What will it Grow Into?
4.4 Conclusions
4 Development and Application of Digital Soil Mapping 49 The soil survey program in the United States is nearing completion of “once-over” coverage of the nation. Many potential soil survey users in the remaining unmapped areas expect to use traditional polygon-based soil maps. Natural-resource planning and management methods have been developed using this type of soil in-formation. The soil map unit polygons serve as indications for management units.
These are made available for nationwide online soil mapping at the Web Soil Sur-vey (see also Section 24.2). While these traditional soil surSur-vey projects are being completed to meet these expectations, raster-based soil-landscape models are being developed and evaluated as input to the mapping and as stand-alone products for use in other models.
We feel that raster soil-landscape models are still a developmental product of soil survey. They are just becoming useful as pre-mapping estimates of the spatial distribution of some individual soil properties. The explicit estimation of all signif-icant soil properties based on a suite of individual models is yet to be developed.
For example, to use raster soil property estimates to make an interpretation of the soil limitation for septic tank installations a separate raster estimate is needed for each soil property used in the rating criteria, i.e., surface water ponding, depth to bedrock, depth to cemented pan, permeability, slope, flooding, and rock fragment content. This is necessary before informed land management decisions can be based on soil property raster estimates.
Since natural resource management methods and regulations are coordinated lo-cally, regionally, and nationally, standards for the creation and implementation of these models are required for consistent and coordinated outputs within a nation. In addition, the model outputs present interpretation challenges to the natural resource planner and manager. How do they interpret or use a raster estimate or even a stack of raster estimates to decide on placement of facilities or practices? How would a regulatory agency such as a regional planning department review how the models were used by conflicting groups to make decisions in order to meet planning regu-lations? For example each regulatory agency would need to be able to objectively evaluate the source of covariate data, pre-processing methods, implementation of the model mathematically, and grouping or filtering of the model outputs. The adoption of raster soil property estimates in the United States as stand-alone products requires the development of these standards and practical interpretation methods. We are just beginning to discuss these topics as the potential uses of these digital raster data are demonstrated and accepted.
50 D. Howell et al.
very limited. These are usually available only by contract and are proprietary. In some project areas, such as extensive desert ecosystems, the variation of the vegeta-tion, precipitavegeta-tion, and geologic data may be small. This leads to models that focus on elevation derivatives and satellite images as the primary covariates, although all available covariates should be evaluated.
These data, along with field soil profile descriptions and laboratory data, provide inputs for explicit, soil-landscape model estimates. These model estimates are useful pre-mapping products. They can help guide field sampling and provide support for extrapolation to unvisited field locations.
We found that increasing the spatial resolution (changing the elevation data res-olution from 30 m in our previous work to 5 m in this project) and increasing the attribute resolution (using ASTER data with 14 bands instead of Landsat data with 7 bands, i.e., more narrowly defined bands) did not increase the performance of the models in a dramatic way. Perhaps the significant elevation variance was portrayed by the 30 m DEM. The surface reflectance captured by the ASTER sensors may not have a significant correlation to subsurface features although in many areas burrowing animals and other disturbances have brought subsurface materials to the surface.
Tools for analyzing soil-landscape relationships need to be developed for easy application by field soil scientists using standard soil survey office software. We will focus on principal component analysis, unsupervised classification, and devel-opment of rule-based models. We will work with field soil scientists to develop these methods.
The biggest infrastructure challenge to implementation of explicit raster esti-mates of soil properties or classes in the United States is the adoption and com-munication of standards for model development and application. Another challenge is the development of methods for interpretation of raster soil estimates. Until con-servation planners know how to use these raster estimates to make decisions or make recommendations for land use management, these maps will not replace traditional polygon soil class maps.
References
Environmental Systems Research Institute (ESRI). 2004. ArcInfo v9.0. Redlands, CA.
Environmental Systems Research Institute (ESRI). 1998. GridSpot70. [Online] Available:
http://arcscripts.esri.com/details.asp?dbid=11037 [May 30, 2002].
Howell, D., Kim, Y., Haydu-Houdeshell, C., Clemmer, P., Almaraz, R., Ballmer, M. 2006. Chap-ter 34. Fitting soil property spatial distribution models in the Mojave Desert for digital soil mapping. In: P. Lagacherie, A.B. McBratney and M. Voltz (Eds.), Digital Soil Mapping, An Introductory Perspective. Developments in Soil Science, Vol. 31. Elsevier, Amsterdam, pp. 465–475.
Haydu-Houdeshell, C. 2003. Soil profile descriptions Johnson Valley Off Highway Vehicle Area Soil Survey Project. Personal Communication. USDA Natural Resources Conservation Service.
Intermap Technologies Incorporated. 2005. Digital Terrain Model. Interferometric Synthetic Aper-ture Radar.
4 Development and Application of Digital Soil Mapping 51
Lagacherie, P., Legros, J.P., Burrough, P.A. 1995. A soil survey procedure using the knowledge of soil pattern established on a previously mapped reference area. Geoderma 65, 283–301.
Lagacherie, P., Robbez-Masson, J.M., Nguyen-The, N., Barth`es, J.P., 2001. Mapping of reference area representativity using a mathematical soilscape distance. Geoderma 101, 105–118.
McBratney, A.B., Mendonc¸a Santos, M.L., Minasny, B., 2003. On digital soil mapping. Geoderma 117, 3–52.
McKenzie, N.J., Ryan, P.J., 1999. Spatial prediction of soil properties using environmental corre-lation. Geoderma 89, 67–94.
Riley, S.J., DeGloria, S.D., Elliot, R., 1999. A terrain ruggedness index that quantifies topographic heterogeneity, Intermountain Journal of Sciences 5, 1–4.
SAS Statistical Software Release 9.1. 2003. SAS Institute Inc. Cary, NC, USA.
Soil Survey Staff. 1999. Soil Taxonomy, Second Edition, Agriculture Handbook No. 436. United States Department of Agriculture, Natural Resources Conservation Service. Washington, D.C.
Schmidt, J., Hewitt, A., 2004. Fuzzy land element classification from DTMs based on geometry and terrain position. Geoderma 121, 243–256.
Yamaguchi, Y., Kahle, A.B., Tsu, H., Kawakami, T., Pniel, M., 1998. Overview of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). IEEE Transactions on Geoscience and Remote Sensing 36, 1062–1071.