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Article(id=1322506381622625143, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322506335908901129, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2024.09.458, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1727625600000, receivedDateStr=2024-09-30, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793239692359, onlineDateStr=2026-10-29, pubDate=1760457600000, pubDateStr=2025-10-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793239692359, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793239692359, creator=13701087609, updateTime=1793239692359, updator=13701087609, issue=Issue{id=1322506335908901129, tenantId=1146029695717560320, journalId=1281212831689347082, year='2025', volume='45', issue='10', pageStart='991', pageEnd='1100', issueExtLink='null', onlineDate='null', pubDate='1760457600000', pubDateStr='2025-10-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793239681460, creator='13701087609', updateTime=1793244369709, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322525999896576487, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322506335908901129, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322525999896576488, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322506335908901129, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1013, endPage=1019, ext={EN=ArticleExt(id=1322506381828146040, articleId=1322506381622625143, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=Impact Analysis of DEM on SBAS-InSAR Deformation Monitoring and Geometric Distortion Identification, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Taking the hilly open-pit mining area in central Shandong as the study region, this research analyzes the impacts of different DEMs(SRTM-1, SRTM-3, ALOS-DEM, ASTER-GDEM, and LiDAR-DEM) and their elevation accuracy/spatial resolution on SBAS-InSAR deformation monitoring performance and geometric distortion identification. Results demonstrate that compared to spatial resolution, DEM elevation accuracy plays a more critical role in deformation monitoring and significantly affects measurement precision. Both DEM elevation accuracy and spatial resolution substantially influence the identification of shadow and layover areas. These findings provide guidance for DEM data selection in subsequent time-series deformation monitoring in complex terrains.

, authors=Zhuopu ZHANG1, Chenxi ZHANG1, *, Zhigang YU1, Guanghui ZHANG1, Xu YANG1, Chunyu LIU1, authorsList=Zhuopu ZHANG, Chenxi ZHANG, Zhigang YU, Guanghui ZHANG, Xu YANG, Chunyu LIU, authorCompany=null, correspAuthors=Chenxi ZHANG, authorNote=

ZHANG Zhuopu, postgraduate, majors in time-series InSAR deformation monitoring, E-mail:

, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322506385296835468, articleId=1322506381622625143, tenantId=1146029695717560320, journalId=1281212831689347082, language=CN, title=DEM对SBAS-InSAR形变监测和几何畸变识别的影响分析, columnId=1322506197874356701, journalTitle=大地测量与地球动力学, columnName=地壳运动, runingTitle=null, highlight=null, articleAbstract=

以山东中部丘陵露天矿区为研究区域，在对比SRTM-1、SRTM-3、ALOS-DEM、ASTER-GDEM和LiDAR-DEM高程精度的基础上，分析不同DEM及DEM的高程精度和空间分辨率对SBAS-InSAR形变监测效果和几何畸变识别的差异。结果表明，相较于空间分辨率，DEM的高程精度在形变监测中起到了更为关键的作用，是影响监测精度的重要因素；同时，DEM的高程精度与空间分辨率均会对阴影、叠掩区域的识别产生显著影响。本文结果为后续在复杂地形中进行时序形变监测提供了DEM数据选择的支持。

, authors=张卓普1, 张晨晰1, *, 于志刚1, 张光辉1, 杨绪1, 刘春雨1, authorsList=张卓普, 张晨晰, 于志刚, 张光辉, 杨绪, 刘春雨, authorCompany=null, correspAuthors=张晨晰, authorNote=

张卓普，硕士生，主要研究方向为时序InSAR形变监测，E-mail：

, correspAuthorsNote=

张晨晰，博士，副教授，主要研究方向为地表形变监测、卫星导航与精密定位数据处理，E-mail:

ZHANG Chenxi, PhD, associate professor, majors in surface deformation monitoring, satellite navigation and precision positioning data processing, E-mail:

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ZHANG Zhuopu, postgraduate, majors in time-series InSAR deformation monitoring, E-mail:

