低山丘陵区多源数字高程模型误差分析

作为多学科交叉与渗透产物的数字高程模型(DEM)已在诸多学科和领域及实际应用中发挥了重要作用,但目前能够免费获取的高分辨全球DEM在不同区域仍存在很大的不确定性,应用之前进行质量评估至关重要。以烟台市为实验区,以大比例尺地形图(1∶10 000)生成的DEM为参照,结合坡度、坡向和土地覆被类型等地学因子,定量分析了目前广泛应用的两个版本ASTER GDEM(先进星载热辐射和反射辐射计全球数字高程模型)ASTETR 1和ASTER 2及不同空间分辨率SRTM DEM(航天飞机雷达地形测绘任务)(SRTM 1:～30m和SRTM 3:～90m)在低山丘陵区高程、坡度及坡向误差。结果表明:在研究区域内,ASTER 1、ASTER 2、SRTM 3、SRTM 1总体高程均方根误差分别为8.7m、6.3m、3.7m和2.9m。ASTER与SRTM的高程精度不同程度地受坡度、坡向以及土地覆被类型等地学因子的影响,DEM误差随坡度增加而增大,其中SRTM 3精度对该因子最敏感。尽管坡向对DEM精度影响不明显(4种DEM在不同坡向上的均方根误差波动范围均不超过2m),但是不同土地覆被类型下这4种DEM精度差异显著。此外,分析4种DEM提取的坡度可知,SRTM 1的均方根坡度误差最低(2.5°)、ASTER 1与ASTER 2的坡度的均方根误差大致相同(3.6°、3.9°)、SRTM 3的坡度均方根误差最高(4.3°)。坡向的精度SRTM 1最高,ASTER 1与ASTER 2次之,SRTM 3最低。研究结果对我国低山丘陵区ASTER GDEM与SRTM DEM的应用与精度评估具有一定的借鉴作用。     

低山丘陵区多源数字高程模型误差分析北大核心CSCD

作为多学科交叉与渗透产物的数字高程模型(DEM)已在诸多学科和领域及实际应用中发挥了重要作用,但目前能够免费获取的高分辨全球DEM在不同区域仍存在很大的不确定性,应用之前进行质量评估至关重要。以烟台市为实验区,以大比例尺地形图(1∶10 000)生成的DEM为参照,结合坡度、坡向和土地覆被类型等地学因子,定量分析了目前广泛应用的两个版本ASTER GDEM(先进星载热辐射和反射辐射计全球数字高程模型)ASTETR 1和ASTER 2及不同空间分辨率SRTM DEM(航天飞机雷达地形测绘任务)(SRTM 1:～30m和SRTM 3:～90m)在低山丘陵区高程、坡度及坡向误差。结果表明:在研究区域内,ASTER 1、ASTER 2、SRTM 3、SRTM 1总体高程均方根误差分别为8.7m、6.3m、3.7m和2.9m。ASTER与SRTM的高程精度不同程度地受坡度、坡向以及土地覆被类型等地学因子的影响,DEM误差随坡度增加而增大,其中SRTM 3精度对该因子最敏感。尽管坡向对DEM精度影响不明显(4种DEM在不同坡向上的均方根误差波动范围均不超过2m),但是不同土地覆被类型下这4种DEM精度差异显著。此外,分析4种DEM提取的坡度可知,SRTM 1的均方根坡度误差最低(2.5°)、ASTER 1与ASTER 2的坡度的均方根误差大致相同(3.6°、3.9°)、SRTM 3的坡度均方根误差最高(4.3°)。坡向的精度SRTM 1最高,ASTER 1与ASTER 2次之,SRTM 3最低。研究结果对我国低山丘陵区ASTER GDEM与SRTM DEM的应用与精度评估具有一定的借鉴作用。     

基于无人机影像的山地人工林景观DEM构建

山地人工林景观的DEM构建是对景观地形信息进行描述的基础的研究内容,也是人工林景观面积、结构、蓄积量等信息提取的重要因子,具有重要的研究意义。通过无人机平台获取影像,采用立体像对拼接的方式生成正射影像并提取DEM信息,并与GPS测量数据、ASTER GDEM、SRTM数据进行比较分析。结果表明:在该区域无人机影像构建的DEM与实测高程差距最小(RMSE=8.96),具有比ASTER GDEM(RMSE=13.68)和SRTM(RMSE=11.81)更高的精度;在每个样方内的最大高程差值与最大树高最为接近(RMSE=1.813),说明无人机DEM能够反映出更多的冠层与地面分层信息,在山地人工林景观DEM构建中表现出较大潜力。     

