Mapping from ASTER stereo image data: DEM validation and accuracy assessment

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on-board the National Aeronautics and Space Administration's (NASA's) Terra spacecraft provides along-track digital stereo image data at 15-m resolution. As part of ASTER digital elevation model (DEM) accuracy evaluation efforts by the US/Japan ASTER Science Team, stereo image data for four study sites around the world have been employed to validate prelaunch estimates of heighting accuracy. Automated stereocorrelation procedures were implemented using the Desktop Mapping System (DMS) software on a personal computer to derive DEMs with 30- to 150-m postings. Results indicate that a root-mean-square error (RMSE) in elevation between ±7 and ±15 m can be achieved with ASTER stereo image data of good quality. An evaluation of an ASTER DEM data product produced at the US Geological Survey (USGS) EROS Data Center (EDC) yielded an RMSE of ±8.6 m. Overall, the ability to extract elevations from ASTER stereopairs using stereocorrelation techniques meets expectations.     

Accuracy assessment of lidar-derived digital elevation models

Despite the relatively high cost of airborne lidar-derived digital elevation models (DEMs), such products are usually presented without a satisfactory associated estimate of accuracy. For the most part, DEM accuracy estimates are typically provided by comparing lidar heights against a finite sample of check point coordinates from an independent source of higher accuracy, supposing a normal distribution of the derived height differences or errors. This paper proposes a new methodology to assess the vertical accuracy of lidar DEMs using confidence intervals constructed from a finite sample of errors computed at check points. A non-parametric approach has been tested where no particular error distribution is assumed, making the proposed methodology especially applicable to non-normal error distributions of the type usually found in DEMs derived from lidar. The performance of the proposed model was experimentally validated using Monte Carlo simulation on 18 vertical error data-sets. Fifteen of these data-sets were computed from original lidar data provided by the International Society for Photogrammetry and Remote Sensing Working Group III/3, using their respective filtered reference data as ground truth. The three remaining data-sets were provided by the Natural Environment Research Council's Airborne Research and Survey Facility lidar system, together with check points acquired using high precision kinematic GPS. The results proved promising, the proposed models reproducing the statistical behaviour of vertical errors of lidar using a favourable number of check points, even in the cases of data-sets with non-normally distributed residuals. This research can therefore be considered as a potentially important step towards improving the quality control of lidar-derived DEMs.     

Accuracy assessment of GDEM,SRTM, and DLR-SRTM in Northeastern China

This paper compares the accuracy of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), Shuttle Radar Topography Mission (SRTM) C-band and German Aerospace Centre (DLR)-SRTM X-band digital elevation models (DEMs) with the Ziyuan 3 (ZY-3) stereoscopic DEM and ground control points (GCPs). To date, the horizontal error of these DEMs has received little attention in accuracy assessments. Using the ZY-3 DEM as reference, this study examines (1) the horizontal offset between the three DEMs and the reference DEM using the normalised cross-correlation method, (2) the vertical accuracy of those DEMs using kinematic GPS data and (3) the relationship between the three DEMs and the reference ZY-3 DEM. The results show that the SRTM and DLR-SRTM have greater vertical accuracy after applying horizontal offset correction, whereas the vertical accuracy of the ASTER GDEM is less than the other two DEMs. These methods and results can be useful for researchers who use DEMs for various applications.     

