Estimating vertical error of SRTM and map-based DEMs using ICESat altimetry data in the eastern Tibetan Plateau

The Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud and land Elevation Satellite (ICESat) provides elevation data with very high accuracy which can be used as ground data to evaluate the vertical accuracy of an existing Digital Elevation Model (DEM). In this article, we examine the differences between ICESat elevation data (from the 1064 nm channel) and Shuttle Radar Topography Mission (SRTM) DEM of 3 arcsec resolution (90 m) and map-based DEMs in the Qinghai-Tibet (or Tibetan) Plateau, China. Both DEMs are linearly correlated with ICESat elevation for different land covers and the SRTM DEM shows a stronger correlation with ICESat elevations than the map-based DEM on all land-cover types. The statistics indicate that land cover, surface slope and roughness influence the vertical accuracy of the two DEMs. The standard deviation of the elevation differences between the two DEMs and the ICESat elevation gradually increases as the vegetation stands, terrain slope or surface roughness increase. The SRTM DEM consistently shows a smaller vertical error than the map-based DEM. The overall means and standard deviations of the elevation differences between ICESat and SRTM DEM and between ICESat and the map-based DEM over the study area are 1.03 ± 15.20 and 4.58 ± 26.01 m, respectively. Our results suggest that the SRTM DEM has a higher accuracy than the map-based DEM of the region. It is found that ICESat elevation increases when snow is falling and decreases during snow or glacier melting, while the SRTM DEM gives a relative stable elevation of the snow/land interface or a glacier elevation where the C-band can penetrate through or reach it. Therefore, this makes the SRTM DEM a promising dataset (baseline) for monitoring glacier volume change since 2000.     

基于机载LiDAR 技术的典型水库水量变化估算

针对传统外业测量水库库容估算方法易受地形、气象条件影响,存在危险性高、效率低等问题,以仑山水库为研究对象,采用机载 LiDAR 技术估算水库面积及水量变化。通过实地数据采集,利用 KD- 树算法剔除点云中的粗差点,并采用渐进加密不规则三角网(PTIND)滤波分离出地面点,完成 LiDAR 点云数据的预处理;根据点云数据建立精细化数字高程模型(DEM),提取不同水位的水库水面面积;最终利用积分和传统棱台体积估算方法对比分析不同水位水量差值变化。结果表明：与高程实测值相比,DEM 高程反演值满足高程精度要求,基于机载 LiDAR 的水量估算受库底坡度变化的影响,估算值低于传统估算方法的估算值,估算结果更为精确,可为监测水库水量变化提供科学依据。     

Comparing the accuracy of estimated terrain elevations across spatial resolution

Terrain is modelled in Geographic Information Science on a grid, assuming that elevation values are constant within any single pixel of a Digital Elevation Model (DEM). Pixels are considered flat and rigid, for computational simplicity (a ‘rigid pixel’ paradigm). This paradigm does not account for the slope and curvature of terrain within each pixel, generating imprecise measurements, particularly as pixel size increases or in uneven terrain. This paper relaxes the rigid pixel assumption, allowing for possible sub-pixel variations in slope and curvature (a ‘surface-adjusted’ paradigm). This paper compares different interpolation methods to investigate sub-pixel variations for estimating elevation of arbitrary points given a regular grid. Tests interpolating elevation values for 20,000 georeferenced off-centroid random points from a regular grid DEM are presented, using a variety of exact and inexact local deterministic interpolation methods within contiguity configurations incorporating first and second order neighbours. The paper examines the accuracy of surface-adjusted estimations across a progression of spatial resolutions (10 m, 30 m, 100 m, and 1,000 m DEMs) and a suite of terrain types varying in latitude, altitude, slope, and roughness, validating off-centre estimates against direct elevation measurements on 3 m resolution lidar DEM. Results illustrate that the Bi-quadratic and Bi-cubic interpolation methods outperform Weighted Average, Linear, and Bi-linear methods at coarse resolutions and in rough or non-uniform terrain. In smooth or flat terrain and at finest resolutions, the interpolation method impacts estimation accuracy less or not at all. The type of contiguity configuration appears to play a role in estimation errors as well, with tighter neighbourhoods exhibiting higher accuracy. The analysis also examined regularized mathematical surfaces, adding autocorrelated randomly distributed noise to simulate terrain. The results of experiments based on regularized smooth mathematical surfaces do not translate directly to terrain modelling. The analysis also considers the balance between the increased computation times needed to measure surface-adjusted elevation against improvements in accuracy.     

