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.     

中国地区3"SRTM高程误差特征

系统评估了中国地区航天飞机雷达地形测绘任务（Shuttle Radar Topography Mision,SRTM）3″高程误差的分布及其与地形和地表覆盖因素的关系。通过单因子分析法,使用从50多万个样本点中提取的地表特征属性确定误差的变化规律。结果显示:SRTM高程误差与不同地形和地表覆盖类型关系密切;坡度增大误差由正变负,误差绝对值增大;正误差集中在偏北坡向,负误差集中在西南坡向;误差随植被覆盖增加而增大;冰川、沙漠、湿地区域误差整体为负,城镇建筑区的误差整体为正;坡度作为主导因素,同时影响其他因素对高程误差的作用。数据在某些区域存在明显高程异常,在平坦地区存在条带现象。整体上SRTM高程误差在中国地区呈现复杂的变化规律。     

Comparative evaluation of horizontal accuracy of elevations of selected ground control points from ASTER and SRTM DEM with respect to CARTOSAT-1 DEM: a case study of Shahjahanpur district,Uttar Pradesh,India

Digital elevation model (DEM) data of Shuttle Radar Topography Mission (SRTM) are distributed at a horizontal resolution of 90 m (30 m only for US) for the world, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM data provide 30 m horizontal resolution, while CARTOSAT-1 (IRS-P5) gives 2.6 m horizontal resolution for global coverage. SRTM and ASTER data are available freely but 2.6 m CARTOSAT-1 data are costly. Hence, through this study, we found out a horizontal accuracy for selected ground control points (GCPs) from SRTM and ASTER with respect to CARTOSAT-1 DEM to implement this result (observed from horizontal accuracy) for those areas where the 2.6-m horizontal resolution data are not available. In addition to this, the present study helps in providing a benchmark against which the future DEM products (with horizontal resolution less than CARTOSAT-1) with respect to CARTOSAT-1 DEM can be evaluated. The original SRTM image contained voids that were represented digitally as ?140; such voids were initially filled using the measured values of elevation for obtaining accurate DEM. Horizontal accuracy analysis between SRTM- and ASTER-derived DEMs with respect to CARTOSAT-1 (IRS-P5) DEM allowed a qualitative assessment of the horizontal component of the error, and the appropriable statistical measures were used to estimate their horizontal accuracies. The horizontal accuracy for ASTER and SRTM DEM with respect to CARTOSAT-1 were evaluated using the root mean square error (RMSE) and relative root mean square error (R-RMSE). The results from this study revealed that the average RMSE of 20 selected GCPs was 2.17 for SRTM and 2.817 for ASTER, which are also validated using R-RMSE test which proves that SRTM data have good horizontal accuracy than ASTER with respect to CARTOSAT-1 because the average R-RMSE of 20 GCPs was 3.7 × 10^?4 and 5.3 × 10^?4 for SRTM and ASTER, respectively.     

Effect of the SRTM global DEM on the determination of a high-resolution geoid model: a case study in Iran

Any errors in digital elevation models (DEMs) will introduce errors directly in gravity anomalies and geoid models when used in interpolating Bouguer gravity anomalies. Errors are also propagated into the geoid model by the topographic and downward continuation (DWC) corrections in the application of Stokes’s formula. The effects of these errors are assessed by the evaluation of the absolute accuracy of nine independent DEMs for the Iran region. It is shown that the improvement in using the high-resolution Shuttle Radar Topography Mission (SRTM) data versus previously available DEMs in gridding of gravity anomalies, terrain corrections and DWC effects for the geoid model are significant. Based on the Iranian GPS/levelling network data, we estimate the absolute vertical accuracy of the SRTM in Iran to be 6.5 m, which is much better than the estimated global accuracy of the SRTM (say 16 m). Hence, this DEM has a comparable accuracy to a current photogrammetric high-resolution DEM of Iran under development. We also found very large differences between the GLOBE and SRTM models on the range of −750 to 550 m. This difference causes an error in the range of −160 to 140 mGal in interpolating surface gravity anomalies and −60 to 60 mGal in simple Bouguer anomaly correction terms. In the view of geoid heights, we found large differences between the use of GLOBE and SRTM DEMs, in the range of −1.1 to 1 m for the study area. The terrain correction of the geoid model at selected GPS/levelling points only differs by 3 cm for these two DEMs.     

