Increased accuracy of geostatistical prediction of nitrogen dioxide in the United Kingdom with secondary data

Five techniques were used to map nitrogen dioxide (NO₂) concentrations in the United Kingdom. The methods used to predict from point data, collected as part of the UK NO₂ diffusion tube network, were local linear regression (LR), inverse distance weighting (IDW), ordinary kriging (OK), simple kriging with a locally varying mean (SKlm) and kriging with an external drift (KED). These techniques may be divided into two groups: (i) those that use only a single variable in the prediction process (IDW, OK) and (ii) those that make use of additional variables as a part of prediction (LR, SKlm and KED). Nitrous oxides emission data were used as secondary data with LR, SKlm and KED. It was concluded that SKlm provided the most accurate predictions based on the summary statistics of prediction errors from cross-validation.     

Determination of complexity factor and its relationship with accuracy of representation for DEM terrain

Based on the estimating rule of the normal vector angles between two adjacent terrain units, we use the concept of terrain complexity factor to quantify the terrain complexity of DEM, and then the formula of terrain complexity factor in Raster DEM and TIN DEM is deduced theoretically. In order to make clear how the terrain complexity factor E _CF and the average elevation h affect the accuracy of DEM terrain representation RMSE _Et , the formula of Gauss synthetical surface is applied to simulate several real terrain surfaces, each of which has different terrain complexity. Through the statistical analysis of linear regression in simulation data, the linear equation between accuracy of DEM terrain representation RMSE _Et , terrain complexity factor E _CF and the average elevation h is achieved. A new method is provided to estimate the accuracy of DEM terrain representation RMSE _Et with a certain terrain complexity and it gives convincing theoretical evidence for DEM production and the corresponding error research in the future.     

干热河谷冲沟DEM插值误差的空间分布研究

为了分析云南元谋干热河谷典型冲沟插值误差的空间分布特征,采用反距离加权（inverse distance weighting,IDW）、局部多项式（local polynomial interpolation,LPI）、张力样条（spline with tension,ST）、析取克里格（disjunctive Kriging,DK）以及不规则三角网（triangulated irregular network,TIN）模型方法对高程采样点进行插值,用交叉验证法、相对差系数及沟谷线差异衡量其插值精度。遴选高程误差大于1 m的误差点,用变异系数（coefficient of variation,CV）、全局Moran指数和Getis-Ord G_i*指数分析其空间格局特征。结果表明:TIN和DK精度较高,IDW精度最低;高程误差均呈聚集分布,聚集程度TIN > LPI > DK > ST > IDW;高程误差均呈空间正自相关,TIN模型插值误差的自相关程度最高;误差热点位于坡度大的区域。     

Zonation of coastal sediments based on the effective properties on the accumulation of heavy metals using the IDW and kriging method (case study: SW Iran)

Abstract

This study investigated the effects of some physicochemical properties of sediments on the accumulation of heavy metals in portions of the Musa creek coasts (Jafari and Petrochemical creeks). Effective properties such as pH, EC, texture, G_S, γ_d, n, CaCO₃ and OM were determined. All variables showed a normal distribution and general trends of NW–SE and NE–SW. After detrending the variables, ordinary kriging was used for modelling. The C₀/σ₂, C₀/σ_2, and search radius criteria were used to select the best semivariogram. All the variables displayed a spatial structure with different intensities. The IDW method was also used for estimation. The cross-validation showed that the results of both IDW and kriging methods are almost similar. Distribution of the sand particle, G_S, n and OM decreases with distance from the waterways, whilst clay–silt deposits. In the center of the studied area, CaCO₃ has the highest value and EC has the lowest value.     

基于协克立金方法的遥感图像融合

 协克立金方法是一种多元最优无偏的线性估值方法，已被广泛应用于许多领域。本文将协克立金方法应用于遥感图像的融合中，并与经典的基于主成分分析方法的融合结果进行定性和定量比较和分析。首先，通过计算实验变异函数，进行变异函数理论模型拟合； 然后，运用3种不同协克立金方法对较低分辨率的多光谱图像和较高分辨率的全色图像进行融合。实验结果表明： 基于简单协克立金方法融合的图像空间分辨率最好； 普通协克立金方法融合的图像光谱保真性最好； 标准化的协克立金方法融合的效果介于两者之间。     

