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。     

ASTER GDEM与SRTM3高程差异影响因素分析

作为最新发布的全球地形数据,ASTER GDEM比目前常用的SRTM3数据有着更高的分辨率和更广的覆盖范围,对于相关地学分析具有重要意义。本文以华中地区为研究区域,对ASTER GDEM与SRTM3数据进行了比较,重点分析了坡度、坡向、地形起伏度、土地利用类型、植被覆盖度、生成ASTER GDEM栅格点高程数据所用的ASTER DEM影像数等因素对2种DEM数据高程差异的影响。结果表明,在研究区域内,ASTER GDEM高程比SRTM3高程平均低5.42 m,两种DEM数据高程差异的RMS值为16.90 m;ASTER GDEM与SRTM3之间的高程差异随着坡度、地形起伏度、植被覆盖度的增大而增大,而ASTER DEM影像数越大,高程差异越小;坡向、土地利用类型对高程差异也有影响。     

我国东部地区ASTER GDEM高程精度评价

为了解我国ASTER GDEM数据高程精度,在考虑空间分布的情况下,选取我国东部辽宁、山东、浙江和海南4个地区的平原、丘陵、山地等作为典型研究区,并以1∶5万DEM为假定真值、以1∶25万DEM为参照,通过DEM面误差可视化分析和DEM面误差信息熵模型等方法对ASTER GDEM数据的高程精度做了分析。结果表明:ASTER GDEM数据高程误差在整个地图上分布是否均匀与其高程精度高低无决定关系;在山地和丘陵地形研究区,其数据高程精度要高于SRTM DEM和1∶25万DEM。总体来看,中国东部地区ASTER GDEM数据高程精度整体上要高于SRTM DEM和1∶25万DEM,但低于1∶10万DEM。     

采用小波分析的SRTM DEM与ASTER DEM数据融合

为了利用航天飞机雷达地形测绘任务数字高程模型(SRTM DEM)与先进星载热反射和反辐射仪数字高程模型(ASTER DEM)的互补信息,提出基于小波分析的多源DEM数据融合方法,以我国秦岭典型高山峡谷地貌类型区为试验样区,选取相同位置的SRTM DEM与ASTER DEM数据,通过重采样、数据配准等步骤形成融合数据源;对小波分解的低频系数作基于邻域像素关联性的融合,高频系数采用像素点绝对值取大的融合,生成融合DEM。并把融合前与融合后的数据分别与1∶5万高程库数据作精度比较,总体统计与抽样检查表明融合DEM精度较源数据均得到了提高。该融合技术为应用SRTM DEM与ASTER DEM生成精度和可靠性更高的DEM产品提供了可行方案。     

多源DEM融合的高差拟合神经网络方法

本文侧重于介绍智能化摄影测量机器学习的高差拟合神经网络方法。观测手段和处理方式等限制导致全球高质量无缝DEM数据的缺乏,进而制约了它在水文、地质、气象及军事等领域的应用。本文提出了一种基于高差拟合神经网络的多源DEM融合方法,尝试融合全球DEM产品SRTM1、ASTER GDEM v2和激光雷达测高数据ICESat GLAS。首先,根据ICESat GLAS的相关参数及与DEM数据的高程差值,结合坡度自适应的思想设置高差阈值对ICESat GLAS进行滤波,剔除异常数据点。然后,以ICESat GLAS数据为控制点,利用神经网络模型拟合ASTER GDEM v2的误差分布。以地形坡度信息和经纬度坐标作为网络输入,ICESat GLAS和ASTER GDEM v2的高程差值作为目标输出,训练得到预测高差,将其与ASTER GDEM v2高程值相加即可获得校正结果。最后,引入TIN差分曲面的方法,利用校正后的ASTER GDEM v2高程值对SRTM1的数据空洞进行填充,融合生成空间无缝DEM。本文通过随机选取数据进行真实试验,对模型进行了精度验证,并给出了处理结果的定量评价和目视效果。结果表明,不论是空洞还是整体区域,本文方法相比其他DEM数据集和其他方法的处理结果都能够在RMSE上表现出优势,同时,本文提出的方法能够有效克服ASTER GDEM中异常值的影响,得到空间无缝DEM。     

SRTM(1″)DEM在流域水文分析中的适用性研究

高精度的数字高程模型(digital elevation model,DEM)数据是流域水文分析应用的基础。美国地质调查局新发布了全球高分辨率数字高程数据产品,其空间分辨率为1″(约为30 m)。为评价该数据在流域水文分析中的适用性,以鹤壁汤河流域为实验区,以机载LiDAR DEM数据为参考,统计了SRTM(1″)数据的高程误差,分析了坡度、坡向、地表覆盖等对误差的影响;在基于地形的水文分析中,统计分析了SRTM(1″)数据误差对地形湿度指数、坡度坡长因子以及汇流动力指数等地形指数计算的影响;最后选取流域汇水区面积、最长水流路径长度、形状系数、弯曲度系数等流域特征参数对两种DEM数据提取结果进行了对比。研究表明SRTM(1″)DEM数据具有较高的精度,原始数据均方根误差为5.98 m,在消除平面位移误差后减小为4.32 m。基于地形的水文分析表明SRTM DEM与LiDAR DEM计算结果具有一定的差异,地形湿度指数平均值略高,坡度坡长因子和汇流动力指数平均值偏低,离散度偏小,这与SRTM DEM在微地貌以及高坡度地形区存在失真相关。两种DEM数据提取流域特征参数差异较小。上述研究表明SRTM DEM(1″)数据在流域水文分析中具有较大的应用潜力。     

