Geomorphometric analysis for characterizing landforms in Morelos State, Mexico

Bolongaro-Crevenna, Andrea; Torres-Rodríguez, Vicente; Sorani, Valentino; Frame, Dawn; Ortíz, Manuel

doi:10.1016/j.geomorph.2004.11.007

Cited by 89 publications

(51 citation statements)

References 9 publications

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“…Terrain features can be described and categorized into simple topographic relief elements or units by parameterizing DEMs (Bolongaro-Crevenna et al, 2005). Using geomorphometric parameters (slope, aspect, azimuth, concavity/convexity, etc.)…”

Section: Digital Analysis Methodsmentioning

confidence: 99%

“…For example, using only relative slope gradients as a geomorphometric parameter, Yong et al (2008) demonstrated that the same parameters used for Pakistan are applicable for a firstapproximation of geomorphology in other regions that have distinctively different depositional environments, such as Turkey and Mozambique. Geomorphometry also offers a variety of additional approaches for defining terrain units, such as the classification of terrain feature parameters, filter- ing techniques, cluster analysis, and multivariate statistics (Pike, 2002;Bolongaro-Crevenna et al, 2005).…”

Section: Digital Analysis Methodsmentioning

confidence: 99%

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Site Characterization Using Integrated Imaging Analysis Methods on Satellite Data of the Islamabad, Pakistan, Region

Yong

Hough

Abrams

et al. 2008

Bulletin of the Seismological Society of America

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We develop an integrated digital imaging analysis approach to produce a first-approximation site characterization map for Islamabad, Pakistan, based on remote-sensing data. We apply both pixel-based and object-oriented digital imaging analysis methods to characterize detailed (1∶50; 000) geomorphology and geology from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. We use stereo-correlated relative digital elevation models (rDEMs) derived from ASTER data, as well as spectra in the visible near-infrared (VNIR) to thermal infrared (TIR) domains. The resulting geomorphic units in the study area are classified as mountain (including the Margala Hills and the Khairi Murat Ridge), piedmont, and basin terrain units. The local geologic units are classified as limestone in the Margala Hills and the Khairi Murat Ridge and sandstone rock types for the piedmonts and basins. Shear-wave velocities for these units are assigned in ranges based on established correlations in California. These ranges include V s 30-values to be greater than 500 m=sec for mountain units, 200-600 m=sec for piedmont units, and less than 300 m=sec for basin units. While the resulting map provides the basis for incorporating site response in an assessment of seismic hazard for Islamabad, it also demonstrates the potential use of remote-sensing data for site characterization in regions where only limited conventional mapping has been done.

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Section: Digital Analysis Methodsmentioning

confidence: 99%

Section: Digital Analysis Methodsmentioning

confidence: 99%

Site Characterization Using Integrated Imaging Analysis Methods on Satellite Data of the Islamabad, Pakistan, Region

Yong

Hough

Abrams

et al. 2008

Bulletin of the Seismological Society of America

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“…A similar scheme was put forward by Dikau [24] and modified by Wood [14]. These classification schemes havesometimes in adapted or extended form -often been applied to derive both crisp [25,26,27,28] and fuzzy [29,30] classifications. Wood [14] proposed multi-scale classification which was extended into fuzzy multi-scale classification based on a range of crisp classifications at different scales [2].…”

Section: Describing Landscapes In Terms Of Surface Formmentioning

confidence: 99%

Delineation of Valleys and Valley Floors

Straumann

Purves

2008

Geographic Information Science

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Methods to automatically derive landforms have typically focused on pixel-based, bottom-up approaches and most commonly on the derivation of topographic eminences. In this paper we describe an object-based, top-down algorithm to identify valley floors. The algorithm is based on a region growing approach, seeded by thalwegs with pixels added to the region according to a threshold gradient value. Since such landforms are fiat we compare the results of our algorithm for a particular valley with a numberof textual sources describing that valley. In a further comparison, we computed a pixel-based six-fold morphometric classification for regions we classified as either being, or not being, valley floor. The regions classified as valley floor are dominated by pla nar slopes and channels,though the algorithm is robust enough to allow local convexities to be classified as within the valley floor. Future work will explore the delineation of valley sides, and thus complete valleys. Abstract. Methods to automatically derive landforms have typically focused on pixel-based, bottom-up approaches and most commonly on the derivation of topographic eminences. In this paper we describe an object-based, top-down algorithm to identify valley floors. The algorithm is based on a region growing approach, seeded by thalwegs with pixels added to the region according to a threshold gradient value. Since such landforms are fiat we compare the results of our algorithm for a particular valley with a number of textual sources describing that valley. In a further comparison, we computed a pixel-based sixfold morphometric classification for regions we classified as either being, or not being, valley floor. The regions classified as valley floor are dominated by planar slopes and channels, though the algorithm is robust enough to allow local convexities to be classified as within the valley floor. Future work will explore the delineation of valley sides, and thus complete valleys. Delineation of Valleys and Valley Floors

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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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Geomorphometric analysis for characterizing landforms in Morelos State, Mexico

Cited by 89 publications

References 9 publications

Site Characterization Using Integrated Imaging Analysis Methods on Satellite Data of the Islamabad, Pakistan, Region

Site Characterization Using Integrated Imaging Analysis Methods on Satellite Data of the Islamabad, Pakistan, Region

Delineation of Valleys and Valley Floors

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

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