对ASTER GDEM数字高程模型的精度评价及修正

针对ASTER GDEM高程精度还未得到充分验证,以江西省莲花县为试验区,使用ICESat-2数据系统分析了ASTER GDEM在坡度、地形起伏度和土地利用类型中的误差分布。结果表明,ASTER GDEM受坡度、地形起伏度影响严重,随坡度、地形起伏度增加,GDEM误差呈上升趋势;对于不同土地利用类型,GDEM误差存在较大差异,在水域误差最大,在建设用地误差最小。最后,使用后向传播神经网络(BPNN)对莲花县ASTER GDEM修正,结果发现BPNN模型可以有效改善其高程精度。     

中国地区30 m分辨率SRTM质量评估

高分辨率、高质量地形数据有利于地震、火山与滑坡地质灾害等环境变化相关的研究。2014年9月,美国国家地理空间情报局宣布30 m分辨率的SRTM DEM数据逐步向全球用户免费开放。本文对中国境内最新发布的SRTM DEM开展了质量评估与验证工作,讨论了传感器波长、植被覆盖、影像数量等影响DEM质量的关键因素。研究结果表明,30 m分辨率的SRTM DEM高程精度（10 m）与SRTMX DEM相当,并优于ASTER GDEM v2、SRTM v4.1和SRTM v3。     

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 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。     

基于DEM的宁明县地形因子分析

以ASTER GDEM 30 m分辨率的DEM为基础数据,ArcGIS为软件平台,提取宁明县坡度、坡向、地形起伏度、地表粗糙度、地表切割深度等地形特征要素,并对地形特征进行定量分析.结果表明,县域内坡度在0°～81 °范围内均有分布,但相对集中于5°～ 35°之间;南坡、北坡坡向面积相差较小,分布相对均衡;西北部、南部地形起伏较大,最大处可达702 m;地表粗糙度处于0.15～1之间;地表切割深度最深处达到340.67 m.     

不同格网密度的DSM/DEM对影像分类精度的影响研究

在无控制点的卫星影像正射校正中,大多采用DSM/DEM数据作为辅助数据来消除或限制因地形起伏引起的形变,然而经不同格网密度的DSM/DEM正射校正后的影像对后续处理会产生不同程度的影响,如对地物分类精度产生影响。针对这一问题,本文分别采用不同的DSM/DEM数据(China DSM 15 m、ASTER GDEM 30 m和SRTM 90 m)对资源三号影像进行正射校正,然后对正射校正后影像利用支持向量机进行分类,比较正射校正后影像结果的分类精度。结果表明:在相同重采样方法下,影像经China DSM 15 m DSM正射校正后结果的分类精度优于ASTER GDEM 30 m DEM和SRTM 90 m DEM。     

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。     

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。     

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。     

多源DEM数据地形因子表达准确性分析

在不同分组条件下,以1∶10 000数字地形图构建的DEM作为参考,对由资源三号正后视立体像对构建的DEM、SRTM和ASTER GDEM提取的高程、坡度和坡长进行对比分析,结果表明:(1)资源三号数据构建的10 m分辨率DEM表现出最高数据质量,增加控制点个数及提高DEM生产的分辨率,并未提高数据质量;(2)在海拔1000~1100 m,分辨率30 m的SRTM和ASTER GDEMv2数据质量凸显绝对优势,但在海拔1100~1300 m及不同土地利用类型分组中,资源三号数据构建的10 m分辨率DEM数据质量更高;(3)建立了基于多组样本数据进行相似性评价的指标——SI指数,综合试验样本的均值和标准差定量化评价与参考样本数据的相似程度,可定量反映数据组之间的相似关系。     

利用车载动态PPP技术检核SRTM、ASTER GDEM和AW3D模型的高程精度

SRTM、ASTER GDEM和AW3D是比较有代表性的全球数字高程模型。本文探讨了采用车载动态PPP技术对上述3类模型的区域高程精度进行检核,首先沿广州至肇庆公路进行连续数据采集,采用动态精密单点定位（PPP）技术解算动态点的WGS-84坐标;然后利用EGM2008重力场模型和仪器高获得动态点的正常高;最后采用4种不同的插值方法对SRTM、ASTER GDEM和AW3D模型进行高程检验。检核结果显示：不同的插值方法具有较好的一致性,SRTM3 V4.1、ASTER GDEM V2、AW3D30的高程标准差分别优于3.4 m、4.1 m和3.3 m,均优于其全球标称高程精度;本文检核方法快速高效,有较好的适用性。     

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.     

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.     

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.     

利用弹性反馈神经网络融合ASTER GDEM和SRTM1高程数据

由于数据获取与后期处理方式不同,先进星载热发射和反射辐射仪全球数字高程模型(advanced spaceborne thermalemissionandreflectionradiometerglobaldigitalelevationmodel,ASTERGDEM)和航天飞机雷达地形测绘任务(shuttle radar topography mission,SRTM)数据在高程精度上存在差异,采用弹性反馈（resilient backpropagation,RProp）神经网络算法对二者进行融合处理,实现优势互补以提升高程精度。选取两个黄土丘陵沟壑地貌样区分别用于模型建立与效果验证,1∶10 000高程精度为参考数据,在建模样区应用RProp神经网络算法构建ASTER GDEM高程校正模型、SRTM1高程校正模型、ASTER GDEM与SRTM1高程融合模型,同时应用误差反向传播(back propagation,BP)神经网络建立ASTER GDEM与SRTM1高程融合模型,将这些模型的高程精度优化效果进行对比,并在验证样区检验RProp融合模型的可行性。结果表明,RProp融合模...     

Characterization of ASTER GDEM elevation data over vegetated area compared with lidar data

Current researches based on areal or spaceborne stereo images with very high resolutions (<1 m) have demonstrated that it is possible to derive vegetation height from stereo images. The second version of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) is the state-of-the-art global elevation data-set developed by stereo images. However, the resolution of ASTER stereo images (15 m) is much coarser than areal stereo images, and the ASTER GDEM is compiled products from stereo images acquired over 10 years. The forest disturbances as well as forest growth are inevitable in 10 years time span. In this study, the features of ASTER GDEM over vegetated areas under both flat and mountainous conditions were investigated by comparisons with lidar data. The factors possibly affecting the extraction of vegetation canopy height considered include (1) co-registration of DEMs; (2) spatial resolution of digital elevation models (DEMs); (3) spatial vegetation structure; and (4) terrain slope. The results show that the accurate coregistration between ASTER GDEM and national elevation dataset (NED) is necessary over mountainous areas. The correlation between ASTER GDEM minus NED and vegetation canopy height is improved from 0.328 to 0.43 by degrading resolutions from 1 arc-second to 5 arc-second and further improved to 0.6 if only homogenous vegetated areas were considered.     

露天矿区多源数字高程模型数据对比分析

针对数字高程模型数据源不同会带来一定的不确定性和差异性的问题,选取德国某露天矿为实验区,以高精度DEM数据TanDEM-X为参照,对比了SRTM、AW3D30、ASTER GDEM与TanDEM-X数据的高程精度,分析了DEM数据的差异.结果表明:(1)露天矿区的开采和复垦活动明显地体现在了不同时期获取的DEM高程变化...