ASTER GDEM V2的南极冰川高程误差校正及精度分析

ASTER GDEM V2是研究南极冰盖表面的一种重要DEM数据源。由于南极冰雪区反射率高且缺乏地形特征,导致ASTER GDEM V2存在大量的坑、隆起等噪声,难以直接用于南极地形分析。本文以ICESat/GLAS激光点高程数据作为参考,采用修正等高线法对南极伯德（Byrd）冰川ASTER GDEM V2进行了误差校正,并将其与ICESat-1 DEM的垂直精度进行了对比分析。结果表明：ASTER GDEM V2的RMSE由校正前的26.56 m下降到校正后的18.77 m,远低于ICESat-1 DEM的RMSE（121.24 m）;校正后的ASTER GDEM V2高程精度受坡度影响较小,不存在明显的系统误差,而ICESat-1 DEM的高程精度受坡度的影响较大。本研究进一步通过地形剖面分析得到：校正前的ASTER GDEM V2噪声主要分布于高程较低、地形平坦的区域,通过修正等高线的方法可以有效去除这些噪声,去除噪声后的ASTER GDEM V2可作为研究伯德冰川理想的DEM数据源。     

利用ASTER数据融合的SRTM空值填补方法

由于雷达干涉采集误差导致SRTM数据出现高程空值,为了保证数据完整性,需要对其进行空值修补。本文总结了国内外对SRTM数据空值的填补方法,提出了一种在内插的基础上利用ASTER数据进行数据融合的空值填补方法。该方法首先对ASTER数据进行预处理,以消除两者之间的高程差异,然后利用处理后的90m间隔ASTER数据对SRTM数据进行融合,从而实现了SRTM数据的空值填补。实例验证了该方法是一种获取完整地形数据的有效途径。     

利用ICESAT/GLAS激光测高数据评估SRTM数据精度

提出利用ICESAT卫星搭载的GLAS激光雷达数据进行SRTM数据的高程精度评价,选择青藏高原东北缘作为研究区,综合利用GIS空间分析方法,计算在不同高程分带和坡度分带上SRTM数据高程精度,并分析其变化规律。结果表明,ICESAT/GLAS与SRTM高程数据间存在明显的相关关系。该地区SRTM数据高程总体精度为5.3m,随着海拔升高,坡度的增大,高程精度呈降低的趋势。在祁连山高山区（4600m-4700m）误差达到12.3m,在40°-50°的坡度带上误差为18.9m,略高于SRTM的标称精度。     

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。     

Evaluation of digital elevation models for delineation of hydrological response units in a Himalayan watershed

This study reports results from evaluation of the quality of digital elevation model (DEM) from four sources viz. topographic map (1:50,000), Shuttle Radar Topographic Mission (SRTM) (90 m), optical stereo pair from ASTER (15 m) and CARTOSAT (2.5 m) and their use in derivation of hydrological response units (HRUs) in Sitla Rao watershed (North India). The HRUs were derived using water storage capacity and slope to produce surface runoff zones. The DEMs were evaluated on elevation accuracy and representation of morphometric features. The DEM derived from optical stereo pairs (ASTER and CARTOSAT) provided higher vertical accuracies than the SRTM and topographic map-based DEM. The SRTM with a coarse resolution of 90 m provided vertical accuracy but better morphometry compared to topographic map. The HRU maps derived from the fine resolution DEM (ASTER and CARTOSAT) were more detailed but did not provide much advantage for hydrological studies at the scale of Sitla Rao watershed (5800 ha).     

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.     

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.     

Evaluation of morphometric parameters derived from ASTER and SRTM DEM — A study on Bandihole sub-watershed basin in Karnataka

Morphometric parameters derived from three different sources viz., Survey of India topographic map (1:50,000), SRTM (Shuttle Radar Topographic Mission 90 m) and DEM derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer — 30 m) are evaluated to examine any difference within the results for the proper planning and management of the watersheds. Extracting drainage network from DEMs is mainly based on the flow of water from higher to lower elevation and steepest descent in a pixel. Common morphometric parameters are considered for analysis. The results show that the morphometric parameters derived from the SRTM and ASTER data provide good and satisfying results. The results will be more efficient when the DEM cell size is smaller or the resolution of the image is higher.     

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.     

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.     

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.