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ZHANG Zhuopu, postgraduate, majors in time-series InSAR deformation monitoring, E-mail:

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张卓普，硕士生，主要研究方向为时序InSAR形变监测，E-mail：

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张卓普，硕士生，主要研究方向为时序InSAR形变监测，E-mail：

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区域1和区域2为露天采矿区，区域3为尾矿库，区域4为丘陵区

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Basic parameters of experimental DEM data

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DEM 空间分辨率/m 采集时间 高程精度/m 采集技术
LiDAR-DEM 2 2018年 0.2 LiDAR
ALOS-DEM 12.5 2006—2011年 10 InSAR
SRTM-1 30 1999—2000年 16 InSAR
ASTER-GDEM 30 1999—2008年 30 立体像对
SRTM-3 90 1999—2000年 20 InSAR
), ArticleFig(id=1322506394100679629, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506381622625143, language=CN, label=表1, caption=

实验DEM数据基本参数

, figureFileSmall=null, figureFileBig=null, tableContent=
DEM 空间分辨率/m 采集时间 高程精度/m 采集技术
LiDAR-DEM 2 2018年 0.2 LiDAR
ALOS-DEM 12.5 2006—2011年 10 InSAR
SRTM-1 30 1999—2000年 16 InSAR
ASTER-GDEM 30 1999—2008年 30 立体像对
SRTM-3 90 1999—2000年 20 InSAR
), ArticleFig(id=1322506394171982798, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506381622625143, language=EN, label=Tab. 2, caption=

Comparison of DEM elevation accuracyin village area

, figureFileSmall=null, figureFileBig=null, tableContent=
DEM 平均误差 绝对误差均值 均方根误差
ALOS-DEM －0.10 1.24 1.58
ASTER-GDEM 0.71 1.46 1.96
SRTM-1 0.15 1.42 1.62
SRTM-3 1.03 1.93 2.79
), ArticleFig(id=1322506394453001167, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506381622625143, language=CN, label=表2, caption=

村镇区DEM高程精度对比

, figureFileSmall=null, figureFileBig=null, tableContent=
DEM 平均误差 绝对误差均值 均方根误差
ALOS-DEM －0.10 1.24 1.58
ASTER-GDEM 0.71 1.46 1.96
SRTM-1 0.15 1.42 1.62
SRTM-3 1.03 1.93 2.79
), ArticleFig(id=1322506394515915728, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506381622625143, language=EN, label=Tab. 3, caption=

Comparison of DEM elevation accuracyin hilly area

, figureFileSmall=null, figureFileBig=null, tableContent=
DEM 平均误差 绝对误差均值 均方根误差
ALOS-DEM 1.12 4.82 6.37
ASTER-GDEM 2.18 4.98 6.94
SRTM-1 0.63 4.94 6.39
SRTM-3 8.43 14.31 18.63
), ArticleFig(id=1322506394587218897, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506381622625143, language=CN, label=表3, caption=

丘陵区DEM高程精度对比

, figureFileSmall=null, figureFileBig=null, tableContent=
DEM 平均误差 绝对误差均值 均方根误差
ALOS-DEM 1.12 4.82 6.37
ASTER-GDEM 2.18 4.98 6.94
SRTM-1 0.63 4.94 6.39
SRTM-3 8.43 14.31 18.63
), ArticleFig(id=1322506394675299282, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506381622625143, language=EN, label=Tab. 4, caption=

Comparison of DEM elevation accuracyin open-pit mining areas

, figureFileSmall=null, figureFileBig=null, tableContent=
DEM 平均误差 绝对误差均值 均方根误差
ALOS-DEM －16.21 16.82 20.49
ASTER-GDEM －15.08 15.92 19.27
SRTM-1 －14.72 15.82 19.43
SRTM-3 －15.01 15.57 19.13
), ArticleFig(id=1322506394939540435, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506381622625143, language=CN, label=表4, caption=