多基线InSAR技术的震区DEM提取及精度分析

针对汶川特大地震发生后的震区地形严重破坏，原有的地形图及DEM数据不再具有时效性，不能准确地描述地质特征，亟待更新重建。收集了2007年8月至2010年7月的20景Envisat ASAR影像数据，采用多基线InSAR技术对研究区域DEM进行提取，并对生成的ASAR DEM与ASTER GDEM和SRTM DEM进行了比较分析。实验结果表明，由于时效性原因，ASTER GDEM和SRTM DEM不能较好反映震后地面高程变化情况；所提取的ASAR DEM能有效弥补震后灾区DEM不足，在一些植被较少和地质稳定区域，ASAR DEM有着较高的精度，多基线InSAR技术提取方法为震后形变区域DEM提取提供了一个很好的途径。     

顾及数据配准的ALOS和SRTM1精度修正北大核心CSCD

目前,ICESat/GLAS是大尺度GDEM(global public digital elevation model)精度评价和修正的主要数据源。然而,现有的修正方法均忽略了两组数据之间的有效配准。为此,选取江西省作为研究区域,以SRTM1和ALOS作为研究对象,使用随机森林(random forest,RF)对配准前、配准后SRTM1和ALOS修正,以验证数据配准对GDEM修正的重要性。研究结果表明:ICESat/GLAS与SRTM1之间不存在位置偏差,而与ALOS之间存在明显的位置偏差;和配准前相比,配准后ALOS的系统误差基本消除,中误差也降低了10.0%,证明数据配准对ALOS精度有重要影响。RF方法可以显著提高SRTM1和ALOS的精度,但配准后ALOS修正精度明显优于配准前,其精度多提高了21.5%,再次证明数据配准对ALOS修正影响较大。和原始DEM相比,修正后SRTM1和ALOS的平均误差均接近于0 m,平均绝对误差和中误差也分别降低了11.9%、37.0%和15.1%、29.2%。     

多源外部DEM对两轨DINSAR测量结果影响分析

在总结两轨差分中参考DEM影响的最新研究成果基础上,以青藏高原上典型平地和山地作为研究区,利用理论上没有形变的ERS Tandem像对以及3种常用外部参考DEM（SRTM,ASTER GDEM,1∶5万DEM）,使用ROI_PAC软件进行两轨差分干涉试验。实例证明：SRTM更适合作为两轨差分中的外部参考DEM,并对此试验结果予以解释分析,即多源DEM数据质量的差异导致干涉图与DEM配准精度的不同,并最终反映在差分干涉相位误差中。本文研究结论对提高DInSAR处理精度有参考价值。     

ASTER-GDEM与SRTM3数据质量精度对比分析

ASTER-GDEM是最新的全球数字高程模型,具有较高的分辨率;SRTM3是目前研究人员广泛应用的地形数据。结合实测数据、矢量化地形图,从高程精度、位置精度和坡度因子3方面对比分析两种数据精度。结果表明:ASTER-GDEM数据高程精度低于SRTM3数据,而水平位置精度较高,二者各级坡度百分率近似,与国家1∶5万数字地形图生成的DEM差异较大。     

利用共线方程的ALOS DEM制作误差分析

卫星遥感是获取DEM数据的重要手段,定量化和降低DEM数据误差是应用DEM数据的前提。在共线方程理论的基础上,模拟分析了DEM数据精度与内外方位元素之间的定量关系,以覆盖鄱阳湖地区的ALOS PRISM立体像对为研究数据,根据SRTM数据计算DEM误差,并求解影像内外方位元素误差。研究表明:影像角度误差是影响DEM误差的主要因素。消除角度元素误差后,DEM数据误差的均值由4.4m降为0.2m,标准差由7.7m降为2.7m。     

多源外部DEM对两轨DINSAR测量结果影响分析

在总结两轨差分中参考DEM影响的最新研究成果基础上,以青藏高原上典型平地和山地作为研究区,利用理论上没有形变的ERS Tandem像对以及3种常用外部参考DEM(SRTM,ASTER GDEM,1:5万DEM),使用ROI_PAC软件进行两轨差分干涉试验.实例证明:SRTM更适合作为两轨差分中的外部参考DEM,并对此试验结果予以解释分析,即多源DEM数据质量的差异导致干涉图与DEM配准精度的不同,并最终反映在差分干涉相位误差中.本文研究结论对提高DInSAR处理精度有参考价值.     

基于ASTER遥感立体像对的DEM提取

卫星遥感立体像对提取DEM是地貌信息获取的一个重要里程碑,ASTER卫星传感器是可以拍摄立体像对传感器中的代表,具有数据质量稳定、覆盖广泛、价格低廉的特点。本文通过实例研究了ASTER立体像对在高山峡谷地区提取DEM的精度。首先简述ASTER的立体像对提取DEM的国内外发展现状,然后针对一处高程变化显著地区在1:10万比例尺地形图采集地面控制点(GCP),用1:5万精度的DEM作检验,获得GCP范围内高程误差为±20.4m,GCP范围外高程误差为±48.2m,平均误差是±34.3m。这证明可以在小区域内选取GCP控制点,由ASTER立体像大范围外推生成大范围DEM,而且采用常规的技术手段和普通的商业软件就可实现。该方法提取DEM对于我国地形资料缺乏的西部地区有很强的实用性。     

Accuracy assessment of the ASTER GDEM and SRTM3 DEM: an example in the Loess Plateau and North China Plain of China

The digital elevation model (DEM) produced by the Shuttle Radar Topographic Mission (SRTM) has provided important fundamental data for topographic analysis in many fields. The recently released global digital elevation model (GDEM) produced by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has higher spatial resolution and wider coverage than the SRTM3 DEM, and thus may be of more value to researchers. Taking two typical study areas—the Loess Plateau and the North China Plain of China—as an example, this article assesses the accuracy of the SRTM3 DEM and ASTER GDEM by collecting ground control points from topographical maps. It is found that both the SRTM3 DEM and the ASTER GDEM are far more accurate for the North China Plain than for the Loess Plateau. For the Loess Plateau, the accuracy of the ASTER GDEM is similar to that of the SRTM3 DEM; whereas for the North China Plain, it is much worse than that of the SRTM3 DEM. Considering the negative bias of the ASTER GDEM for flat or gentle regions, we improve its accuracy by adding the difference of the mean value between the SRTM3 DEM and ASTER GDEM for the North China Plain; then, the root mean square error (RMSE) of ±7.95 m from the original ASTER GDEM is improved to ±5.26 m, which demonstrates that it is a simple but useful way to improve the accuracy of the ASTER GDEM in flat or gentle regions.     

Evaluation of ASTER GDEM using GPS benchmarks and SRTM in China

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) has generated one of the most complete high-resolution digital topographic data sets of the world to date. The ASTER GDEM covers land surfaces between 83° N and 83° S at a spatial resolution of 1 arc-second (approximately 30 m at the equator). As an improvement over Shuttle Radar Topography Mission (SRTM) coverage, the ASTER GDEM will be a very useful product for many applications, such as relief analysis, hydrological studies, and radar interferometry. In this article, its absolute vertical accuracy in China was assessed at five study sites using ground control points (GCPs) from high-accuracy GPS benchmarks and also using a DEM-to-DEM comparison with the Consultative Group on International Agriculture Research Consortium for Spatial Information (CGIAR-CSI) SRTM DEM Version 4.1. It is demonstrated that the vertical accuracy of ASTER GDEM is 26 m (root mean square error (RMSE)) against GPS-GCPs, while for the SRTM DEM it is 23 m. Furthermore, height differences in the GDEM-SRTM comparison appear to be overestimated in the areas with a south or southwest aspect in the five study areas. To a certain extent, the error can be attributed to variations in heights due to land-cover effects and undefined inland waterbodies. But the ASTER GDEM needs further error-mitigating improvements to meet the expected accuracy specification. However, as for its unprecedented detail, it is believed that the ASTER GDEM offers a major alternative in accessibility to high-quality elevation data.     

Tectonic Interpretation and Analysis based on Multi-source Remote Sensing Data：A Case Study of JitaiRiver in Chayi，Tibet

Linear Structures and Ring Structures are of great important to distinguish and analyze faults,folds and magmatic emplacement on the surface.Extracting linear structures from multi-source remote sensing data with the approach of Human-Computer-Interaction can understand the overall and individual geometrical characteristics of Linear Structures and Ring Structures objectively and comprehensively.Taking Jitai river as an example,three sets of Linear Structures with characteristic of clustering and abundant Ring Structures were extracted in working area based on remote sensing data from Google Earth,Landsat 8/OLI,ASTER GDEM and high-resolution DEM.The results of the analysis show that the working area is in a dextral shear zone with northwest direction and the southwest structure of Jitai River is still in a relatively active stage,which may be an unstable area of the engineering geology and the prone areas of geological disasters.     

