中国区域SRTM DEM与ASTER GDEM误差空间分布特征

SRTM DEM和ASTER GDEM数据作为目前全球最完整的高精度地形数据,自发布以来,就一直是全球范围内应用最为广泛的DEM数据,其精度评价问题也是历来研究的焦点。但目前研究多集中在中小区域或典型地形区,且多以DEM高程数据精度分析为主,缺乏误差空间分布规律的探究。该文以中国大陆为研究区域,以GLA14点数据为参考高程,研究两类DEM的误差统计特征和空间分布特征。研究表明:SRTM DEM和ASTER GDEM在我国的精度稍优于标称精度,但整体上存在约1m的系统性误差;SRTM DEM误差在平坦区域呈菱形分布,而在地势复杂区域,误差与地形具有较强的相关性;ASTER GDEM误差呈明显的条带状分布且误差正负值间隔分布,条带方向为东北-西南方向,与SRTM DEM相比,ASTER GDEM受地形影响较小。研究结论对两类DEM的应用、融合等研究有着一定的参考价值。     

DEM的不确定性对温度插值精度的影响

以2001-2010年全国711个气象站点温度观测数据为数据源,在薄板样条插值方法和ANUSPLIN软件支持下,对比分析了使用GTOP30、SRTM3和ASTER GDEM三种DEM数据作为协变量开展空间插值得到的温度成果数据精度。研究发现:1)GDEM的平均相对误差(MRE)、平均绝对误差(MAE)和均方根误差(RMSE)明显高于其他两套数据,数据质量最差;SRTM3和GTOP30的数据精度较高,各误差评估指标大致相同。2)在全国尺度上,在MAE方面,基于ASTER GDEM所得温度插值结果比其他两组DEM得到的温度插值结果高0.4℃左右;在RMSE方面,基于ASTER GDEM获得的温度比其他两组DEM得到的温度高0.5℃左右。基于ASTER GDEM所得温度插值数据的误差明显高于其他两套数据。3)在温度插值误差的空间分布格局上,在我国中东部地区,三种DEM温度插值误差分布规律基本相同;在我国西南部地区,基于GTOP30和SRTM3的温度插值结果的数据精度明显好于基于ASTER GDEM的温度插值结果。4)温度插值误差与DEM高程误差呈现明显的耦合特性,这表明DEM数据的精度是影响温度插值结果的重要因素。     

高山地区多源地面高程数据误差分析

地面高程数据作为诸多科研领域都需要的一种数据源,其重要性不言而喻,但就目前国内外能够免费获取到的数字高程数据来说,在数据误差方面仍存在很大不足,特别是在高山地区,误差更为明显。以中国1∶50 000比例尺DEM为参考数据,结合坡度,坡向,剖面,地形起伏度等地学因子,对目前国内外常用的3种数字高程模型(SRTM,ASTER GDEM,ICESatGLAS14)在高山地区的误差情况进行较为详细地对比分析。结果表明:坡度和地形起伏度对于3种高程数据的精度都有不同程度的影响,其中,SRTM数据受到影响最为严重,ASTER的绝对精度较差,但在地形起伏较大区域,其精度要高于SRTM,ICESat GLAS14整体情况表现良好。     

数字高程模型地形描述误差的量化模拟--以黄土丘陵沟壑区的实验为例

本研究以陕北黄土高原丘陵沟壑区为试验样区 ,综合运用统计分析及比较分析的原理与方法 ,通过数学模拟、对比分析、误差可视化分析 ,研究不同空间尺度下DEM地形描述误差的数学转换模型。实验结果证明 ,目前适用于欧洲地区的地形描述误差的模拟方程EtD=q·a·(EtD′) b,经过参数的修正 ,在我国黄土高原丘陵沟壑区具有较高的模拟精度。该模型的建立可以实现应用低空间分辨率的DEM数据在栅格水平模拟高空间分辨率DEM所提取的地形描述误差 ,从而 ,在一定精度范围内大大提高Et的计算速度和计算效率。并且 ,对我国其它地貌类型区类似模型的建立 ,都具有重要的参考意义     