露天采矿区DEM高程精度对比

, figureFileSmall=null, figureFileBig=null, tableContent=
DEM 平均误差 绝对误差均值 均方根误差
ALOS-DEM －16.21 16.82 20.49
ASTER-GDEM －15.08 15.92 19.27
SRTM-1 －14.72 15.82 19.43
SRTM-3 －15.01 15.57 19.13
), ArticleFig(id=1322506395027620820, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506381622625143, language=EN, label=Tab. 5, caption=

Deformation information in study area

, figureFileSmall=null, figureFileBig=null, tableContent=
DEM 平均形变速率/mm·a－1 均方差/mm·a－1 监测点位数量
LiDAR-DEM －0.227 2.126 30 975
ALOS-DEM －0.004 2.136 31 819
ASTER-GDEM －0.861 2.160 31 852
SRTM-1 －0.345 2.130 31 778
SRTM-3 －0.948 2.181 31 325
重采样12.5 m －0.120 2.122 29 661
重采样30 m －0.074 2.121 28 880
重采样90 m －0.210 2.119 26 881
), ArticleFig(id=1322506395287667669, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506381622625143, language=CN, label=表5, caption=

研究区内形变信息

, figureFileSmall=null, figureFileBig=null, tableContent=
DEM 平均形变速率/mm·a－1 均方差/mm·a－1 监测点位数量
LiDAR-DEM －0.227 2.126 30 975
ALOS-DEM －0.004 2.136 31 819
ASTER-GDEM －0.861 2.160 31 852
SRTM-1 －0.345 2.130 31 778
SRTM-3 －0.948 2.181 31 325
重采样12.5 m －0.120 2.122 29 661
重采样30 m －0.074 2.121 28 880
重采样90 m －0.210 2.119 26 881
), ArticleFig(id=1322506395350582230, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506381622625143, language=EN, label=Tab. 6, caption=

Mean square error of deformationin different areas

, figureFileSmall=null, figureFileBig=null, tableContent=
DEM 村镇形变量均方差 丘陵形变量均方差 露天采矿区形变量均方差
LiDAR-DEM 3.28 7.41 9.01
ALOS-DEM 3.70 8.12 9.01
ASTER-GDEM 3.62 8.62 9.88
SRTM-1 2.90 7.92 9.23
SRTM-3 2.69 11.71 13.70
重采样12.5 m 2.78 7.52 9.15
重采样30 m 3.66 7.56 9.18
重采样90 m 3.32 7.91 9.28
), ArticleFig(id=1322506395430274007, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506381622625143, language=CN, label=表6, caption=

不同区域形变量均方差

, figureFileSmall=null, figureFileBig=null, tableContent=
DEM 村镇形变量均方差 丘陵形变量均方差 露天采矿区形变量均方差
LiDAR-DEM 3.28 7.41 9.01
ALOS-DEM 3.70 8.12 9.01
ASTER-GDEM 3.62 8.62 9.88
SRTM-1 2.90 7.92 9.23
SRTM-3 2.69 11.71 13.70
重采样12.5 m 2.78 7.52 9.15
重采样30 m 3.66 7.56 9.18
重采样90 m 3.32 7.91 9.28
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DEM对SBAS-InSAR形变监测和几何畸变识别的影响分析
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张卓普 1 , 张晨晰 1, * , 于志刚 1 , 张光辉 1 , 杨绪 1 , 刘春雨 1
大地测量与地球动力学 | 地壳运动 2025,45(10): 1013-1019
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大地测量与地球动力学 |地壳运动 2025 , 45 (10) : 1013 -1019
DEM对SBAS-InSAR形变监测和几何畸变识别的影响分析
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张卓普1 , 张晨晰1, * , 于志刚1, 张光辉1, 杨绪1, 刘春雨1
作者信息
  • 山东科技大学资源学院，山东省泰安市岱宗大街223号，271000
通讯作者:

张晨晰，博士，副教授，主要研究方向为地表形变监测、卫星导航与精密定位数据处理，E-mail:

ZHANG Chenxi, PhD, associate professor, majors in surface deformation monitoring, satellite navigation and precision positioning data processing, E-mail:

作者简介:

张卓普，硕士生，主要研究方向为时序InSAR形变监测，E-mail：

Impact Analysis of DEM on SBAS-InSAR Deformation Monitoring and Geometric Distortion Identification
Zhuopu ZHANG1 , Chenxi ZHANG1, * , Zhigang YU1, Guanghui ZHANG1, Xu YANG1, Chunyu LIU1
Affiliations
  • College of Resources, Shandong University of Science and Technology, 223 Daizong Street, Taian 271000, China

    • ZHANG Zhuopu, postgraduate, majors in time-series InSAR deformation monitoring, E-mail:

出版时间: 2025-10-15 doi: 10.14075/j.jgg.2024.09.458
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以山东中部丘陵露天矿区为研究区域，在对比SRTM-1、SRTM-3、ALOS-DEM、ASTER-GDEM和LiDAR-DEM高程精度的基础上，分析不同DEM及DEM的高程精度和空间分辨率对SBAS-InSAR形变监测效果和几何畸变识别的差异。结果表明，相较于空间分辨率，DEM的高程精度在形变监测中起到了更为关键的作用，是影响监测精度的重要因素；同时，DEM的高程精度与空间分辨率均会对阴影、叠掩区域的识别产生显著影响。本文结果为后续在复杂地形中进行时序形变监测提供了DEM数据选择的支持。

SBAS-InSAR  /  DEM  /  露天矿  /  几何畸变  /  形变精度

Taking the hilly open-pit mining area in central Shandong as the study region, this research analyzes the impacts of different DEMs(SRTM-1, SRTM-3, ALOS-DEM, ASTER-GDEM, and LiDAR-DEM) and their elevation accuracy/spatial resolution on SBAS-InSAR deformation monitoring performance and geometric distortion identification. Results demonstrate that compared to spatial resolution, DEM elevation accuracy plays a more critical role in deformation monitoring and significantly affects measurement precision. Both DEM elevation accuracy and spatial resolution substantially influence the identification of shadow and layover areas. These findings provide guidance for DEM data selection in subsequent time-series deformation monitoring in complex terrains.