A regional ASTER GDEM error model for the Chinese Loess Plateau

Accuracy of the global ASTER GDEM (Advanced Space-borne Thermal Emission and Reflection Radiometer Global Digital Elevation Model) version 2 (v2) elevation data product is highly variable regionally, as are its empirical correlations with landscape variables. This paper investigates GDEM error along a 49-site geomorphologic gradient within the core region of the Chinese Loess Plateau, notable for its heterogeneous terrain. The error is modelled using its associations with MODIS (Moderate Resolution Imaging Spectroradiometer) composite forest cover percentage, GlobeLand30 land cover, and key elevation derivatives, including two indices, terrain roughness index (TRI) and topographic position index (TPI), not previously evaluated in GDEM accuracy studies. Overall root mean squared error (RMSE) is 20.33 m, in excess of the GDEM v2 accuracy specifications, while RMSE at each site varies substantially, from 10.67 m for a low relief area to 21.84 m for the most rugged site. Strong associations between covariates, especially slope, aspect, TRI, and forest cover are identified. A regression model using these variables is developed to formally characterize and predict GDEM error. External validation with independent checkpoints across all sites demonstrates that this model can reduce mean error by about 4 m.     

埃塞俄比亚西部岩浆熔离型铁矿遥感找矿模型

为解决在地质资料匮乏的情况下发挥多源遥感数据优势开展地质找矿研究这一科学问题,以非洲埃塞俄比亚西部研究区为例,选取ASTER及ALOS PALSAR单极化雷达数据为数据源,针对研究区重点类型铁矿--岩浆熔离型铁矿建立了其遥感找矿模型,提取与成矿作用相关的蚀变、构造、岩体信息。研究基于ASTER遥感数据,通过比值、主成分方法进行蚀变信息提取研究,并基于ASTER及ALOS PALSAR单极化雷达融合数据进行控矿构造及赋矿岩体信息提取研究。最终通过综合分析,基于ArcGIS平台圈定了3处遥感找矿靶区。研究结果与1∶25万地球化学数据对比,具有很高一致性,表明遥感找矿模型能够快速、准确地进行找矿靶区预测。     

基于数字高程模型的古滑坡区提取

针对古滑坡的滑前影像无法获得,植被、纹理信息都已恢复,无法通过对比分析滑坡滑动前后的植被、纹理等信息的变化来提取滑坡区域的问题,提出了一种新的基于数字高程模型的滑坡区域范围提取方法。该方法基于简化的滑坡体模型及特征分析,对滑坡区进行水流方向、坡度、山脊山谷线提取,通过流域分析获取滑坡区域范围;利用坡度图实现滑坡壁与滑坡体提取。实验利用全球30m分辨率ASTER GDEM数据,提取了四川理县3个古滑坡体区域范围,验证了该方法的有效性。     

Geomorphological changes associated with underground coal mining in the Fushun area,northeast China revealed by multitemporal satellite remote sensing data

Fushun is a famous coal-mining city in northeastern China with more than 100 years of history. Long-term underground coal mining has caused serious surface subsidence in the eastern part of the city. In this study, multitemporal and multisource satellite remote sensing data were used to detect subsidence and geomorphological changes associated with underground coal mining over a 10-year period (1996–2006). A digital elevation model (DEM) was generated through Synthetic Aperture Radar (SAR) interferometry processing using data from a pair of European Remote Sensing Satellite (ERS) SAR images acquired in 1996. In addition, a Shuttle Radar Topography Mission (SRTM) DEM obtained from data in 2000 and an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM from 2006 were used for this study. The multitemporal DEMs indicated that the maximum vertical displacement due to subsidence was around 13 m from 1996 to 2006. Multitemporal ASTER images showed that the flooded water area associated with subsidence had increased by 1.73 km² over the same time period. Field investigations and ground level measurements confirmed that the results obtained from the multitemporal remote sensing data agreed well with ground truth data. This study demonstrates that DEMs derived from multisource satellite remote sensing data can provide a powerful tool to map geomorphological changes associated with underground mining activities.     

Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping

The aim of this study is to extract landslide-related factors from remote-sensing data, such as Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery, and to examine their applicability to landslide susceptibility near Boun, Korea, using a geographic information system (GIS). Landslide was mapped from interpretation of aerial photographs and field surveying. Factors that influence landslide occurrence were extracted from ASTER imagery. The slope, aspect and curvature were calculated from the digital elevation model (DEM) with 25.77 m root mean square error (RMSE), which was derived from ASTER imagery. Lineaments, land-cover and normalized difference vegetation index (NDVI) layers were also estimated from ASTER imagery. Landslide-susceptible areas were analysed and mapped using the occurrence factors by a frequency ratio and logistic regression model. Validation results were 84.78% in frequency ratio and 84.20% in logistic regression prediction accuracy for the susceptibility map with respect to ground-truth data.