坡度和粗糙度对ICESat-GLAS回波特征及其光斑脚点高程误差的影响研究

为评价ICESat-GLAS数据在青藏高原地区冰川表面高程变化中的不确定性,采用GLA01产品和GLA14产品,基于SRTM DEM数据所派生的冰川区和非冰川区表面坡度、粗糙度数据,利用多元线性回归关系模型分析了表面粗糙度、坡度对GLAS光斑脚点高程的不确定误差。结果表明:当GLAS光斑脚点坡度小于10°时,GLAS光斑脚点高程偏差为0.1m,且高程偏差随着坡度的增加而增大。在受坡度和粗糙度影响复杂地形下,GLAS光斑的回波展宽较大,不确定性偏差为0.3m。此外,冰川区GLAS光斑脚点高程误差可通过高斯滤波进行展宽偏差校正,但当冰川表面坡度大于45°时,相应的GLAS光斑脚点高程在冰川表面高程变化估算中不可用。在后续的研究中,需考虑冰川运动、GLAS回波在冰雪表面渗透对冰川DEM高程精度以及GLAS光斑脚点水平位移的高程偏差影响。     

基于DEM的山区气温地形修正模型——以陕西省耀县为例

提出基于DEM的山区气温地形修正模型,以具有多种地貌类型的陕西省耀县为实验样区,以DEM模拟的坡面与平面太阳总辐射量为地形调节因子,实现对传统山区温度空间推算模型的改进,并与TM6热波段反映的地表温度进行了对比验证。实验结果显示,该方法能够较为精细地刻画山区局地温度随地形的空间分异规律,一定程度上提高了山区地面温度推算的精度。     

基于DEM的黄土高原面积高程积分研究

面积高程积分(Hypsometric Integral,HI)是通过统计流域地表的高程组合信息,从而揭示流域地貌形态与发育特征的重要指标。本文以1:10000比例尺5 m分辨率DEM数据分析流域面积高程积分计算时的影响因素,以SRTM数字高程模型数据为基本信息源,研究黄土高原重点水土流失区的面积高程积分空间分异特征。研究工作首先讨论并总结了面积高程积分的地学含义,明确了DEM分辨率以及分析面积对于面积高程积分计算的影响,并分析各地貌对象面积高程积分的相关性;然后,面向黄土高原重点水土流失区,采用面向多尺度分割的方法,基于小流域面积高程积分,实现了黄土高原重点水土流失区地貌分区。研究结果表明,DEM分辨率对于小流域面积高程积分计算影响较小,当小流域面积阈值达到10 km2时,面积高程积分趋于稳定;各地貌对象中,流域面—正地形—沟沿线、山顶点—山脊线—流域边界这两组组内面积高程积分值相关性非常强;基于面积高程积分的地貌分区,与黄土高原地区水土流失分区图和输沙模数分区图具有相当程度的耦合关系,并细化了原有分区结果。     

数字地形模型数据产品特点 与评估分析

数字高程模型是支撑地理过程模型研究的重要基础数据。DEM数据获取、加工及压缩精度直接影响地学相关模型及地理过程的模拟效果和研究质量。在分析比较当今国际地学界应用DEM数据源的基础上，我们采用绝对差值法和三维剖面法对国家基础地理信息中心生产的基于1：50,000扫描地形图DEM产品(NFGIS_DEM)、GTOPO30_DEM数据及基于SRTM数据生产的DEM数据集进行了精度检验及评估。同时分析了影响由SRTM数据所生产的全球范围DEM数据集精度与数据质量。研究结果表明30-90m分辨率的SRTM_DEM产品在精度和数据质量控制方面远优于GTOPO30_DEM数据，与NFGIS_DEM数据质量相当。大范围乃至全球范围的30m分辨率SRTM_DEM产品，将极大促进基于DEM数据的地理过程模型的研究，尤其会大幅提升对DEM空间分辨率较敏感的地学相关研究能力。     

DEM地面形态重构方法的精度差异特征研究

依据DEM地形建模过程,阐述了DEM地面形态重构概念,并以黄土丘陵1∶5万DEM数据为例,采用高程数值误差场和局地坡面形态误差场相结合的分析技术,实验分析了双线性、三次卷积、局部二次多项式、规则样条函数等常用插值方法在基于加密格网的DEM地面形态重构过程中的精度差异,以及DEM地面形态可重构的基本条件等问题。研究表明:在高程数值误差的极值大小、离散程度、空间分布等方面,规则样条函数插值法具有最好的地面形态重构精度,其次为局部二次多项式、三次卷积和双线性插值法;对于已确定综合尺度的地形,存在满足高保真地面形态描述要求的最佳DEM格网分辨率阈值,只有当实际DEM格网分辨率等于或高于该阈值时,才有可能无歧义、高精度地重构出DEM所描述的综合地形的地面形态。     

