Logo of Humboldt-Universität zu BerlinLogo of Humboldt-Universität zu Berlin
edoc-Server
Open-Access-Publikationsserver der Humboldt-Universität
de|en
Header image: facade of Humboldt-Universität zu Berlin
View Item 
  • edoc-Server Home
  • Artikel und Monographien
  • Zweitveröffentlichungen
  • View Item
  • edoc-Server Home
  • Artikel und Monographien
  • Zweitveröffentlichungen
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
View Item 
  • edoc-Server Home
  • Artikel und Monographien
  • Zweitveröffentlichungen
  • View Item
  • edoc-Server Home
  • Artikel und Monographien
  • Zweitveröffentlichungen
  • View Item
2020-09-09Zeitschriftenartikel DOI: 10.18452/21888
Classification of Landforms for Digital Soil Mapping in Urban Areas Using LiDAR Data Derived Terrain Attributes: A Case Study from Berlin, Germany
Mohamed, Mohamed Ali cc
Mathematisch-Naturwissenschaftliche Fakultät
In this study, a knowledge-based fuzzy classification method was used to classify possible soil-landforms in urban areas based on analysis of morphometric parameters (terrain attributes) derived from digital elevation models (DEMs). A case study in the city area of Berlin was used to compare two different resolution DEMs in terms of their potential to find a specific relationship between landforms, soil types and the suitability of these DEMs for soil mapping. Almost all the topographic parameters were obtained from high-resolution light detection and ranging (LiDAR)-DEM (1 m) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-DEM (30 m), which were used as thresholds for the classification of landforms in the selected study area with a total area of about 39.40 km2. The accuracy of both classifications was evaluated by comparing ground point samples as ground truth data with the classification results. The LiDAR-DEM based classification has shown promising results for classification of landforms into geomorphological (sub)categories in urban areas. This is indicated by an acceptable overall accuracy of 93%. While the classification based on ASTER-DEM showed an accuracy of 70%. The coarser ASTER-DEM based classification requires additional and more detailed information directly related to soil-forming factors to extract geomorphological parameters. The importance of using LiDAR-DEM classification was particularly evident when classifying landforms that have narrow spatial extent such as embankments and channel banks or when determining the general accuracy of landform boundaries such as crests and flat lands. However, this LiDAR-DEM classification has shown that there are categories of landforms that received a large proportion of the misclassifications such as terraced land and steep embankments in other parts of the study area due to the increased distance from the major rivers and the complex nature of these landforms. In contrast, the results of the ASTER-DEM based classification have shown that the ASTER-DEM cannot deal with small-scale spatial variation of soil and landforms due to the increasing human impacts on landscapes in urban areas. The application of the approach used to extract terrain parameters from the LiDAR-DEM and their use in classification of landforms has shown that it can support soil surveys that require a lot of time and resources for traditional soil mapping.
Files in this item
Thumbnail
land-09-00319.pdf — Adobe PDF — 4.862 Mb
MD5: d82c890057b17fc50e0dd57de7282b3c
Cite
BibTeX
EndNote
RIS
(CC BY 4.0) Attribution 4.0 International(CC BY 4.0) Attribution 4.0 International
Details
DINI-Zertifikat 2019OpenAIRE validatedORCID Consortium
Imprint Policy Contact Data Privacy Statement
A service of University Library and Computer and Media Service
© Humboldt-Universität zu Berlin
 
DOI
10.18452/21888
Permanent URL
https://doi.org/10.18452/21888
HTML
<a href="https://doi.org/10.18452/21888">https://doi.org/10.18452/21888</a>