Monday, 10 July 2006 - 4:20 PM

Refining the Pedogenic Understanding Raster Classification Methodology.

Janis L. Boettinger1, Nephi J. Cole2, Amy M. Saunders2, Shawn J. Nield2, Suzann Kienast-Brown2, Jedd M. Bodily2, and Alexander K. Stum1. (1) Utah State Univ, 4820 Old Main Hill, Logan, UT 84322-4820, (2) USDA Natural Resources Conservation Service, 11 S Spruce Street, Buffalo, WY 82834-2305

Vast areas of the earth need new or updated soil survey data, but traditional methods of soil survey can be inefficient, expensive, and inaccurate. We developed, tested, adapted, and refined a methodology that incorporates Geographic Information Systems (GIS), remote sensing, and modeling to predict and map soil distribution. This Pedogenic Understanding Raster Classification (PURC) methodology can be applied to initial or update soil surveys, and allows the entire process to be completed in a digital environment. Based on the conceptual model that unique soils are the products of unique sets of soil-forming factors, spatially explicit digital data are selected to represent different soil forming factors (environmental covariates). Topographic data (e.g., slope, compound topographic index, topographic ruggedness index) are derived from Digital Elevation Models (DEMs). Digital data proxies for vegetation (e.g., normalized difference vegetation index, fractional vegetation cover) and parent material (e.g., band ratios diagnostic for gypsic, sodic, and calcareous materials) are derived from Landsat spectral data. These digital data are analyzed using commercially available GIS and image processing software or programming and visualization in the Interactive Data Language. Unsupervised, supervised, and simple knowledge-based classifications can be used in the preliminary stage to recognize soil-landscape patterns and to plan for field data collection. As more is learned about the survey area from data collection and expert knowledge, various classification techniques (e.g., supervised classification, classification tree analysis with and without boosting and point buffering) can be employed. The resulting maps are evaluated qualitatively by local experts and, ideally, quantitatively using an accuracy assessment with independent field observation to show the agreement between predicted and observed soil components or map units. PURC methodology pre-mapping, model development, validation, and final map generation were completed in a digital environment. The PURC methodology can be adapted for digital soil mapping for an initial soil survey (e.g., Powder River and Green River Basins, Wyoming), or for targeted problem solving and soil survey updates (e.g., refining distribution and temporality of wet and saline soils, identifying limestone rock outcrop and potential endemic species habitat, Utah). Continued technological advancements and availability of spatial data and improved GIS and modeling expertise of soil scientists should increase the accuracy and efficiency of the soil survey process.

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