eprintid: 4378
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/43/78
datestamp: 2023-11-09 16:16:03
lastmod: 2023-11-09 16:16:03
status_changed: 2023-11-09 15:58:18
type: article
metadata_visibility: show
creators_name: Hani, A.F.M.
creators_name: Sathyamoorthy, D.
creators_name: Asirvadam, V.S.
title: Computing uncertainty of physiographic features extracted from multiscale digital elevation models
ispublished: pub
keywords: Change classes; Digital elevation model; Fuzzy classification; Ground truth; Lifting schemes; Physiographic features; Spatial resolution, Entropy; Errors; Geomorphology, Landforms, computer simulation; digital elevation model; entropy; fuzzy mathematics; numerical model; uncertainty analysis
note: cited By 3
abstract: In this paper, it is proposed that the mapping of uncertainties of the three predominant physiographic features of terrains, which are mountain, basins and piedmont slopes, using variation in the spatial resolution over which these landforms are defined, can be performed with fuzzy classification. The proposed methodology allows for the generation of fuzzy certainty maps which assign high levels of uncertainty to regions with high levels of change across scales. This paper demonstrates that fuzzy certainty maps provide a better quantification of landform character than Boolean landform maps alone. In terms of sensitivity to noise, the methodology is able to identify narrow bridges, and spurious landforms, and assign these errors with low certainty values. However, it is unable to identify spurious modifications to landform shape, with these errors being assigned high certainty values. Ground truth maps are required to identify these errors. © 2013 Elsevier Ltd.
date: 2014
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890854702&doi=10.1016%2fj.cageo.2013.11.005&partnerID=40&md5=bf3d894a62e730398b700494878ec044
id_number: 10.1016/j.cageo.2013.11.005
full_text_status: none
publication: Computers and Geosciences
volume: 64
pagerange: 15-23
refereed: TRUE
issn: 00983004
citation:   Hani, A.F.M. and Sathyamoorthy, D. and Asirvadam, V.S.  (2014) Computing uncertainty of physiographic features extracted from multiscale digital elevation models.  Computers and Geosciences, 64.  pp. 15-23.  ISSN 00983004