DEM Points Research Articles

A NEW FRAMEWORK FOR ACCURACY ASSESSMENT OF LIDAR-DERIVED DIGITAL ELEVATION MODELS

Abstract. Existing approaches to accuracy assessment of LiDAR-derived DEM are typically based on statistical methodology and focus on vertical accuracy. This paper presents a new framework which calls for assessment of not only a DEM’s vertical accuracy, but also its ability to preserve a point’s elevation rank in the bare earth topographic surface. New methods to assess each aspect are presented. For DEM’s vertical accuracy, approximation theory from numerical analysis is used to quantify the total error at each DEM point as well as its three error components – sensor error, ground error, and interpolation error. For DEM’s elevation order which is critical to model terrain structure, the concept of isomorphism in set theory is drawn as the mathematical rationale and Kendall’s rank correlation efficient is used to quantify the accuracy. The new framework is illustrated using a DEM derived from LiDAR for sea-level rise vulnerability assessment of a tidal salt marsh. Compared to conventional methods based on statistics, the new framework and methods produce detailed mapping of error distribution thus enable the identification of the main sources of error and where improvement is needed most. Results call for re-evaluation of the current practice of assessing filtering accuracy in LiDAR data processing and further research on relative elevation and isomorphism.

Delineating planner surfaces from correlation-based DEMS

Planner surfaces play an important role in several mapping and planning applications in urban areas. Automated algorithms to extract such surfaces are essential since they reduce the time and cost when compared to editing these DEMs manually. These surfaces could be constructed from either correlation-based DEMs or LIDAR point clouds. The advantages of the first over the second make it more appealing in generating 3D planer surfaces. In this paper we outline an automated algorithm to delineate 3D surfaces in DEMs compiled from a set of high resolution digital aerial photographs. Each cell in the DEM is assigned three attributes representing: height from bare ground, local statistics of neighboring elevations, and homogeneity of pixel intensities in corresponding aerial photos. These attributes are used to classify DEM points to ground and non-ground points using a feedforward back-propagation neural network. Candidate non-ground points are further segmented into different surfaces based on their tendency to lie in a plane. The 3D parameters of roof planes are then estimated using a robust estimator and regression analyses. DEM points contiguous to the roof patches are augmented if they pass an intensity compatibility check. Results showed an average increase in the precision of the DEMs’ heights for roof posts from 48 cm to 15 cm.

Dense DSM and DTM Point Cloud Generation Using CARTOSAT-2E Satellite Images for High-Resolution Applications

The primary objective of this study is to provide a methodology to generate a dense point cloud of digital surface model (DSM) and digital terrain model (DTM) from 0.6 m GSD stereo images acquired by CARTOSAT-2E satellite of the Indian Space Research Organization. These products are required for many high-resolution applications such as mapping of watersheds and watercourses; river flood modeling; analysis of flood depth, landslide, forest structure, and individual trees; design of highway and canal alignment. The proposed method consists of several processes such as orienting the stereo images, DEM point cloud extraction using the semi-global matching, and DSM to DTM filtering. The stereo model is built by performing aero triangulation and block adjustment using the ground control points. The semi-global matching algorithm is used on the epipolar images to generate the DSM in the form of dense point cloud corresponding to one height point for each pixel. The planimetric and height accuracies are evaluated using orthoimages and DSM and found to be within 3-pixel (~ 2 m) range. A method for extracting DTM by ground point filtering, to discriminate the probable ground points and the non-ground points, is provided by using discrete cosine transformation interpolation. This robust method uses a weight function to differentiate the noise points from the ground points. The overall classification efficiency kappa (κ) value averages at 0.92 for ground point classification/DTM extraction. The results of benchmarking of the GPS-aided GEO augmented navigation GPS receiver by operating it over IGS station, in static mode for collecting the checkpoints, also are presented.

Comparison of two methods for multiresolution terrain modelling in GIS

Very important points (VIPs) and important points and edges (IPEs) methods have been compared in accordance with the TINs obtained by: (1) Delaunay triangulation using DEM points determined by VIP and (2) constrained Delaunay triangulation using DEM points and triangle edges determined by IPE. It was ensured that the number of points in each TIN was approximately equal to the number calculated by Töpfer’s formula, and that the vertical error of each TIN was less than the error calculated by Koppe’s formula. According to the results, (1) both methods are quality prioritized, (2) IPE is more sensitive to local surface changes, (3) important triangle edges determined by IPE make a significant contribution to the TIN, (4) some of the points selected by IPE are more important points than that of VIP, and (5) IPE-based TINs are more structural fidelity than VIP-based TINs.

