Soil survey mapping units are designed such that the dominant soil represents the major proportion of the unit. At times, soil mapping delineations do not adequately represent conditions as stated in the mapping unit descriptions. Digital analysis of LANDSAT multispectral scanner (MSS) data provides a means of accurately describing and quantifying soil mapping unit composition. Digital analysis of LANDSAT MSS data collected on 9 June 1973 was used to prepare a spectral soil map for a 430-hectare area in Clinton County, Indiana. Fifteen spectral classes were defined, representing 12 soil and 3 vegetation classes. The 12 soil classes were grouped into 4 moisture regimes based upon their spectral responses; the 3 vegetation classes were grouped into one all-inclusive class

Hinzel, E. J.

Kaminsky, S. A.

Kirschner, F. R.

Sinclair, H. R.

Weismiller, R. A.

English

NASA Technical Reports Server

N78-14602
QUANTIFICATION OF SOIL MAPPING
BY DIGITAL ANALYSIS OF LANDSAT DATA*
F. R. Kirschner (USDA/Soil Conservation Service),
S. A. Kaminsky and E. J. Hinzel
Laboratory for Applications of Remote Sensing
Purdue University
West Lafayette, Indiana 47906
H. R. Sinclair
USDA/Soil Conservation Service
Indianapolis, Indiana
R. A. Weismiller
Laboratory for Applications of Remote Sensing
Purdue University
West Lafayette, Indiana 47906
ABSTRACT
Soil survey mapping units are designed such that the
dominant soil represents the major proportion of the unit.
At times, soil mapping delineations do not adequately
represent conditions as stated in the mapping unit descrip-
tions. Digital analysis of Landsat multispectral scanner
(MSS) data provides a means of accurately describing and
quantifying soil mapping unit composition.
Digital analysis of Landsat MSS data collected on
9 June 1973 was used to prepare a spectral soil map for
a 430-hectare area in Clinton County, Indiana. Fifteen
spectral classes were defined, representing 12 soil and
3 vegetation classes. The 12 soil classes were grouped
into 4 moisture regimes based upon their spectral responses;
the 3 vegetation classes were grouped into one all-inclusive
class.
Using these groupings, the spectral map was compared
to a conventionally prepared soil map. Three mapping units
were investigated in detail: a) Mahalasville silty clay
loam, b) Reesville silt loam, 0 to 2 percent slopes, and
c) Xenia silt loam, 2 to 6 percent slopes, eroded.
Results indicate that the percentage of soil mapping
unit, inclusions can be readily ascertained according to
their soil moisture regimes and that soil complexes can
be easily quantified. Thus, the composition of soil
mapping units can be accurately determined.
INTRODUCTION
Soil maps depict soil conditions in a particular landscape scene with
varying degrees of precision depending primarily upon the type of survey con-
ducted and the ability of the mapper to analyze the landscape and identify the
*This work was accomplished under the National Aeronautics and Space Adminis-
tration, Office of University Affairs, Grant No. NGL-15-005-186. Journal
Paper No. 6690 Purdue University, Agricultural Experiment Station.
1567
https://ntrs.nasa.gov/search.jsp?R=19780006659 2020-03-22T05:42:15+00:00Z
components of the mapping units delineated. Due to the subjective nature of
soil surveys and the vast areas of land involved, it is often difficult to
evaluate the accuracy of the soil surveys. Currently, field methods such as
spot checking and line and point intercept transects are used to evaluate the
composition of mapping units. (1) Various studies (2,3,4) to determine mapping
unit composition suggest that many delineations do not adequately represent
conditions as stated in the mapping unit descriptions. Also, many separations
on a soil map often represent soil complexes rather than taxonomic units with
minor inclusions as indicated. A study was undertaken to determine the feasi-
bility of using digital analysis of Landsat multispectral scanner (MSS) data
as a means of accurately describing and quantifying soil mapping unit composi-
tion. This paper examines three distinctly different soil mapping units, com-
paring their composition as described by conventional field mapping tech-
niques and digital analysis of Landsat MSS data.
A 430-hectare tract located in Clinton County, Indiana was selected as
the study area. Soils in this area developed from loess deposited over glacial
till derived from the late Wisconsin glaciation and localized lacustrine de-
posits. The surface topography ranges from 0 to 6 percent slope but the
slopes are commonly less than 2 percent.
Three soil mapping units were investigated in detail: a) Mahalasville