SBAS-InSAR  /  DEM  /  open-pit mine  /  deometric distortion  /  deformation accuracy
张卓普, 张晨晰, 于志刚, 张光辉, 杨绪, 刘春雨. DEM对SBAS-InSAR形变监测和几何畸变识别的影响分析. 大地测量与地球动力学, 2025 , 45 (10) : 1013 -1019 . DOI: 10.14075/j.jgg.2024.09.458
Zhuopu ZHANG, Chenxi ZHANG, Zhigang YU, Guanghui ZHANG, Xu YANG, Chunyu LIU. Impact Analysis of DEM on SBAS-InSAR Deformation Monitoring and Geometric Distortion Identification[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (10) : 1013 -1019 . DOI: 10.14075/j.jgg.2024.09.458
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在利用小基线集干涉测量技术(SBAS-InSAR)进行地表形变监测时，需借助外部参考数字高程模型(DEM)数据来去除地形相位误差。尽管在SBAS-InSAR数据处理流程中可通过时间序列信息和模型的协方差矩阵对DEM误差进行校正，但残留的DEM误差仍会影响观测结果，且不同类型的DEM数据在高程精度和分辨率上存在差异，会对InSAR形变监测结果产生不同的影响[1-3]。
几何畸变能否被正确识别也是InSAR形变监测的关键挑战之一。由于SAR采用侧视成像方式，当波束斜向照射地表时，在地形起伏较大区域的雷达图像中可能会出现阴影、叠掩和透视收缩等现象。在地形坡度方向与成像方位角相似的区域，应用InSAR技术难以准确观测到形变信息，因此进行SAR雷达的可视性分析显得尤为重要[4]。目前对SAR成像几何带来的阴影、叠掩问题大多需通过外部参考DEM和SAR卫星几何参数进行计算。研究指出，阴影、叠掩区域的检测能力对相位解缠过程影响显著，进而对形变监测的精度造成重要影响[5-6]，但对于不同DEM对几何畸变识别的影响仍缺乏相关分析。
针对上述问题，本文采用5组不同高程精度、不同空间分辨率的外部DEM数据(LiDAR-DEM、ALOS-DEM、ASTER-GDEM、SRTM-1、SRTM-3)作为研究对象，探讨DEM的高程精度、时效性和空间分辨率对SBAS-InSAR技术形变监测结果的影响，并分析不同DEM在几何畸变识别上的差异。
1 研究区与实验数据
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1.1 研究区域及DEM数据
研究区域位于山东省日照市莒县境内，该地区属低山丘陵区，海拔在150~430 m。研究区域中部及北部有露天采矿区，矿区西侧还存在一处尾矿库，南部为丘陵区(图 1)。近年来，由于采矿活动，在露天矿区形成了一个巨大的人工矿坑，改变了当地的地形地貌，使早期的DEM数据产生较大误差。此外，研究区域存在较多地势平缓的村镇。
本文采用的DEM数据见表 1，其中LiDAR-DEM是2018年通过机载激光雷达获取的[7]，相比其他几种DEM数据，LiDAR-DEM具有更高的精度和空间分辨率，且在时效性方面更优秀。
1.2 SAR数据
本文采用89景C波段的Sentinel-1A升轨数据，重访周期为12 d，获取日期为2017-01-03—2019-12-31，距离向分辨率和方位向分辨率分别为5 m和20 m，成像模式为IW，极化方式为VV。轨道数据采用精密定轨星历数据，定位精度优于5 cm。
2 研究方法
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2.1 SBAS-InSAR技术
小基线集干涉测量技术是一种利用雷达卫星数据进行地表形变监测的方法。在SBAS技术中，需引入外部DEM数据模拟并除去地形相位[8]，地形相位计算公式为：
$\varphi_{\mathrm{Topo}}=\frac{4 \pi B}{\lambda R \sin \theta} h$
式中，B为基线垂直于视线向的分量；λ为雷达波长；R为雷达与地面目标的距离；θ为雷达入射角；h为地表高程。
