Land surface in gravity points classification by a complete system of curvatures

Shary, P. A.

doi:10.1007/bf02084608

Cited by 174 publications

(129 citation statements)

References 9 publications

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“…The quadratic polynomial can be given as follows (Shary, 1995): (1) and the Lagrange polynomial can be given as follows (Florinsky, 1998): (2) The values of the coefficients in these polynomials for the square and circular neighborhoods are listed in Tables 1 and 2. Details of the derivation can be found in the Appendix of this paper.…”

Section: Implementation Of Circular Neighborhoodmentioning

confidence: 99%

An Experiment Using a Circular Neighborhood to Calculate Slope Gradient from a DEM

Shi¹,

Zhu²,

Burt³

et al. 2007

photogramm eng remote sensing

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The traditional 3 ϫ 3 cell neighborhood used in a focal operation on a raster layer has a square shape that results in a dimensional neighborhood of which the orientation is eventually arbitrary to the physical features represented. This paper presents an experiment using a circular neighborhood IntroductionThe traditional 3 ϫ 3 cell neighborhood used in a focal operation on a raster layer has two characteristics: its size is determined by the resolution of the input layer, and its shape is usually square. The resolution-determined size causes inconsistency in the terrain attribute values computed from gridded DEMs with different resolutions. This problem has been studied by many researchers (e.g., Chang and Tsai, 1991;Hodgson, 1995;Gao, 1997;Kienzle, 2004;Zhou and Liu, 2004). Conclusions drawn by these researchers do not totally agree with each other (mainly due to the different benchmarks or "true values" they used), but there is a consensus that the DEM resolution (eventually the size of the neighborhood determined by this resolution) has a strong effect on the accuracy of the terrain attributes derived from the DEM, and in turn on the outcome from the erosion or hydrological models based on these attributes. The resolution-determined neighborhood size also leads to the problem of mismatch between human-perceived and computer-calculated values of terrain attributes, people doing fieldwork (e.g., soil surveyors) always have their own measuring scales. This mismatch has become an important source of error in knowledge-based digital soil mapping. Ironically, this is becoming a more An Experiment Using a Circular Neighborhood to Calculate Slope Gradient from a DEM Xun Shi, A-Xing Zhu, James Burt, Wes Choi, Rongxun Wang, Tao Pei, Baolin Li, and Chengzhi Qin serious problem since high-resolution DEMs are becoming more readily available. Burt and Zhu (2002) solved this mismatch problem by employing a user-defined neighborhood to replace the resolution-determined neighborhood. In this method, the user specifies the size of the neighborhood, and the computer selects the eight cells (not necessarily contiguous) that make up the square with the specified size to calculate the terrain attributes for the cell at the center of the neighborhood. This method allows the user to specify a neighborhood size that matches the scale in their mind, regardless of the resolution of the input DEM.On the other hand, the square shape results in a dimensional neighborhood: the values in the diagonal directions are farther from the center of the neighborhood than the values in the cardinal directions. This dimensional property has a significant effect on derived terrain attributes (Zhou and Liu, 2004) while from a physical standpoint, the orientation of a DEM grid is arbitrary. To reduce this effect, some algorithms (e.g., Horn, 1981) assign different weights to the cells in different directions. The square shape, after all, is merely a convenient setting for the nine contiguous cells that define the neighborhood. Ideally, the ...

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Section: Implementation Of Circular Neighborhoodmentioning

confidence: 99%

An Experiment Using a Circular Neighborhood to Calculate Slope Gradient from a DEM

Shi¹,

Zhu²,

Burt³

et al. 2007

photogramm eng remote sensing

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“…Soil properties vary predictably within the landscape due to the influence of topography on hydrologic and pedogenic processes (Gerrard 1981). As such, information on soil series or landscape posi- (Evans 1980;Moore et al 1993;Shary 1995). For personal use only.…”

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confidence: 99%

Evaluating the use of digital terrain modelling for quantifying the spatial variability of 2,4-D sorption by soil within agricultural landscapes

Farenhorst

Florinsky²,

Monreal³

et al. 2003

Can. J. Soil. Sci.

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Farenhorst, A., Florinsky, I. V., Monreal C. M. and Muc, D. 2003. Evaluating the use of digital terrain modelling for quantifying the spatial variability of 2,4-D sorption by soil within agricultural landscapes. Can. J. Soil Sci. 83: 557-564.The most sensitive input parameter in many herbicide fate and transport models is the sorption-partitioning coefficient (Kd), a measure of herbicide sorption by soil. Spatial analyses of Kd are traditionally performed using geostatistics, but this approach requires intensive soil sampling and herbicide analysis. This study examined the use of digital terrain modelling as an alternative tool for quantifying the distribution of herbicide sorption within agricultural fields. Soil samples from a conventional-till (CT) and zero-till (ZT) field were analysed for soil organic carbon (SOC), soil pH, clay content, and the 2,4-D ([(2,4-dichlorophenoxy)acetic acid]) soil-water partitioning coefficient. Digital terrain models were used to calculate topographical variables (elevation, slope gradient, slope aspect, horizontal curvature, vertical curvature, mean curvature, specific catchment area, topographic index and stream power index) for each sampling point. Results indicated that topographic variables were adequate predictors of all soil properties in ZT (R 2 ranging from 0.64 to 0.76), and of SOC in CT (R 2 = 0.65, P < 0.001). For CT, 2,4-D sorption by soil was very well predicted with soil properties alone (R 2 = 0.82, P < 0.001) and with soil properties in combination with topographic variables (R 2 = 0.85, P < 0.001), but was less predicted by topographic variables alone (R 2 = 0.50, P < 0.001). For ZT, the level of prediction of 2,4-D sorption by soil was weak with soil properties alone (R 2 = 0.53, P < 0.001) or when topographical variables alone (R 2 = 0.56, P < 0.001) were used, but a substantial increase in the level of prediction was achieved when both soil properties and topographic variables were used (R 2 = 0.73, P < 0.001). We conclude that digital terrain modelling, in combination with soil properties data, is an appropriate approach for predicting the spatial distribution of 2,4-D sorption within undulating-to-hummocky glacial till landscapes in western Canada.Key words: herbicide sorption, topography, zero-tillage, conventional-tillage, digital terrain modelling.Farenhorst, A., Florinsky, I. V., Monreal, C. M. et Muc, D. 2003. Evaluation de l'utilisation de la modélisation numérique du terrain pour quantifier la variabilité de la sorption du 2,4-D par la terre dans les paysages agricoles. Can. J. Soil Sci. 83: 557-564. Pour beaucoup de modèles sur la dispersion et le transport des herbicides dans le sol le paramètre d'entrée le plus variable est le coefficient de distribution de la sorption (Kd). Les analyses du Kd sont généralement réalisées en utilisant des méthodes géostatistiques, mais cette approche requière d'intensifs échantillonnages de terre avec recherche d'herbicides. Notre étude se propose de vérifier l'utilité de la modélisation numérique du terrain comm...

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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%

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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Land surface in gravity points classification by a complete system of curvatures

Cited by 174 publications

References 9 publications

An Experiment Using a Circular Neighborhood to Calculate Slope Gradient from a DEM

An Experiment Using a Circular Neighborhood to Calculate Slope Gradient from a DEM

Evaluating the use of digital terrain modelling for quantifying the spatial variability of 2,4-D sorption by soil within agricultural landscapes

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

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