基于星载激光雷达和雷达高度计数据的南极冰盖表面高程制图

南极数字高程模型DEMs(Digital Elevation Models)是研究极区大气环流模式,南极冰盖动态变化和南极科学考察非常重要的基础数据。目前,科学家已经发布了五种不同的南极数字表面高程模型。这些数据都是由卫星雷达高度计,激光雷达和部分地面实测数据等制作而成。尽管如此,由于海洋与冰盖交接的南极冰盖边缘区随时间的快速变化,有必要根据新的卫星数据及时更新南极冰盖表面高程数据。因此,我们利用雷达高度计数据(Envisat RA-2)和激光雷达数据(ICESat/GLAS)制作了最新的南极冰盖高程数据。为提高ICESat/GLAS数据的精度,本文采用了五种不同的质量控制指标对GLAS数据进行处理,滤除了8.36%的不合格数据。这五种质量控制指标分别针对卫星定位误差、大气前向散射、饱和度及云的影响。同时,对Envisat RA-2数据进行干湿对流层纠正、电离层纠正、固体潮汐纠正和极潮纠正。针对两种不同的测高数据,提出了一种基于Envisat RA-2和GLAS数据光斑脚印几何相交的高程相对纠正方法,即通过分析GLAS脚印点与Envisat RA-2数据中心点重叠的点对,建立这些相交点对的高度差(GLAS-RA-2)与表征地形起伏的粗糙度之间的相关关系,对具有稳定相关关系的点对进行Envisat RA-2数据的相对纠正。通过分析南极冰盖不同区域的测高点密度,确定最终DEM的分辨率为1000 m。考虑到南极普里兹湾和内陆地区的差异性,将南极冰盖分为16个区,利用半方差分析确定最佳插值模型和参数,采用克吕金插值方法生成了1000 m分辨率的南极冰盖高程数据。利用两种机载激光雷达数据和我国多次南极科考实测的GPS数据对新的南极DEM进行了验证。结果显示,新的DEM与实测数据的差值范围为3.21—27.84 m,其误差分布与坡度密切关系。与国际上发布的南极DEM数据相比,新的DEM在坡度较大地区和快速变化的冰盖边缘地区精度有较大改进。     

Artificial Neural Networks as a Method of Spatial Interpolation for Digital Elevation Models

This paper examines the performance of artificial neural networks (ANNs) as a method of spatial interpolation, when presented with irregular and regular samples of elevation data. The results of the ANN interpolation are compared with results obtained by kriging. Tests of spatial bias in the systematic errors contained in each of the neural network-derived DEMs were conducted using four attributes: slope, aspect, average direction and average distance from the nearest sampled value. Based on RMS and other evaluation measures, the accuracy of estimated DEMs from regular and irregular sample distributions using neural networks is lower than the accuracy level derived from kriging. The accuracy level of the ANN interpolators also decreases as the range of elevation values in DEMs increases. As reported in the literature, ANNs are approximate interpolators, and the pattern of under-prediction and over-prediction of elevation values in this study revealed that all estimated values fell within the range of sample elevations. Neural networks cannot predict values outside the range of elevation values contained in the sample, a property shared by other interpolators such as inverse weighted distance.     

顾及地形坡度的SRTM和ASTER加权融合

为了克服现有SRTM和ASTER各自缺陷，提升公共DEM精度，本文提出了一种顾及地形坡度的SRTM和ASTER加权融合方法。首先对两种DEM进行地理配准；然后计算不同坡度等级下SRTM和ASTER的高程误差，并得到DEM融合权重；最后采用加权平均法对SRTM和ASTER进行融合。高精度控制点的检验表明：融合后DEM精度有明显提高，相比于原始SRTM和ASTER高程误差，标准差分别降低了5.65 m和1.20 m。     

Effect of Contour Intervals and Grid Cell Size on the Accuracy of DEMs and Slope Derivatives

Digital Elevation Models (DEMs) are indispensable tools in many environmental and natural resource applications. DEMs are frequently derived from contour lines. The accuracy of such DEMs depends on different factors. This research investigates the effect of sampling density used to derive contours, vertical interval between contours (spacing), grid cell size of the DEM (resolution), terrain complexity, and spatial filtering on the accuracy of the DEM and the slope derivative. The study indicated different alternatives to achieve an acceptable accuracy depending on the contour interval, the DEM resolution and the complexity of the terrain. The effect of these factors on the accuracy of the DEM and the slope derivative was quantified using models that determine the level of accuracy (RMSE). The implementation of the models will guide users to select the best combination to improve the results in areas with similar topography. For areas with variable terrain complexity, the suggestion is to generate DEMs and slope at a suitable resolution for each terrain separately and then to merge the results to produce one final layer for the whole area. This will provide accurate estimates of elevation and slope, and subsequently improve the analyses that rely on these digital derivatives.     