Qualitative and quantitative assessment of TanDEM-X DEM over western Himalayan glaciated terrain

Glaciers have a high impact in the socio-economic sectors including water supply, energy production, flood and avalanches. A high precision digital elevation model (DEM) is required to monitor glaciers and to study various glacier processes. The present study deals with the qualitative and quantitative evaluation of the DEM generated from the bistatic TanDEM-X data by comparing it with GPS, Ice, Cloud, and land Elevation Satellite (ICESat) data and standard global DEMs such as Shuttle Radar Topography Mission (SRTM) and Advanced Space-borne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM). The study area consists of highly undulating glaciated terrain in western Himalaya, India. The results reveal that TanDEM-X is slightly better than SRTM both qualitatively and quantitatively, whereas ASTER GDEM showing maximum discrepancy among the three DEMs. The Root Mean Square Error (RMSE) of the TanDEM-X DEM with respect to GPS is 3.5 m at lower relief and 11.9 m at glaciated terrain, against 6.7 and 12.5 m for SRTM and 9.3 and 19.8 m for ASTER GDEM, respectively, for the same sites. On an average, for the whole study area, the RMSE of TanDEM-X is 7.9 m, SRTM is 9.3 m and ASTER GDM is 14.2 m. The RMSE of TanDEM-X, SRTM and ASTER GDEM with respect to ICESat are 16.3, 19.9 and 101.1 m, respectively. It is evident from the analysis that though SRTM is closer to TanDEM-X in terms of accuracy in the mountainous terrain, however, TanDEM-X will be more useful for studying glacier dynamics and topography.     

国产资源三号测绘卫星DSM数据质量评价--以高海拔山区为例

为了评价国产资源三号测绘卫星DSM数据精度,在顾及地貌类型的情况下,以涵盖平原、台地、丘陵等地貌的高海拔山区为研究案例,并以1∶1万实测地形图DEM为假定真值,以90m分辨率SRTM DEM为评价参照,从高程精度和地形描述精度两个方面,对15m分辨率ZY-3DSM进行精度评价分析。研究结果表明:ZY-3DSM高程精度优于SRTM DEM,前者高程中误差仅为后者的1/6;就地形描述精度来讲,ZY-3DSM与SRTM DEM相比,其地形描述精度更接近理论值,前者RMS Et实际值仅为理论值0.99倍,而后者的实际值却是理论值5.13倍。由此看来,ZY-3DSM数据精度整体上高于SRTM DEM。     

The Influence of Land Cover on Shuttle Radar Topography Mission (SRTM) Elevations in Low‐relief Areas

The Shuttle Radar Topography Mission (SRTM), the first relatively high spatial resolution near‐global digital elevation dataset, possesses great utility for a wide array of environmental applications worldwide. This article concerns the accuracy of SRTM in low‐relief areas with heterogeneous vegetation cover. Three questions were addressed about low‐relief SRTM topographic representation: to what extent are errors spatially autocorrelated, and how should this influence sample design? Is spatial resolution or production method more important for explaining elevation differences? How dominant is the association of vegetation cover with SRTM elevation error? Two low‐relief sites in Louisiana, USA, were analyzed to determine the nature and impact of SRTM error in such areas. Light detection and ranging (LiDAR) data were employed as reference, and SRTM elevations were contrasted with the US National Elevation Dataset (NED). Spatial autocorrelation of errors persisted hundreds of meters spatially in low‐relief topography; production method was more critical than spatial resolution, and elevation error due to vegetation canopy effects could actually dominate the SRTM representation of the landscape. Indeed, low‐lying, forested, riparian areas may be represented as substantially higher than surrounding agricultural areas, leading to an inverted terrain model.     

