Thesis (M.S. in soil science)--Washington State University, August 2009. / Title from PDF title page (viewed on July 27, 2009). "Department of Crop and Soil Sciences." Includes bibliographical references (p. 94-96).

Thesis (Ph. D.)--Oregon State University, 2008. / Printout. Includes bibliographical references. Also available on the World Wide Web.

Thesis (M.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 98-102). Also available on the World Wide Web.

There is a need to understand the spatial distribution of soil taxonomic classes, the spatial distribution of potential biological soil crust, and soil properties related to wind erosion to address land use and management decisions in arid and semi-arid areas of the western USA. Digital soil mapping (DSM) can provide this information. Chapter 2 compared multiple DSM functions and environmental covariate sets at three geographically distinct semi-arid study areas to identify combinations that would best predict soil taxonomic classes. No single model or type of model was consistently the most accurate classifier for all three areas. The use of the “most important” variables consistently resulted in the highest model accuracies for all study areas. Overall classification accuracy was largely dependent upon the number of taxonomic classes and the distribution of pedons between taxonomic classes. Individual class accuracy was dependent upon the distribution of pedons in each class. Model accuracy could be increased by increasing the number of pedon observations or decreasing the number of taxonomic classes. Potential biological soil crust level of development (LOD) classes were predicted over a large area surrounding Canyonlands National Park in Chapter 3. The moderate LOD class was modeled with reasonable accuracy. The low and high LOD classes were modeled with poor accuracy. Prediction accuracy could likely be improved through the use of additional covariates. Spatial predictions of LOD classes may be useful for assessing the impact of past land uses on biological soil crusts. Threshold friction velocity (TFV) was measured and then correlated with other, easier-to-measure soil properties in Chapter 4. Only soils with alluvial surficial rocks or weak physical crusts reached TFV in undisturbed conditions. All soil surfaces reached TFV after disturbance. Soils with weak physical crusts produced the most sediment. Future work on wind erosion in the eastern Great Basin should focus on non-crusted/weakly crusted soils and soils formed in alluvium overlying lacustrine materials. Soils with other crust types are likely not susceptible to wind erosion. Threshold friction velocity in undisturbed soils with weak physical crusts and undisturbed soils with surficial rocks was predicted using a combination of penetrometer, rock cover, and silt measurements.

The aim of this thesis was to examine environmental covariate-related issues, the resolution dependency, the contribution of vegetation covariates, and the use of LiDAR data, in the purposive sampling design for fuzzy logic-based digital soil mapping. In this design fuzzy c-means (FCM) clustering of environmental covariates was employed to determine proper sampling sites and assist soil survey and inference. Two subsets of the Laurel Creek Conservation area were examined for the purposes of exploring the resolution and vegetation issues, respectively. Both conventional and LiDAR-derived digital elevation models (DEMs) were used to derive terrain covariates, and a vegetation index calculated from remotely sensed data was employed as a vegetation covariate. A basic field survey was conducted in the study area. A validation experiment was performed in another area. The results show that the choices of optimal numbers of clusters shift with resolution aggregated, which leads to the variations in the optimal partition of environmental covariates space and the purposive sampling design. Combining vegetation covariates with terrain covariates produces different results from the use of only terrain covariates. The level of resolution dependency and the influence of adding vegetation covariates vary with DEM source. This study suggests that DEM resolution, vegetation, and DEM source bear significance to the purposive sampling design for fuzzy logic-based digital soil mapping. The interpretation of fuzzy membership values at sampled sites also indicates the associations between fuzzy clusters and soil series, which lends promise to the applicability of fuzzy logic-based digital soil mapping in areas where fieldwork and data are limited.

