The Digital Elevation Model Intercomparison Experiment Demix, a Community-Based Approach at Global Dem Benchmarking

Strobl, Peter; Bielski, Conrad; Guth, Peter L.; Grohmann, Carlos Henrique; Müller, Jan-Peter; López-Vázquez, Carlos; Gesch, Dean B.; Amatulli, Giuseppe; Riazanoff, Serge; Carabajal, C. C.

doi:10.5194/isprs-archives-xliii-b4-2021-395-2021

Cited by 13 publications

(14 citation statements)

References 11 publications

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“…For example, GDAL (Perry, Rouault, Butler, & Yesson, 2021) suggests reprojecting the DEM before creating slope maps. MICRODEM (Guth, 2009, 2010), GRASS (Shapiro & Waupotitsch, 2021), and ArcGIS's geodesic model (Esri, 2020) can correctly compute slope and aspect for geographic DEMs (Strobl et al., 2021). The slope results from those three programs agree, and the results from equivalent UTM and geographic grids agree within the caveats to be discussed below.…”

Section: Methodsmentioning

confidence: 99%

“…We thank Høydedata, NOAA, and USGS for freely hosting the LiDAR and other mapping data. We greatly appreciate the members of DEMIX (Strobl et al, 2021) for many stimulating discussions on DEM quality, particularly Carlos López-Vázquez and Peter Strobl. This work was done in MICRODEM (Guth, 2009); Delphi code for the slope, aspect, and hillshade algorithms is posted at https://github.com/prof-pguth/ Slope -for-arc-secon d-DEMs.…”

Section: Ack N Owled G M Entsmentioning

confidence: 99%

“…Reprojection computes new elevations at locations different from those in the original DEM, changing its characteristics. Some current GIS software correctly and automatically converts the arc‐second spacing of the DEMs into meters and deals with the rectangular pixels; examples include MICRODEM, GRASS, and ArcGIS (Strobl et al., 2021). This article will demonstrate the required modifications to the slope, aspect, and hillshade algorithms and demonstrate how they can be adapted not only to geographic DEMs, but also to those in a variety of other projections.…”

Section: Introductionmentioning

confidence: 99%

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Slope, aspect, and hillshade algorithms for non‐square digital elevation models

Guth

Kane

2021

Transactions in GIS

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Focal operations operate on raster cells and their neighbors, and provide some of the most useful displays for digital elevation models (DEMs), including slope, aspect, and hillshade displays. Computation of these parameters requires data spacing, and many GIS programs have required that the spacing be the same in both directions of the grid. At the same time, the mapping agencies supplying DEMs produce most of the moderate-scale

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Section: Methodsmentioning

confidence: 99%

Section: Ack N Owled G M Entsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Slope, aspect, and hillshade algorithms for non‐square digital elevation models

Guth

Kane

2021

Transactions in GIS

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“…These user communities and data producers do not read the same literature and consequently many have adopted various definitions for digital topography and other digital representations (see Section 3.1) of topographic surfaces, as well as their derivatives. This paper aims to consolidate the definitions as agreed upon by participants of the 2019 Joint Research Centre (JRC) meeting and the Digital Elevation Model Intercomparison eXperiment (DEMIX) working group [1], which began work in 2020.…”

Section: Introductionmentioning

confidence: 99%

Digital Elevation Models: Terminology and Definitions

et al. 2021

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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.

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“…Given the high applicability of DEMs, it is important to assess their accuracy in order to understand their respective qualities, capabilities and limitations [21]. This is also the focus of the recently launched initiative, called the Digital Elevation Model Intercomparison Experiment (DEMIX), which targets global DEM benchmarking [22]. The vertical accuracy of a DEM, or its ability to represent the true elevation, is a key consideration when selecting between different DEMs for a particular application.…”

Section: Introductionmentioning

confidence: 99%

Error Characteristics of Pan-Arctic Digital Elevation Models and Elevation Derivatives in Northern Sweden

2021

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Many biochemical processes and dynamics are strongly controlled by terrain topography, making digital elevation models (DEM) a fundamental dataset for a range of applications. This study investigates the quality of four pan-Arctic DEMs (Arctic DEM, ASTER DEM, ALOS DEM and Copernicus DEM) within the Kalix River watershed in northern Sweden, with the aim of informing users about the quality when comparing these DEMs. The quality assessment focuses on both the vertical accuracy of the DEMs and their abilities to model two fundamental elevation derivatives, including topographic wetness index (TWI) and landform classification. Our results show that the vertical accuracy is relatively high for Arctic DEM, ALOS and Copernicus and in our study area was slightly better than those reported in official validation results. Vertical errors are mainly caused by tree cover characteristics and terrain slope. On the other hand, the high vertical accuracy does not translate directly into high quality elevation derivatives, such as TWI and landform classes, as shown by the large errors in TWI and landform classification for all four candidate DEMs. Copernicus produced elevation derivatives with results most similar to those from the reference DEM, but the errors are still relatively high, with large underestimation of TWI in land cover classes with a high likelihood of being wet. Overall, the Copernicus DEM produced the most accurate elevation derivatives, followed by slightly lower accuracies from Arctic DEM and ALOS, and the least accurate being ASTER.

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The Digital Elevation Model Intercomparison Experiment Demix, a Community-Based Approach at Global Dem Benchmarking

Cited by 13 publications

References 11 publications

Slope, aspect, and hillshade algorithms for non‐square digital elevation models

Slope, aspect, and hillshade algorithms for non‐square digital elevation models

Digital Elevation Models: Terminology and Definitions

Error Characteristics of Pan-Arctic Digital Elevation Models and Elevation Derivatives in Northern Sweden

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