-mentioned criteria and scoring system. After the reclassification process, the weighting of each criteria which affect the analysis was determined by the researched literature and an overlapping process was carried out. According to the results map produced as a result of the overlay analysis, the appropriate area has been determined for the electric charging stations working with solar energy. On the defined route within the scope of the study, a proposal has been made for a total of 13 stations, 8 in Ankara, 3 in Bolu, 1 in Kocaeli, and 1 in Istanbul. This study, it is aimed to encourage automobile users to make greater use of electric motor vehicles, which would be a more environmentally friendly and sustainable choice, and ultimately more economical.

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• MakaleAçık Erişim2021/r/gumushane/13314
  Investigation of the Effect of the Dataset Size and Type in the Earthquake-Triggered Landslides Mapping: A Case Study for the 2018 Hokkaido Iburu Landslides

  Comert, Resul

  Rapid mapping of landslides that occur after an earthquake is important for rapid crisis management. In this study, experimental research was conducted on the size of the model area and the data types used in developing classifiers for the supervised classification approaches used in rapid landslide mapping. The Hokkaido Iburu earthquake zone that occurred on September 6, 2018, was selected as the study area. PlanetScope pre-event and post-event images and ALOS-PALSAR Digital Elevation Model (DEM) were used in the analysis processes. In this context, five model areas with different sizes and one test area were determined. Object-based image analysis (OBIA) was used as a landslide mapping approach. Random Forest classifier, which is a supervised classification algorithm, was performed in the mapping of image objects produced by the segmentation stage of OBIA. Two different data sets were created for landslide mapping: change-based dataset and post-event dataset. The change-based dataset is generated from change data such as the difference of normalized difference vegetation index (δNDVI), change detection Image (CDI), princiable component analysis (PCA), and Independent component analysis (ICA) which are used in change detection applications. The post-event dataset was created from data generated from post-event image bands. When the obtained results were examined, higher accuracy results were obtained with the post-event dataset. Increasing the size of the model area, in other words, increasing the training data slightly increases the accuracy of landslide mapping. However, a model area that represents the region to be mapped in small sizes to make rapid decisions provides a 94% F-measure accuracy for earthquake-triggered landslide detection.

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• MakaleAçık Erişim2021/r/gumushane/12867
  Investigation of the effect of urbanization on land surface temperature example of Bursa

  Yamak, Beyzanur; Yagci, Zekeriya; Bilgilioglu, Burhan Baha; Comert, Resul

  Today, industrialization and urbanization in large cities cause an increase in the surfaces covered with man-made objects such as concrete and asphalt. The expansion of urban areas and increase in the using materials with high heat storage properties, directly affect the Land Surface Temperature (LST), which shows an increase in the sensible temperature of the region. LST is directly connected to the natural green vegetation of the land and varies widely in large urban areas, these differences related to the temperature is defined as the ‘urban heat island’. In this study, it was investigated the temporal variation of land surface temperature due to urbanization. Bursa, which is considered as a study area, has shown a rapid population increase since 1990 and a rapid increase in the number of buildings with the impact of urbanization and industrialisation. To determine the effect of this urbanization and industrialisation on land surface temperature, temporal change analysis of the surface temperature in the Bursa was observed with Landsat satellite images of 1988, 1998, 2008 and 2018. From produced maps, it was observed that the amount of green vegetation in urban areas decreased and as a consequence of this decrease, the surface temperature increased in the urban areas.

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• MakaleAçık Erişim2020/r/gumushane/12918
  Comparison of tree-based classification algorithms in mapping burned forest areas; [Primerjava drevesnih algoritmov razvrščanja pri zajemu območij pogorelih gozdov]

  Matci, Dilek Kucuk; Comert, Resul; Avdan, Ugur

  In this study, we compared the performance of tree-based classification algorithms – Random Forest (RF), Rotation Forest (RotF), J48, The Alternating Decision Tree (ADTree), Forest by Penalising Attributes (Forest PA), Logical Analysis of Data Algorithm (LADTree) and Functional Trees (FT) – for mapping burned forest areas within the Mediterranean region in Turkey. Object-based image analysis (OBIA) was performed to pan-sharpened the Landsat 8 images. Four different burned areas, namely Kumluca, Adrasan, Anamur, and Alanya, were used as study areas. Kumluca, Anamur, and Alanya regions were used as training areas, and Adrasan region was used as the test area. Obtained results were evaluated with confusion matrix and statistically significant analysis. According to the results, FT and RotF produced more accurate results than other algorithms. Also, the results obtained with these algorithms are statistically significant.