SRTM约束的无地面控制立体影像区域网平差

针对SRTM(shuttle radar topography mission)数据在平坦地形或局部区域的高程精度远远高于其标称精度的特点,研究设计了一种无地面控制条件下利用SRTM作为高程约束的立体区域网平差方法。通过构建一个较大范围区域网并匹配密集连接点,将SRTM作为连接点物方高程初值,并在平差解算过程中确保分布于地形平坦区域(根据经验,在该类区域SRTM精度较高)的连接点的物方高程严格趋近SRTM高程,最终实现大范围区域内影像高程精度的整体提升。通过以覆盖湖北省全境的资源三号卫星三线阵立体影像作为试验影像的试验验证表明,采用该平差方案,在无地面控制点条件下资源三号立体影像的高程中误差从7.2m提升到2.0m,其中地形平坦区域高程中误差1.44m,山地区域高程中误差3.0m,达到了我国1∶25 000比例尺测图应用的高程精度要求。     

小型消费级无人机地形数据精度验证

低空遥感是近几年快速发展、应用非常广泛的新兴技术。小型消费级无人机集成可见光传感器,具有快速、灵活、高性价比等优势,受到广泛关注。然而目前有关该类无人机综合测量精度的研究不足,影响其进一步的推广应用。为此,本文开展了针对大疆(Phantom 3 professional)小型消费级无人机地形测量数据精度验证工作,设定6种航高(50 m、60 m、70 m、80 m、90 m和100 m)获取研究区的立体像对,生成影像点云(point cloud)、数字表面模型(DSM)以及数字正射影像图(DOM)等结果。在测量精度验证中,首先,在标准实验场均匀布设地面控制点(GCP),利用差分GPS测出GCP的高精度3维坐标;然后,通过GCP对立体像对进行绝对定位;最后,利用误差统计方法分析上述结果的测量精度。验证表明,在50—100m航高时,无人机影像结果的分辨率为2.22—4.23 cm,水平方向平均误差为±0.51 cm,垂直方向平均误差为±4.39 cm,相对均方根误差(RMSE)水平方向为±2.79 cm,垂直方向为±9.98 cm。研究结果表明,小型消费级无人机在飞控系统下的测量精度可达厘米级,这不仅为野外地理和生态调查工作者提供一种低成本、快速、灵活与精确获取地形信息的新型测量手段,同时还对使用此类无人机做航测应用及飞行参数设置提供一定参考。     

Analysis of the factors affecting LiDAR DTM accuracy in a steep shrub area

The creation of a quality Digital Terrain Model (DTM) is essential for representing and analyzing the Earth in a digital form. The continuous improvements in the acquisition and the potential of airborne Light Detection and Ranging (LiDAR) data are increasing the range of applications of this technique to the study of the Earth surface. The aim of this study was to determine the optimal parameters for calculating a DTM by using an iterative algorithm to select minimum elevations from LiDAR data in a steep mountain area with shrub vegetation. The parameters were: input data type, analysis window size, and height thresholds. The effects of slope, point density, and vegetation on DTM accuracy were also analyzed. The results showed that the lowest root mean square error (RMSE) was obtained with an analysis window size of 10 m, 5 m, and 2.5 m, rasterized data as input data, and height thresholds equal to or greater than 1.5 m. These parameters showed a RMSE of 0.19 m. When terrain slope varied from 0–10% to 50–60%, the RMSE increased by 0.11 m. The RMSE decreased by 0.06 m when point density was increased from 4 to 8 points/m², and increased by 0.05 m in dense vegetation areas.     