SRTM vs ASTER elevation products. Comparison for two regions in Crete,Greece

The Shuttle Radar Topography Mission (SRTM) collected elevation data over 80% of earth's land area during an 11‐day Space Shuttle mission. With a horizontal resolution of 3 arc sec, SRTM represents the best quality, freely available digital elevation models (DEMs) worldwide. Since the SRTM elevation data are unedited, they contain occasional voids, or gaps, where the terrain lay in the radar beam's shadow or in areas of extremely low radar backscatter, such as sea, dams, lakes and virtually any water‐covered surface. In contrast to the short duration of the SRTM mission, the ongoing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is continuously collecting elevation information with a horizontal resolution of 15 m. In this paper we compared DEM products created from SRTM data with respective products created from ASTER stereo‐pairs. The study areas were located in Crete, Greece. Absolute DEMs produced photogrammetricaly from ASTER using differentially corrected GPS measurements provided the benchmark to infer vertical and planimetric accuracy of the 3 arc sec finished SRTM product. Spatial filters were used to detect and remove the voids, as well as to interpolate the missing values in DEMs. Comparison between SRTM‐ and ASTER‐derived DEMs allowed a qualitative assessment of the horizontal and vertical component of the error, while statistical measures were used to estimate their vertical accuracy. Elevation difference between SRTM and ASTER products was evaluated using the root mean square error (RMSE), which was found to be less than 50 m.     

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。     

Deriving terrain and textural information from stereo RADARSAT data for mountainous land cover mapping

A new procedure is proposed for land cover classification in a mountainous area using stereo RADARSAT-1 data. The method integrates a few types of information that can be extracted from the same stereo RADARSAT images: (1) the Digital Elevation Model (DEM) generated from the stereo RADARSAT images; (2) terrain information (elevation, slope and aspect) extracted from the derived DEM; and (3) textural information derived from the same RADARSAT images. An Artificial Neural Network (ANN) classifier is applied for the land cover classification. Performance of the proposed method is evaluated using a mountainous study area in Southern Argentina, where there is a lack of up-to-date information for environmental monitoring. The results show that the integration of textural and terrain information can greatly improve the accuracy of the classification using the ANN classifier. It demonstrates that stereo RADARSAT images provide valuable data sources for land cover mapping, especially in mountainous areas where cloud cover is a problem for optical data collection and topographical data are not always available.     

Terrain Super‐resolution through Aerial Imagery and Fully Convolutional Networks

Despite recent advances in surveying techniques, publicly available Digital Elevation Models (DEMs) of terrains are low‐resolution except for selected places on Earth. In this paper we present a new method to turn low‐resolution DEMs into plausible and faithful high‐resolution terrains. Unlike other approaches for terrain synthesis/amplification (fractal noise, hydraulic and thermal erosion, multi‐resolution dictionaries), we benefit from high‐resolution aerial images to produce highly‐detailed DEMs mimicking the features of the real terrain. We explore different architectures for Fully Convolutional Neural Networks to learn upsampling patterns for DEMs from detailed training sets (high‐resolution DEMs and orthophotos), yielding up to one order of magnitude more resolution. Our comparative results show that our method outperforms competing data amplification approaches in terms of elevation accuracy and terrain plausibility.     

Effects of terrain-induced shade removal using global DEM data sets on land-cover classification

Free access to global data sets of satellite images and digital elevation models (DEMs) such as Aster Global DEM (GDEM) and Shuttle Radar Topography Mission (SRTM) digital topography are expected to contribute to various study areas that deal with land cover and land use. To assess the capabilities of these global DEM data sets and to provide guidelines for performing shade removal under various terrain and illumination conditions, we evaluated the results of shade removal using the Minnaert correction and C-correction. These corrections were applied, using the GDEM (versions 1 and 2), SRTM, and a DEM derived from a local map (local DEM), to 30 sample images from 20 scenes of 10 path-rows in global land survey (GLS) Landsat-TM/ETM+ images, in terms of statistical indices and the accuracy of land-cover discrimination. The analysis indicated that the results of shade removal depended mainly on the correlation between the cosine of the sunshine incidence angle (cos(i)) and the radiance before shade removal, except in some cases with inferior illumination conditions. Of the three global DEMs, GDEM version 2 had the highest performance in shade removal. However, this study also indicated that successful shade removal was only one of the several factors that increased the accuracy of land-cover classification. In practical applications, shade removal can be recommended only for images where the terrain shade obviously disturbs the original spectral reflection characteristics of each land-cover type and no significant dependence of the land-cover distribution on the slope aspect is assumed. In such cases, also global DEMs evaluated in this study can be used for shade removal.     

Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: A meta analysis

This study compared the suitability of LIDAR (LIght Detection And Ranging) data, three-band multispectral data, and LIDAR data integrated with multispectral information, for classifying spatially complex vegetation in the Aspen Parkland of western Canada. Classifications were performed for both a) general vegetation classes limited to three major formations of deciduous forest, shrubland and grassland, and b) eight detailed vegetation classes including upland mixed prairie and fescue grasslands, closed and semi-open aspen forests, western snowberry and silverberry shrublands, and fresh and saline riparian (lowland) meadows. A Digital Elevation Model (DEM) and Surface Elevation Model (SEM) developed from LIDAR data incorporated both topographic and biological biases in community positioning across the landscape. Using multispectral data, the original digital image mosaic, its hybrid color composite, and an intensity-hue-saturation (IHS) image were each tested. Final vegetation classification was done through integration of information from both digital images and LIDAR data to evaluate the improvement in classification accuracy. Among the land cover schedules with three and eight classes of vegetation, classification from the multispectral imagery, specifically the hybrid color composite image, had the highest accuracy, peaking at 74.6% and 59.4%, respectively. In contrast, the LIDAR classification schedules led to an average classification accuracy of 64.8% and 52.3%, respectively, for the general and detailed vegetation data. Subsequent integration of the LIDAR and digital image classification schedules resulted in accuracy improvements of 16 to 20%, resulting in a superior final accuracy of 91% and 80.3%, respectively, for the three and eight classes of vegetation. A final land cover map including 8 classes of vegetation, fresh and saline water, as well as bare ground, was created for the study area with an overall accuracy of 83.9%, highlighting the benefit of integrating LIDAR and multispectral imagery for enhanced vegetation classification in heterogenous rangeland environments.     

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.     

Accuracy assessment of automatically derived digital elevation models from aster data in mountainous terrain

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite was designed to generate along‐track stereo images. The data are available at low cost, providing a feasible opportunity for generating digital elevation models (DEMs) in areas where little or no elevation data are yet available. This study evaluates the accuracy of DEMs extracted from ASTER data covering mountainous terrain. For an assessment of the achieved accuracies in the Andean study site, comparisons were made to similar topographical conditions in Switzerland, where reference data were available. All raw DEMs were filtered and interpolated by the post‐processing tools included with PCI Geomatica, the software package used. After carefully checking the DEM quality, further post‐processing was undertaken to eliminate obvious artefacts such as peaks and sinks. Accuracy was tested by comparing the DEMs in the Swiss Alps to three reference models. The achieved results of the generated DEMs are promising, considering the extreme terrain. Given accurate and well‐distributed ground control points (GCPs), it is possible to generate DEMs with a root mean square (RMS) error between 15?m and 20?m in hilly terrain and about 30?m in mountainous terrain. The DEMs are very accurate in nearly flat regions and on smooth slopes with southern expositions: errors are generally within ±10?m in those cases. Larger errors do appear in forested, snow covered or shady areas and at steep cliffs and deep valleys with extreme errors of a few hundred metres. The evaluation showed that the quality of the DEMs is sufficient for enabling atmospheric, topographic and geometric correction to various satellite datasets and for deriving additional products.     

Combination of stereo SAR and InSAR for DEM generation using TanDEM-X spotlight data

The objective of this work is to generate accurate digital elevation models (DEMs) from synthetic aperture radar (SAR) interferograms using the spotlight and staring spotlight modes during the TanDEM-X (TDX) science phase. We use stereo SAR with TDX or TerraSAR-X (TSX) to estimate absolute heights of natural or man-made reflectors for input to the unwrapped interferograms. The accuracy of the absolute heights in the DEMs was a few decimetres in flat regions and a few metres in the hilly and rugged terrain.     

