ASTER立体像对提取玛尔挡坝区DEM及精度评价

ASTER立体像对提取DEM已经成为近年来DEM提取研究的热点问题。本文基于ENVI软件,利用AS-TER立体像对提取青藏高原玛尔挡坝区DEM,并对其进行精度评价和误差来源分析。结果表明,利用ENVI软件提取ASTER-DEM方法可行,提取的DEM效果较好,能与地形图重叠,高程精度可达30m,而且地形较平坦地区精度高于地形陡峭地区;控制点的多少及精度、成像时的环境和气象条件、波段特性、影像空间分辨率等都影响着DEM的精度。     

土地整理中数字高程模型的应用

数字高程模型(DEM)是地貌的一种数字表达,已被广泛应用到多个领域。我国的基本国情是人口多、人均耕地少、耕地后备资源不足,而土地整理是当前我国实现耕地总量动态平衡、保证国家粮食安全和促进土地可持续利用的重要措施。据此,将DEM应用到丘陵地区土地整理项目中,分析DEM数据的获取、处理、分析的全过程,提出利用DEM计算土石方量和提取路渠纵断面信息的注意事项,可供相关工作提供借鉴。     

土地开发整理项目中土方量计算的精度分析

土方量计算是土地开发整理项目中土地平整工程的重要组成部分,其工程量的大小直接影响着工程的预算及投资。本文通过建立理想模型,对土方量的计算精度进行了分析、探讨。并以国家投资重点项目——山西省隰县土地开发整理项目为例进行示范研究,通过分析对提高土方量计算精度提出了一些观点和建议。     

利用SPOT5 HRS/HRG立体影像提取DEM方法的探讨

本文结合生产实践讨论了利用SPOT5 HRS/HRG立体影像提取DEM的技术和方法,通过试验证明了SPOT5 HRS/HRG立体影像在提取高山峡谷地区DEM的优势,并对提取出的DEM成果精度进行了分析。     

不同软件在ASTER数据中提取DEM的精度对比

利用3个不同的软件对四川省龙门山中段ASTER 15 m分辨率的立体像对进行了DEM提取,并对其精度进行了初步评价。分别使用立体测量法和干涉测量法提取DEM,并通过检验点法和剖面线法对比分析。结果表明,利用ERDAS的干涉测量法提取出的DEM效果较好,高程精度可达30 m,对后续数据深挖掘和高层次地形分析具有应用价值。     

无人机影像数据提取大比例尺DEM的方法研究

以新疆某个试验测区为例,介绍了利用Pix4DMapper全自动摄影测量软件结合Terrasolid LDAR点云数据处理软件提取高精度DEM的方法与基本流程。通过实测的路面高程点与获取的DEM高程值做对比进行精度评价,试验结果表明,通过差分GPS无人机影像数据提取的DEM,可满足1∶2 000大比例尺地形图平坦地区DEM的精度要求。     

基于SPOT 5异轨立体像对提取DEM试验与精度评估

介绍了异轨立体像对提取DEM的基本原理和数据处理流程,利用两景影像进行了DEM提取试验,分析了结果的精度,提出了利用卫星立体影像提取DEM这一方法的发展方向。     

基于数字高程模型制作山区坡度分级栅格数据

山区的坡度分析可为规划建设和土地利用提供所需的地理底图,对此较为准确、有效的方法是利用高精度的数字高程模型DEM进行坡度计算及分析,数字高程模型相比于其他数据更适用于数字地形分析.本文阐述了基于1∶50 000比例尺的数字高程模型并利用ArcGIS软件进行山区坡度的计算及坡度分级栅格数据的制作,将结果与高分辨率正射影像进行叠加以初步检查结果的正确性,最后将坡度在25°以上的区域提取出来并以矢量文件形式输出.     

MapGIS技术支持下的典型田块土方量计算及成图

土地平整工程是土地整理中最重要的组成部分之一,而土方量的计算则关系到土地平整的投资与预算,因而快速准确地计算土方量意义重大。本文以传统的方格网法计算土方量作为研究对象,利用MapGIS DTM分析模块中对数字高程模型的空间分析功能和Microsoft Excel方便的统计功能,从而实现土地平整中土方量的快速计算及自动成图。利用这种方法,自动提取特定点的高程,减小了由手工计算而带来的人为误差,在确保计算精度的情况下有效提高工作效率。     

基于OEAD平台的航片DEM提取方法研究

数字高程模型是数字地球技术和各种数字化模型的重要基础,目前已有多种DEM生成技术,以全数字摄影测量方法最具前途。本文介绍了利用OEAD(OrthoEngineAirphotoDEM)软件进行DEM提取的方法和技术流程,以某水库库区金龙山地区为例进行了DEM提取,并对生成的DEM进行分析。     