GF-1卫星WFV影像几何定位稳定性研究

针对GF-1卫星WFV传感器高分辨率与大视场相结合的特点,本文将ZY-3卫星传感器校正产品作为参考,通过分析同名点对的地理信息,对北京地区WFV传感器4台相机影像相对几何定位精度进行长时间序列评价,分析其误差特征。试验结果表明,单景影像均出现了明显的系统几何定位误差,但是不同时相影像的几何定位误差方向并无明显规律。针对该现象本文基于相机成像原理,从理论上证明了仅利用某一时相影像的有理函数模型补偿参数是无法有效消除其他不同时相影像系统误差的。     

Vertical accuracy evaluation of freely available latest high-resolution (30 m) global digital elevation models over Cameroon (Central Africa) with GPS/leveling ground control points.

Digital Elevation Models (DEMs) contain topographic relief data that are vital for many geoscience applications. This study relies on the vertical accuracy of publicly available latest high-resolution (30?m) global DEMs over Cameroon. These models are (1) the ALOS World 3D-30?m (AW3D30), (2) the Shuttle Radar Topography Mission 1 Arc-Second C-Band Global DEM (SRTM 1) and (3) the Advanced Spaceborne Thermal Emission and Reflection Global DEM Version 2 (ASTER GDEM 2). After matching their coordinate systems and datums, the horizontal positional accuracy evaluation was carried out and it shows that geolocation errors significantly influence the vertical accuracy of global DEMs. After this, the three models are compared among them, in order to access random and systematic effects in the elevation data each of them contains. Further, heights from 555 GPS/leveling points distributed all over Cameroon are compared to each DEM, for their vertical accuracy determination. Traditional and robust statistical measures, normality test, outlier detection and removal were used to describe the vertical quality of the DEMs. The test of the normality rejected the hypothesis of normal distribution for all tested global DEMs. Overall vertical accuracies obtained for the three models after georeferencing and gross error removal in terms of Root Mean Square (RMS) and Normalized Median Absolute Deviation (NMAD) are: AW3D30 (13.06?m and 7.75?m), SRTM 1 (13.25?m and 7.41?m) and ASTER GDEM 2 (18.87?m and 13.30?m). Other accuracy measures (MED, 68.3% quantile, 95% quantile) supply some evidence of the good quality of AW3D30 over Cameroon. Further, the effect of land cover and slope on DEM vertical accuracy was also analyzed. All models have proved to be worse in the areas dominated by forests and shrubs areas. SRTM 1 and AW3D30 are more resilient to the effects of the scattering objects respectively in forests and cultivated areas. The dependency of DEMs accuracy on the terrain roughness is evident. In all slope intervals, AW3D30 is performing better than SRTM 1 and ASTER GDEM 2 over Cameroon. AW3D30 is more representative of the external topography over Cameroon in comparison with two others datasets and SRTM 1 can be a serious alternative to AW3D30 for a range of DEM applications in Cameroon.     

Improving impervious surface estimation: an integrated method of classification and regression trees (CART) and linear spectral mixture analysis (LSMA) based on error analysis

Classification and regression tree (CART) has been widely implemented to estimate impervious surface, an important indicator of urbanization and environmental quality. Although the CART algorithm gains higher overall accuracy than linear regression models, only very few studies have noticed that reliability of CART is affected by systematic errors. Especially, CART typically overestimates impervious surfaces in low-density urban areas and underestimates them in high-density urban areas. The primary objective of this study is to develop an improved integrated method to estimate impervious surface with higher accuracy by reducing the systematic errors of CART. This improved method was applied to three urban areas, Chicago (United States), Venice (Italy), and Guangzhou (China) to examine its effectiveness. When compared with the conventional CART, overall mean average error (MAE) and root mean square error (RMSE) of improved method are decreased by 22.64% and 20.93%, respectively, and R² rises from 0.9 to 0.96. In high-density impervious surfaces, where intensely developed urban area is located, the MAE and RMSE for the improved method are 0.066 and 0.088, respectively, largely improved from 0.100 to 0.130. Since accurate estimation of high-density impervious surfaces is the fundamental issue for monitoring and understanding the urban environment, the improved method demonstrated in this study is significant.     

