DIVISION S-5—NOTES

UPDATE AND RECORRELATIONOF SOIL SURVEYS USING CIS AND

STATISTICAL ANALYSIS

G. R. BRANNON* AND B. F. HAJEK

AbstractThe introduction of U.S. soil taxonomy and the increased pressure

on land use and development has generated the need to update soilsurveys that were published before 1965. A portion of a pre-1965 soilsurvey from Montgomery county in Alabama was selected to evaluatean update approach using geographic information system (GIS) andstatistical analysis. The update included map recompilation, correla-tion, interpretation, and presentation methods. Sampling points wereidentified with a stratified random sampling and data obtained at eachpoint were analyzed by traditional statistical methods. The taxonomicaccuracy was 75 to 83% at a confidence level of 90%. Interpretativereliability was 90 to 95% for dwellings without basements, 95 to 98%for septic tank absorption fields, and 93 to 98% for local roads andstreets. Updating old soil surveys by using GIS technology and statisti-cal evaluation can produce a quality soil survey that meets or exceedsNational Cooperative Soil Survey (NCSS) standards. Using thismethod, an experienced soil scientist can update, recorrelate, andrecompile = 40180 ha (100000 acres) yr~'. This is an increase inproduction of 22 090 to 24100 ha (55 000-60 000 acres) or =120 to150% compared with conventional remapping methods.

THE PRODUCTION of soil surveys is undergoing a fun-damental change as we approach the 21st century.

The NCSS traditionally focused on producing soil sur-veys by counties or political regions (Indorante et al.,1996). The NCSS is moving away from a county basisto the administration, correlation, maintenance, andproduction of soil surveys on Major Land ResourceAreas (MLRAs), which includes use of GIS. This ap-proach improves reliability by applying consistent stan-dards to the entire geographic area while providing easyaccess to data and products. (McLeese et al., 1991;McLeese, 1992; Soil Survey Staff, 1993a). In addition,many published soil surveys were completed before1965 and need to be updated using current U.S. soiltaxonomy (Soil Survey Staff, 1975).

In October, 1995, 17 MLRA offices were created ina major reorganization of the NCSS to implement andmanage the Soil Survey by geographic regions. Reduc-tions in funding and numbers of personnel make thischallenge increasingly difficult by forcing the federalgovernment to do more with less. The reinvention ofgovernment initiatives set forth in 1993 by the NationalPerformance Review called for a major reduction of thefederal work force (Kettl, 1994).

G.R. Brannon, USDA-NRCS, P.O. Box 311, Auburn, AL 36830; B.F.Hajek, Agronomy and Soils Dep., Auburn Univ., Auburn, AL 36830.Received 5 Apr. 1999. *Corresponding author (greg.brannon®al.usda.gov).Published in Soil Sci. Soc. Am. J. 64:679-680 (2000).

Due to constraints of personnel and resources, alter-native methods to remapping are needed. In this study,a combination of GIS and statistical analysis using strati-fied random sampling and binomial statistics were usedto evaluate remapping and recorrelation of a portionof a pre-1965 county soil survey.

Materials and MethodsThe study area is in eastern Montgomery County, Alabama

and extends west from the Bullock and Macon County lineto 86°7'30" N, and north of Pike County to the TallapoosaRiver. The area is in MLRAs 133a and 135, the SouthernCoastal Plain and Alabama, Mississippi, and Arkansas Black-land Prairies, respectively. The study area encompasses=62306 ha (153840 acres), comprising about 30% of thecounty (U.S. Department of Commerce, 1990).

The legend was evaluated by an experienced soil scientistand soil map units with similar profile characteristics, land-scape positions, slope, and geology (parent material) werecombined in an office recorrelation. Lines separating soil mapunit delineations were transferred by hand from the publishedsoil survey atlas sheets (Burgess et al., 1960) to the correspond-ing 7.5' U.S. Geological Survey topographic quad sheets. Soilmap units that were combined in the office recorrelation weremerged on the topographic maps by combining the map unitswhile transferring the lines. The soil map unit polygons wereadjusted to follow map features.

A field reconnaissance of the study area was done to checkthe accuracy of the office correlation, line placement, validityof the soil map units, and to identify potential problem areas.This reconnaissance was restricted to primary and secondaryroads. Very few off-road investigations were conducted sincethis study was not designed for intensive field investigationsand remapping. Soil map units were added as needed.

