Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature

Iwahashi, Junko; Pike, Richard J.

doi:10.1016/j.geomorph.2006.09.012

Cited by 423 publications

(354 citation statements)

References 29 publications

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“…These two geomorphometric parameters clearly highlight the presence of a concave upward break in slope, typical of the connection between the edifice and the basement (Euillades et al, 2013). This methodology simplifies those proposed by Iwahashi and Pike (2007) and by Gorini (2009) for landform classification. Slope is a suitable landform parameter in the case of monogenetic edifices, generally characterized by a simple shape often showing a radial symmetry, which clearly stands out with respect to the surrounding basement.…”

Section: Slope-total Curvature (Stc) Algorithmmentioning

confidence: 83%

Tracing the boundaries of Cenozoic volcanic edifices from Sardinia (Italy): a geomorphometric contribution

et al. 2014

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Abstract. Unequivocal delimitation of landforms is an important issue for different purposes, from sciencedriven morphometric analysis to legal issues related to land conservation. This study is aimed at giving a new contribution to the morphometric approach for the delineation of the boundaries of volcanic edifices, applied to 13 monogenetic volcanoes (scoria cones) related to the Pliocene-Pleistocene volcanic cycle in Sardinia (Italy). External boundary delimitation of the edifices is discussed based on an integrated methodology using automatic elaboration of digital elevation models together with geomorphological and geological observations. Different elaborations of surface slope and profile curvature have been proposed and discussed; among them, two algorithms based on simple mathematical functions combining slope and profile curvature well fit the requirements of this study. One of theses algorithms is a modification of a function introduced by Grosse et al. (2011), which better performs for recognizing and tracing the boundary between the volcanic scoria cone and its basement. Although the geological constraints still drive the final decision, the proposed method improves the existing tools for a semi-automatic tracing of the boundaries.

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Section: Slope-total Curvature (Stc) Algorithmmentioning

confidence: 83%

Tracing the boundaries of Cenozoic volcanic edifices from Sardinia (Italy): a geomorphometric contribution

et al. 2014

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“…In this study, a radius between 5 m and 25 m was applied to determine the slope positions. Iwahashi and Pike had developed a Landforms classification unsupervised method based on only three terrain attributes: slope gradient, surface texture and local convexity (Iwahashi & Pike, 2007).…”

Section: Topographic Position Index (Tpi) the Analysis Was Performedmentioning

confidence: 99%

Multiscale Landforms Classification Based on UAV Datasets

Doumit¹

2018

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“…Automated and semi-automated terrain analysis from the Digital Elevation Models (DEM) has become widely used in geomorphological researches and landform classifications during recent years. Landform features as physical constituents of terrain has been extracted from DEMs using various approaches including combination of geomorphometric parameters (Dikau 1989, Iwahashi, Pike 2007, fuzzy logic and unsupervised classification (Irvin et al 1997, Burrough et al 2000, Adediran et al 2004, supervised classification (Brown et al 1998, Hengl, Rossiter 2003, Prima et al 2006, probabilistic clustering algorithms (Stepinski, Vilalta 2005), multivariate descriptive statistics (Evans 1972, Dikau 1989, Dehn et al 2001, double ternary diagram classification (Crevenna et al 2005), object-oriented image analysis (Dragut, Blaschke 2006) and artificial neural networks (Ehsani, Quiel 2008).…”

Section: Introductionmentioning

confidence: 99%

Semi-Automated Classification of Landform Elements in Armenia Based on SRTM DEM using K-Means Unsupervised Classification

Piloyan

Konečný

2017

Quaestiones Geographicae

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PiloyAn A., Konečný M., 2017. Semi-automated classification of landform elements in Armenia based on SRTM DEM using k-means unsupervised classification. Quaestiones Geographicae 36(1), Bogucki Wydawnictwo Naukowe, Poznań, pp. 93-103, 5 figs, 4 tables.AbstrAct: Land elements have been used as basic landform descriptors in many science disciplines, including soil mapping, vegetation mapping, and landscape ecology. This paper presents a semi-automatic method based on k-means unsupervised classification to analyze geomorphometric features as landform elements in Armenia. First, several data layers were derived from DEM: elevation, slope, profile curvature, plan curvature and flow path length. Then, k-means algorithm has been used for classifying landform elements based on these morphomertic parameters. The classification has seven landform classes. Overall, landform classification is performed in the form of a three-level hierarchical scheme. The resulting map reflects the general topography and landform character of Armenia.

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Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature

Cited by 423 publications

References 29 publications

Tracing the boundaries of Cenozoic volcanic edifices from Sardinia (Italy): a geomorphometric contribution

Tracing the boundaries of Cenozoic volcanic edifices from Sardinia (Italy): a geomorphometric contribution

Multiscale Landforms Classification Based on UAV Datasets

Semi-Automated Classification of Landform Elements in Armenia Based on SRTM DEM using K-Means Unsupervised Classification

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