You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics
Chen, Jun

ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001
priori information existed indicating the influence of other
environmental factors, e.g. geology, vegetation, landuse, etc.,
the above landscape metrics were considered of primary
importance in the Medina study area by default. GIS data
layers on these five environmental variables were derived from
a 3 by 3 meter resolution DEM using conventional digital
terrain analysis (Zevenbergen and Thorne, 1987).
3.2.2 Identifying unique environmental combinations using
FCM. An unsupervised FCM classification described in Section
2.2.2 was carried out on the environmental dataset compiled in
Section 3.2.1. The FCM were applied on the environmental
data across three different weighting exponents (m = 1.5, 2.0,
and 2.5, after Bezdek (1981)). For each run (per m) the
number of clusters examined ranges from 2 to 15. By
examining the improvements in partition coefficient (F) and
entropy (H) for all three sets, we observed the consistent
improvement in both entropy and partition coefficient across all
three runs at ten clusters. No other cluster values improved
consistently across all three run, we argue that there are ten
clusters (ten unique combinations of environmental conditions)
within the data. Figure 1 shows the membership distributions
of Class 7 and Class 9.
Figure 1: Membership distributions, (a): Class 7; (b): Class 9
3.2.3 Investigating the class centroids in the field. Over 20 field
observations (2 to 3 per class) were made to determine the
soil classes associated with each of the environmental
classes. The field observations were guided by the
membership values. For each class, the observation sites
were at locations where the membership values for the class
are very high.
The association between the environmental classes and soil
types observed in the field is summarized in Table 1. It is clear
that there is a good association between the environmental
classes and the soil types in the area. However, the
association is not one-to-one. A soil type can occur under
different unique environmental conditions. It is important to
point out that we did not observed different soil types at a
single environmental class. This validates the utility of fuzzy
membership maps in allocating field investigation efforts since
the membership distribution allowed us to avoid sampling in
the transitional areas.
Table 1: Association between environmental clusters (for
m= 2.0) and soil types.
Cluster ID
Observed Soil Class
St. Charles
St. Charles
St. Charles
3.2.4 Distilling the soil-landscape model for the area. By
interpreting the cluster centroids in comparison with field
observations and examining the membership distribution of
each environmental cluster, the local soil scientist created an
environment description for each of soil class. Examples of
these descriptions are shown in Table 2. These environmental
descriptions constitute the essence of the soil-landscape
model of the areas. Local soil scientists can map the
distribution of each soil series using the environmental
description for a given soil series.
Table 2: Descriptions of environmental conditions of Soil
Series Kidder and McHenry in the study area.
For Soil Series Kidder
Environmental Variable
Environmental Conditions
850-1050 feet
Profile Curvature
Slightly concave to convex
Planform Curvature
Slightly concave to convex
Upstream Drainage Area
Landform Position
Drumlin tops and upslopes
For Soil Series McHenry
Environmental Variable
Environmental Conditions
850 - 940 feet
Profile Curvature
Slightly concave to convex
Planform Curvature
Slightly concave to slightly
Upstream Drainage Area
Moderately low
Landform Position
Strongly sloping back-slopes and
3.3 Evaluating the so-derived soil-landscape model
3.3.1 Applying the soil-landscape model using SoLIM. To
assess the validity of the soil-landscape model constructed
using the FCM approach, the soil-landscape model was used
under the SoLIM approach to generate soil maps for the area.
The SoLIM approach is a knowledge-based approach for soil
mapping. It combines the knowledge of soil-environmental
relationships (soil-landscape model) with the environmental
conditions characterized in a GIS to infer the spatial
distribution of soils (Zhu, 1997; Zhu 1999; Zhu et al., 2001).
Case studies have demonstrated that the SoLIM approach to
soil mapping is successful (Zhu et al, 2001). However, the
quality of the soil maps from SoLIM largely depends on the
quality of the soil-landscape model. Thus, the SoLIM approach
provides us with the opportunity to examine the quality of the
soil-landscape model constructed with the use of FCM.
3.3.2 Evaluating the quality of the soil-landscape model. The
soil map derived from SoLIM with the use of the soil-landscape
model above is shown in Figure 2. The soil map shows a
catenary sequence of the soils in the area: soil series Kidder at
the ridge (drumlin) tops and upper part of the slopes; McHenry
at the back-slopes; St. Charles at the foot-slopes; Mayville at
the toe-slope; Virgil on the flat area of valleys and Sable at the
wet areas besides the streams. This pattern matches field
observation of catenary sequence in the area well.
To validate this soil map, observations of soils at 50 field sites
were made. Soil type at each field site was identified at the
series level. The field observed soil series at these sites were
then compared with the soil series obtained from the inferred
soil map at these locations. Soil series from the inferred soil
map match field observed soil series at 38 of the 50 sites,