Сибирский федеральный университет

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Multidisciplinary Scientific Geoconference (SGEM 2016); Albena, BULGARIA; Albena, BULGARIA

Год издания: 2016

Ключевые слова: digital elevation model, relief features, accuracy assessment

Аннотация: Nowadays, an accuracy of digital elevation models (DEM) is increasing and it can be used to investigate relationships between land-cover dynamics and orography. Relief elements, such as slopes, aspects, and curvature can be easily calculated using GIS-tools. However, it is known that existing DEMs have errors. Some investigators assessed accuracy of the existing high-resolution DEMs obtained by remote sensing techniques. In addition, it is crucial to know an accuracy of the calculated relief features (slope, aspect, and curvature) for proper interpretation of results, for example, vegetation distribution relative to azimuthal directions of slopes. In this research, equations to estimate errors for terrain features extracted from DEM were determined based on the equations realized in Erdas Imagine and ESRI ArcGIS software, and using error assessment theory. Equations to estimate root mean square error (RMSE) were realized using Erdas Imagine modeler. The analysis of SRTM DEM showed that RMSE of aspects depends on the average changes in elevation by x and y directions and relative elevation error of DEM. RMSE of aspects decreases with increasing of the average changes in elevation by x and y directions. RMSE for aspects was less than 13 degrees for 90% of the area within test site. RMSE of slopes depends on spatial resolution and relative elevation error of DEM, and the average changes in elevation by x and y directions. With increasing of slope steepness, its RMSE decreases. RMSE for slope was less than 4.5 for 90% of the area within test site.

Журнал: INFORMATICS, GEOINFORMATICS AND REMOTE SENSING CONFERENCE PROCEEDINGS, SGEM 2016, VOL I

Номера страниц: 859-866

ISSN журнала: 13142704

Место издания: SOFIA

Издатель: STEF92 TECHNOLOGY LTD

• Im Sergei (VN Sukachev Inst Forest SB RAS, Krasnoyarsk, Russia; Siberian Fed Univ, Krasnoyarsk, Russia; MF Reshetnev Siberian State Aerosp Univ, Krasnoyarsk, Russia)

• Web of Science Core Collection