ANALISIS KUALITAS HASIL PREDIKSI KLASIFIKASI PENGGUNAAN LAHAN MENGGUNAKAN CA MARKOV MODEL BERDASARKAN PETA RENCANA TATA RUANG

Fauzi Janu Amarrohman, T. Putri, Bambang Sudarsono, M. Awaluddin, S. Subiyanto
{"title":"ANALISIS KUALITAS HASIL PREDIKSI KLASIFIKASI PENGGUNAAN LAHAN MENGGUNAKAN CA MARKOV MODEL BERDASARKAN PETA RENCANA TATA RUANG","authors":"Fauzi Janu Amarrohman, T. Putri, Bambang Sudarsono, M. Awaluddin, S. Subiyanto","doi":"10.14710/elipsoida.2020.9200","DOIUrl":null,"url":null,"abstract":"Land use changes due to community activities and mobility occur because of the increasingly complex need for land. Spatial analysis is needed to identify land use changes which are subsequently reviewed by the Regional Spatial Plan in accordance with Government Regulation Number 8 of 2013 concerning the accuracy of the RTRW map. In this study, the study area taken was Pati Regency around the South Ring Road which includes four districts. From the acquisition of high resolution satellite imagery data in 2009, 2015 and 2019, predictions were made for the years 2023 and 2030 to determine the development of the area around the South Ring Road. The results of the prediction of land use using CA Markov in 2023 will be compared with the prediction in 2030 to determine the quality of the prediction results of the classification of land use in the prediction year with the same input data interval and exceeding the input data interval by conducting a suitability analysis with the RTRW. In 2023, the category of conformity is 95.41341%,  and in 2030 amounting to 95.41340%. This shows that the prediction results of land use change with CA Markov for the same year with the time interval of the input data have insignificant differences with the predicted results with longer intervals when compared to the current RTRW.","PeriodicalId":190139,"journal":{"name":"Elipsoida : Jurnal Geodesi dan Geomatika","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Elipsoida : Jurnal Geodesi dan Geomatika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14710/elipsoida.2020.9200","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Land use changes due to community activities and mobility occur because of the increasingly complex need for land. Spatial analysis is needed to identify land use changes which are subsequently reviewed by the Regional Spatial Plan in accordance with Government Regulation Number 8 of 2013 concerning the accuracy of the RTRW map. In this study, the study area taken was Pati Regency around the South Ring Road which includes four districts. From the acquisition of high resolution satellite imagery data in 2009, 2015 and 2019, predictions were made for the years 2023 and 2030 to determine the development of the area around the South Ring Road. The results of the prediction of land use using CA Markov in 2023 will be compared with the prediction in 2030 to determine the quality of the prediction results of the classification of land use in the prediction year with the same input data interval and exceeding the input data interval by conducting a suitability analysis with the RTRW. In 2023, the category of conformity is 95.41341%,  and in 2030 amounting to 95.41340%. This shows that the prediction results of land use change with CA Markov for the same year with the time interval of the input data have insignificant differences with the predicted results with longer intervals when compared to the current RTRW.
根据空间平面图的模型对土地使用的分类预测结果的质量分析
由于对土地的需求日益复杂,社区活动和人口流动引起的土地利用变化也随之发生。根据2013年关于RTRW地图准确性的第8号政府法规,需要进行空间分析,以确定随后由区域空间规划审查的土地利用变化。本研究选取的研究区域为南环路附近的帕蒂摄政区,包括四个区。通过获取2009年、2015年和2019年的高分辨率卫星图像数据,对2023年和2030年进行预测,确定南环路周边地区的发展情况。将2023年CA Markov土地利用预测结果与2030年的预测结果进行对比,通过RTRW进行适宜性分析,确定在相同输入数据区间和超过输入数据区间的预测年土地利用分类预测结果的质量。2023年符合性类别为95.41341%,2030年符合性类别为95.41340%。这表明,与当前RTRW相比,CA马尔可夫方法在输入数据的时间间隔内对当年土地利用变化的预测结果与较长时间间隔的预测结果差异不显著。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信