TERRA: Terrain Extraction from elevation Rasters through Repetitive Anisotropic filtering

Over the past decades, several filters have been developed to derive a Digital Terrain Model (DTM) from a Digital Surface Model (DSM), by means of filtering out aboveground objects such as vegetation. In this filtering process, however, one of the major challenges remains to precisely distinguish sharp terrain features, e.g. ridges, agricultural terraces or other anthropogenic geomorphology such as open-pit mines, riverbanks or road ramps. Hence, loss of elevation data around terrain edges (and consequent smoothing) is very common with existing algorithms. In terraced landscapes, the preservation of precise geomorphology is of key importance in digital terrain analyses, such as hydrologic and erosion modelling, or automatic feature recognition and inventorying. In this work, we propose a new filtering method called TERRA (Terrain Extraction from elevation Rasters through Repetitive Anisotropic filtering). The novelty of the algorithm lies within its usage of terrain aspect to guide the anisotropic filtering direction, therefore maximising the preservation of terrain edges. We derived six DTMs from DSMs using UAV Structure from Motion (SfM) photogrammetry, laser altimetry and satellite sources (grid resolutions ranging from 0.1–1.0 m). The results indicated a close agreement of DTMs filtered using the TERRA algorithm and reference DTMs, while terrace risers were well preserved even under thick canopies of vines and trees. Compared to existing filtering approaches, TERRA performed well in minimising Type I errors (false ground removal), while Type II errors occurred locally where vegetation was covering the terrace edges. Given the promising filtering performance, and supported by the minimal requirements of parameterisation and computation, the TERRA algorithm could be a useful tool in DTM preparation for digital terrain analysis of agricultural terraces and similar hillslopes characterised by a complex mosaic of sharp terrain and non-terrain features.     

基于DEM的水土流失分析

数字高程模型（DEM）是地理信息系统地理数据库中最为重要的空间信息资料和赖以进行地形分析的核心数据。数字地形模型从已知3维坐标的散乱点和已知高程的等高线出发,构筑地形表面,以数字的形式表示实际地形特征的空间分布,从而建立起相关区域内任一点的地形情况。利用数字高程模型进行宏观地形因子和微观地形因子的分析,直观地显示研究区域的地貌形态,为水土流失监测、分析、治理提供了重要的科学依据。     

Algorithm for generating dem based on cone

Digital elevation model (DEM) has a variety of applications in GIS and CAD. It is the basic model for generating three-dimensional terrain feature. Generally speaking, there are two methods for building DEM. One is based upon the digital terrain model of discrete points, and is characterized by fast speed and low precision. The other is based upon triangular digital terrain model, and slow speed and high precision are the features of the method. Combining the advantages of the two methods, an algorithm for generating DEM with discrete points is presented in this paper. When interpolating elevation, this method can create a triangle which includes interpolating point and the elevation of the interpolating point can be obtained from the traingle. The method has the advantage of fast speed, high precision and less memory.     

Algorithm for generating dem based on cone

Digital elevation model (DEM) has a variety of applications in GIS and CAD.It is the basic model for generating three-dimensional terrain feature.Generally speaking,there are two methods for building DEM.One is based upon the digital terrain model of discrete points,and is characterized by fast speed and low precision.The other is based upon triangular digital terrain model,and slow speed and high precision are the features of the method.Combining the advantages of the two methods,an algorithm for generating DEM with discrete points is presented in this paper.When interpolating elevation,this method can create a triangle which includes interpolating point and the elevation of the interpolating point can be obtained from the triangle.The method has the advantage of fast speed,high precision and less memory.     

Creating high-resolution bare-earth digital elevation models (DEMs) from stereo imagery in an area of densely vegetated deciduous forest using combinations of procedures designed for lidar point cloud filtering

For areas of the world that do not have access to lidar, fine-scale digital elevation models (DEMs) can be photogrammetrically created using globally available high-spatial resolution stereo satellite imagery. The resultant DEM is best termed a digital surface model (DSM) because it includes heights of surface features. In densely vegetated conditions, this inclusion can limit its usefulness in applications requiring a bare-earth DEM. This study explores the use of techniques designed for filtering lidar point clouds to mitigate the elevation artifacts caused by above ground features, within the context of a case study of Prince William Forest Park, Virginia, USA. The influences of land cover and leaf-on vs. leaf-off conditions are investigated, and the accuracy of the raw photogrammetric DSM extracted from leaf-on imagery was between that of a lidar bare-earth DEM and the Shuttle Radar Topography Mission DEM. Although the filtered leaf-on photogrammetric DEM retains some artifacts of the vegetation canopy and may not be useful for some applications, filtering procedures significantly improved the accuracy of the modeled terrain. The accuracy of the DSM extracted in leaf-off conditions was comparable in most areas to the lidar bare-earth DEM and filtering procedures resulted in accuracy comparable of that to the lidar DEM.     

