Header

UZH-Logo

Maintenance Infos

Assessment of height accuracy of the DEM for species habitat analysis and modeling


Haller, R M; Imfeld, S (2007). Assessment of height accuracy of the DEM for species habitat analysis and modeling. In: 5th International Symposium Spatial Data Quality 2007. Theme: Modelling qualities in space and time , Enschede (NL), 13 June 2007 - 15 June 2007, online.

Abstract

In mountainous areas, the use of DEM as environmental parameter in local and global models is widely accepted. Derived information like slope, aspect and others are often used as substitutes for missing data on specific habitat parameters to model the probability of species occurrence or absence. For habitat analysis of species, point observations were combined with underlying terrain information. The use of DEM with a resolution of 25 m is still accepted as sufficient. Different studies have shown the influence of specific topographic elements like ridges, depression, steep and flat areas on species distributions. Data providers often provide an overall accuracy description for the total area. The spatial correlation of error is hardly ever described, although this influences notably the derived data from DEM and could consequently be a major source of uncertainty in models using such derivatives.
We investigated the error of neighbouring cells of two different DEMs with a resolution of 20 m and 25 m, derived from photogrammetry and digitized from contour lines, respectively. 5 sampling areas each with 5*5 control points of the precise lattice coordinate of the DEM were established in the area of the Swiss National Park to investigate error correlations in open and forested as well as in planar and steep areas. Moreover, 246 survey points of a slightly irregular grid in the open area were used. The reference points were measured with surveying techniques.
The results show a spatial correlation of the error on all plots as well as significant differences between the different subareas. Nevertheless, the average difference of error between neighbouring points is 1.01 m. The derived mean slope error from cell to cell (20m) is therefore 2.9°. The maximum error between two neighbouring cells of 25 m is 8.14m which is resulting in a slope mismeasurement of 19.0°.
These results show the importance of the knowledge about spatial correlation of error in DEM, especially its consequences on derived data of DEM used in more complex model calculations such as species habitat models.

Abstract

In mountainous areas, the use of DEM as environmental parameter in local and global models is widely accepted. Derived information like slope, aspect and others are often used as substitutes for missing data on specific habitat parameters to model the probability of species occurrence or absence. For habitat analysis of species, point observations were combined with underlying terrain information. The use of DEM with a resolution of 25 m is still accepted as sufficient. Different studies have shown the influence of specific topographic elements like ridges, depression, steep and flat areas on species distributions. Data providers often provide an overall accuracy description for the total area. The spatial correlation of error is hardly ever described, although this influences notably the derived data from DEM and could consequently be a major source of uncertainty in models using such derivatives.
We investigated the error of neighbouring cells of two different DEMs with a resolution of 20 m and 25 m, derived from photogrammetry and digitized from contour lines, respectively. 5 sampling areas each with 5*5 control points of the precise lattice coordinate of the DEM were established in the area of the Swiss National Park to investigate error correlations in open and forested as well as in planar and steep areas. Moreover, 246 survey points of a slightly irregular grid in the open area were used. The reference points were measured with surveying techniques.
The results show a spatial correlation of the error on all plots as well as significant differences between the different subareas. Nevertheless, the average difference of error between neighbouring points is 1.01 m. The derived mean slope error from cell to cell (20m) is therefore 2.9°. The maximum error between two neighbouring cells of 25 m is 8.14m which is resulting in a slope mismeasurement of 19.0°.
These results show the importance of the knowledge about spatial correlation of error in DEM, especially its consequences on derived data of DEM used in more complex model calculations such as species habitat models.

Statistics

Downloads

32 downloads since deposited on 25 Apr 2013
2 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Conference or Workshop Item (Paper), not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:15 June 2007
Deposited On:25 Apr 2013 13:38
Last Modified:05 Apr 2016 16:46
Publisher:ISPRS
Official URL:http://www.isprs.org/proceedings/XXXVI/2-C43/Session4/paper_haller.pdf
Related URLs:http://www.isprs.org/proceedings/XXXVI/2-C43/ (Organisation)

Download

Preview Icon on Download
Preview
Content: Published Version
Language: English
Filetype: PDF
Size: 2MB

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations