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United States Department of Agriculture

Agricultural Research Service

Research Project: DEVELOPMENT OF ALTERNATIVE PRACTICES FOR IMPROVED WATERSHED MANAGEMENT Title: Identification and Quantification Soil Redoximorphic Features by Digital Image Processing

Authors
item O'Donnell, Thomas -
item Goyne, Keith -
item Miles, Randall -
item Baffaut, Claire
item Anderson, Stephen -
item Sudduth, Kenneth

Submitted to: Geoderma
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 22, 2010
Publication Date: May 1, 2010
Citation: O'Donnell, T.K., Goyne, K.W., Miles, R.J., Baffaut, C., Anderson, S.H., Sudduth, K.A. 2010. Identification and Quantification Soil Redoximorphic Features by Digital Image Processing. Geoderma. 157(2010):86-96

Interpretive Summary: Soil redoximorphic features (SRFs) are patches of different color soil material found in soil horizons. They are formed by chemical reactions associated with alternating periods of aeration interspersed with water-saturated or oxygen-deprived conditions. They provide scientists and land managers with information about past and present soil moisture regimes. The objective of this study was to develop a new method of SRF identification and quantification from soil cores using a digital camera and image classification software. In addition, the effects of soil moisture at time of assessment and image processing on the quantification and interpretation of SRF metrics were determined. Sections from soil cores were photographed under controlled light conditions and SRFs were determined by grouped Munsell soil colors. Quantification of SRFs at increasing moisture content showed few differences once a specific moisture state was reached but large differences between air-dry and moist surfaces were observed. Metrics characterizing SRF boundaries, shapes, number of SRFs, and mean area of SRFs were sensitive to post-classification image smoothing. Methods demonstrated by this study provide an opportunity to better integrate pedology with other related earth sciences by allowing standardized quantification of SRFs as well as a determination of human error associated with current visual estimates. These assessments will benefit land managers and scientists who depend on SRF characterization to make inferences on soil moisture.

Technical Abstract: Soil redoximorphic features (SRFs) have provided scientists and land managers with insight into relative soil moisture for approximately 60 years. The overall objective of this study was to develop a new method of SRF identification and quantification from soil cores using a digital camera and image classification software. Additional objectives included a determination of soil moisture effects on quantified SRFs and image processing effects on interpretation of SRF metrics. Eighteen horizons from selected landscapes in the Central Claypan Area, northcentral Missouri, USA were photographed from exposed soil cores under controlled light conditions. A 20 cm2 area was used for SRF quantification following a determination of the initial gravimetric water content of horizon faces. Overall color determination accuracy was 99.6% based on Munsell soil color groupings used for SRF identification. Rewetting of air-dry horizon faces by successive application of 1mL of deionized water demonstrated little change in identified SRFs after seven applications. Mean change in identified Low Chroma and High Chroma between the seventh and tenth rewetting sequences was 2% (SD ± 4) and 0.03% (SD ± 0.3), respectively. However, ten of eighteen horizons contained greater area of Low Chroma after ten rewetting sequences compared to the same horizon at the initial moisture state. Metrics characterizing SRF boundaries, shapes, number of SRFs, and mean area of SRFs were sensitive to post-classification image smoothing. Methods demonstrated by this study provide an opportunity to better integrate pedology with other related earth sciences by allowing standardized quantification of SRFs as well as a determination of human error associated with current visual estimates.

Last Modified: 10/20/2014
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