DEM-induced errors in developing a quasi-geoid model for Africa

Errors in digital elevation models (DEMs) will introduce errors in geoid and quasi-geoid models, via their use in interpolating free-air gravity anomalies and (in the case of the quasi-geoid) their use in computing the Molodensky G ₁ term. The effects of these errors and those of datum shifts are assessed using three independent DEMs for a test region in South Africa. It is shown that these effects are significant and that it is important to choose the best-possible DEM for use in geoid and quasi-geoid modelling. Acknowledgments.The land gravity data used for this research were provided by the South African Council for Geoscience. Marine gravity anomalies were provided by the Danish National Survey and Cadastre (Kort & Matrikelstyrelsen). The GLOBE DEM was provided by the US National Geophysical Data Centre, and the CDSM DEM was provided by the South African Chief Directorate for Surveying and Mapping. The constructive comments of the reviewers are gratefully acknowledged.     

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。     

An application of Coons patch to generate grid-based digital elevation models

We have presented a new method for generating grid-based digital elevation models (DEMs) based on Coons patch. A series of Gaussian synthetic surfaces with various surface complexities were employed to comparatively analyze the simulation accuracies of Coons patch and the classical interpolation methods, such as IDW, Natural neighbor, SPLINE and KRIGING. Results indicate that irrespective of terrain complexity, Coons patch is much more accurate than the classical interpolation methods, and the accuracy of Coons patch is about 327.30, 15.19, 3.66 and 40.07 times as high as the accuracies of IDW, Natural neighbor, SPLINE and KRIGING on average. A real-world test area was used to test the effect of sampling density on the simulation accuracy. The results indicate that Coons patch has a higher accuracy than the classical interpolation methods regardless of sampling density. In a word, Coons patch as an accurate interpolation method can be used for grid-based DEM construction.     

基于高斯过程回归的SRTM数据空洞填补方法

针对现有插值方法对复杂地表模拟能力不足的问题,发展了一种基于非平稳(神经网络)核的高斯过程回归(GPR)方法,并将其应用于SRTM DEM空缺数据填补。以山区SRTM DEM的模拟数据空洞为研究对象,将GPR模拟结果与传统插值方法(TIN、SPLINE和IDW)比较表明:GPR填补精度高于传统插值方法,且空缺区域模拟曲面较好的保持了地形特征。     

DEM Fusion using a modified k-means clustering algorithm

Digital elevation models (DEMs) are a necessary dataset for modelling the Earth’s surface; however, all DEMs contain error. Researchers can reduce this error using DEM fusion techniques since numerous DEMs can be available for a region. However, the use of a clustering algorithm in DEM fusion has not been previously reported. In this study a new DEM fusion algorithm based on a clustering approach that works on multiple DEMs to exploit consistency in the estimates as indicators of accuracy and precision is presented. The fusion approach includes slope and elevation thresholding, k-means clustering of the elevation estimates at each cell location, as well as filtering and smoothing of the fusion product. Corroboration of the input DEMs, and the products of each step of the fusion algorithm, with a higher accuracy reference DEM enabled a detailed analysis of the effectiveness of the DEM fusion algorithm. The main findings of the research were: the k-means clustering of the elevations reduced the precision which also impacted the overall accuracy of the estimates; the number of final cluster members and the standard deviation of elevations before clustering both had a strong relationship to the error in the k-means estimates.     

Street‐level Spatial Interpolation Using Network‐based IDW and Ordinary Kriging

This study proposes network‐based spatial interpolation methods to help predict unknown spatial values along networks more accurately. It expands on two of the commonly used spatial interpolation methods, IDW (inverse distance weighting) and OK (ordinary kriging), and applies them to analyze spatial data observed on a network. The study first provides the methodological framework, and it then examines the validity of the proposed methods by cross‐validating elevations from two contrasting patterns of street network and comparing the MSEs (Mean Squared Errors) of the predicted values measured with the two proposed network‐based methods and their conventional counterparts. The study suggests that both network‐based IDW and network‐based OK are generally more accurate than their existing counterparts, with network‐based OK constantly outperforming the other methods. The network‐based methods also turn out to be more sensitive to the edge effect, and their performance improves after edge correction. Furthermore, the MSEs of standard OK and network‐based OK improve as more sample locations are used, whereas those of standard IDW and network‐based IDW remain stable regardless of the number of sample locations. The two network‐based methods use a similar set of sample locations, and their performance is inherently affected by the difference in their weight distribution among sample locations.     