High Resolution DEM Generation in High‐Alpine Terrain Using Airborne Remote Sensing Techniques

Up‐to‐date and accurate digital elevation models (DEMs) are essential for many applications such as numerical modeling of mass movements or mapping of terrain changes. Today the Federal Department of Topography, swisstopo, provides Digital Terrain Models (DTMs) and Digital Surface Models (DSMs) derived from airborne LiDAR data with a high spatial resolution of 2 m covering the entire area of Switzerland below an elevation of 2000 m a.s.l.. However, above an elevation of 2000 m a.s.l., which is typical for high‐alpine terrain, the best product available is the a DTM with a spatial resolution of 25 m. This spatial resolution is insufficient for many applications in complex terrain. In this study, we investigate the quality of DSMs derived from opto‐electronic scanner data (ADS80; acquired in autumn 2010) using photogrammetric image correlation techniques based on the multispectral nadir and backward looking sensor data. As reference, we take a high precision airborne LiDAR data set with a spatial resolution of ca. 0.5 m, acquired in late summer 2010, covering the Grabengufer/Dorfbach catchment near Randa, VS. We find the deviations between the two datasets are surprisingly low. In terrain with inclination angles of less than 30° the RMSE is below 0.5 m. In extremely steep terrain of more than 50° the RMSE goes up to 2 m and outliers increase significantly. We also find dependencies of the deviations on illumination conditions and ground cover classes. Finally we discuss advantages and disadvantages of the different data acquisition methods.     

地形特征线提取方法的研究进展

地形与地貌是数字地形分析中重要的自然地理要素,数字高程模型中蕴含着丰富的地形地貌信息,从中提取地形特征线信息是当前地学研究的热点之一。目前,很多学者使用不同的方法来研究地形特征线的提取,但是仍然存在着许多不足之处。本文对这些方法进行了介绍,对各种方法的研究现状展开综述并分析了优点和不足,最后对特征线提取方法存在的主要问题进行总结和展望。     

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.     

Characterization of ASTER GDEM elevation data over vegetated area compared with lidar data

Current researches based on areal or spaceborne stereo images with very high resolutions (<1 m) have demonstrated that it is possible to derive vegetation height from stereo images. The second version of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) is the state-of-the-art global elevation data-set developed by stereo images. However, the resolution of ASTER stereo images (15 m) is much coarser than areal stereo images, and the ASTER GDEM is compiled products from stereo images acquired over 10 years. The forest disturbances as well as forest growth are inevitable in 10 years time span. In this study, the features of ASTER GDEM over vegetated areas under both flat and mountainous conditions were investigated by comparisons with lidar data. The factors possibly affecting the extraction of vegetation canopy height considered include (1) co-registration of DEMs; (2) spatial resolution of digital elevation models (DEMs); (3) spatial vegetation structure; and (4) terrain slope. The results show that the accurate coregistration between ASTER GDEM and national elevation dataset (NED) is necessary over mountainous areas. The correlation between ASTER GDEM minus NED and vegetation canopy height is improved from 0.328 to 0.43 by degrading resolutions from 1 arc-second to 5 arc-second and further improved to 0.6 if only homogenous vegetated areas were considered.     

数字地形分析与地形三维可视化表达研究

数字地形分析是基于数字高程模型(DEM)进行地形分析的一种数字分析技术,是常规纸质地图分析、DEM应用范围的拓广和延伸。地形的可视化表达是地形地貌、景观建模、地形分析等领域的基本技术手段之一,通过和地形分析技术的有机结合,可使地形的表达更加逼真形象。本文简要论述了数字地形分析的基本内容和原理,总结归纳了地形三维可视化表达的相关模型。     

滩涂数字地形模型的建模与应用

采用数字高程模型描述上海市滩涂地形,研究和设计了数字高程模型的数据结构,较好地解决了海量数据的存储、查询和检索问题,由此建立了多年的滩涂数字地形模型。在此基础上研究了滩涂地形分析方法,并在地理信息系统平台上开发了滩涂地形分析系统。试用结果表明,该系统数据结构合理、功能齐全、运行快速稳定,较好地满足了滩涂海塘管理工作的需要。     

Methods and applications of a geomorphological GIS: a case study in the Ordos region of China

An information system designed for geomorphology has been established employing GIS tools, statistics, fuzzy mathematics, digital image processing and finite element analysis. In addition, we have reorganized and proposed a series of quantitative geomorphic analysis modules (quantitative analysis, information compound, division and assessment, dynamic simulation and prediction), and developed new technology in computer-aided geomorphic mapping.Then, by the methods mentioned above, we have made a quantitative analysis in a case study and worked out a map series which consists of about 100 maps on morphotectonics in the Ordos region in China.This work contributes to the development of geomorphology, the theory and applications of GIS, and research on regional morphotectonics, which shows that a GIS-based approach to geomorphic modelling has distinct advantages over traditional methods.     

