Name
Abstract | ||
|---|---|---|
This paper focuses on slope unit derivation from DEM by object-based image analysis. A landform classification system consisting of ridge, valley, slope unit, and flat terrain classes is firstly constructed considering the detailed characteristics of slope unit. Based on the classification system, a procedure for slope unit derivation from DEM, which takes into consideration the topographic factors including slope gradient, slope aspect and curvature, is designed within multi-scale OBIA framework. The method to determine the optimum segmentation scale by COLV is also introduced. A case study of Shenzhen is carried out to verify the feasibility and accuracy of the designed procedure. The results of the case study shows that when the scale parameter is 20, the derived slope units remarkably denote the detailed landform structure and the bounds of the derived units are largely consistent to the concave and convex terrain bounds. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/Geoinformatics.2013.6626065 | Geoinformatics |
Keywords | Field | DocType |
slope gradient,convex terrain bounds,digital elevation model,gis,object-based image analysis,flat terrain classes,dem,slope unit,slope aspect,concave terrain bounds,slope units,digital elevation models,landform classification system,image classification,geophysical image processing,topographic factors,colv,optimum segmentation scale,multiscale obia framework,dem-based slope unit derivation,image segmentation,surface topography,geographic information systems,image analysis,remote sensing | Curvature,Computer science,Terrain,Remote sensing,Slope stability probability classification,Image segmentation,Digital elevation model,Landform,Contextual image classification,Scale parameter | Conference |
Volume | Issue | ISSN |
null | null | 2161-024X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenchao Xiao | 1 | 2 | 1.76 |
| Yuan Tian | 2 | 3 | 5.43 |
| Ting Li | 3 | 0 | 0.34 |
| Zhi-Fang Gao | 4 | 4 | 1.06 |