Bare-earth extraction and DTM generation from photogrammetric point clouds including the use of an existing lower-resolution DTM

A method of extracting bare-earth points from photogrammetric point clouds by partially using an existing lower resolution digital terrain model (DTM) is presented. The bare-earth points are extracted based on a threshold defined by local slope. The local slope is estimated from the lower resolution DTM. A gridded DTM is then interpolated from the extracted bare-earth points. Five different interpolation algorithms are implemented and evaluated to identify the most suitable interpolation method for such non-uniformly scattered data. The algorithm is tested on four test sites with varying topographic and ground cover characteristics. The results are evaluated against a reference DTM created using aerial laser scanning. The deviations of the extracted bare-earth points, and the interpolated DTM, from the reference DTM increases with increasing forest canopy density and terrain roughness. The DTM created by the method is significantly closer to the reference DTM than the lower resolution national DTM. The ANUDEM (Australian National University Digital Elevation Modelling) interpolation method is found to be the best performing interpolation method in terms of reducing the deviations and in terms of modelling the terrain realistically with minimum artefacts, although the differences among the interpolation methods are not considerably large.     

Evaluating error associated with lidar-derived DEM interpolation

Light detection and ranging (lidar) technology is capable of precisely measuring a variety of vegetation metrics, the estimates of which are usually based on relative heights above a digital elevation model (DEM). As a result, the development of these elevation models is a critical step when processing lidar observations. A number of different algorithms exist to interpolate lidar ground hits into a terrain surface. We tested seven interpolation routines, using small footprint lidar data, collected over a range of vegetation classes on Vancouver Island, British Columbia, Canada. The lidar data were randomly subsetted into a prediction dataset and a validation dataset. A suite of DEMs were then generated using linear, quintic, natural neighbour, regularized spline, spline with tension, a finite difference approach (ANUDEM), and inverse distance weighted interpolation routines, at spatial resolutions of 0.5, 1.0 and 1.5 m. In order to examine the effects of terrain and ground cover on interpolation accuracies, the study area was stratified by terrain slope, vegetation structural class, lidar ground return density, and normalized difference vegetation indices (NDVI) derived from Quickbird and Landsat7 ETM+ imagery. The root mean square (RMS) and mean absolute errors of the residuals between the surfaces and the validation points indicated that the 0.5 m DEMs were the most accurate. Of the tested approaches, the regularized spline and IDW algorithms produced the most extreme outliers, sometimes in excess of ±6 m in sloping terrain. Overall, the natural neighbour algorithm provided the best results with a minimum of effort. Finally, a method to create prediction uncertainty maps using classification and regression tree (CART) analysis is proposed.     

The impact of misregistration on SRTM and DEM image differences

Image differences between Shuttle Radar Topography Mission (SRTM) data and other Digital Elevation Models (DEMs) are often performed for either accuracy assessment or for estimating vegetation height across the landscape. It has been widely assumed that the effect of sub-pixel misregistration between the two models on resultant image differences is negligible, yet this has not previously been tested in detail. The aim of this study was to determine the impact that various levels of misregistration have on image differences between SRTM and DEMs. First, very accurate image co-registration was performed at two study sites between higher resolution DEMs and SRTM data, and then image differences (SRTM–DEM) were performed after various levels of misregistration were systematically introduced into the SRTM data. It was found that: (1) misregistration caused an erroneous and dominant correlation between elevation difference and aspect across the landscape; (2) the direction of the misregistration defined the direction of this erroneous and systematic elevation difference; (3) for sub-pixel misregistration the error due solely to misregistration was greater than, or equal to the true difference between the two models for substantial proportions of the landscape (e.g., greater than 33% of the area for a half-pixel misregistration); and (4) the strength of the erroneous relationship with aspect was enhanced by steeper terrain. Spatial comparisons of DEMs were found to be sensitive to even sub-pixel misregistration between the two models, which resulted in a strong erroneous correlation with aspect. This misregistration induced correlation with aspect is not likely specific to SRTM data only; we expect it to be a generic relationship present in any DEM image difference analysis.     

Benchmarking stereo-derived DSMs to lidar by land cover and slope for resource development in northern Ontario,Canada

Topographic and elevation data are essential in the development of supporting infrastructure around mining sites. The de facto standard for acquiring elevation data is through light detection and ranging (lidar). The high labour and monetary cost of acquiring lidar has fostered more cost-effective approaches for creating elevation models that use stereo photogrammetry. To assess the accuracy of stereo-photogrammetry-derived elevation models and their potential application, we benchmark satellite (Worldview-2) and aircraft (South Central Ontario Orthoimagery Project; SCOOP) stereo-derived digital surface models (DSMs) against a lidar-derived DSM. Our results show that both stereo-derived DSMs have strong monotonic correlations with lidar across a range of land-cover types and slopes. The overall vertical accuracy of Worldview-2 and SCOOP DSMs are similar and do not meet the United States National Digital Elevation Program (NDEP) standards. However, accuracy assessment across land-cover types and slope categories show that specific land cover types (i.e. grass, row crops/pasture, sparse vegetation and marsh) on gently sloping terrain compare well to lidar data and meet NDEP accuracy standards. We situate the presented research in the context of northern resource development and discuss opportunities to improve the vertical accuracy of stereo-derived DSMs, for example, through unmanned aerial systems.     

Differentiating mine-reclaimed grasslands from spectrally similar land cover using terrain variables and object-based machine learning classification

Incorporating ancillary, non-spectral data may improve the separability of land use/land cover classes. This study investigates the use of multi-temporal digital terrain data combined with aerial National Agriculture Imagery Program imagery for differentiating mine-reclaimed grasslands from non-mining grasslands across a broad region (6085 km²). The terrain data were derived from historical digital hypsography and a recent light detection and ranging data set. A geographic object-based image analysis (GEOBIA) approach, combined with two machine learning algorithms, Random Forests and Support Vector Machines, was used because these methods facilitate the use of ancillary data in classification. The results suggest that mine-reclaimed grasslands can be mapped accurately, with user’s and producer’s accuracies above 80%, due to a distinctive topographic signature in comparison with other spectrally similar grasslands within this landscape. The use of multi-temporal digital elevation model data and pre-mining terrain data only generally provided statistically significant increased classification accuracy in comparison with post-mining terrain data. Elevation change data were of value, and terrain shape variables generally improved the classification. GEOBIA and machine learning algorithms were useful in exploiting these non-spectral data, as data gridded at variable cell sizes can be summarized at the scale of image objects, allowing complex interactions between predictor variables to be characterized.     

Accuracy assessment of the processed SRTM-based elevation data by CGIAR using field data from USA and Thailand and its relation to the terrain characteristics

Shuttle radar topographic mission (SRTM) has created an unparalleled data set of global elevations that is freely available for modeling and environmental applications. The global availability (almost 80% of the Earth surface) of SRTM data provides baseline information for many types of the worldwide research. The processed SRTM 90 m digital elevation model (DEM) for the entire globe was compiled by Consultative Group for International Agriculture Research Consortium for Spatial Information (CGIAR-CSI) and made available to the public via internet mapping interface. This product presents a great value for scientists dealing with terrain analysis, thanks to its easy download procedure and ready-to-use format. However, overall assessment of the accuracy of this product requires additional regional studies involving ground truth control and accuracy verification methods with higher level of precision, such as the global positioning system (GPS).The study presented in this paper is based on two independent datasets collected with the same GPS system in Catskill Mountains (New York, USA) and Phuket (Thailand). Both datasets were corrected with differential base station data. Statistical analysis included estimation of absolute errors and multiple regression analysis with slope and aspect variables. Data were analyzed for each location separately and in combination. Differences in terrain and geographical location allowed independent interpretation of results.The results of this study showed that absolute average vertical errors from CGIAR dataset can range from 7.58 ± 0.60 m in Phuket to 4.07 ± 0.47 m in Catskills (mean ± S.E.M.). This is significantly better than a standard SRTM accuracy value indicated in its specification (i.e. 16 m). The error values have strong correlation with slope and certain aspect values. Taking into account slope and aspect considerably improved the accuracy of the CGIAR DEM product for terrain with slope values greater than 10°; however, for the terrain with slope values less than 10°, this improvement was found to be negligible.     

Derivation of surface topography and terrain parameters from single satellite image using shape-from-shading technique

This paper evaluates and compares two area-based shape-from-shading algorithms for deriving detailed surface topography and terrain parameters. A Fourier transform-based linear algorithm and an iterative minimization algorithm with integrability constraint are devised and implemented using C++ programming language, and applied to a SPOT panchromatic image. The relative height measurements from the shape-from-shading technique are calibrated into an absolute elevation model using a parametric least-squares adjustment procedure based on a number of ground control points. Surface slope, orientation, and structural lines are also calculated based on the digital elevation model derived from the shape-from-shading technique. The comparative advantages of the two algorithms are discussed, and the combination of two algorithms is also explored. The fundamental requirement for the shape-from-shading algorithms is that the terrain surface under investigation has a relatively homogenous land cover. Source codes in C++ and test data sets are available.