The revised soil lines contained on the topographic mapswere traced onto mylar sheets and scanned on an LDS 4000Plus Scanner (Summagraphics, 1991). The digitized data thenwere loaded down into a Unix SUNSPARCstation 1 + work-station system (Sun Microsystems, 1994) and imported intothe LT4X Release 3.21 for Sun (SunOS/SOLARIS) program(Infotec Development, 1993) and registered. The individualmaps were cleaned, edited, and the lines along the map edgeswere adjusted to match the joining maps. Individual mapswere then imported into ARC/INFO (Environmental SystemsResearch Institute, 1994) for joining into a composite mapand conducting further editing, which primarily consisted ofdissolving the map edges and matching the adjoining soil mapunit polygons.

A stratified random sampling method was used to selectthe points to check the accuracy of the recorrelation. Pointswere stratified by map unit. This method is frequently usedby geographers and some of the early applications were forland use studies (Wood, 1955). This method has been founduseful when the study area is extremely variable (Cole andKing, 1968). This method has two advantages: (i) it is possibleto sample in proportion to the size of the units sampled and

Abbreviations; GIS, geographic information system; MLRA, MajorLand Resource Area; NCSS, National Cooperative Soil Survey.

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680 SOIL SCI. SOC. AM. J., VOL. 64, MARCH-APRIL 2000

Table 1. Confidence intervals at a confidence level of 90% of thesubsamples before and after the soil map legend was adjustedto reflect the observations in the field.

Confidence interval before themap unit legend was adjusted

Confidence interval after themap unit legend was adjusted

64.6-73.4% 74.7-92.5

(ii) it increases the precision of sampling (Shaw andWheeler, 1985).

The composite soil map was imported from ARC/INFOinto IDRISI (Clarke University, 1992), a DOS-based GISsoftware package. The stratified random sampling programin IDRISI generated the points that were used to evaluate therevised soil map. Ninety-eight sample points were randomlyselected throughout the study area. The UTM coordinatesfor the points were identified, recorded, and plotted on thecorresponding topographic maps. Three subsamples, or ped-ons, were randomly selected within the polygon at each pointand were checked for taxonomic accuracy, slope, and seriesidentification. A total of 294 observations were evaluated.Series identification was accomplished by using Keys to SoilTaxonomy (Soil Survey Staff, 1996) and the Soil Survey Man-ual (Soil Survey Staff, 1993b), and the corresponding officialseries descriptions. Each subsample was also evaluated for thefollowing interpretation ratings: dwellings without basements,septic tank absorption fields, local roads and streets, and campareas. The subsamples were analyzed using soil interpretationsratings guides to determine whether the observations had thesame interpretation rating as the correlated map unit.

ResultsThe interpretative data were assessed using statistics

applicable to binomial data (Steel and Torrie, 1960). A90% confidence level was selected to evaluate the data.The initial evaluation for taxonomic accuracy revealedthat 91 of the 294 subsamples, or =31%, did not meettaxonomic accuracy (Table 1). Additional review of thedata point observations resulted in renaming four soilmap units to better reflect the point observation on mapunit composition. The change in soil map unit namesresulted in a decrease to 63 subsamples that did notmeet taxonomic accuracy, or an increase to 78.6% intaxonomic accuracy of the map units.

The interpretations were evaluated and the resultsare listed in Table 2. The confidence intervals rangedfrom 89.7 to 97.6%, reflecting a high degree of confi-dence in the recorrelated map units.

Using this method of soil survey, in this region, anexperienced soil scientist can recorrelate and recompile

Table 2. Confidence intervals of selected interpretations at a con-fidence level of 90%._______________________

Dwellings without Septic tank Local roadsbasements absorption fields and streets Camp areas

89.7-94.8% 94.9-98.3% 93.3-97.6% 93.3-97.6%

=40180 ha (100000 acres) in a fiscal year. This is anincrease in production of 22 090 to 24100 ha (55 GOO-60 000 acres) or approximately 120 to 150% more thanconventional remapping methods.

The study was designed to address updates of pre-U.S. soil taxonomy soil surveys made and published ona photobase in MLRAs 133a (Southern Coastal Plain)and 135 (Alabama, Mississippi, and Arkansas BlacklandPrairies). They will have to be tested and revised asneeded to address local needs, existing data, and qualityof existing soil surveys.