The aim of this thesis was to examine environmental covariate-related issues, the resolution dependency, the contribution of vegetation covariates, and the use of LiDAR data, in the purposive sampling design for fuzzy logic-based digital soil mapping. In this design fuzzy c-means (FCM) clustering of environmental covariates was employed to determine proper sampling sites and assist soil survey and inference. Two subsets of the Laurel Creek Conservation area were examined for the purposes of exploring the resolution and vegetation issues, respectively. Both conventional and LiDAR-derived digital elevation models (DEMs) were used to derive terrain covariates, and a vegetation index calculated from remotely sensed data was employed as a vegetation covariate. A basic field survey was conducted in the study area. A validation experiment was performed in another area. The results show that the choices of optimal numbers of clusters shift with resolution aggregated, which leads to the variations in the optimal partition of environmental covariates space and the purposive sampling design. Combining vegetation covariates with terrain covariates produces different results from the use of only terrain covariates. The level of resolution dependency and the influence of adding vegetation covariates vary with DEM source. This study suggests that DEM resolution, vegetation, and DEM source bear significance to the purposive sampling design for fuzzy logic-based digital soil mapping. The interpretation of fuzzy membership values at sampled sites also indicates the associations between fuzzy clusters and soil series, which lends promise to the applicability of fuzzy logic-based digital soil mapping in areas where fieldwork and data are limited.

Thesis (M.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 78-90). Also available on the World Wide Web.

A fundamental knowledge gap in understanding land-atmosphere interactions is accurate, high-resolution soil properties. Remote sensing and spatial modeling techniques can bridge the gap between site-specific soil properties and landscape variability, thereby improving predictions of soil attributes. Three studies were completed to advance soil prediction models in semiarid areas. The first study developed a soil pre-mapping technique using automated image segmentation that utilized soil-landscape relationships and surface reflectance to produce an effective map unit design in a 160,000 ha soil survey area. Overall classification accuracy of soil taxonomic units at the suborder was 58 % after including soil temperature regime. Physical soil properties were not significantly different for individual transects; however, properties were significantly different between soil pre-map units when soils from the entire study area were compared. Other studies used a raster approach to predict physical soil properties at a 5 m spatial resolution for a 6,265 ha area using digital soil mapping. The second study utilized remotely-sensed auxiliary data to develop a sampling design and compared three geostatistical techniques for predicting surface soil properties. Ordinary kriging had the smallest prediction error; however, regression kriging preserved landscape features present in the study area and demonstrated the potential of this technique for quantifying variability of soil components within soil map units. The third study applied quantitative data from soil prediction models in study 2 and additional models of subsurface properties to a pedotransfer function for predicting hydraulic soil parameters at the landscape scale. Saturated hydraulic conductivity and water retention parameters were used to predict water residence times for loss to gravity and evapotranspiration across the landscape. High water residence time for gravitational water corresponded to both low drainage density and high clay content, whereas high residence of plant available water was related to increased vegetation response. These studies illustrate the utility of digital soil mapping techniques for improving soil information at landscape scales, while reducing required resources. Resulting soil information is useful for quantifying landscape-scale processes that require constraint of spatial variability and prediction error of soil properties to better model hydrological and ecological responses to climate and land use change.

Soil property variability is a large component of the overall environmental variability that Precision Agriculture practices seek to address. Thus, the creation of accurate field soil maps from field soil samples is of utmost importance to practitioners of Precision Agriculture, as understanding and characterizing variability is the first step in addressing it. Today, growers often interpolate their soil maps in a “black-box” fashion, and there is a need for an easy to use, accurate method of interpolation. In this study, current interpolation practices are examined as a benchmark, a Random Forest (RF) based prediction framework utilizes public data to aid predictions, and the RF framework is exposed via a webtool. A high density (0.20 ha/sample) field soil sample dataset provides 28 training points and 82 validation points to be used as a case study. In the prediction of soil percent organic matter (OM), the grid and ordinary kriging interpolations both had higher Mean Absolute Error (MAE) scores than a field average prediction, though the difference was not statistically significant at a 5\% confidence level. A RF framework interpolation utilizing a high resolution (1.52 m) DEM and distances to known points as the feature set had a significantly lower MAE score than the field average, grid, and ordinary kriging interpolations. The results suggest that for the study site, RF framework performed better compared to a field average, a grid based, and an ordinary kriging interpolation methods.

Thesis (Ph. D.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references (p. 117-122).