Effect of Contour Intervals and Grid Cell Size on the Accuracy of DEMs and Slope Derivatives

Digital Elevation Models (DEMs) are indispensable tools in many environmental and natural resource applications. DEMs are frequently derived from contour lines. The accuracy of such DEMs depends on different factors. This research investigates the effect of sampling density used to derive contours, vertical interval between contours (spacing), grid cell size of the DEM (resolution), terrain complexity, and spatial filtering on the accuracy of the DEM and the slope derivative. The study indicated different alternatives to achieve an acceptable accuracy depending on the contour interval, the DEM resolution and the complexity of the terrain. The effect of these factors on the accuracy of the DEM and the slope derivative was quantified using models that determine the level of accuracy (RMSE). The implementation of the models will guide users to select the best combination to improve the results in areas with similar topography. For areas with variable terrain complexity, the suggestion is to generate DEMs and slope at a suitable resolution for each terrain separately and then to merge the results to produce one final layer for the whole area. This will provide accurate estimates of elevation and slope, and subsequently improve the analyses that rely on these digital derivatives.     

浅谈大比例尺数字测图的生产实践与认识

数字测图的基本思想是将地面上的地形和地物要素转换为数字量,然后由电子计算机对其进行处理,得到内容丰富的电子地图,需要时由图形输出设备(如显示器、绘图仪)输出地形图或各种专题图。数字测图就是要实现丰富的地形信息和地理信息数字化和作业过程的自动化或半自动化。本文就北京规划市区1:500地形图更新测绘工程中应用数字测图技术的生产流程以及对数字测图的认识浅谈一下想法。     

Terrain complexity and reduction of topographic data

Digital terrain data are useful for a variety of applications in mapping and spatial analysis. Most available terrain data are organized in a raster format, among them being the most extensively-used Digital Elevation Models (DEM) of the U.S. Geological Survey. A common problem with DEM for spatial analysis at the landscape scale is that the raster encoding of topography is subject to data redundancy and, as such, data volumes may become prohibitively large. To improve efficiency in both data storage and information processing, the redundancy of the terrain data must be minimized by eliminating unnecessary elements. To what extent a set of terrain data can be reduced for improving storage and processing efficiency depends on the complexity of the terrain. In general, data elements for simpler, smoother surfaces can be substantially reduced without losing critical topographic information. For complex terrains, more data elements should be retained if the topography is to be adequately represented. In this paper, we present a measure of terrain complexity based on the behavior of selected data elements in representing the characteristics of a surface. The index of terrain complexity is derived from an estimated parameter which denotes the relationship between terrain representation (percentage surface representation) and relative data volume (percentage DEM elements). The index can be used to assess the required volume of topographic data and determine the appropriate level of data reduction. Two quadrangles of distinct topographic characteristics were examined to illustrate the efficacy of the developed methodology.     

基于DEM的地形曲率计算模型误差分析

地形曲率是地形表面几何形态和地学建模的基本变量之一。本文首先对曲率计算模型进行了归纳,然后通过数据独立的DEM误差分析方法和实际DEM的分析验证,对目前九种曲率计算的三类曲率计算模型进行了量化分析比较。研究结果表明,当DEM数据精度比较高时,高次曲面(四次曲面)能给出较高精度的曲率计算结果,而当DEM误差较大时,低次曲面(二次曲面)由于具有误差的平滑作用而能产生较高精度的曲率值。     

A novel approach for constructing a 3D model based on registering a mono image on a 3D model,applicable in Digital Earth

The effect of Digital Earth on our life is vital. Developing and updating Geospatial data in Digital Earth is also essential. This paper presents the application of a new approach of image registration in Digital Earth. The approach was developed based on registering a mono photograph on a master 3D model. The result is a 3D vector model, which can be broadly applied in visualisation, mapping, geographic information system (GIS), planning, change detection, as well as Digital Earth. The approach does not require parameters of correction for transformation. The accuracy of the output depends on the accuracy of the master data. This approach is very versatile and able to register any image on the digital elevation model, digital surface model and topographic 3D model.     

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.     

Cartographically Wordy but not Necessarily Worthy

Abstract

A novel method called multidirectional visibility index (MVI) has been developed and verified. The MVI improves standard cartographic analytical shading with a number of enhancements to topographic detail and prominent structures, i.e. the portrayal of flat areas in lighter tones, the accentuation of morphologic edges, and the multiscale visualisation of morphologic terrain features. The procedure requires a digital elevation model (DEM) and involves the following steps: visibility mask computation; the respective multidirectional altering of the azimuth and elevation angle; the generation of continuous grid MVIs that indicate upper/lower views, quasi-slope, and relative relief; and an appropriate visualisation of the relevant MVI as a standalone technique or in combination with standard hill-shaded relief. The modelling parameters are robust and therefore highly adaptive to different landforms.     

Integration of multi-sensor data for landscape modeling using a region-based approach

The increased availability of multi-sensor data, and elevation information in particular, leads to the need of advanced processing methods. In the context of landscape modeling tasks, we concentrate on one central component, the extraction of terrain surface from a Digital Surface Model (DSM). In contrast to conventional mathematical grey value morphology approaches (filtering methods) or to stochastical procedures, we propose an alternative methodology for this task by applying a region-based and multi-scale approach. It consists of segmentation and follow-up fuzzy logic classification based on several features derived from elevation and multi-spectral image data. The satisfying results obtained with a multi-sensor as well as with other datasets show the applicability of the approach.