silty clay loam, b) Reesville silt loam, 0 to 2 percent slopes and c) Xenia
silt loam, 2 to 6 percent slopes, eroded. The Reesville and Xenia soils were
developed in the loess over glacial till and the Mahalasville soil in the
lacustrine deposits. The soils of this area had previously been mapped using
conventional techniques by USDA/Soil Conservation Service (SCS) personnel as
part of an on-going progressive survey.
DATA
Landsat-1 MSS data collected on 9 June 1973 were used as the main data
source for this study. This scene was selected because the data were: a) of
high quality, b) acquired when most cropland was in a bare soil state and c)
free of interfering atmospheric and surface conditions (i.e., clouds, haze and
standing water). However, Clinton County had received approximately 2.90 inches
of precipitation in the week prior to the Landsat overpass.
The Landsat MSS data were geometrically corrected (i.e., rotated, deskewed
and rescaled to a scale of 1:20,000) (5), and registered to ground control points
selected from U.S. Geological Survey 7% minute topographic quadrangle maps.
These procedures produced a data set of an exact scale of 1:20,000 with points
in the data registered to their exact ground position. The aerial photography
and field sheets used by the USDA/SCS personnel are also at a scale of 1:20,000.
These matching scales allowed for convenient comparisons between the conven-
tionally developed soil map and the spectral soil map derived from computer-
aided analysis of Landsat MSS data.
PROCEDURES
Landsat MSS data covering the study area was input into a clustering algo-
rithm program. This algorithm divided the MSS data into groups of sample
points of similar spectral characteristics. A statistics processor was uti-
lized to calculate the mean relative reflectance values and covariance matrices
for each of these individual cluster groupings. Cluster groupings were either
deleted retained or combined based upon their statistical separability char-
acteristics. This procedure indicated that there were 15 spectrally separable
classes within the study area. The statistics developed on each of these 15
classes were used by computer-implemented pattern recognition techniques as
implemented by LARSYS (6) and a maximum likelihood Gaussian classifier to
assign each of the data points to one of the 15 spectrally separable classes.
1568
A ratio (A = yR)* and the summed response (total magnitude of the relative in-
tensity values of all four Landsat bands) calculated for each spectral class
were used to identify 12 soil and 3 vegetation classes within the 15 spectral
classes.
After classification, these classes were grouped into four major soil
classes and one all-inclusive vegetation class. Each major soil class was
assigned to one of four soil moisture regimes based upon the magnitudes of
their respective summed responses (Table I). The class with the highest total
reflectance represented moderately well drained soils; the class with the
lowest reflectance represented poorly drained soils. These groupings were
verified by detailed field checking.
An alphanumeric spectral map delineating the 12 soil and 3 vegetation
classes was produced at a scale of 1:20,000. Field checks were conducted to
evaluate the agreement between the conventionally developed soil map and the
spectral soil map. Field observations included a) precise location of the
three mapping units on both types of soil maps, b) notation of the various soil
types and their respective moisture regimes included in the three mapping units
and c) notation of the boundaries (agreements and disagreements) of the three
mapping units and of their individual soil components.
RESULTS
The conventionally prepared soil map of the study area and the enhanced
boundaries of the 1) poorly drained Mahalasville, 2) moderately well drained
Xenia and 3) somewhat poorly drained Reesville mapping units are shown in
Figure 1. For comparitive purposes the three mapping units as delineated on
the conventional soil map were superimposed upon the spectral soil map (Fig-
ure 2). Distinct boundary differences existed between the two maps. Also,
significant inclusions not delineated on the conventionally prepared soil map
were noted on the spectral soil map. In all cases, the spectral map identified
inclusions within each mapping unit that had different drainage characteristics
than were identified by the named mapping unit. For example, significant por-
tions of the moderately well drained Xenia mapping unit were shown to be poorly
and somewhat poorly drained according to the computer classification. Field
checks of a major portion of the questionable areas revealed the spectral
