Multifractal analysis in soil properties: Spatial signal versus mass distribution

Morató, M.C.; Castellanos, María Teresa; Bird, N. R. A.; Tarquis, Ana M.

doi:10.1016/j.geoderma.2016.08.004

Cited by 23 publications

(13 citation statements)

References 39 publications

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“…It is in this section where we can contemplate a closer isotropic behaviour. However, all of the studied transects presented a strong persistence character or positive long memory because all of the H(q) values exceeded 0.5 (Morato et al, 2016).…”

Section: Directional Generalised Hurst Exponentsmentioning

confidence: 88%

“…There are several MF methods that can be used to characterise scaling properties, and several relations among them can be found in the literature (Morato, Castellanos, Bird, & Tarquis, 2016). In the context of soils, the most popular method applied to soil transect data, including altitude, is the moment method developed by Halsey, Jensen, Kadanoff, Procaccia, and Shraiman (1986).…”

Section: Introductionmentioning

confidence: 99%

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Detrended fluctuation analysis for spatial characterisation of landscapes

Castellanos

Morató

Cortijo³

et al. 2018

Biosystems Engineering

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The interactions among abiotic, biotic, and anthropic factors and their influence at different scales create a complex dynamic in landscape evolution. Scaling and multifractal analysis have the potential to characterise landscapes in terms of the statistical signature of the selected measure, in this case, altitude. This work evaluates the multifractality of altitude data points along transects that are obtained in several directions using Detrended Fluctuation Analysis (DFA) in a protected area adjacent to Madrid. The study data set consist of a matrix 2048 x 2048 pixels obtained at a 5 m resolution and extracted from a digital terrain model (DTM) using a Geographic Information System (GIS). We found that the distribution of altitude fluctuations at small scales revealed a non-Gaussian character in the statistical moments, indicating that Fractional Brownian modelling is not appropriate. Generalised Hurst dimensions (H(q)) were calculated on several transects crossing the area under study, all of which exhibited multifractality within a certain scale range. The results show a persistent behaviour in all directions because all of the H(q) values exceeded 0.5 and because there were differences in the intensities of the multifractality. The analysis of the directionality by means of a generalised Hurst rose plot showed differences in the scaling characteristics both along and across rivers and reservoirs. This indicates a clear anisotropy that is mainly due to the directions of the two river basins located in the area and the basement movement as a consequence of gradual tectonic displacement, which must be considered in two-dimensional DFAs.

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Section: Directional Generalised Hurst Exponentsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Detrended fluctuation analysis for spatial characterisation of landscapes

Castellanos

Morató

Cortijo³

et al. 2018

Biosystems Engineering

Self Cite

View full text Add to dashboard Cite

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“…For comparison, we also plotted in Fig. 9 the line = ζ q ( ) q 2 , which corresponds to Brownian motion or a noise without any correlation (Morató et al, 2017). In the four cases, the ζ q ( ) function is significantly different from the Brownian line.…”

Section: Multiscaling Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Some authors have applied these techniques to soil science to look for statistical self-similar series (Pozdnyakova et al, 2005;Kravchenko et al, 2002;Garcia Moreno et al, 2010;Morató et al, 2017). Likewise, other works (Tarquis et al, 2008;Morató et al, 2017) have studied wider scaling behaviors which cannot be captured in a consistent way by the MFA. These analyses include linear relations between log structure functions, and frequency distributions of the variables' increments tending to be symmetric with increasing kurtosis as the lags between pairs of values decreases (Guadagnini et al, 2015;Molinari et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Assessing soil water content variability through active heat distributed fiber optic temperature sensing

Zubelzu

Rodríguez-Sinobas

Saá-Requejo

et al. 2019

Agricultural Water Management

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Soil spatial variability is a key point for the sustainable water management in agriculture. Fractal techniques provide proper tools to analyze soil spatial variability searching for statistical self-similarity patterns among different scales. Although they have been extensively applied to study the soil properties variability, its applicability for the soil water content (SWC) distribution is complicated because requires many data difficult to obtain with the typical point soil water sensors. Recently, a fiber optic distributed temperature sensor has been used to measure soil thermal properties which relate to SWC. These sensors provide large amount of data with high spatial and temporal resolution, thus filling the gap of point soil water sensors. In the present work, soil temperature was measured with a Distributed Temperature Sensing (DTS) and SWC was estimated by different fitting functions which have been studied with focus on spatial variability. Temperature was measured in a 133 m fiber optic cable laid in a sandy soil field plot. A The Active Heat Fiber Optic (AHFO) method was used, with 12 cm sampling resolution, and heat pulses (19,4 W/m during 2 min) were applied. The temperature data were correlated to SWC, considering the integration of temperature during the heat pulse T cum , and then the datasets Tcum-SWC were fitted to the best fit statistical function (exponential, potential and polynomial). The results showed that the T cum distribution presented a non-Gaussian pattern. Additionally, highly anti-persistent patterns have been detected for the larger spatial scaling lags. The function`s performance was different thus, the exponential function reproduced better the absolute moments of the temperature profile but it failed reproducing the non-Gaussian behavior.

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Multifractal Detrended Fluctuation Analysis of Regional Precipitation Sequences Based on the CEEMDAN-WPT

Liu

Chen

et al. 2018

Pure Appl. Geophys.

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Multifractal analysis in soil properties: Spatial signal versus mass distribution

Cited by 23 publications

References 39 publications

Detrended fluctuation analysis for spatial characterisation of landscapes

Detrended fluctuation analysis for spatial characterisation of landscapes

Assessing soil water content variability through active heat distributed fiber optic temperature sensing

Multifractal Detrended Fluctuation Analysis of Regional Precipitation Sequences Based on the CEEMDAN-WPT

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