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.     

DEM地形信息提取对比研究——以坡度为例

由于DEM数据本身多尺度因素,加之地形、地貌特征具有宏观性与区域分异性的特点,直接的信息提取往往很难达到预期的目的。利用DEM制作坡度图高效、省力,但其精度有很大的不确定性,同时DEM制作过程中的误差传播、转移对坡度信息的影响缺少系统的判断依据。选取位于陕北黄土高原上的两个不同地区作为实验样区,在不同DEM生产的基础上,以高精度的1∶10 000DEM为准值,通过对1∶5万和1∶1万DEM提取定量地形要素的叠合、比较与统计分析,探讨具有不同地貌类型的区域1∶5万DEM提取地形信息的精度及其统计意义上的数量百分比关系。     

DEM内插算法与精度评定研究

本文分析了DEM数据产品的误差源、内插算法的误差模型,简要介绍了目前DEM数据产品精度评定的指标与方法途径.通过介绍DEM引出了数字高程模型内插方法,比较了各种DEM内插方法,并且分析了模型精度.     

基于ICESat-2 ATLAS数据的DEM高程精度评价

为探究ASTER GDEMV3、SRTM1 DEM和AW3D30 DEM 3种开源DEM数据的高程精度,本文以高精度ICESat-2 ATLAS测高数据为参考数据,利用GIS统计分析、误差相关分析及数理统计对DEM的高程精度进行对比评价。结果表明：①AW3D30的质量最稳定;SRTM1 DEM在平原精度最高;在高原山地精度由高到低依次为AW3D30 DEM、ASTER GDEMV3、SRTM1 DEM。②DEM数据高程精度受地表覆盖影响较大,且与地形因素密切相关,在相同地表覆盖的两个研究区中DEM数据高程精度表现情况不一致,SRTM在平原地表覆盖下精度表现最好,平均误差为3.15 m,AW3D30 DEM在山地地表覆盖下精度表现最好,平均误差为7.61 m。③坡度对DEM数据的高程精度影响较大,在两个研究区3种DEM数据的高程误差均随坡度的增加而增加;坡向对DEM数据的高程精度影响较小,未发现明显的规律。     

Evaluation of vertical accuracy of open source Digital Elevation Model (DEM)

Digital Elevation Model (DEM) is a quantitative representation of terrain and is important for Earth science and hydrological applications. DEM can be generated using photogrammetry, interferometry, ground and laser surveying and other techniques. Some of the DEMs such as ASTER, SRTM, and GTOPO 30 are freely available open source products. Each DEM contains intrinsic errors due to primary data acquisition technology and processing methodology in relation with a particular terrain and land cover type. The accuracy of these datasets is often unknown and is non-uniform within each dataset. In this study we evaluate open source DEMs (ASTER and SRTM) and their derived attributes using high postings Cartosat DEM and Survey of India (SOI) height information. It was found that representation of terrain characteristics is affected in the coarse postings DEM. The overall vertical accuracy shows RMS error of 12.62 m and 17.76 m for ASTER and SRTM DEM respectively, when compared with Cartosat DEM. The slope and drainage network delineation are also violated. The terrain morphology strongly influences the DEM accuracy. These results can be highly useful for researchers using such products in various modeling exercises.     

Fusion of high-resolution DEMs derived from COSMO-SkyMed and TerraSAR-X InSAR datasets

