Fundamental quantitative methods of land surface analysis

Shary, P. A.; Sharaya, L. S.; Mitusov, A.V.

doi:10.1016/s0016-7061(01)00136-7

Cited by 356 publications

(222 citation statements)

References 47 publications

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“…Profile, plan and a variety of additional curvature measures (cf. [13]) are second order derivatives of elevation. All these measures can be approximated from a moving window through a focal operation, typically with a neighbourhood of 3x3 cells, though Wood [14] and others have emphasised the importance of scale (and thus varying window size) on the derivation of such attributes.…”

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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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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“…Elevation, slope, plan and profile curvatures according to Evans (1972) and Shary (1995, Shary et al 2002 schemas and the flow path length according to Horton method (Horton 1945, Mitasova, Hofierka 1993, Zavoianu et al 2013 were used as a DEM-based geomorphometric layers for semi-automated landform classification of the territory of Armenia. Evans (1972), Lastochkin (1991), andSimonov (1999) point out that elevation, slope and aspect are compulsory for every kind of geomorphometric analysis.…”

Section: Geomorphometric Parametersmentioning

confidence: 99%

“…Plan curvature is a measure of flow convergence (kh < 0) and divergence (kh > 0) and determines soil water or the deposition of particles. Profile curvature controls water flow acceleration (kh > 0) and deceleration (kh < 0) and therefore the erosion potential of an area (Shary 1995, Shary et al 2002, Schillaci et al 2015. For both parameters units are expressed in 1/m.…”

Section: Geomorphometric Parametersmentioning

confidence: 99%

“…There are many approaches and classification schemes for the landform classification in digital geomorphology (Pennock et al 1987, Dikau 1989, Wood 1996, Irvin et al 1997, Ventura, Irvin 2000, Burrough et al 2001, Shary et al 2002, Schmidt, Hewitt 2004, Dragut, Blaschke 2006, Ehsani, Quiel 2008, MacMillan, Shary 2009). Here a combination of the classification criterias of Dikau (1989), and Dragut, Blaschke (2006) with small modifications is used.…”

Section: Landform Classificationmentioning

confidence: 99%

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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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“…From this, primary (slope and plan curvature) and secondary (compound topographic index (CTI) and potential drainage density (PDD)) terrain features were calculated according to Moore et al (1993), Dobos et al (2000), Shary et al (2002), Gessler et al (1995), and McBratney et al (2003).…”

Section: Derivation Of Terrain Featuresmentioning

confidence: 99%

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Alves

Demattê

Barros

2015

Rev. Bras. Ciênc. Solo

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in recent years, geotechnologies as remote and proximal sensing and attributes derived from digital terrain elevation models indicated to be very useful for the description of soil variability. however, these information sources are rarely used together. therefore, a methodology for assessing and specialize soil classes using the information obtained from remote/proximal sensing, Gis and technical knowledge has been applied and evaluated. two areas of study, in the state of são Paulo, Brazil, totaling approximately 28.000 ha were used for this work. first, in an area (area 1), conventional pedological mapping was done and from the soil classes found patterns were obtained with the following information: a) spectral information (forms of features and absorption intensity of spectral curves with 350 wavelengths -2,500 nm) of soil samples collected at specific points in the area (according to each soil type); b) obtaining equations for determining chemical and physical properties of the soil from the relationship between the results obtained in the laboratory by the conventional method, the levels of chemical and physical attributes with the spectral data; c) supervised classification of Landsat TM 5 images, in order to detect changes in the size of the soil particles (soil texture); d) relationship between classes relief soils and attributes. subsequently, the obtained patterns were applied in area 2 obtain pedological classification of soils, but in GIS (ArcGIS). finally, we developed a conventional pedological mapping in area 2 to which was compared with a digital map, ie the one obtained only with pre certain standards. the proposed methodology had a 79 % accuracy in the first categorical level of Soil Classification System, 60 % accuracy in the second category level and became less useful in the categorical level 3 (37 % accuracy).

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Fundamental quantitative methods of land surface analysis

Cited by 356 publications

References 47 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

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

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