Suzann’s research focuses on applying digital soil mapping techniques in the NRCS Soil Survey Program to advance soil survey methods and products.
Spatial Prediction of Surface Soil Temperature Classes from Soil Climate Analysis Network (SCAN) Data
Utah NRCS installed 35 SCAN sites throughout the state in 2007 and 2010. SCAN sites record soil moisture and temperature data at several depths in the soil profile. In addition, the SCAN sites also record selected climatic data. The goal is to eventually use SCAN data to predict soil climate across the state of Utah at different temporal scales (annual, monthly, daily). We are focusing the initial phase of this project on predicting annual surface soil moisture classes in a subset area of MLRA 28A, extending from the Utah-Nevada border to the Wasatch front.
Annual surface soil moisture data will be calculated from SCAN, SNOTEL, and active weather stations in the subset area and used as training data for the classification. Predictor variables will be a combination of remote sensing and DEM derivative data, surface soil property data from SSURGO products, vegetation data from SWGap data, and other possible data sources. The Random Forests classifier will be used to predict annual surface soil moisture classes across the subset area. This project is in the preliminary stage with no results to report, but will be complete by the end of 2011.
Applying the Optimum Index Factor to Multiple Data Types for Soil Survey
Digital soil mapping in production soil survey requires simple, straight-forward methods that can be easily implemented into daily activities of soil mapping. The Optimum Index Factor (OIF) was developed by Chavez et al. (1982; 1984) as a method for determining the three-band combination that maximizes the variability in a particular multispectral scene. The OIF is based on the amount of total variance and correlation within and between all possible band combinations in the dataset. Although the OIF method was developed for Landsat TM data, the concept and methodology are applicable to any multilayer dataset. We used the OIF method in a subset area of the initial soil survey of the Duchesne Area, Utah, USA, to help determine which combination of data layers would be most useful for modeling soil distribution. Unique multiband images created from layers of multiple data types (elevation and remote sensing derivatives) were evaluated using the OIF method to determine which data layers would maximize the biophysical variability in the study area. A multiband image was created from the optimum combinations of data layers and used for classification and modeling in ERDAS Imagine. The output from the classification and modeling are being evaluated as pre-maps for soil mapping activities in the study area.
Kienast-Brown, S. and J.L. Boettinger. 2010. Applying the optimum index factor to multiple data types for soil survey. p. 385-398. In: J.L. Boettinger, D.W. Howell, A.C. Moore, A.E. Hartemink, and S. Kienast-Brown (eds.) Digital soil mapping: Bridging research, environmental application, and operation. Springer Science+Business Media, Dordrecht.
Land Cover Classification from Landsat Imagery for Mapping Dynamic Wet and Saline Soils
Wet and saline soils have been recognized as an important and complex component of wetland ecosystems in arid environments. Analysis and classification of remotely sensed spectral data is an effective method for discerning the spatial and temporal variability of soils. The East Shore Area (ESA) of the Great Salt Lake soil survey update is focused on updating soil map units containing wet and saline soils. The ESA provides a unique environment for the use of remotely sensed spectral data for map unit refinement because of low relief and a large extent of soils that are wet and saline to various degrees. Map units in the ESA containing wet and saline soils were updated and refined using Landsat 7 imagery. Five land-cover classes are related to dominant soil types that vary in soil wetness, salinity, calcium carbonate concentration and vegetation cover type. Supervised classification of the imagery was performed using the five land cover classes. The final classification resulted in 14 land cover classes, including nine additional classes that help describe the variability in the original five classes. The classification results were validated using visual inspection in the field, a priori knowledge of the area and an error matrix. The results of the classification were used to enhance original soil map units and calculate map unit composition in the final soil mapping process. This information was then incorporated into the updated soil map. Temporal variation in land cover classes has the potential to be considered in map-unit refinement to reflect the dynamic nature of the margins of the Great Salt Lake, Utah.
Kienast-Brown, S., and J.L. Boettinger. 2007. Land cover classification from Landsat imagery for mapping dynamic wet and saline soils. p. 235-244. In: P. Lagacherie, A.B. McBratney, and M. Voltz (eds.) Digital SoilMapping: An introductory perspective. Developments in Soil Science Vol. 31, Elsevier, Amsterdam.
Soil Survey Using Traditional and Landscape Analysis Methods – Grand Staircase-Escalante National Monument, Utah
The overall goal of this research was to develop methodology to advance the soil survey of the Circle Cliffs area of the Grand Staircase-Escalante National Monument. A soil survey of the Circle Cliffs area was completed using traditional soil survey methods, and then enhanced using GIS-based methods. A lithosequence of soils was also examined to enhance the understanding of arid soil genesis and the soil-landscape relationships in the Circle Cliffs area. GIS was shown to be beneficial for soil survey data analysis, and was useful for quantifying and validating map unit concepts. The lithosequence study showed that soils formed in lithified parent material exhibited varying degrees of soil development, and the soil formed in non-lithified parent material appeared to be polygenetic. GIS was very useful for accelerating the soil survey, and may be applicable in other remote areas for examining soil-landscape relationships.
Kienast, S. 2002. Soil survey using traditional and landscape analysis methods – Grand Staircase-Escalante National Monument, Utah. M.S. thesis. Utah State University, Logan.
NRCS Soils http://soils.usda.gov/
Soil Data Mart http://soildatamart.nrcs.usda.gov/
Web Soil Survey http://websoilsurvey.nrcs.usda.gov/app/HomePage.htm
UT NRCS Soil Survey http://www.ut.nrcs.usda.gov/technical/soils/index.html
UT NRCS SCAN Sites http://www.wcc.nrcs.usda.gov/scan/Utah/utah.html
IUSS Working Group on Digital Soil Mapping http://www.digitalsoilmapping.org/
GlobalSoilMap.net project http://www.globalsoilmap.net/
Click here to see Suzann's February 2011 presentation for the Natural Resources Conservation Service. This presentation summarizes the research projects of staff and students in Dr. Janis Boettinger's lab.