海岛礁卫星影像无地面控制点定位方法

针对海岛礁卫星影像的定位问题,提出一种利用航天飞机雷达地形测绘任务（shuttle radar topography mission,SRTM）DEM辅助的无地面控制点定位方法。该方法分为概略定位和精定位两个阶段,各阶段均包括DEM提取和DEM匹配等主要步骤,可分别对影像中的相对误差和绝对误差进行补偿。SRTM DEM被充分应用到方法各环节中,以发挥其高精度的特性:提取DEM时既用于剔除海域点,也用于确定求解陆域点高程时的高程搜索范围,从而避免海域影像的不利影响,同时保证计算效率;DEM匹配时其作为基准数据。利用多景天绘一号卫星海岛礁地区的立体影像进行验证。实验结果表明,所提出的方法对具有不同陆域比例、不同生产方式的天绘一号海岛礁影像均能得到较稳定且较高的定位精度,平面和高程精度分别优于6.2 m、5.2 m,能较好地满足1:50 000比例尺地形图的精度要求。定位精度基本不受待匹配DEM分辨率的影响,计算效率取决于陆域比例和待匹配DEM的分辨率。     

Vertical accuracy evaluation of SRTM-GL1, GDEM-V2, AW3D30 and CartoDEM-V3.1 of 30-m resolution with dual frequency GNSS for lower Tapi Basin India

Abstract

Shuttle Radar Topography Mission (SRTM-GL1), Advanced Space Borne Thermal Emission and Reflection Radiometer- Global DEM (GDEM-V2), recently released Advanced Land Observing Satellite (‘DAICHI’) DEM (AW3D30) and Indian National Cartosat-1 DEM v3 (CartoDEM-V3.1) provide free topographic data at a 30-m resolution for Indian peninsula. In this research study, the vertical accuracy of DEM is evaluated for above data-sets and compared with high accuracy dual frequency GNSS of a millimetre accuracy. The extensive field investigation is carried out using a stratified random fast static DGPS survey for collecting 117 high accuracy ground control points in a predominantly agriculture catchment. Further, the effect of land cover, slope and low-lying coastal zone on DEM vertical accuracy was also analysed and presented in this study.     

ArcGIS支持下的海岸带DEM构建与精度评估分析

以福建九龙江口海岸带DEM数据构建为实例,介绍了基于ArcGIS软件综合陆域、海域以及海陆交界处干出滩数据,构建海岸带DEM并进行精度评估的方法。精度评估结果显示所构建的数字高程模型是满足国家DEM构建相关标准精度要求,从而为GIS在海岸带资源信息化综合管理中应用提供了技术支撑。     

Vertical accuracy assessment of freely available digital elevation models over low-lying coastal plains

The frequency of coastal flood damages is expected to increase significantly during the twenty-first century as sea level rises in the coastal floodplain. Coastal digital elevation model (DEM) data describing coastal topography are essential for assessing future flood-related damages and understanding the impacts of sea-level rise. The Shuttle Radar Topography Mission (SRTM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) are currently the most accurate and freely available DEM data. However, an accuracy assessment specifically targeted at DEMs over low elevation coastal plains is lacking. The present study focuses on these areas to assess the vertical accuracy of SRTM and ASTER GDEM using Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System (ICESat/GLAS) and Real Time Kinematic (RTK) Global Positioning System (GPS) field survey data. The findings show that DEM accuracy is much better than the mission specifications over coastal plains. In addition, optical remote sensing image analysis further reveals the relationship between DEM vertical accuracy and land cover in these areas. This study provides a systematic approach to assess the accuracy of DEMs in coastal zones, and the results highlight the limitations and potential of these DEMs in coastal applications.     

Creating high-resolution bare-earth digital elevation models (DEMs) from stereo imagery in an area of densely vegetated deciduous forest using combinations of procedures designed for lidar point cloud filtering

For areas of the world that do not have access to lidar, fine-scale digital elevation models (DEMs) can be photogrammetrically created using globally available high-spatial resolution stereo satellite imagery. The resultant DEM is best termed a digital surface model (DSM) because it includes heights of surface features. In densely vegetated conditions, this inclusion can limit its usefulness in applications requiring a bare-earth DEM. This study explores the use of techniques designed for filtering lidar point clouds to mitigate the elevation artifacts caused by above ground features, within the context of a case study of Prince William Forest Park, Virginia, USA. The influences of land cover and leaf-on vs. leaf-off conditions are investigated, and the accuracy of the raw photogrammetric DSM extracted from leaf-on imagery was between that of a lidar bare-earth DEM and the Shuttle Radar Topography Mission DEM. Although the filtered leaf-on photogrammetric DEM retains some artifacts of the vegetation canopy and may not be useful for some applications, filtering procedures significantly improved the accuracy of the modeled terrain. The accuracy of the DSM extracted in leaf-off conditions was comparable in most areas to the lidar bare-earth DEM and filtering procedures resulted in accuracy comparable of that to the lidar DEM.     

