Yazar "Yucer, Emre" seçeneğine göre listele

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    Classification of RASAT Satellite Images Using Machine Learning Algorithms
    (Springer International Publishing Ag, 2022) Abujayyab, Sohaib K. M.; Yucer, Emre; Karas, I. R.; Gultekin, I. H.; Abali, O.; Bektas, A. G.
    The development in the remote sensing and geographic information systems facilitated the monitoring processes of changes in land cover and use. This article aimed to evaluate the classification accuracy of five supervised classification methods: Neural Network, Naive Bayes, K-nearest neighbors, discriminant analysis and Decision Tree using the Turkish RASAT satellite images. The Bursa area in Turkey was taken as a study area to examine the RASAT satellite images. MATLAB and Python programming languages were employed to develop the training dataset and generated the five classifiers. According to the performance analysis using confusion matrix metric, the best overall accuracy was achieved by K-nearest neighbors. the K-nearest neighbors method produced 100% performance accuracy using RASAT satellite image. This comparative analysis showed that the K-nearest neighbors can be used as a trusted method for satellite image classification.
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    Examining Urbanization Dynamics in Turkey Using DMSP-OLS and Socio-Economic Data
    (Springer, 2018) Yucer, Emre; Erener, Arzu
    The present study tried to determine the spatial expansion of urban areas in all the cities in Turkey and to examine the relationship between this spatial expansion with the related demographic, employment, educational, industrial and other indicators using the Geographical Information Systems. The present study was made up of three parts. In the first part, the urban areas in Turkey were determined using the pixel-based image classification methods. In the second part, the development levels of the cities, one of socio-economic indicators, were determined using the Principle Components Analysis. In the last part, statistical analyses were conducted to examine the relationship between the development levels of cities and urban areas. The results obtained revealed that the cities with larger urban area were more in the western part of the country and while those with less urban area were in the eastern part of the country. A similar distribution was also true for the socio-economic development order. The Pearson correlation coefficient of 0.711 between these variables demonstrated that there was a linear positive correlation in between. According to the results of Moran's I spatial auto-correlation analysis, the distribution of both urban area and socio-economic development throughout the country had a relationship with place. According to geographically weighted regression analysis, the demographic, education and health indicators had the biggest influence on urban area.
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    GIS Based Urban Area Spatiotemporal Change Evaluation Using Landsat and Night Time Temporal Satellite Data
    (Springer, 2018) Yucer, Emre; Erener, Arzu
    This study aims at determining the spatiotemporal change in urban areas by using multi-temporal satellite images with geographic information systems integration. In this study, the city of Erzincan was selected as the sample case. The analyses of change were conducted by using the optical satellite images from LANSAT TM dated 1987 and the LANDSAT ETM+ dated 2006, besides the night images from 1998, 2006 and 2010. Spatial change maps were created for the qualitative analysis, and change matrixes were formed for the quantitative assessment of these changes. The outcomes of these change analyses were then evaluated and interpreted in the light of the demographics of the population living in the area. The results obtained from the Landsat satellite images indicate that the area of the city expanded at the annual average rate of 1.65% in 1987-2006. Night images indicate that the city area grew at an annual average rate of 4.04% in 1998-2006, while this rate was 20.28% in the period of 2006-2010. The results of the study demonstrate that the usability and contribution of satellite images is quite significant in tracking and monitoring temporal and spatial change in the area.
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    A Land Use Regression Model to Estimate Ambient Concentrations of PM10 and SO2 in Izmit, Turkey
    (Springer, 2023) Yucer, Emre; Erener, Arzu; Sarp, Gulcan
    The goal of this study is to develop a land use regression (LUR) model, for estimating the intraurban variation of PM10 and SO2 in a highly dense industrialized city of Izmit, Kocaeli, Turkey. The method allows for the simultaneous consideration of transportation, demography, topography, traffic patterns, road patterns, and land use characteristics as estimators of pollution variability. In the study, PM10 and SO2 Concentrations were obtained hourly from National Air Quality Monitoring Network. The mean annual pollution parameters of 2019 were used to evaluate the temporal differences of estimator variables. 102 sample points were used in the study. 72 of the sampling points were used to establish the LUR model and 30 of them were used to test the accuracy of the model. In the model results, the R square value between the pollutant concentrations of the independent variables was 0.876 for SO2 and 0.919 for PM10. It has been determined that the distance to the roads, the density of the industrial areas, and the population density are the main variables that affect the PM10 and SO2 concentrations. In addition, it has been revealed that meteorological variables are effective in the concentration of pollutants. R square values between the observed and predicted values in the validation analysis of the model were determined as 0.90 for SO2 and 0.94 for PM10.This study showed that it can make accurate estimations about air pollution in areas with complex topographic factors, variable meteorological conditions, and industrial activities.