本文采用控制变量法进行研究，将外部DEM数据作为唯一变量进行SBAS-InSAR形变监测，分析形变速率、形变量及监测点位数量的差异，分别研究DEM的高程精度和分辨率对形变监测结果的影响。处理参数设置为：时间基线阈值为180 d，空间基线阈值为300 m，距离向视数为4，方位向视数为1，采用同一组地面控制点。相位解缠采用最小费用流法，经轨道误差、大气延迟等校正后，最终得到地表视线向的形变信息。
2.2 SAR几何畸变
几何畸变是指由于雷达成像的几何特性和地形影响，导致雷达图像中目标点的空间分布与实际地理位置发生偏差或扭曲的现象。几何畸变可能会影响InSAR监测的质量和解译，根据入射角和不同地形地貌特征，主要会产生透视收缩、叠掩、阴影3种几何畸变。
透视收缩是距离压缩的一种现象(图 2)。当SAR卫星观测斜坡AB时，SAR影像上显示的距离ab会小于AB的实际距离；当观测方向与斜坡AB垂直时，ab在SAR影像上可能会重合为一个点。叠掩是一种特殊的距离压缩现象，当山顶的反射信号比山脚的反射信号更早到达接收器时，会在SAR影像的距离方向上出现山顶和山脚影像倒置的情况(图 2)。斜坡DE在SAR影像中对应为ed，随着斜坡坡度的减缓，叠掩现象会逐渐消失。阴影是另一种几何畸变现象，发生在地形坡度较大时，斜坡背后的区域无法被卫星观测到，从而在影像中形成阴影区，见图 2中gj区域。由此可知，受透视收缩影响的区域得到的InSAR监测结果会失真，受阴影及叠掩影响的区域无法在InSAR监测中探测到[9]。
有学者在已有地形可视性分析总结的基础上，提出基于DEM和SAR观测提取几何畸变的R指数法[10]。R指数表示斜距(雷达几何距离)与地面距离之间的比例，其计算需卫星的入射角(θ)、方位角(ω)及利用DEM数据模型提取的坡度(α)、坡向(β)等地形因子。首先利用山体阴影模型初步获取阴影参数(SH)和叠掩参数(L)，阴影参数(SH)需通过视线方位角(卫星方位角ω+90°)和卫星入射角余角(90°－θ)获得，叠掩参数(L)需通过卫星视线方位角补角(ω+270°)和卫星入射角(θ)获得；其次通过哨兵一号数据获取卫星飞行参数，并结合坡度、坡向地形因子计算得到R指数。
$R=-\sin (\alpha \cdot \sin (A)-\theta)$
式中，α为坡度；A为地形校正因子，对于本文升轨卫星A=β+ω+180°；θ为卫星入射角。最后将所得R指数与上述阴影参数(SH)、叠掩参数(L)进行逻辑乘法运算，即可进行雷达可视性分析。当0＜R≤sinθ(β=0时，R=sinθ)且位于阴影参数(SH)和叠掩参数(L)之外时，为透视收缩；当R≥sinθ且位于阴影参数(SH)内时，为阴影；当－1≤R≤0且位于叠掩参数(L)内时，为叠掩。
3 实验与分析
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3.1 DEM高程精度分析
通过表 1可以看出，相较于其他DEM数据，LiDAR扫描获取的DEM数据的高程精度和空间分辨率最高，能够更准确地反映地表信息。本文以LiDAR-DEM作为参考，对比分析其他不同DEM数据的高程精度。首先采用自然邻域法[11]将LiDAR-DEM分别重采样至空间分辨率为12.5 m、30 m和90 m(不涉及空间分辨率时统称为LiDAR-DEM，其余4种DEM统称为其他DEM)，同时将所有DEM转换至WGS84坐标系，并统一将高程基准调整为EGM96，在此基础上对LiDAR-DEM与其他DEM进行高程精度的对比分析。
图 3为LiDAR-DEM与其他DEM的高程差值分布。可以看出，LiDAR-DEM与其他DEM的差异主要集中在露天采矿区，最大高程差值可达56 m。在丘陵地区，LiDAR-DEM与SRTM-3存在较大差异，最大高程差值达60 m，与其余3种DEM数据的差异较小。将SRTM-3与其余3种DEM数据进行对比后发现，丘陵地区均存在较大的高程差值，表明SRTM-3在丘陵地区的地表高程精度较低。同时，其余3种DEM在露天采矿区的高程差异较小，说明这些区域的地形在DEM数据采集期间未发生较大变化。LiDAR-DEM与3组重采样DEM之间的差异较小，差异主要集中在丘陵地区，且随着分辨率的降低，差异逐渐增大。由于该区域地势起伏复杂，分辨率的降低会导致高程信息的失真，尽管如此，3组重采样DEM仍在很大程度上保留了LiDAR-DEM的地表信息。