ВИКОРИСТАННЯ МЕТЕОРОЛОГІЧНИХ ТА ГЕОМОРФОЛОГІЧНИХ ДАНИХ ПРИ РОЗРАХУНКУ ПАВОДКОВИХ СТОКІВ

Виконано аналіз катастрофічних паводків у басейні Дністра в Карпатах, найбільшим з яких є червневий паводок 1969 р., коли було досягнуто сторічного максимального стоку. Проаналізовано сучасні методи розрахунку паводкових стоків. Запропоновано метод розрахунку, заснований на генетичній формулі стоку та цифровій моделі рельєфу. Розроблено цифрові моделі рельєфу верхніх частин басейнів річок Свіча та Бистриця Солотвинська, карти ізохрон стоку. Виконано розрахунок максимального стоку на основі генетичної формули. Виконані дослідження показують, що обмеженість та низька точність вихідних метеорологічних та геоморфологічних даних не гарантують точних розрахунків та захисту від небезпечних паводків навіть з використанням найсучасніших геоінформаційних систем. Тому важливими є аналіз і використання результатів багаторічних спостережень за паводками у попередні роки. Вони дають змогу певною мірою прогнозувати наслідки дощових паводків. Подальший прогрес у гідрологічних розрахунках пов'язаний з детальними знаннями про рельєф, які дає цифрова модель рельєфу. На основі цифрової моделі рельєфу створено просторово-часову модель паводкового стоку, в якій для кожної вершини регулярної сітки визначено координати, ухили поверхні, віддалі до водорозділу та до водного потоку, інтенсивність і шар опадів. Використання цифрової моделі рельєфу дасть змогу розробити для кожного басейну регіональну методику розрахунку паводків, врахувати максимум параметрів поверхневого стоку, а також зміну їх у часі.

Automatic Correction of DEM Surface Errors

The automation of extracting planner surfaces is a main field of research in digital photogrammetry. These surfaces are essential to generate three dimensional GIS databases. Surfaces are usually determined from either DEMs or images. Each dataset provides a different type of information. Thus, the combination of the two datasets should enhance the surface reconstruction process. This paper presents a new technique for generating 3D surfaces by combining both correlation-based DEMs and aerial images. The process starts by discriminating DEM points that represent planner surfaces using local statistics of neighboring elevations and intensities and point elevations. A segmented orthophoto is then used to group these points into different regions. The elevations of the points in each region are fed into a least squares adjustment model to compute the best-fit planner surface parameters. Refinement of surface borders is then performed using a region growing algorithm. The RMSE for five test sites showed a spatial accuracy of 5 - 8 cm.

Cross-validation as a means of investigating DEM interpolation error

Studies of the detailed characteristics of DEM error have been hampered by the difficulty in obtaining a large sample of error values for a DEM. The approach proposed in this paper is to resample a DEM to a lower resolution and then reinterpolate back to the original resolution which produces a large sample of error values well distributed across the DEM. This method is applied to a sample area from Scotland, which contains a variety of terrain types. The results show that the standard measure of error, the root mean square error (RMSE) of elevation, shows only moderate correlation with a visual assessment of the quality of DEMs produced by a range of interpolation methods. The frequency distribution and strength of spatial autocorrelation are shown to vary with the initial data density and interpolation method. When the source data density is low, the error has strong spatial autocorrelation and a distribution that is close to being Gaussian. However, as the data density increases, levels of spatial autocorrelation drop and the distribution becomes leptokurtic with values very strongly clustered around zero. At the level of the individual DEM point, elevation error is shown to be a poor predictor of error in slope derivatives which depend on the spatial pattern of elevation errors around the point and are also sensitive to differences in terrain. At the level of a whole DEM, however, RMSE of elevation is a good predictor of RMSE in gradient and aspect but not of curvature.

Accuracy Assessment of Digital Elevation Models based on Approximation Theory

Empirical research in DEM accuracy assessment has observed that DEM errors are correlated with terrain morphology, sampling density, and interpolation method. However, theoretical reasons for these correlations have not been accounted for. This paper introduces approximation theory adapted from computational science as a new framework to assess the accuracy of DEMs interpolated from topographic maps. By perceiving DEM generation as a piecewise polynomial simulation of the unknown terrain, the overall accuracy of a DEM is described by the maximum error at any DEM point. Three linear polynomial interpolation methods are examined, namely linear interpolation in 1D, TIN interpolation, and bilinear interpolation in a rectangle. Their propagation error and interpolation error, whose sum is the total error at a DEM point, are derived. Based on the results, the theoretical basis for the correlation between DEM error and terrain morphology and source data density is articulated for the first time.