classification to be correct.
Statistics derived from the spectral classification of the study site
(Table II) indicate that 51 percent of the Mahalasville mapping unit is appro-
priately classified as poorly drained. The majority of the other 49 percent
of the mapping unit was classified as very poorly and somewhat poorly drained.
Similarly, the spectral classification indicates that 46 percent of the Rees-
ville and 30 percent of the Xenia mapping units were appropriately classified
as the named mapping unit. Exclusive of vegetation the remainder of the Rees-
ville mapping unit was classified as poorly and moderately well drained. Sim-
ilarly, the remainder of the Xenia mapping unit was classified as poorly and
somewhat poorly drained. In both the Reesville and Xenia mapping units con-
trasting inclusions constituted a large enough percentage of the named unit
to justify additional separations or the mapping of a soil complex.
CONCLUSIONS
Digital analysis of Landsat multispectral scanner data can provide detail
and definition of soil features not readily discernible through visual inter-
pretation of Landsat imagery. It is apparent from this study that digital
*where V is the relative intensity of the mean spectral responses of the visi-
ble wavelengths {(0.5 to 0.6ym) + (0.6 to 0.7pm)} and IR is the relative inten-
sity of the mean spectral response of the reflective infrared wavelengths
{(0.7 to O.Sum) + (0.8 to l.lum)}
1569
analysis of Landsat MSS data can provide quantification of soil mapping. The
percentage of soil mapping unit inclusions can readily be ascertained according
to their soil moisture regimes, and soil complexes can be easily quantified.
Thus, the composition of mapping units can be accurately determined.
The use of digital analysis of Landsat data as a mapping tool to quantify
soil mapping unit compositions should greatly increase the accuracy of soil
surveys. By utilizing this quantification procedure, other aspects of the
soil survey, such as soil interpretations for urban uses, may be also greatly
enhanced.
REFERENCES
1. Soil Survey Manual. August 1951. U.S. Department of Agriculture Handbook
No. 18.
2. Amos, D. F. and E. P. Whiteside. 1975. Mapping accuracy of a contemporary
soil survey in an urbanizing area. Soil Sci. Soc. Am. Proc. 39:937-942.
3. McCormack, D. E. and L. P. Wilding. 1969. Variations in soil properties
within mapping units of soils with contrasting substratas in northwestern
Ohio. Soil Sci. Soc. Am. Proc. 33:587-593.
4. Powell, J. C. and M. E. Springer. 1965. Composition and precision of
classification of several mapping units of the Applying, Cecil and Lloyd
series in Walton County. Georgia Soil Sci. Soc. Proc. 29:454-458.
5. Anuta, P. E. 1973. Geometric correction of ERTS-1 digital MSS data.
Laboratory for Applications of Remote Sensing. Information Note 103073.
Purdue University.
6. Phillips, T. L. (editor). June 1973. LARSYS User's Manual, Vol. 1, 2.
Laboratory for Applications of Remote Sensing. Purdue University. West
Lafayette, Indiana.
1570
TABLE I. RELATIVE SPECTRAL RESPONSES OF GROUPED CLASSES.
Spectral
Map Symbol
/
+
X
M
Blank
Drainage
Class
Moderately
Well
Somewhat
Poorly
Poorly
Very Poorly
(Vegetation)
Range of
Summed Response
184.47-218.13
162.47-176.20
136.31-151.05
118.78
140.93-150.49
Range of
Ratio A
1.03-1.33
1.32-1.35
1.16-1.35
1.40
0.59-0.74
TABLE II. COMPOSITION OF SOIL MAPPING UNITS.
Characteristics of Named Mapping Unit
Mapping Unit
Mahalasville
Silty clay loam
Reesville silt loam,
0 to 2 percent slopes
Xenia silt loam,
2 to 6 percent
slopes, eroded
Spectral
Class
X
+
/
Internal Size of
Drainage Mapping Unit
Class (Hectares)
Poorly 47.65
Somewhat 51.21
Poorly
Moderately 13.36
Well
% and Area (Hectares)
of Unit Represented by
Named Series
51.40%
24.49
46.08%
23.60
30.00%
4.01
Percent and Area (Hectares) of Inclusions within the Above
_ Mapping Units Identified by Drainage Classes _
Spectral
Symbol
Very Poorly
Drained
21.49%
10.24
.88%
0.45
Poorly
Drained
20.00%
10.24
16.67%
2.23
Somewhat
Poorly Drained
17.75%
8.46
40.00%
5.34
Moderately
Well Drained
9.34%
4.45
18.26%
9.35
(Vegetation)
.02%
14.78%
7.57
13.33%
1.78
1571
ORIGINAL PAGE IS
OF POOR QUALITY
FIGURE 1. CONVENTIONAL SOIL MAP INDICATING THE
(1) MAHALASVILLE, (2) XENIA, AND
(3) REESVILLE MAPPING UNITS.
1572
ORIGINAL PAGE t§
OF POOR QUALITY
SOIL DRAINAGE
•1 Moderately well
IH Somewhat poorly
Poorly
BBVery poorly
Vegetation
MAPPING UNIT
1 Mahalasville
2 Xenia
3 Reesville
FIGURE 2. CONVENTIONALLY DELINEATED MAPPING UNITS SUPERIMPOSED
UPON THE SPECTRAL SOIL MAP.
1573


Quantification of soil mapping by digital analysis of LANDSAT data

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