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A Time Series Analysis of Satellite Imageries for Land Use & Land Cover (LULC) Change Detection of Gujranwala City, Pakistan from 1999–2019

机译:巴基斯坦古吉兰瓦拉市1999-2019年土地利用和土地覆被(LULC)变化卫星图像的时间序列分析

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Objectives: Information on land use and land cover changes are very valuable for the viable expansion of the city. LULC are interrelated attributes in which LC means which type of land is covered with cropland, farmland and grassland and LU means which type of land is used for residential, commercial, Agriculture or Marshland. Methods: This study is an effort to monitor land use/ land cover change by using remote sensing and GIS from 1999 to 2019. The use of remote sensing data has proved to be very helpful in LULC change detection. Accuracy assessment is a vital part of image classification and accuracy assessment is an important technique that defines the quality of the information obtained from the remotely sensed data and is considered an important tool for classification image. Maximum Likelihood supervised classification which was used to create a signature class for land cover. Findings: During the last 20 years our results indicate water, agriculture and mix vegetation decrease 0.1%, 7.2% and 4.1% but there is more increase in the other two classes barren land increase 2.9% and built-up increase 7.4%. Mostly agriculture land has been converted into barren and urban land. For the accuracy assessment, overall accuracy assessment was performed. Accuracy assessment was calculated through the Kappa co-efficient index. Applications: For calculating accuracy, we use accuracy statistics, overall accuracy. Temporal changes are time to time changes, in this perspective the change/increase in built-up land is 174.71 sq km. The results of this study would be helpful for decision making, urban development and future planning.
机译:目标:关于土地使用和土地覆盖变化的信息对于城市的可持续发展非常有价值。 LULC是相互关联的属性,其中LC表示哪种类型的土地被耕地,农田和草地覆盖,LU表示哪种类型的土地用于住宅,商业,农业或沼泽地。方法:本研究旨在通过使用遥感和GIS来监测1999年至2019年的土地利用/土地覆盖变化。事实证明,使用遥感数据对LULC变化的检测非常有帮助。准确性评估是图像分类的重要组成部分,准确性评估是一项重要技术,它定义了从遥感数据中获取的信息的质量,并且被认为是对图像进行分类的重要工具。最大似然监督分类,用于创建土地覆盖的签名类。结果:在过去的20年中,我们的结果表明水,农业和混合植被减少了0.1%,7.2%和4.1%,但其他两个类别的增加幅度更大,其中荒地增加2.9%,建成区增加7.4%。大多数情况下,农业用地已被转化为贫瘠的土地和城市土地。对于准确性评估,执行了总体准确性评估。通过Kappa系数指数计算准确性评估。应用程序:为了计算准确性,我们使用准确性统计数据,整体准确性。时间变化是指时间的变化,从这个角度来看,建筑用地的变化/增加为174.71平方公里。这项研究的结果将有助于决策,城市发展和未来规划。

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