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The Effects of Spatial Resolution on Digital Soil Attribute Mapping

Shaffer, Jared M
http://rave.ohiolink.edu/etdc/view?acc_num=osu1374004706

Abstract Details

2013, Master of Science, Ohio State University, Environment and Natural Resources.
Given the rapid changes our environment experiences, soil resource data on various spatial and temporal scales are critical to monitor and mitigate soil degradation. Realistic and useful soil data can be obtained through digital soil mapping. The prediction models are based on deterministic relationships between soil attributes and one or more landscape variables. This concept argues that the relationship between soil and its environment provides the information necessary to infer what soil might occur at a given point by assessing the environmental conditions there. It is known that environmental processes occur at natural scales, describing the hierarchical organization of the environment. Although the scale for observation should be related to the object or function under investigation, there is no clear method to determine the optimal scale. The appropriate scale is identified by the data and the process being studied. In hierarchical systems, there is often a range of scales over which a process remains stable as scale changes. These ranges are separated by transition points, a scale or range of scales where the importance of explanatory variables on the process changes. By changing the raster cell size of digital terrain models, various scales of data can be created to match the relative scale of the studied process. Statistical analyses such as correlation and model performance can then be used to explore the relationships between environmental variables, processes and scale. Five methods were used to determine the most appropriate spatial resolution for environmental variables: (a) significant differences in cumulative distribution functions, (b) using the RMS slope measure, (c) using the maximum RMS measure, (d) using the peak variable value, and (e) using the peak local variance. Analysis of the CFDs revealed that altitude and aspect do not change significantly with resolution, and a general decrease in slope and an increase in TWI as data resolution becomes coarse. It was also observed that terrain curvature tends to become more extreme as grid cell size increases. RMS analysis suggested that an optimum for catchment area, MRVBF, PRR, TWI, VRM and the curvature variables may occur in the 20-35 m range. This range was also indicated by the maximum RMS and local variance measures for MRVBF, NDVI, PRR, and VRM. Geostatistical models were found to be inferior in predicting soil depth than GAM and MLR models. Soil depth was strongly correlated with elevation and maintained this correlation as grid cell size increased. Altitude and PRR were the most frequently used variables in the best fitting models, along with GEOL, NDVI and Gaussian curvature. The most precise soil depth prediction model was found to be a GAM at 40 m resolution. Although a direct relationship between model accuracy, environmental variables and spatial resolution was not found, models which included predictors at a resolution which maximizes their local variance and RMS values tended to perform well. It was shown that soil depth processes do not display linear scaling behavior. It is therefore a more complicated matter to extend local soil data to a regional scale.
Brian Slater, PhD (Advisor)
Kristin Jaeger, PhD (Committee Member)
Edward McCoy, PhD (Committee Member)
175 p.
Geographic Information Science; Soil Sciences
digital soil mapping; geostatistics; multi-scale analysis; scaling; soil depth; terrain analysis

Recommended Citations

Citations

  • Shaffer, J. M. (2013). The Effects of Spatial Resolution on Digital Soil Attribute Mapping [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1374004706

    APA Style (7th edition)

  • Shaffer, Jared. The Effects of Spatial Resolution on Digital Soil Attribute Mapping. 2013. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1374004706.

    MLA Style (8th edition)

  • Shaffer, Jared. "The Effects of Spatial Resolution on Digital Soil Attribute Mapping." Master's thesis, Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1374004706

    Chicago Manual of Style (17th edition)

osu1374004706
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This open access ETD is published by The Ohio State University and OhioLINK.