High-resolution landform classification using fuzzy -means

Burrough, P.A.; Gaans, P.F.M. van; MacMillan, R.A.

doi:10.1016/s0165-0114(99)00011-1

Cited by 261 publications

(161 citation statements)

References 13 publications

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“…Examples are the distance to a local depression, the elevation above local depression [16] or ridge proximity [17].…”

Section: Describing Landscapes In Terms Of Surface Formmentioning

confidence: 99%

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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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“…Examples are the distance to a local depression, the elevation above local depression [16] or ridge proximity [17].…”

Section: Describing Landscapes In Terms Of Surface Formmentioning

confidence: 99%

“…The latter first choose attributes on which the clustering process is to take place, before forming either crisp or fuzzy clusters by minimising intra-class variance and maximising inter-class variance (e.g. [20,17,21]). …”

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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“…It has been successfully used in geohydrology, soil science and vegetation mapping (Vriend et al 1988, de Bruin, Stein 1998, Burrough. McDonnell 1998, Burrough et al 2000, 2001, Schmidt, Hewitt 2004.…”

Section: K-means Unsupervised Classification Algorithmmentioning

confidence: 99%

“…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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“…For example, the TWI has been used as an index to indicate potential saturated and unsaturated areas in a catchment and predict the distribution of local soil moisture [12], [13], [14]. TWI is also used as input data to predict spatially varying evapotranspiration, liability to erosion or nutrient transport [15], [16] and in the automatic delineation/classification of landforms [17], [18], [19], since it represents an intuitive notion of wetness or proneness to generate surface flow.…”

Section: Introductionmentioning

confidence: 99%

Resolution Sensitivity of a Compound Terrain Derivative as Computed from LiDAR-Based Elevation Data

Straumann

Purves

2007

Lecture Notes in Geoinformation and Cartography

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New technologies such as Light Detection And Ranging (LiDAR) provide high resolution digital elevation data. These data offer new possibilities in the field of terrain modelling and analysis. However, not very much is known about the effects when these data are used to compute broadly applied terrain derivatives. In this paper the sensitivity of the Topographic Wetness Index (TWI) and its two constituting components gradient and Specific Catchment Area (SCA) regarding the resolution of LiDARbased elevation data is examined. For coarser resolutions a shift in the TWI distribution to higher values is noted. TWI distributions at different resolutions differ significantly from each other. These findings have an impact on aspatial and spatial modelling based on the TWI. Abstract. New technologies such as Light Detection And Ranging (LiDAR) provide high resolution digital elevation data. These data offer new possibilities in the field of terrain modelling and analysis. However, not very much is known about the effects when these data are used to compute broadly applied terrain derivatives. In this paper the sensitivity of the Topographic Wetness Index (TWI) and its two constituting components gradient and Specific Catchment Area (SCA) regarding the resolution of LiDAR-based elevation data is examined. For coarser resolutions a shift in the TWI distribution to higher values is noted. TWI distributions at different resolutions differ significantly from each other. These findings have an impact on aspatial and spatial modelling based on the TWI.

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High-resolution landform classification using fuzzy -means

Cited by 261 publications

References 13 publications

Delineation of Valleys and Valley Floors

Delineation of Valleys and Valley Floors

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

Resolution Sensitivity of a Compound Terrain Derivative as Computed from LiDAR-Based Elevation Data

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