地形地貌是评估DEM垂直误差的关键因素[12]，选取村镇、丘陵区和露天采矿场3种具有不同地形特征的区域为样本进行统计分析，计算平均误差(用于评估各地DEM数据的系统误差)、绝对误差均值(防止平均误差计算时正负误差相抵消)及均方根误差(用于反映误差的总体大小)[13]，结果见表 2~4(单位mm)。可以看出，由于地形变化较大，4种DEM在露天采矿区的高程误差较大，不能反映真实地表高程信息。通过对村镇和丘陵区DEM误差数据进行分析，其他DEM的高程精度依次为ALOS-DEM、SRTM-1、ASTER-GDEM、SRTM-3。
3.2 不同DEM数据的形变监测结果分析
通过SBAS-InSAR技术，分别利用5组外部DEM数据与3组重采样DEM数据对研究区域进行观测，并对得到的8组形变监测结果进行线性回归方程分析，8组监测结果都具有较高的可靠性。图 4为8组DEM数据的形变速率监测结果，均显示在露天采矿区和丘陵区发生明显形变。以露天采矿区(区域1)和丘陵区(区域4)为例进行形变速率(图 5)分析，可以看出，露天采矿区存在地面抬升现象，而丘陵区则表现为沉降。在丘陵区，基于LiDAR-DEM数据与重采样DEM数据的监测信息出现明显的缺失现象。
将8组DEM数据实验中所有监测点位进行统计分析，结果见表 5。可以看出，基于ASTER-GDEM与SRTM-3得到的平均形变速率与其他数据存在较大差异。在监测点数量分析中发现，基于LiDAR-DEM和重采样DEM得到的监测点数据较少，且随着分辨率的降低，监测点数量也呈下降趋势，而其他4组外部DEM得到的监测点数据比较接近。通过对比8组实验数据相干系数发现，外部DEM的差异对相干性的影响较小，特别是在强相干区域，因此相干性不是造成基于重采样DEM得到的监测点数据较少的主要原因。
为深入研究不同地形形变量的稳定性，在形变趋势较显著的丘陵区和露天采矿区(图 5红框)内选取全部同名点位，并在形变较为平稳的村镇区选取监测点进行形变稳定性对比分析，不同DEM在3处地形的形变量均方差见表 6(单位mm)。在地形平缓的村镇区域，8组实验的形变量均方差值相近；在丘陵区，基于SRTM-3的形变量均方差值最大，且显著高于其他数据集，在露天采矿区观察到了类似趋势，即SRTM-3最大，ASTER-GDEM次之。在高程精度相似的情况下，对比分析基于LiDAR-DEM与3组重采样DEM数据的监测结果发现，3组基于重采样DEM数据得到的形变量均方差值与基于LiDAR-DEM数据的均方差值相近，表明DEM的空间分辨率对时序处理结果影响较小。将3组重采样DEM数据的监测结果分别与空间分辨率相同的全球DEM监测结果进行对比发现，除重采样分辨率为12.5 m的数据结果比ALOS-DEM在露天采矿区的形变量均方差值大外，同分辨率下高程精度较高的重采样数据表现更好，其中重采样分辨率为90 m的数据结果与SRTM-3的最为明显，且重采样分辨率为12.5 m的数据结果与ALOS-DEM的形变量均方差值差异较小，存在一定的偶然误差。值得注意的是，5组外部DEM数据之间的形变量均方差值排序与DEM的高程精度相关，表明相较于DEM的空间分辨率，高程精度是影响监测精度的关键因素。
3.3 斜视成像几何畸变分析
为分析不同外部DEM数据引起的几何畸变差异，根据SAR卫星的轨道信息、入射角等参数，采用R指数法对8组DEM数据产生的几何畸变进行分析，结果见图 6和7。从图 6可以看出，与其他DEM数据相比，基于LiDAR-DEM及重采样DEM数据在丘陵区识别出更多的阴影和叠掩区域。从图 7可以看出，采用LiDAR-DEM和重采样DEM数据识别到的阴影和叠掩面积明显大于其他DEM数据，且随着重采样DEM分辨率的降低而增加。
坡度是造成几何畸变现象的重要因素，随着坡度的增大，可视性逐渐降低[14]。8组DEM数据的坡度计算结果见图 8，可以看出，在丘陵区，LiDAR-DEM及其重采样数据表现出更明显的大坡度特征，而其他数据在该区域的表现较为平缓。3组重采样DEM数据随分辨率的降低，坡度细节信息逐渐损失，坡度起伏减小。以LiDAR-DEM识别的阴影和叠掩为例，阴影及叠掩的分布与大坡度区域基本重合。由图 9可知，8组DEM数据识别到的阴影和叠掩面积与DEM的35°以上大坡度占比呈正相关。
其他4种全球DEM数据在制作过程中会对原始数据进行噪声去除(如SRTM-1采用多重回波用于减少地表植被、建筑物带来的高程误差)和平滑处理(如高斯滤波器、插值使DEM表面更加连续平滑)[15]，造成局部地形失真，不能表现真实地形。在相同分辨率下，重采样DEM数据能够识别到更多的阴影和叠掩，对比LiDAR-DEM与3组重采样DEM数据识别到的阴影和叠掩发现，随着分辨率的降低，识别到的阴影和叠掩面积在增大，而增加的区域有很大一部分在LiDAR-DEM中是低缓坡度，不能准确反映真实地表，所以存在过度识别，甚至错误识别现象。说明随着DEM空间分辨率的降低，观测精准度也随之降低。阴影和叠掩被LiDAR-DEM和3组重采样DEM数据大面积识别，可能是导致表 5中对应监测点数据较少的原因，而其他DEM数据没有识别到较多区域的阴影和叠掩，这些没有被识别到的区域在解缠过程中可能会引入更多误差，导致观测精度的下降。
