Maria Laurensyelen Wulu Beda Rianghepat, I. W. Nuarsa, Ida Bagus Mandhara Brasika
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摘要

沿海地区是大陆和海洋的交汇处。泗水布拉克区沿海地区的旅游潜力扩大。如2015年泗水大桥的施工所示。该建设将影响土地利用变化。遥感技术是监测土地利用变化的收购之一。本研究的重点是识别泗水布拉克区沿海地区2014年和2020年的土地利用变化,并确定用于绘制2020年土地利用变化图的分类方法的准确性。2014年收购数据的应用被用作桥梁建设计划,而2020年收购数据应用被用作上一年土地分类系统的前提。沿海地区土地利用分类有两种方法,即基于像素的分类(最大似然算法)和基于对象的分类(最近邻算法)。研究表明,研究区共有6类土地利用类型:建成区、稻田、森林、灌木林、非建成区和海洋。通过应用这两种方法,结果显示出不同的面积变化。应用基于像素的分类法转换的最高大陆分别出现在建成区(+23.03公顷)和稻田(-24.84公顷),而应用基于对象的分类法的面积变化分别出现在建设区(+32.75公顷)和水稻田(-26.91公顷)。应用基于像素和基于对象的方法的准确率分别为89%和92%,这表明解释效果良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pemetaan Perubahan Penggunaan Lahan Wilayah Pesisir di Kecamatan Bulak, Surabaya Tahun 2014 dan 2020
The coastal area is an intersection between mainland and ocean. The tourism potential in the coastal area of Bulak District in Surabaya is expanded. It is shown by the construction of Surabaya Bridge in 2015. This construction will affect land use change. Remote sensing technology is one of the acquisitions to monitor land use change. This research focuses on identifying the land use change in the coastal area in Bulak District, Surabaya, in 2014 and 2020, as well as to determine the accuracy of classification method applied for mapping the land use change in 2020. The application of 2014 acquisitions data was used as the bridge construction plan, while the application of 2020 acquisitions data was used as the premise for the land classification system in the previous year. There are two methods used to classify land use in coastal areas, that is pixel-based classification (maximum likelihood algorithm) and object-based classification (nearest neighbor algorithm). The research shows that there are 6 land use classes in study area: built-up land, rice fields, forests, shrubs, non-built-up land, and ocean.  By applying these two methods, the result shows different area changes. The conversion of the highest mainland by applying a pixel-based classification was found in built-up land (+23.03 ha) and rice fields (-24.84 ha), while the area changes by applying object-based classification method were found in built-up land (+32.75 ha) and rice fields (-26.91 ha), respectively. The accuracy by applying the pixel and object-based method is 89% and 92%, respectively, from the percentage indicates good interpretation.
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