首页> 外文期刊>Fresenius environmental bulletin >MONITORING SHORELINE CHANGE OF ACIGOL ANDBURDUR LAKES IN TURKEY OVER 44 YEARS USINGREMOTE SENSING AND GIS APPROACHES
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MONITORING SHORELINE CHANGE OF ACIGOL ANDBURDUR LAKES IN TURKEY OVER 44 YEARS USINGREMOTE SENSING AND GIS APPROACHES

机译:在44年中监测土耳其Acigol湖湖的海岸线变化,使用Remote Sensing和GIS方法

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In this study, the main aim was to analyze the spatial changes of Burdur and Acigol Lakes by us-ing Remote Sensing approaches and geographical in-formation system (GIS) applications. Five m ulti-temporal satellite images from Landsat 2, Landsat 5 TM and Landsat 8 O LI were used to monitor and map the shoreline changes for both lakes. Support Vector Machine (SVM ) classification was used as an effective way of monitoring the changes between specified time intervals accurately. Object-Based Image Analysis (O BIA) is the second classification approach that was applied to all data in this study. Besides both classification applications, spectral wa-ter indexes including the Normalized Difference Water Index (NDW I) and M odified Normalized D if-ference Water Index (M NDW I) were used for the ex-traction of the water body area and digitization of these areas in the GIS platform. The results of both SVM and O BIA classifications indicated that Burdur Lake lost 40% of its water body as the total area of Burdur lake was 209 km2 in 1975 and about 125 km2 in 2019, while Acigol Lake lost about 70% of its wa-ter body, w ith a total area of about 77 km2 in 1975 and 24.6 km2 in 2019. Spatiotemporal changes of Burdur and Acigol Lakes based on the applied meth-ods show a significant diminishing trend in surface area between the time period of 1975 and 2019 in this study. The results show that remote sensing ap-plications including water indexes and classification algorithms are effective ways of identifying changes between spatial and specific time intervals.
机译:在这项研究中,主要目的是通过遥感方法和地理形式系统(GIS)应用来分析Burdur和Acigol Lakes的空间变化。来自Landsat 2的五米Ulti-Temporal卫星图像,Landsat 5 TM和Landsat 8 O Li用于监测和映射两个湖泊的海岸线变化。支持向量机(SVM)分类用作准确监测指定时间间隔之间的变化的有效方法。基于对象的图像分析(o BIA)是应用于本研究中的所有数据的第二种分类方法。除了分类应用外,包括归一化差异水指数(NDW I)和M个ODIED归一化D IF-Ference水指数(M NDW I)的光谱WA-TER指标用于水体积的前牵引和数字化这些区域在GIS平台中。 SVM和O BIA分类的结果表明,Burdur Lake在1975年的Burdur Lake的总面积为209公里2和2019年约125公里,而Acigol Lake占其遗产约70% Ter Body,1975年的总面积约为77平方公里,2019年的24.6公里。基于所应用的Meth-ODS的Burdur和Acigol Lakes的时空变化显示了1975年和2019年的时间段内表面积的显着递减趋势在这个研究中。结果表明,遥感容纳犁包括水指标和分类算法是识别空间和特定时间间隔之间的变化的有效方法。

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