Voids caused by shadow, layover, and decorrelation usually occur in digital elevation models (DEMs) of mountainous areas that are derived from interferometric synthetic aperture radar (InSAR) datasets. The presence of voids degrades the quality and usability of the DEMs. Thus, void removal is considered as an integral part of the DEM production using InSAR data. The fusion of multiple DEMs has been widely recognized as a promising way for the void removal. Because the vertical accuracy of multiple DEMs can be different, the selection of optimum weights becomes a key problem in the fusion and is studied in this article. As a showcase, two high-resolution InSAR DEMs near Mt. Qilian in northwest China are created and then merged. The two pairs of InSAR data were acquired by TerraSAR-X from an ascending orbit and COSMO-SkyMed from a descending orbit. A maximum likelihood fusion scheme with the weights optimally determined by the height of ambiguity and the variance of phase noise is adopted to syncretize the two DEMs in our study. The fused DEM has a fine spatial resolution of 10 m and depicts the landform of the study area well. The percentage of void cells in the fused DEM is only 0.13 %, while 6.9 and 5.7 % of the cells in the COSMO-SkyMed DEM and the TerraSAR-X DEM are originally voids. Using the ICESat/GLAS elevation data and the Chinese national DEM of scale 1:50,000 as references, we evaluate vertical accuracy levels of the fused DEM as well as the original InSAR DEMs. The results show that substantial improvements could be achieved by DEM fusion after atmospheric phase screen removal. The quality of fused DEM can even meet the high-resolution terrain information (HRTI) standard.     

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.     

Orthorectification of IKONOS and Impact of Different Resolution DEM

IKONOS image has been wildly used in city planning, precision agriculture and emergence response. However, the accuracy of IKONOS Geo product is limited due to distortion caused by terrain relief. Orthorectification was performed to remove the distortion and the impact of different DEM on orthorectification were evaluated. 38 ground control points （GCPs） and 25 independent check points （ICPs） were collected. DEMs were generated from 1 ： 10 000 and 1 ： 50 000 topographic maps. Results show that RMS error at the check points is 1. 554 0 m using DEM generated from 1 ： 10 000 topographic map, which can meet the accuracy requirement of IKONOS Precision product （1.9 m RMSE）. While RMS error is 2. 572 4 m using DEM generated from 1 ： 50 000 topographic map.     

DEM融合算法及其在困难地区测图中的应用

对于我国西部高山区,如横断山脉,高程起伏明显,常年被云雾覆盖,日照稀少,采用传统方法进行地形图测绘存在较大困难,依赖单一方法获取的DEM往往难以满足测图的精度要求。为充分利用不同传感器和不同方法生成的DEM的优点,本文根据各方法的特点,结合小比例尺地形图中低精度的DEM,基于绝对精度等先验知识确定优先级别、相关/干系数确定融合权重,提出了一种包括雷达干涉测量、光学立体摄影测量、不同侧视方向像对雷达立体测图生成的四种多源DEM的像素级融合算法。在横断山脉地区使用所提融合算法进行了实验,获得了一个总体精度得到提高的无缝DEM,实验结果表明新算法为地形复杂的测图困难地区DEM获取提供了一种可能的解决方案。     

一种局部稀疏地面点云与已有DEM的融合方法

机载LiDAR采集的点云数据中会存在一些局部区域地面点稀疏的情况，利用这些稀疏地面点构建DEM时会出现“三角面片化”的问题，严重影响DEM的质量。为此，本文提出了一种局部稀疏地面点云与已有DEM的融合方法：将稀疏点云作为高精度控制点，在尽量保持原始DEM的地形形态特征的前提下，通过高斯核函数加权迭代插值算法对DEM进行高程局部改正，实现稀疏点云与DEM的一致性融合。试验分析表明，融合后的点云数据得到了较好的补充，由此构建的DEM地形形态自然，在精度上相对于融合前的稀疏地面点云有一定改善，在弱精度区域的可靠性有显著提升。     

QUALITY CONTROL OF PHOTOGRAMMETRICALLY SAMPLED DIGITAL ELEVATION MODELS

The object of this paper is to study the geometric accuracy of photogrammetrically sampled digital elevation models (DEMs)obtained by using an on line graphical DEM editor. As a reference, DEMs obtained by regular grid measurements have been used. Three different test areas are used in the study, each representing different ground types and degrees of homogeneity. The DEMs have been evaluated with respect to their standard errors in elevation, slope and curvature. The results show that the standard errors are not improved by using the on line DEM editor. It is also demonstrated that an increased point density decreases the standard error in elevation while its effect on the standard error in slope is limited. The standard error in curvature is almost independent of the point density.