Ability to detect and locate gross errors on DEM matching algorithm

Abstract

Digital elevation model (DEM) matching techniques have been extended to DEM deformation detection by substituting a robust estimator for the least squares estimator, in which terrain changes are treated as gross errors. However, all existing methods only emphasise their deformation detecting ability, and neglect another important aspect: only when the gross error can be detected and located, can this system be useful. This paper employs the gross error judgement matrix as a tool to make an in-depth analysis of this problem. The theoretical analyses and experimental results show that observations in the DEM matching algorithm in real applications have the ability to detect and locate gross errors. Therefore, treating the terrain changes as gross errors is theoretically feasible, allowing real DEM deformations to be detected by employing a surface matching technique.     

A practical method for SRTM DEM correction over vegetated mountain areas

Digital elevation models (DEMs) are essential to various applications in topography, geomorphology, hydrology, and ecology. The Shuttle Radar Topographic Mission (SRTM) DEM data set is one of the most complete and most widely used DEM data sets; it provides accurate information on elevations over bare land areas. However, the accuracy of SRTM data over vegetated mountain areas is relatively low as a result of the high relief and the penetration limitation of the C-band used for obtaining global DEM products. The objective of this study is to assess the performance of SRTM DEMs and correct them over vegetated mountain areas with small-footprint airborne Light Detection and Ranging (Lidar) data, which can develop elevation products and vegetation products [e.g., vegetation height, Leaf Area Index (LAI)] of high accuracy. The assessing results show that SRTM elevations are systematically higher than those of the actual land surfaces over vegetated mountain areas. The mean difference between SRTM DEM and Lidar DEM increases with vegetation height, whereas the standard deviation of the difference increases with slope. To improve the accuracy of SRTM DEM over vegetated mountain areas, a regression model between the SRTM elevation bias and vegetation height, LAI, and slope was developed based on one control site. Without changing any coefficients, this model was proved to be applicable in all the nine study sites, which have various topography and vegetation conditions. The mean bias of the corrected SRTM DEM at the nine study sites using this model (absolute value) is 89% smaller than that of the original SRTM DEM, and the standard deviation of the corrected SRTM elevation bias is 11% smaller.     

A total error-based multiquadric method for surface modeling of digital elevation models

Digital elevation model (DEM) source data are subject to both horizontal and vertical errors owing to improper instrument operation, physical limitations of sensors, and bad weather conditions. These factors may bring a negative effect on some DEM-based applications requiring low levels of positional errors. Although classical smoothing interpolation methods have the ability to handle vertical errors, they are prone to omit horizontal errors. Based on the statistical concept of the total least squares method, a total error-based multiquadric (MQ-T) method is proposed in this paper to reduce the effects of both horizontal and vertical errors in the context of DEM construction. In nature, the classical multiquadric (MQ) method is a vertical error regression procedure, whereas MQ-T is an orthogonal error regression model. Two examples, including a numerical test and a real-world example, are employed in a comparative performance analysis of MQ-T for surface modeling of DEMs. The numerical test indicates that MQ-T performs better than the classical MQ in terms of root mean square error. The real-world example of DEM construction with sample points derived from a total station instrument demonstrates that regardless of the sample interval and DEM resolution, MQ-T is more accurate than classical interpolation methods including inverse distance weighting, ordinary kriging, and Australian National University DEM. Therefore, MQ-T can be considered as an alternative interpolator for surface modeling with sample points subject to both horizontal and vertical errors.     

Obtaining High Fidelity Triangular Regular Network from Only DEM Points

Abstract

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When the source data for the digital elevation model (DEM) are not known and any additional information or features such as skeleton lines of terrain is not available, a triangular regular network (TRN) is constructed with simple subdivision using one or two diagonals uniformly. Such a model gives inaccurate directions for interpolation because of the inaccurate diagonals used in triangulation and thereby, results in inaccurate contours representing artificial terrain features. In this study, a new method is developed based on slope information computed at DEM points determining accurate diagonals in the subdivision process, which is beneficial not only through the skeleton lines of a terrain but also all over the DEM. Consequently, it is shown that the proposed method is able to build a high fidelity TRN from a DEM without any additional information or features.     

