HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Moreno, R. G. Articles by Tarquis, A. M. Search for Related Content PubMed Articles by Moreno, R. G. Articles by Tarquis, A. M. GeoRef GeoRef Citation Agricola Articles by Moreno, R. G. Articles by Tarquis, A. M. Related Collections Sustainable Agriculture Soil Erosion Tillage

SPECIAL SECTION: MULTISCALE MAPPING

Multifractal Analysis of Soil Surface Roughness

^a Soil Science and Climatology, School of Agricultural Engineering, Polytechnic Univ. of Madrid, Ciudad Universitaria s.n. Madrid 28040, Spain
^b Applied Mathematics, School of Agricultural Engineering, Polytechnic Univ. of Madrid, Ciudad Universitaria s.n. Madrid 28040, Spain
^* Corresponding author (chgarcia{at}terra.es).

Received 17 January 2007.

Soil surface roughness (SSR) is a parameter highly suited to the study of soil susceptibility to wind and water erosion. The development of a methodology for quantifying SSR is therefore instrumental to soil evaluation. We developed such a method, based on the multifractal analysis (MFA) of soil elevation measurements collected at the intersections on a 2- by 2-cm² grid in a 200- by 200-cm² plot. Samples were defined using the gliding box algorithm (GB), in which a box of a given size "glides" across the grid map in all possible directions. The advantage of the GB over the box counting algorithm is that it yields a greater number of large sample sizes, which usually leads to better statistical results. Standard deviation, semivariogram fractal dimension, and semivariogram crossover length were estimated for all scenarios to compare the results of SSR multifractal analysis to indices found with traditional techniques. For its high sensitivity to the spatial arrangement implicit in a data set, MFA appears to be better suited than classical indices to compare plots tilled under different management criteria. The results showed that MFA is able to effectively reflect the heterogeneity and complexity of agricultural SSR. Based on this type of analysis, two new indices have been defined to compare the multifractal spectrum characteristics of the raw data to the characteristics of a random field with the same average and SD.

Abbreviations: GB, gliding box algorithm • MFA, multifractal analysis • MFS, multifractal spectrum • RD, random data set • SMV, semivariogram • SSR, soil surface roughness

This article has been cited by other articles:

				S. D. Logsdon, E. Perfect, and A. M. Tarquis Multiscale Soil Investigations: Physical Concepts and Mathematical Techniques Vadose Zone J., May 27, 2008; 7(2): 453 - 455. [Full Text] [PDF]