Station, and made available to users as publication
AY 209 (1980). The map at a scale 1:500,000 was pu
blished by the Cooperative Extension Service of Purdue
University in cooperation with the state Soil and Wa
ter Conservation Committee of the Indiana Department
of Natural Resources and the Soil Conservation Servi
ce of the United States Department of Agriculture.
The soil association map was manually digitized using
the Purdue University/LARS digitizing system. This
system is composed of a Talos table digitizer and an
APPLE II Plus microcomputer. A complete documentation
of this menu-driven system was prepered by Phillips
(1983).
The data capture (map digitization) consisted in the
transformation of three map primitives, i.e. control
points, boundaries (limits of soil units), and centroids
into a format compa 4- ! 11 Q with digital computers. After
he proce cc dp*-- capture was completed, the computer
.om""'--'' 1 uata were transferred from the APPLE II Plus
mj-^rocornpucer to the host (main) computer (IBM 370/158)
where the data were stored and the activities of editing,
coordinate transformation, and rasterization were per
formed. Editing the digitized data was accomplished
by manual and automatic editing routines using a gra
phics terminal Tektronics 4045.
Twelve control points, as illustrated in Figure 2,
were used to derived statistically a biquadratic re
gression model which was used to transform the digiti
zed values in X and Y into longitude and latitude geo-•
graphic coordinates. These data were subsequently
transformed into an Albers equal-area cartographic pro
ject. This was the projection (cartographic) selected
for the Indiana geographic information system imple
mented at the Laboratory for Applications of Remote
Sensing (LARS) of Purdue University.
The final step of the map input procedure was the
rasterization process. During this process, the boun
dary and centroid files, stored in addresses corres
ponding to the Albers equal-area cartographic projec
tion, were converted into an image file. The map units
were filled-in with cells according to a predefined
grid (500 m x500 m on the ground) and subsequently
each cell was assigned a class code associated with
the centroid file (0- 255). Figure 3 illustrates the
different steps performed during the map input proce
dure. The coding (fill characters) assigned to each
of the 55 soil associations existing in Indiana and
to the portion of Lake Michigan in the state, is shown
in Table 1.
For the construction of the attribute database (hie
rarchical) , extensive use was made of the available
information generated for the state soil associations
of Indiana (Galloway et al., 1975). Other information
not readily available in tables or as maps, at this
level of detail, were obtained by interpretation, ex
trapolation and generalization of the information pre
sent in the description of the soil series forming
each soil association (Galloway and Stainhardt, 1981;
Franzmeier and Sinclair, 1982).
For displaying purposes and generation of color out
puts of the computer generated interpretive soil maps,
the rasterized image was transferred to the image pro
cessing device IBM 7350 "HACIENDA".
3 RESULTS AND DISCUSSION
Once the input of the data is completed and the ras
terized data set and the corresponding attribute da
ta set are stored in the database, the spatial infor
mation can be easily retrieved, handled, analyzed and
displayed. The degree of the analitical capabilities
implemented in a system depends on the nature, purpo
se and general objectives of the user. However, a well
thought-out system will be one that is flexible enough
to respond to the needs for input, analysis and display
of different kinds of data required by the main user
of the system.
Regardless of the objective of the principal user,
one element seems to be present in almost every digi
tal information system. It is the element soils, de
picting soil types as obtained from soil surveys. It
occurs because of its relation to the fauna, vegeta
tion and climate, and its strong interaction with o-
ther natural resources elements. Soils, landuse and
infrastructure constitute the fundamental and basic
elements forming part of the database of geographic
information systems for natural resources. The natu
re and types of information available in a soil sur
vey enables the generation of several interpretive
soil maps. These maps can be used as new variables
for analysis or modeling of resources to predict chan
ges that may occur through time.
The soil associations of Indiana in digital format
displayed in the High Level Image Processing System
(HLIPS) device IBM 7350 "HACIENDA", is shown in Figu
re 3. The area estimates and percentage of occurran-
ce of each soil association in Indiana is presented
in Table 1. Soil association Crosby-Brookston present
on nearly level surfaces of Wisconsinan age glacial
till plains in central Indiana, constitutes the lar
gest association in Indiana covering an area of ap
proximately 703,050 ha or 7.4 % of the state, followed
by the Morley-Blount-Pewamo association, occurring on
end moraines and on rolling areas near streams that
dissect till plains. This association covers an area
of 636,825 ha or 6.7 % of the state. The smallest as
sociations are Riddles-Tracy-Chelsea on the end morai
nes in northwesten Indiana and Lyles-Ayrshire-Princen-
ton developed on calcareous outwash sand and eolian
fine sand deposited in Wisconsinan time covering an
area of 13,500 and 15,600 ha respectively, or, 0.14
and 0.16 percent of the state.
The potential soil erosion was calculated using the
Universal Soil Loss Equation. The factors of the
USLE for each soil association were estimated by
Brentlinger et al.(1979). This information was used
to reclassify the digital soil association map into
four potential soil erosion groups: low, medium, high
and very high. The potential soil erosion map is il
lustrated in Figure 4, and the area estimates for each
erosion group are shown in Table 2. This interpreti
ve information can be used in conjuction with landuse
data to predict the erosion hazard or gross erosion
in the state. It can also be related to slope, land-
use and proximity to streams to determine agricultu
ral pollution due to erosion and to estimate sedimen
tation hazards and the related dangers of floodings.
Soil maps in Indiana are used in reassessment of a-
gricultural land. The basic aim of any assessment ac
tivity is the equal treatment of all individual land-
owners. Yahner (1979) described the procedures used
in agricultural land reassessment using estimates of
corn yields. Each state soil association has been as
signed an estimated corn yield ^alue. Figure 5 illus
trates the corn yield estimate map of Indiana after
grouping the values in high, medium and low yield va
lues for each soil association. Because of the reso
lution (scale) of the data and the generalization in
volved in the creation of the soil associations, so
me problems and difficultities may exist in the actual
assessment of individual farm evaluation. However,
it can be used to obtain rapid information on the ap
proximate value of agricultural land.
The possibility of deriving different interpretive
maps from the soil association map can be very useful
in creating a set of illustrative material for didac
tical purposes . One such example is the possibility
of showing graphically the influence of the soil for
ming factors in determining the actual soil characte
ristics. Figure 6 illustrates the parent material
from which the Indiana soils were developed. It de
picts the various kinds of materials including old
sedimentary rocks in the southern part of the state,
defferent thickness of loess deposits over glacial
till, alluvial, lacustrine and eolian deposits from
which the soils were developed.
Topography or relief has a great influence on the
processes of weathering and soil formation. It in-