联合GPS和SARAL卫星测高数据构建南极Dome A区域DEM

南极冰盖数字高程模型(digital elevation model,DEM)对南极环境变化和地形研究具有重要作用,利用GPS实测数据和卫星测高数据建立DEM是构建南极冰盖表面DEM的重要方法。考虑到实测GPS数据的精度较高,而卫星测高的空间分辨率占优,本文探讨综合利用这两种数据构建南极Dome A区域DEM。法国国家空间研究中心和印度空间研究组织共同研制的SARAL卫星是Envisat的后续卫星,搭载的Alti Ka雷达高度计首次采用了Ka波段,可以极大减小电离层的影响,提高测距精度和卫星数据的空间分辨率。本文首先利用中国南极第29次科学考察在Dome A区域的实测GPS数据对SARAL数据进行精度评定,然后利用实测GPS数据对SARAL测高数据进行高程修正,联合GPS数据获取得到了Dome A区域300 m分辨率的DEM。结果表明SARAL的高程精度为0.615 m,而联合GPS数据能改善DEM精度,提高到0.261 m。     

无人机影像三维重建在沙丘形态监测中的应用

沙丘形态监测对开展土地沙化防治工作具有重大意义。应用无人机,分别在2016年7月、2017年3月、7月和9月对青海湖克土沙区4个沙丘进行影像采集,借助PhotoScan软件进行三维重建生成高分辨率的沙丘正射影像和DEM数据,使用ArcGIS软件获取沙丘形态参数并分析变化。实验结果表明：基于无人机影像监测沙丘形态,具有可行性和准确性,正射影像精度较高,DEM数据通过校正后可正确反映沙丘形态,为逐天高精度监测变化提供了可能。     

MODIS大气廓线产品在青海省空气温度遥感估算中的应用研究

气温是反映生态环境的重要参数之一,准确估算气温的时空分布对于气候变化研究具有重要意义。论文基于2011—2019年青海省气温实测数据、MODIS产品和SRTM DEM数据,在像元尺度分别开展了晴天条件和有云条件下瞬时空气温度的遥感估算研究,并评价了不同气温估算方法的精度差异,进而通过多元回归模型生成研究区高精度月空气温度产品,对青海省气温的时空分布格局进行分析。研究结果表明,在未使用气温实测数据进行校准的情况下,通过将MOD07_L2大气廓线产品反演的空气温度与MOD06_L2地表温度平均的方法,能够显著提高气温的估算精度。晴天条件下相关系数(r)为0.93,均方根误差(RMSE)为4.71 ℃;有云条件下r为0.89,RMSE为5.16 ℃。在使用气温观测值进行校准的情况下,通过引入高程参数,多元回归模型月尺度空气温度估算的决定系数(R²)和RMSE总体分别保持在0.8以上和2.5 ℃以下。将上述回归模型应用到栅格尺度,从而生成整个青海省高精度卫星过境时刻的逐月气温产品,进而分析其时空分布格局。具体来说,青海省月最高气温出现在7月,全省平均气温为13.59 ℃,最低气温出现在1月,全省平均气温为-9.44 ℃;气温的空间分布主要受海拔控制,全省平均气温直减率为4 ℃/km。上述研究表明MODIS大气廓线产品在全天气气温估算方面具有独特优势,特别是在地面气温实测数据的支持下能够有效降低遥感估算的系统性误差,实现大尺度复杂地形条件下气温的高精度估算。     

基于DEM的中国地形起伏度适宜计算尺度研究

基于SRTM和ASTER DEM数据,在全国范围内选取13个实验区,在渐变尺度下计算平均起伏度变化曲线的"突变点",据此确定中国地形起伏度的适宜计算尺度;结合山地界定标准计算各实验区山地面积,并采用人工解译的山地范围对计算结果进行检验。研究结果表明:1)地形起伏度适宜计算尺度与所采用的DEM数据有关,DEM分辨率越小,地形起伏度适宜计算尺度越大;2)针对同一分辨率DEM数据,中国境内的地形起伏度适宜计算尺度随地貌特征变化而变化,但总体变化幅度不大;3)针对SRTM和ASTER DEM两种常用数据源,分别选择4.72km2和3.20km2作为地形起伏度适宜计算尺度是合理的,山地界定精度达90%以上。     

The impact of DEM data source on prediction of flooding and erosion risk due to sea-level rise

