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    Predictive Soil Mapping in Southern Arizona's Basin and Range

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    Author
    Levi, Matthew Robert
    Issue Date
    2012
    Keywords
    Remote Sensing
    Soil Survey
    Spatial Modeling
    Soil, Water & Environmental Science
    Digital Soil Mapping
    GIS
    Advisor
    Rasmussen, Craig
    
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    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    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.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Soil, Water and Environmental Science
    Degree Grantor
    University of Arizona
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