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

This Article Abstract Full Text Free Full Text (PDF) Free 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 Google Scholar Google Scholar Articles by Kulli, B. Articles by Flühler, H. Search for Related Content PubMed Articles by Kulli, B. Articles by Flühler, H. GeoRef GeoRef Citation Agricola Articles by Kulli, B. Articles by Flühler, H. Related Collections Structure and Properties Preferential Flow Infiltration

Discrimination of Flow Regions on the Basis of Stained Infiltration Patterns in Soil Profiles

^a Institute of Terrestrial Ecology, Soil Physics Group, Swiss Federal Institute of Technology (ETH Zürich), Grabenstrasse 3, CH-8952 Schlieren, Switzerland
^b Swiss Federal Institute for Environmental Sciences and Technology (EAWAG), Water and Agriculture, CH-8600 Dübendorf, Switzerland

View larger version (152K): [in a new window]   Fig. 1. Layering in flow patterns reflects layers of hydraulically different materials.

View larger version (32K): [in a new window]   Fig. 2. Methodological steps presented in this chapter.

View larger version (80K): [in a new window]   Fig. 3. Sequence of the image processing.

View larger version (21K): [in a new window]   Fig. 4. Analysis of the width distributions of the stained areas. Top: width distributions are determined for every horizontal line (1-mm depth increment). Bottom: for each depth zL the width distributions below and above the respective depth are pooled and compared.

View larger version (31K): [in a new window]   Fig. 5. Maximizing Z to detect layer boundaries. Top: iterative discrimination of dissimilar layers. Bottom: all images of a given plot lined up.

View larger version (14K): [in a new window]   Fig. 6. Top: number of boundaries depending on the threshold (black) and best fit of the two straight line segments with the least chi-square error (gray). Bottom: sum of squared errors for the fit of the line segments depending on the threshold.

View larger version (75K): [in a new window]   Fig. 7. Overview of the results for six sites (rows). From left to right: background image with textural and structural layer boundaries observed in the field; example flow pattern of each plot; overlay image of all flow patterns of the plot with the layer boundaries detected on the patterns as red horizontal lines; results of the cluster analysis for a total of 20, 7, and 3 clusters given by color bars. The color scale on the right identifies the cluster numbers by the colors.

View larger version (11K): [in a new window]   Fig. 8. Cluster number optimized by the method of Xu et al. (1993).

View larger version (33K): [in a new window]   Fig. 9. Box plots, showing the variability of the variables for every cluster for a total number of clusters of 3 (above) and 7 (below).