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• MakaleAçık Erişim2019/r/gumushane/15032
  OBJECT BASED BURNED AREA MAPPING WITH RANDOM FOREST ALGORITHM

  Comert, Resul; Matcı, Dilek Kucuk; Avdan, Ugur

  It is very important to map the burned forest areas economically, quickly and with the high accuracy of issues such as damage assessment studies, fire risk analysis, and management of forest regeneration processes. Remote sensing methods give advantages such as fast, easy-to-use and high accuracy for burned area mapping. Recent years machine learning algorithms have become more popular in satellite image classification, due to the effective solutions for the analysis of complex datasets which have a large number of variables. In this study, the success of object based random forest algorithm was investigated for burned forest area mapping. For this purpose, Object based image analysis (OBIA) was performed using Landsat 8 image of the Adrasan and Kumluca fires which occurred in 24 – 27 June 2016. The study consisted of five steps. In the first step, the multi-resolution image segmentation was performed for obtaining image objects from Landsat 8 spectral bands. In the second step, the image object metrics such as spectral index and layer values were calculated for all image objects. In the third step, a random forest classifier model was developed. Then, the developed model applied to the test site for classification of the burned area. Finally, the obtained results evaluated with confusion matrix based on the randomly sampled points. According to the results, we obtained 0.089 commission error (CE) with 0.014 omission error (OE). An overall accuracy was obtained as 0.99. The results show that this approach is very useful to be used to determine burned forest areas.

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• MakaleYalnız künye2019/r/gumushane/12659
  Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data

  Comert, Resul; Avdan, Ugur; Gorum, Tolga; Nefeslioglu, Hakan A.

  The Black Sea Region of Turkey is one of the most landslide prone areas due to its high slope topography, heavy rainfall, and highly weathered hillslope material conditions. Preparation of landslide inventory maps is the first step in producing landslide susceptibility maps. Ground-based methods for mapping landslide occurrences are time-consuming and expensive. Additionally, landslide mapping based on satellite imageries and aerial photographs has some limitations, including climatic conditions, cost, and limited repetitive measurement capacity. Visual interpretation-based landslide mapping, which is based on satellite imageries and aerial photographs, is a time-consuming procedure that requires an experience-based expert opinion. Therefore, the data acquisition based on unmanned aerial vehicle (UAV) and landslide event inventory maps using an object-based classification approach can be superior to other methods in terms of speed and cost. In this study, we developed a semiautomatic model using object-based image analyses for rapid mapping of shallow landslides from the data obtained from UAVs after major landslide events in the Black Sea Region of Turkey. For this purpose, two test sites—Kurucasile (Bartin) and Cayeli (Rize)—were selected. Landslide mapping models were developed in the investigation sites, and the performance of the models was evaluated. The landslides' data obtained with the developed models were compared to the landslides' data produced by the experts. The comparison process revealed that landslides mapped by using UAV data have an accuracy rate higher than 86% according to the number of landslides and 83% according to the landslide area.

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• MakaleAçık Erişim2018/r/gumushane/18289
  Detection of collapsed building from unmanned aerial vehicle data with object based image classification

  Avdan, Uğur; Çömert, Resul; Küçük Matcı, Dilek

  Buildings are most affected the objects by earthquake disaster. Detection of collapsed buildings after an earthquake isimportant both for determining the current situation and quick response. Unmanned aerial vehicles that have evolved inrecent years, can provide very high resolution images of the earth surface using camera systems attached to them.Information for the intended purpose can be obtained through the products produced from these images.In this study, collapsed buildings were detected in the area where high-resolution images were obtained whit unmanned aerialvehicle in 2015 and 2014. Building detection process was made based on a scenario events. In this context, 2015 images weretaken before the earthquake and 2014 images were taken after the earthquake. The images of both years were processedseparately to produce the digital elevation model and orthophoto image of the study area. building of the study area wereobtained by applying the object-based classification process to the generated data. 11 buildings which were available in thearea in 2015 and not available in the area in 2014, were detected successfully comparison of building classes of two years.

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