Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Lucian Drăguţ1, Clemens Eisank

  • 1Department of Geography and Geology, University of Salzburg, Hellbrunnerstraße 34, Salzburg 5020, Austria.

Geomorphology (Amsterdam, Netherlands)
|April 10, 2012
PubMed
Summary

This study presents an automated object-based method for classifying topography using Shuttle Radar Topography Mission (SRTM) data. The approach effectively segments and classifies land-surface complexity, yielding results comparable to manual mapping.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Influence of sampling design on landslide susceptibility modeling in lithologically heterogeneous areas.

Scientific reports·2022
Same author

A journey on plate tectonics sheds light on European crayfish phylogeography.

Ecology and evolution·2019
Same author

Assessment of multiresolution segmentation for delimiting drumlins in digital elevation models.

Geomorphology (Amsterdam, Netherlands)·2014
Same author

Local variance for multi-scale analysis in geomorphometry.

Geomorphology (Amsterdam, Netherlands)·2011
Same author

Object representations at multiple scales from digital elevation models.

Geomorphology (Amsterdam, Netherlands)·2011

Area of Science:

  • Geosciences
  • Remote Sensing
  • Geomorphometry

Background:

  • Topographical classification is crucial for understanding land surface processes.
  • Existing methods often require manual intervention or extensive pre-processing.
  • Automated, data-driven approaches are needed for efficient topographical analysis.

Purpose of the Study:

  • To introduce an automated object-based method for topography classification using SRTM data.
  • To decompose land-surface complexity into homogeneous domains at multiple scales.
  • To provide a fully automated and efficient tool for topographical mapping.

Main Methods:

  • Object-based image analysis applied to SRTM elevation data.
  • Multi-scale segmentation using self-adaptive, data-driven techniques based on local variance.
  • Classification of segmented objects using elevation and standard deviation thresholds.
  • Implementation within eCognition® software with a web-based visualization and download application.

Main Results:

  • Automated segmentation and classification of topography at three scale levels.
  • Generated topographical classifications comparable in detail to manually drawn maps.
  • Statistical validation confirming internal homogeneity and external heterogeneity of classified regions.
  • Identified object boundaries aligning with natural regional discontinuities.

Conclusions:

  • The proposed object-based method offers a simple, fully automated approach to topography classification.
  • The method requires minimal pre-processing and relies on only two key parameters.
  • Results demonstrate high accuracy and efficiency, suitable for regional and global applications.

Related Experiment Videos