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A Geoinformatics Approach to Water ErosionSpatial Variation in Soils

A Geoinformatics Approach to Water Erosion: Spatial Variation in Soils [Soil variation can be modeled either by dividing the space into discrete units, or by quantifying autocorrelationAutocorrelation in space from known points, using geostatistical tools. Four discrete units are discussed in this chapter: the pedogeomorphological unit of the hillslope catenaHillslope catena, the subcatchment unit, the parcel (an agricultural field) representing data aggregation, based on administrative units, and the rasterRaster grid-cell, or pixel unit. As an alternative to these four discrete units, a set of continuous data analysis tools can be applied to map soil propertiesSoil properties from point measurements, using geostatistical equations. The arsenal of geostatistical tools described here includes: samplingSampling procedures, variogram envelopesVariogram envelopeand Moran’s IMoran’s I analyses for testing spatial autocorrelationSpatial autocorrelation, and the various krigingKriging interpolation techniques (with or without ancillary data). Geostatistical models are advantageous for several reasons: they can improve samplingSampling strategy, introduce and quantify the Tobler Law in a bid to understand patterns of variation in space, and offer an account of the roles of various climatic and environmental factorsEnvironmental factors in water erosion processesErosion processes. However, they may also suffer from a large degree of noise, and not all variables are spatially continuous. In this chapter, we focus on the geoinformatics data modelsData model needed for water erosionErosion studies. We present the procedures for applying spatial data modelsData model in a discrete fashion, as well as the equations necessary for soil samplingSampling, autocorrelationAutocorrelation analysis, and interpolation. These methods make it possible to map the spatial variation in the catchmentCatchment characteristics that are needed for the water erosionErosion data analyses, with relatively high certainty.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Geoinformatics Approach to Water ErosionSpatial Variation in Soils

Svoray, Tal
Springer Journals — Feb 17, 2022
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43 pages

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Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2022
ISBN
978-3-030-91535-3
Pages
107 –150
DOI
10.1007/978-3-030-91536-0_4
Publisher site
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Abstract

[Soil variation can be modeled either by dividing the space into discrete units, or by quantifying autocorrelationAutocorrelation in space from known points, using geostatistical tools. Four discrete units are discussed in this chapter: the pedogeomorphological unit of the hillslope catenaHillslope catena, the subcatchment unit, the parcel (an agricultural field) representing data aggregation, based on administrative units, and the rasterRaster grid-cell, or pixel unit. As an alternative to these four discrete units, a set of continuous data analysis tools can be applied to map soil propertiesSoil properties from point measurements, using geostatistical equations. The arsenal of geostatistical tools described here includes: samplingSampling procedures, variogram envelopesVariogram envelopeand Moran’s IMoran’s I analyses for testing spatial autocorrelationSpatial autocorrelation, and the various krigingKriging interpolation techniques (with or without ancillary data). Geostatistical models are advantageous for several reasons: they can improve samplingSampling strategy, introduce and quantify the Tobler Law in a bid to understand patterns of variation in space, and offer an account of the roles of various climatic and environmental factorsEnvironmental factors in water erosion processesErosion processes. However, they may also suffer from a large degree of noise, and not all variables are spatially continuous. In this chapter, we focus on the geoinformatics data modelsData model needed for water erosionErosion studies. We present the procedures for applying spatial data modelsData model in a discrete fashion, as well as the equations necessary for soil samplingSampling, autocorrelationAutocorrelation analysis, and interpolation. These methods make it possible to map the spatial variation in the catchmentCatchment characteristics that are needed for the water erosionErosion data analyses, with relatively high certainty.]

Published: Feb 17, 2022

Keywords: Autocorrelation; Catchment analysis; Geostatistics; Interpolation; Sampling; Terrain characterization

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