Quantitative representation of mountain objects extracted from the global digital model (GTOPO30)

Miliaresis, GCh; Argialas, DP

dc.contributor.author	Miliaresis, GCh	en
dc.contributor.author	Argialas, DP	en
dc.date.accessioned	2014-03-01T01:18:16Z
dc.date.available	2014-03-01T01:18:16Z
dc.date.issued	2002	en
dc.identifier.issn	0143-1161	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14906
dc.subject	Digital Elevation Model	en
dc.subject	Object Extraction	en
dc.subject.classification	Remote Sensing	en
dc.subject.classification	Imaging Science & Photographic Technology	en
dc.subject.other	Algorithms	en
dc.subject.other	Catchments	en
dc.subject.other	Geomorphology	en
dc.subject.other	Maps	en
dc.subject.other	Global digital elevation models	en
dc.subject.other	Remote sensing	en
dc.subject.other	classification	en
dc.subject.other	digital elevation model	en
dc.subject.other	image analysis	en
dc.subject.other	mountain region	en
dc.subject.other	United States	en
dc.title	Quantitative representation of mountain objects extracted from the global digital model (GTOPO30)	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/01431160110070690	en
heal.identifier.secondary	http://dx.doi.org/10.1080/01431160110070690	en
heal.language	English	en
heal.publicationDate	2002	en
heal.abstract	A methodology was developed previously by the authors for the segmentation of the Global Digital Elevation Model (GTOPO30) to three terrain classes (mountains, basins and pieidmont slopes) and it was applied to the Great Basin Section (south-west USA). In the present research effort, mountain objects were identified through a connected component-labelling algorithm applied on the mountain terrain class. Taking into account the physical and perceptual attributes of the Great Basin mountain features, 12 morphometric attributes were defined for the mountain objects and were used as descriptors in their parametric representation. Finally, classification of mountain objects through the implementation of a K-means clustering algorithm resulted in four clusters of mountain objects that appeared to be spatially arranged to distinct geographic regions. The results were compared with existing maps and they were found to be in accordance with existing physiographic descriptions. It is concluded that the derived parametric representation of mountain objects carried sufficient physiographic information and it can be used for mountain classification. The conclusions point out the physiographic information content of GTOPO30 and its value and applications to regional geology and space geomorphology.	en
heal.publisher	TAYLOR & FRANCIS LTD	en
heal.journalName	International Journal of Remote Sensing	en
dc.identifier.doi	10.1080/01431160110070690	en
dc.identifier.isi	ISI:000174122400009	en
dc.identifier.volume	23	en
dc.identifier.issue	5	en
dc.identifier.spage	949	en
dc.identifier.epage	964	en