基于特征点修正的SRTM数据在风能资源微观评估中的应用

在风能资源微观评估中,高精度地形数据通常较难获取,寻找一种可替代数据尤为重要。本文以全球免费共享的SRTM数据(90m水平分辨率)为地形数据源,利用风图谱分析及应用程序(WAsP)作为风资源评估工具,对广东遮浪岛区域进行了定点风能资源评估。通过将评估结果与1:5千地形数据条件下的结果进行对比,分析了SRTM数据用于风能资源微观评估的精度。接着,本文提出了地形数据的特征点修正方法,利用该方法处理后的SRTM数据,平均风速在两种不同地形数据源下的相对误差由10%降为5%,使得修正后的SRTM数据可适用于风能资源微观评估。     

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。     

中国及周边区域ASTERGDEM与SRTMDEM高程对比分析及互补修复

本文分析了ASTER GDEM和SRTM DEM的获取方式,通过对两者在中国及周边区域高程的对比分析,得出两者高程间存在系统误差,前者高程比后者平均低4.9m。ASTER GDEM在许多区域特别是水域及高山区常存在明显粗差;SRTM DEM在特别是高山区域会出现空白区域,但其有效区域层次清晰、细节分明,无明显粗差,可靠性高。经过填补及高差约束限制修复,生成了无空白区域的SRTM DEM和可靠性更高的ASTER GDEM。     

Effects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm

This paper suggested simulation approaches for quantifying and reducing the effects of National Forest Inventory (NFI) plot location error on aboveground forest biomass and carbon stock estimation using the k-Nearest Neighbor (kNN) algorithm. Additionally, the effects of plot location error in pre-GPS and GPS NFI plots were compared. Two South Korean cities, Sejong and Daejeon, were chosen to represent the study area, for which four Landsat TM images were collected together with two NFI datasets established in both the pre-GPS and GPS eras. The effects of plot location error were investigated in two ways: systematic error simulation, and random error simulation. Systematic error simulation was conducted to determine the effect of plot location error due to mis-registration. All of the NFI plots were successively moved against the satellite image in 360° directions, and the systematic error patterns were analyzed on the basis of the changes of the Root Mean Square Error (RMSE) of kNN estimation. In the random error simulation, the inherent random location errors in NFI plots were quantified by Monte Carlo simulation. After removal of both the estimated systematic and random location errors from the NFI plots, the RMSE% were reduced by 11.7% and 17.7% for the two pre-GPS-era datasets, and by 5.5% and 8.0% for the two GPS-era datasets. The experimental results showed that the pre-GPS NFI plots were more subject to plot location error than were the GPS NFI plots. This study’s findings demonstrate a potential remedy for reducing NFI plot location errors which may improve the accuracy of carbon stock estimation in a practical manner, particularly in the case of pre-GPS NFI data.     

线性变化消光S-RVoG模型的多基线PolInSAR森林高度反演

随机地体散射(random volume over ground,RVoG)模型广泛应用于极化干涉合成孔径雷达(polari-metric synthetic aperture radar interferometry,PolInSAR)森林高度反演当中.该模型假设森林是随机均匀同质体,模型中消光系数为恒定值,未充分考...     

结合NASA DEM和AW3D30 DEM的太原市DEM数据融合

本文首先以DEM数据为例,将输入数据与参考数据配准至同一像素位置,然后分别将均方根误差和标准差作为参考指标,在不同坡度等级的区域内,通过权重系数从0至1的遍历探寻最佳加权融合系数,从而确定融合方案并进行NASA DEM与AW3D30 DEM的数据融合,最后对融合效果进行定量评价。结果表明:配准前,NASA DEM沿x、y、z方向的位移分别为-2.65、2.41、0.60m,AW3D30 DEM位移分别为1.04、7.51、-3.33m;配准后,原始数据各项误差均减小,且NASA DEM的系统误差基本消失。融合DEM相较于NASA DEM,平均误差和均方根误差分别减小了25.0%和36.8%;对于AW3D30 DEM,误差降幅分别为86.5%和13.2%。