Digital elevation model (DEM) elevation accuracy and spatial resolution are typically considered before a given DEM is used for the assessment of coastal flooding, sea-level rise or erosion risk. However, limitations of DEMs arising from their original data source can often be overlooked during DEM selection. Global elevation error statistics provided by DEM data suppliers can provide a useful indicator of actual DEM error, but these statistics can understate elevation errors occurring outside of idealised ground reference areas. The characteristic limitations of a range of DEM sources that may be used for the assessment of coastal inundation and erosion risk are tested using high-resolution photogrammetric, low- and medium-resolution global positioning system (GPS)-derived and very high-resolution terrestrial laser scanning point data sets. Errors detected in a high-resolution photogrammetric DEM are found to be substantially beyond quoted error, demonstrating the degree to which quoted DEM accuracy can understate local DEM error and highlighting the extent to which spatial resolution can fail to provide a reliable indicator of DEM accuracy. Superior accuracies and inundation prediction results are achieved based on much lower-resolution GPS points confirming conclusions drawn in the case of the photogrammetric DEM data. This suggests a scope for the use of GPS-derived DEMs in preference to the photogrammetric DEM data in large-scale risk-mapping studies. DEM accuracies and superior representation of micro-topography achieved using high-resolution terrestrial laser scan data confirm its advantages for the prediction of subtle inundation and erosion risk. However, the requirement for data fusion of GPS to remove ground-vegetation error highlighted limitations for the use of side-scan laser scan data in densely vegetated areas.     

顾及风向和风速的气温空间插值方法

当前气温插值方法多将高程、经纬度等作为影响因素,无法解决风向、风速对气温空间分布的影响问题。该文提出一种顾及风向和风速的气温空间插值方法:1)根据气象观测站点的风向、风速数据进行插值,生成连续的风场表面;2)基于该风场数据利用高斯扩散模型构建成本表面;3)利用Dijkstra最短路径算法计算观测点与待求点的累计移动成本最短路径(SPOCMC);4)将SPOCMC作为协变量进行薄盘光滑样条插值以实现气温插值。为验证该方法的有效性和可靠性,选取山东省109个气象观测点样本数据,以SPOCMC、DEM和SPOCMC-DEM分别作为协变量对气温数据进行薄盘光滑样条插值,结果显示:SPOCMC-DEM法的MAE和RMSE均值(分别为0.517、0.779)略低于SPOCMC法(0.583、1.016),显著低于DEM法(0.809、1.231),表明添加SPOCMC作为协变量可有效提高气温空间插值的准确性。在江苏省与贵州省的插值实验结果证明了该方法的普适性。     

Impacts of DEM resolution,source, and resampling technique on SWAT-simulated streamflow

The sensitivity of streamflow simulated with the Soil and Water Assessment Tool (SWAT) model to Digital Elevation Model (DEM) resolution, DEM source and DEM resampling technique is still poorly understood. The objective of this study is to compare SWAT model streamflow estimates in the Johor River Basin (JRB), Malaysia for DEMs differing in resolution (from 20 to 1500 m), sources (Shuttle Radar Topography Mission: SRTM v4.1, Advanced Space-borne Thermal Emission and Reflection Radiometer: ASTER GDEM2, EarthEnv-DEM90 and Global Multi-resolution Terrain Elevation Data 2010: GMTED2010) and resampling technique (nearest neighbour, bilinear interpolation, cubic convolution and majority). The key findings were as follows: (1) SRTM v4.1 (Root Mean Square Error (RMSE) = 11.16 m) and EarthEnv-DEM90 (RMSE = 12.4 m) had better vertical accuracy over the JRB compared to the ASTER GDEM2 (RMSE = 16.95 m); (2) Accurate annual streamflow simulations were obtained by using nearly all of the DEM resolutions, as pointed out by a relative error (RE) lower than 7% from 20 to 50 m and from 100 to 800 m DEMs; (3) Prediction errors were the lowest for ASTER GDEM2 (RE = 3.9%), followed by SRTM v4.1 (RE = 5.4%), EarthEnv-DEM90 (RE = 6.3%), and GMTED2010 (RE = 7.3%); (4) the majority and nearest neighbour resampling techniques performed the best (RE of 6.0%), followed by bilinear interpolation (RE of 7.2%) and cubic convolution (7.5%). The study indicates that DEM resolution is the most sensitive SWAT model DEM parameter compared to DEM source and DEM resampling technique for streamflow simulation within SWAT.     

Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature

An iterative procedure that implements the classification of continuous topography as a problem in digital image-processing automatically divides an area into categories of surface form; three taxonomic criteria–slope gradient, local convexity, and surface texture–are calculated from a square-grid digital elevation model (DEM). The sequence of programmed operations combines twofold-partitioned maps of the three variables converted to greyscale images, using the mean of each variable as the dividing threshold. To subdivide increasingly subtle topography, grid cells sloping at less than mean gradient of the input DEM are classified by designating mean values of successively lower-sloping subsets of the study area (nested means) as taxonomic thresholds, thereby increasing the number of output categories from the minimum 8 to 12 or 16. Program output is exemplified by 16 topographic types for the world at 1-km spatial resolution (SRTM30 data), the Japanese Islands at 270 m, and part of Hokkaido at 55 m. Because the procedure is unsupervised and reflects frequency distributions of the input variables rather than pre-set criteria, the resulting classes are undefined and must be calibrated empirically by subsequent analysis. Maps of the example classifications reflect physiographic regions, geological structure, and landform as well as slope materials and processes; fine-textured terrain categories tend to correlate with erosional topography or older surfaces, coarse-textured classes with areas of little dissection. In Japan the resulting classes approximate landform types mapped from airphoto analysis, while in the Americas they create map patterns resembling Hammond's terrain types or surface-form classes; SRTM30 output for the United States compares favorably with Fenneman's physical divisions. Experiments are suggested for further developing the method; the Arc/Info AML and the map of terrain classes for the world are available as online downloads.     

基于不同算法等高线曲率的提取与分析

等高线曲率是一个重要的地形属性，反应了地形表面在水平方向的凹凸性，表达了地表物质运动的发散和汇合模式。基于安塞县县南沟小流域的矢量等高线数据和DEM，分别利用圆拟合算法（相邻三点法、间隔三点法和最小二乘法）和曲面拟合模型（E模型、S模型和Z模型）提取等高线曲率，通过对实地地形的对比分析，结果表明：（1） 在矢量等高线数据的计算结果中，三点拟合法计算结果相比最小二乘法结果差异大，对等高线曲率空间格局分布描述更准确；（2） 最小二乘法计算的结果频数分布集中程度最高，两种三点拟合法计算结果频数曲线差别微小；（3） 在栅格数字高程模型的计算结果中，基于S模型计算结果在空间格局上较E模型和Z模型的结果差别大，基于E模型的计算结果对等高线曲率描述效果更好。结果能准确说明采用不同算法计算等高线曲率的差别，对在实际数字地形分析中有重要的意义，可为等高线曲率计算提供重要参考。     

Exploring the sensitivity of coastal inundation modelling to DEM vertical error

As sea level is projected to rise throughout the twenty-first century due to climate change, there is a need to ensure that sea level rise (SLR) models accurately and defensibly represent future flood inundation levels to allow for effective coastal zone management. Digital elevation models (DEMs) are integral to SLR modelling, but are subject to error, including in their vertical resolution. Error in DEMs leads to uncertainty in the output of SLR inundation models, which if not considered, may result in poor coastal management decisions. However, DEM error is not usually described in detail by DEM suppliers; commonly only the RMSE is reported. This research explores the impact of stated vertical error in delineating zones of inundation in two locations along the Devon, United Kingdom, coastline (Exe and Otter Estuaries). We explore the consequences of needing to make assumptions about the distribution of error in the absence of detailed error data using a 1 m, publically available composite DEM with a maximum RMSE of 0.15 m, typical of recent LiDAR-derived DEMs. We compare uncertainty using two methods (i) the NOAA inundation uncertainty mapping method which assumes a normal distribution of error and (ii) a hydrologically correct bathtub method where the DEM is uniformly perturbed between the upper and lower bounds of a 95% linear error in 500 Monte Carlo Simulations (HBM+MCS). The NOAA method produced a broader zone of uncertainty (an increase of 134.9% on the HBM+MCS method), which is particularly evident in the flatter topography of the upper estuaries. The HBM+MCS method generates a narrower band of uncertainty for these flatter areas, but very similar extents where shorelines are steeper. The differences in inundation extents produced by the methods relate to a number of underpinning assumptions, and particularly, how the stated RMSE is interpreted and used to represent error in a practical sense. Unlike the NOAA method, the HBM+MCS model is computationally intensive, depending on the areas under consideration and the number of iterations. We therefore used the HBM+ MCS method to derive a regression relationship between elevation and inundation probability for the Exe Estuary. We then apply this to the adjacent Otter Estuary and show that it can defensibly reproduce zones of inundation uncertainty, avoiding the computationally intensive step of the HBM+MCS. The equation-derived zone of uncertainty was 112.1% larger than the HBM+MCS method, compared to the NOAA method which produced an uncertain area 423.9% larger. Each approach has advantages and disadvantages and requires value judgements to be made. Their use underscores the need for transparency in assumptions and communications of outputs. We urge DEM publishers to move beyond provision of a generalised RMSE and provide more detailed estimates of spatial error and complete metadata, including locations of ground control points and associated land cover.