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An approach to DEM analysis for landform classification based on local gradients
- Source :
- Earth Science Informatics. 11:287-305
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- In this paper we propose an exploratory methodology, devoted to classify the pixels of a Digital Elevation Model (DEM), to be used as a morphometric basis for studies in other frameworks. The aim is to classify the terrain units according to eight topographical local gradients, computed as differences between each pixel and the eight surrounding ones. A partition of the pixels into homogeneous classes is carried out, emphasizing different details of the terrain units characteristics. Each class is described by a complete set of statistics of terrain attributes, including elevation, slope, and aspect, and graphical tools are used to ease the understanding of the partition. In addition, appropriate colours are attributed to each class, in order to build a thematic map that may simulate a three-dimensional effect. They are defined by computing their hue and saturation according to each class mean slope and aspect, respectively. Applications to Mount Soratte (Italy) and Cephalonia island (Greece) show how the results may be successfully used to describe in different ways the morphometric structure of the terrain under study and provide appropriate graphical representations.
- Subjects :
- geography
geography.geographical_feature_category
010504 meteorology & atmospheric sciences
Pixel
business.industry
Computer science
Landform
Pattern recognition
Terrain
010502 geochemistry & geophysics
01 natural sciences
Thematic map
Homogeneous
General Earth and Planetary Sciences
Partition (number theory)
Artificial intelligence
Digital elevation model
business
0105 earth and related environmental sciences
Hue
Subjects
Details
- ISSN :
- 18650481 and 18650473
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Earth Science Informatics
- Accession number :
- edsair.doi...........b2ce01cbf39a69076de8d80c4883fa92