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.     

DEM数据辅助的星载SAR俯仰向数字波束形成

俯仰向数字波束形成(DBF)处理是星载合成孔径雷达(SAR)实现高分辨率宽测绘带成像的关键。然而,在处理较大地形起伏区域的回波信号时,传统俯仰向波束扫描(SCORE)方法会出现波束指向偏差问题,导致接收回波的增益降低,影响SAR系统成像性能。针对这一问题,本文详细分析了高分宽幅星载SAR系统的俯仰向DBF接收波束扫描指向问题,提出了一种基于数字高程图(DEM)的俯仰向DBF处理方法。该方法基于星载SAR成像几何模型,首先利用成像场景的DEM数据和卫星轨道参数计算距离门单元对应的地面高程值,并进一步计算距离门单元对应的目标波达角,然后根据该对应关系计算每个距离门单元的俯仰向DBF加权矢量,从而确保在俯仰向DBF处理过程中接收波束指向正确。通过X波段的星载SAR系统进行仿真实验,结果表明(1)当地面高程值大于1.9 km时,传统SCORE方法处理得到的目标信号幅度下降都超过2.8 dB,本文方法处理得到的目标信号幅度下降都小于0.4 dB,本文方法优于传统SCORE方法;(2)由DEM数据误差导致的地面目标高程偏差对本文方法影响较小。因此本文方法能够有效改善地形起伏较大区域的回波信号接收增益。     

Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi,Vietnam

ABSTRACT

This study applied GIS-based statistical analytic techniques to investigate the influence of accident Severity Index (SI) on temporal-spatial patterns of accident hotspots related to the specific time intervals of day and seasons. Road Traffic Accident (RTA) data in 3 years (2015 ? 2017) in Hanoi, Vietnam were used to analyze and test this approach. Firstly, the RTA data were divided into four seasons in accordance with Hanoi’s weather conditions and the time intervals such as the daytime, nighttime, or peak hours. Then, the Kernel Density Estimation (KDE) method was applied to analyze hotspots according to the time intervals and seasons. Finally, the results were presented by using the comap technique. This study considered both analyses with and without SI. The accident SI measures the seriousness of an accident. The approach method is to give higher weights to the more serious accidents, but not with the extremely high values calculated on a direct rate to the accident expenditures. The results showed that both analyses determined the relatively similar hotspots, but the rankings of some hotspots were quite different due to the integration of SI. It is better to take into account SI in determining RTA hotspots because the gained results are more precise and the rankings of hotspots are more accurate. From there, the traffic authorities can easily understand the causes behind each accident and provide reasonable solutions to solve the most dangerous hotspots in case of limited budget and resources appropriately. This is also the first study about this issue in Vietnam, so the contribution of the article will help the traffic authorities easily solve this problem not only in Hanoi but also in other cities.     

Tectonic activity classes along the nahan thrust (NT) in nw sub-himalaya

Morphotectonic analysis using remote sensing images and GIS techniques have been utilized to identify areas experiencing rapid tectonic deformation and to estimate relative variations of tectonic activity along the mountain front in the Morni Hills, marked by the Nahan Thrust (NT) zone. The NT separates the Upper Morni Hills (UMH) from the highly dissected Lower Morni Hills (LMH). The digital elevation model (DEM) of the area draped with the satellite image was used to identify different geomorphic zones mainly based on altitude and dissection by streams. The NT is also marked by incising streams on the hanging wall block that become braided on the footwall block, across the NT, which can be very well picked on the satellite image. The topographic profiles drawn across the NT, using DEM show a sharp break in slope at the mountain front. Morphometric analysis of the mountain front was carried out determining mountain front sinuosity (S_mf) derived from remote sensing images and valley floor/valley width ratio (V_f) indices obtained from topographic profiles using the DEM. The S_mf values vary from 1.1 to 2.4 that are characteristic of low sinuosity mountain fronts. On the other hand, V_f values (0.1 to 0.33) are quite low. Both these indices indicate active uplift of the mountain front and consequent downcutting by streams in the hanging wall block. Further, using the S_mf and V_f indices, two tectonic activity classes: 1 (active) and 2 (moderately active) are identified from the study area.