A practical method for SRTM DEM correction over vegetated mountain areas

Digital elevation models (DEMs) are essential to various applications in topography, geomorphology, hydrology, and ecology. The Shuttle Radar Topographic Mission (SRTM) DEM data set is one of the most complete and most widely used DEM data sets; it provides accurate information on elevations over bare land areas. However, the accuracy of SRTM data over vegetated mountain areas is relatively low as a result of the high relief and the penetration limitation of the C-band used for obtaining global DEM products. The objective of this study is to assess the performance of SRTM DEMs and correct them over vegetated mountain areas with small-footprint airborne Light Detection and Ranging (Lidar) data, which can develop elevation products and vegetation products [e.g., vegetation height, Leaf Area Index (LAI)] of high accuracy. The assessing results show that SRTM elevations are systematically higher than those of the actual land surfaces over vegetated mountain areas. The mean difference between SRTM DEM and Lidar DEM increases with vegetation height, whereas the standard deviation of the difference increases with slope. To improve the accuracy of SRTM DEM over vegetated mountain areas, a regression model between the SRTM elevation bias and vegetation height, LAI, and slope was developed based on one control site. Without changing any coefficients, this model was proved to be applicable in all the nine study sites, which have various topography and vegetation conditions. The mean bias of the corrected SRTM DEM at the nine study sites using this model (absolute value) is 89% smaller than that of the original SRTM DEM, and the standard deviation of the corrected SRTM elevation bias is 11% smaller.     

Effect of sensor modelling methods on computation of 3-D coordinates from Cartosat-1 stereo data

The orbital and the rational polynomial coefficients (RPC) models are the two most commonly used models to compute a three-dimensional coordinates from an image stereo-pair. But it is still confusing that with the identical user provided inputs, which one of these two models provides more accurate digital elevation model (DEM), especially for mountainous terrain. This study aimed to find out the answer by evaluating the impact of used models on the vertical accuracy of DEM extracted from Cartosat-1 stereo data. We used high-accuracy photogrammetric DEM as the reference DEM. Apart from general variations in statistics, surprisingly in a few instances, both the DEMs provided contrasting results, thus proving the significance of this study. The computed root mean square errors and linear error at 90% (LE90) were lower in case of RPC DEM for various classes of slope, aspect and land cover, thus suggesting its better relative accuracy.     

POS辅助机载LiDAR无地面控制点DEM,DOM制作

DEM和DOM已成为航空遥感生产的重要产品,机载Li DAR系统获取的具有三维坐标信息的点云数据,经系统误差校正和滤波分类后,结合机载POS数据和数码影像,可进行无地面控制点的DEM和DOM制作。经实践分析,成果精度满足1∶2 000比例尺DEM和1∶5 000比例尺DOM精度指标,该方法产品制作周期短,成果质量较高,满足了无人区和应急情况下的测绘需求。     

基于MODIS数据的环北京地区土地资源监测研究

本文基于MODIS 16天合成的NDVI时间序列数据及其他辅助数据,首先用PCA方法对NDVI时间序列数据进行信息增强与压缩处理,结合LST数据、DEM数据及降雨温度数据,利用模糊K-均值非监督分类法,进行环北京地区的土地覆盖分类,得到土地资源现状情况。然后利用变化矢量(CVA)分析方法对环北京地区的土地利用及植被覆盖的多年变化状况进行了分析。结果表明,MODIS数据能很好的应用于大范围的土地资源监测中,并能得到较好的结果。     

我国数字高程模型与数字地形分析研究进展

融合多源数字高程模型(digital elevation model, DEM)数据能有效利用数据间的互补优势,提升单一源数据的质量。提出一种基于深度置信网络(deep belief networks, DBN)的点面融合模型,在DBN的框架下考虑地形坡度、地表覆盖和空间位置信息等因素对DEM高程误差空间分布的影响,建立DEM高程值与高精度激光雷达测高数据之间的回归关系,从而实现多源栅格DEM与激光雷达测高点数据的空间融合,提升栅格DEM的垂直精度。对于空洞数据,根据空洞和非空洞区域的范围建立相应的输入数据集,分别进行融合,再利用不规则三角网差分曲面方法实现融合结果的无缝拼接。实验结果表明,相比原始DEM数据和两两融合的结果,所提出的多源DEM点面融合模型能够大幅度提升数据精度,有效解决原始数据中存在的空洞、噪声和异常值等问题。