4 结语
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本文以地形变化较大的露天采矿区和丘陵区为研究区域，通过5组外部DEM与3组重采样DEM数据(高程精度与LiDAR-DEM相近)，分别探讨DEM的高程精度和空间分辨率对形变监测精度和几何畸变识别的影响。
在地表发生明显变化的区域，全球DEM数据不能准确反映当前高程信息。在形变监测方面，通过对比8组形变监测实验发现，3组重采样DEM与LiDAR-DEM监测结果相近，且均比同分辨率下全球DEM数据的形变监测结果表现优秀。进一步分析表明，在形变监测中，相较于DEM的空间分辨率，高程精度是影响监测精度的关键因素，而在地形平缓的区域，不同DEM的形变监测结果差异较小。
在几何畸变识别方面，由于在制作过程中采用降噪和平滑处理，全球DEM数据虽然提高了数据的整体稳定性，但同时也失去了局部地形细节，从而影响几何畸变的准确识别，因此DEM的高程精度是影响几何畸变识别的重要因素。通过对比LiDAR-DEM与3组重采样DEM的识别结果发现，随着分辨率的降低，识别到的几何畸变面积增大，但低分辨率DEM不能反映真实的地表坡度及坡向，从而造成过度识别，甚至错误识别，因此DEM的空间分辨率也是影响几何畸变识别的重要因素。全球DEM数据在地势起伏较大区域存在细节丢失，在几何畸变识别方面应用困难，需要高精度、高分辨率DEM数据进行辅助。
本文研究表明，针对SBAS-InSAR监测，尤其在地形复杂区域，选择高精度、高分辨率的DEM数据，能显著提高形变监测的准确性和几何畸变识别的精度。
基金
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  • 教育部“春晖计划”合作科研项目(HZKY20220484)
  • 泰安市科技创新发展项目(2023GX036)
文献
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2025年第45卷第10期
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doi: 10.14075/j.jgg.2024.09.458
  • 接收时间：2024-09-30
  • 首发时间：2026-10-29
  • 出版时间：2025-10-15
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  • 收稿日期：2024-09-30
基金
Chunhui Project Foundation of the Education Department of China(HZKY20220484)
教育部“春晖计划”合作科研项目(HZKY20220484)
Taian Science and Technology Innovation Development Program(2023GX036)
泰安市科技创新发展项目(2023GX036)
作者信息
    山东科技大学资源学院，山东省泰安市岱宗大街223号，271000

通讯作者:

张晨晰，博士，副教授，主要研究方向为地表形变监测、卫星导航与精密定位数据处理，E-mail:

ZHANG Chenxi, PhD, associate professor, majors in surface deformation monitoring, satellite navigation and precision positioning data processing, E-mail:

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https://castjournals.cast.org.cn/joweb/ddcl/CN/10.14075/j.jgg.2024.09.458
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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