基于DGPS数据的GIS DEM插值方法的详细评价

C. A. Rishikeshan, S. Katiyar, V. N. Vishnu Mahesh
{"title":"基于DGPS数据的GIS DEM插值方法的详细评价","authors":"C. A. Rishikeshan, S. Katiyar, V. N. Vishnu Mahesh","doi":"10.1109/CICN.2014.148","DOIUrl":null,"url":null,"abstract":"Digital Elevation Model (DEM) is a numerical representation of topography and is made up of equal-sized grid cells, each with a value of elevation. The DEMs that were generated from the spot heights by general interpolation techniques namely Inverse Distance Weighted (IDW), Kriging, Topo to Raster, Natural Neighbor (NN) and Spline approaches have been compared. The relative performance of each method depends on various ground parameters and spatial distribution of sampling points. In this research investigation, performance of the above mentioned five interpolation methods have been evaluated by generating and validating the DEM from Differential Global Positioning System (DGPS) data in the ArcGIS software. With respect to our sample observations' spatial distribution and densities, the investigation results have shown that IDW method is giving better performance in plane and mild slope area, Natural Neighbor provides better performance in steep slope and whole area as compared to other methods.","PeriodicalId":6487,"journal":{"name":"2014 International Conference on Computational Intelligence and Communication Networks","volume":"42 1","pages":"666-671"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Detailed Evaluation of DEM Interpolation Methods in GIS Using DGPS Data\",\"authors\":\"C. A. Rishikeshan, S. Katiyar, V. N. Vishnu Mahesh\",\"doi\":\"10.1109/CICN.2014.148\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Digital Elevation Model (DEM) is a numerical representation of topography and is made up of equal-sized grid cells, each with a value of elevation. The DEMs that were generated from the spot heights by general interpolation techniques namely Inverse Distance Weighted (IDW), Kriging, Topo to Raster, Natural Neighbor (NN) and Spline approaches have been compared. The relative performance of each method depends on various ground parameters and spatial distribution of sampling points. In this research investigation, performance of the above mentioned five interpolation methods have been evaluated by generating and validating the DEM from Differential Global Positioning System (DGPS) data in the ArcGIS software. With respect to our sample observations' spatial distribution and densities, the investigation results have shown that IDW method is giving better performance in plane and mild slope area, Natural Neighbor provides better performance in steep slope and whole area as compared to other methods.\",\"PeriodicalId\":6487,\"journal\":{\"name\":\"2014 International Conference on Computational Intelligence and Communication Networks\",\"volume\":\"42 1\",\"pages\":\"666-671\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Computational Intelligence and Communication Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CICN.2014.148\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Computational Intelligence and Communication Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CICN.2014.148","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17

摘要

数字高程模型(DEM)是地形的数字表示,由大小相等的网格单元组成,每个网格单元都有一个高程值。比较了常用插值方法(逆距离加权法、Kriging法、Topo to Raster法、自然邻域法和样条法)对斑点高度产生的dem。每种方法的相对性能取决于各种地面参数和采样点的空间分布。本研究通过在ArcGIS软件中对差分全球定位系统(DGPS)数据生成DEM并进行验证,对上述五种插值方法的性能进行了评价。就样本观测的空间分布和密度而言,调查结果表明,IDW法在平面和缓坡区域具有较好的效果,而Natural Neighbor法在陡坡和全区域具有较好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Detailed Evaluation of DEM Interpolation Methods in GIS Using DGPS Data
Digital Elevation Model (DEM) is a numerical representation of topography and is made up of equal-sized grid cells, each with a value of elevation. The DEMs that were generated from the spot heights by general interpolation techniques namely Inverse Distance Weighted (IDW), Kriging, Topo to Raster, Natural Neighbor (NN) and Spline approaches have been compared. The relative performance of each method depends on various ground parameters and spatial distribution of sampling points. In this research investigation, performance of the above mentioned five interpolation methods have been evaluated by generating and validating the DEM from Differential Global Positioning System (DGPS) data in the ArcGIS software. With respect to our sample observations' spatial distribution and densities, the investigation results have shown that IDW method is giving better performance in plane and mild slope area, Natural Neighbor provides better performance in steep slope and whole area as compared to other methods.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Research on Flow Control of all Vanadium Flow Battery Energy Storage Based on Fuzzy Algorithm Synthetic Aperture Radar System Using Digital Chirp Signal Generator Based on the Piecewise Higher Order Polynomial Interpolation Technique Frequency-Domain Equalization for E-Band Transmission System A Mean-Semi-variance Portfolio Optimization Model with Full Transaction Costs Detailed Evaluation of DEM Interpolation Methods in GIS Using DGPS Data
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1