Comparing Methods for Interpolation to Improve Raster Digital Elevation Models

Digital elevation models (DEMs) are available as raster files at 100m, 30m, and 10m resolutions for the contiguous United States and are used in a variety of geographic analyses. Some projects may require a finer resolution. GIS software offers many options for interpolating data to higher resolutions. We compared ten interpolation methods using 10m sample data from the Ouachita Mountains in central Arkansas. We interpolated the 10m DEM to 5m, 2.5m, and 1m resolutions and compared the absolute mean difference (AMD) for each using surveyed control points. Overall, there was little difference in the accuracy between interpolation methods at the resolutions tested and minimal departure from the original 10m raster

Camera geolocation using digital elevation models in hilly area

he geolocation of skyline provides an important application in unmanned vehicles, unmanned aerial vehicles, and other fields. However, the existing methods are not effective in hilly areas. In this paper, we analyze the difficulties to locate in hilly areas and propose a new geolocation method. According to the vegetation in hilly area, two new skyline features, enhanced angle chain code and lapel point, are proposed. In order to deal with the skyline being close to the camera, we also propose a matching method which incorporates skyline distance heatmap and skyline pyramid. The experimental results show that the proposed method is highly effective in hilly area and has a robust performance against noise and rotation effects

W42 - a scalable spatial database system for holding Digital Elevation Models

The design of a scalable system for holding spatial data in general and digital elevation models (DEMs) in specific has to account for the characteristics of data from various application fields. The data can be heterogeneous in coverage, as well as in resolution, information content and quality. A database aiming at the representation of world-wide DEMs has to consider these differences in the design of the system with respect to the structure and the algorithms. The database system W42, which is presented in the work at hand, is a scalable spatial database system capable of holding, extracting, mosaicking, and fusing spatial data represented in raster- as well as in vector-format. Design aspects for this task can be specified as holding spatial data in unique data structures and providing unique access functions to the data. These are subject of this work as well as first experiences gained from the implementation of part of the extensions made for the TanDEM-X mission

Digital Elevation Models in Geomorphology

This chapter presents place of geomorphometry in contemporary geomorphology. The focus is on discussing digital elevation models (DEMs) that are the primary data source for the analysis. One has described the genesis and definition, main types, data sources and available free global DEMs. Then we focus on landform parameters, starting with primary morphometric parameters, then morphometric indices and at last examples of morphometric tools available in geographic information system (GIS) packages. The last section briefly discusses the landform classification systems which have arisen in recent years

Analsysis of ASTER GDEM elevation models

Digital elevation models (DEM) are of fundamental importance for remote sensing. With a DEM the three-dimensional positioning, requiring a stereo model can be reduced to a two-dimensional solution just based on a single image. With the free of charge availability of the SRTM-height models, covering the land area from 56 degrees southern up to 60.25 degrees northern latitude a nearly world wide coverage is given. But especially in mountainous regions and dry sand deserts the original SRTM DEMs have gaps in the original SRTM data. Now with the also free of charge available ASTER GDEM the area from 83 degrees southern up to 83 degrees northern latitude is covered. For areas where both height models exist, it is the question which height model should be preferred. Outside the USA the SRTM height data have a spacing of 3 arcsec (nearly 90m), while the ASTER GDEM has a spacing of just 1 arcsec (nearly 30m). The decision for the selection of the DEM is based on accuracy, homogeneity, reliability, completeness and morphologic details. In test areas with precise reference height models, located in the USA, Germany, France, Poland, Turkey and Jordan and with different morphology as mountainous, rolling, flat and urban and also with different land classes, the ASTER GDEM has been analyzed and compared with SRTM DEM as well as with SPOT 5 HRS and Cartosat 1 height models. ASTER GDEM in most cases shows improved accuracy with a higher number of number of stacks (number of images used for overlapping height models). But the accuracy improvement with more stacks is smaller as it should be for random data. The number of used stacks per DEM-point varies strongly depending upon the area. Especially in areas with low cloud coverage and higher imaging priority a high number of stacks have been used opposite to areas often covered by clouds and having lower imaging priority, where the dominating number of DEM-points may be located only in 2 stacks. Based on own matching results with ASTER images quite more morphologic details have been expected in ASTER GDEM having 1 arcsec point spacing as in SRTM height models with 3 arcsec spacing, but the analyzed data show only slightly more morphologic details as the SRTM 3" height model. SRTM as well as ASTER height models are strongly depending upon the morphology and the land coverage, so not a homogenous accuracy can be expected. In addition, as all height models, the accuracy depends usually linear upon the tangent of terrain slope, so the standard deviation of height (SZ) should be given in the form SZ = a + b*tan(terrain slope). Not only the standard deviation is important, the height models have different systematic errors (bias). The bias in X, Y and Z is larger for ASTER GDEM as for SRTM DEMs. Horizontal shifts have been determined by adjustment of the ASTER GDEMs against the reference height model. In general the SRTM height models are slightly more accurate as the ASTER GDEM

Creating HiRISE digital elevation models for Mars using the open-source Ames Stereo Pipeline

The present availability of sub-decametre digital elevation models on Mars – crucial for the study of surface processes – is scarce. In contrast to low-resolution global datasets, such models enable the study of landforms 3000 stereo pairs at 0.25 m pixel−1 resolution, enabling the creation of high-resolution digital elevation models (1–2 m pixel−1). Hitherto, only ∼500 of these pairs have been processed and made publicly available. Existing pipelines for the production of digital elevation models from stereo pairs, however, are built upon commercial software, rely upon sparsely available intermediate data, or are reliant on proprietary algorithms. In this paper, we present and test the output of a new pipeline for producing digital elevation models from HiRISE stereo pairs that is built entirely upon the open-source NASA Ames Stereo Pipeline photogrammetric software, making use of freely available data for cartographic rectification. This pipeline is designed for simple application by researchers interested in the use of high-resolution digital elevation models. Implemented here on a research computing cluster, this pipeline can also be used on consumer-grade UNIX computers. We produce and evaluate four digital elevation models using the pipeline presented here. Each are globally well registered, with accuracy similar to those of digital elevation models produced elsewhere

Digital Elevation Models: Terminology and Definitions

Digital elevation models (DEMs) provide fundamental depictions of the three-dimensional shape of the Earth’s surface and are useful to a wide range of disciplines. Ideally, DEMs record the interface between the atmosphere and the lithosphere using a discrete two-dimensional grid, with complexities introduced by the intervening hydrosphere, cryosphere, biosphere, and anthroposphere. The treatment of DEM surfaces, affected by these intervening spheres, depends on their intended use, and the characteristics of the sensors that were used to create them. DEM is a general term, and more specific terms such as digital surface model (DSM) or digital terrain model (DTM) record the treatment of the intermediate surfaces. Several global DEMs generated with optical (visible and near-infrared) sensors and synthetic aperture radar (SAR), as well as single/multi-beam sonars and products of satellite altimetry, share the common characteristic of a georectified, gridded storage structure. Nevertheless, not all DEMs share the same vertical datum, not all use the same convention for the area on the ground represented by each pixel in the DEM, and some of them have variable data spacings depending on the latitude. This paper highlights the importance of knowing, understanding and reflecting on the sensor and DEM characteristics and consolidates terminology and definitions of key concepts to facilitate a common understanding among the growing community of DEM users, who do not necessarily share the same background