Ecosystem mapping in the Lower Foothills Subregion of
Alberta: Application of fuzzy logic
by L.B. Nadeau1, C. Li2 and H. Hans2
Predictive ecosite mapping involves developing computer models that consistently identify and map ecosystems. This method of
predicting ecosystem occurrence on the landscape uses basic inventory information and expert knowledge, and is an effective integrated planning tool for providing a record of the location and spatial distribution of ecosystems within a management area. Fuzzy logic
technology can be used to computerize essential elements of ecosystem identification, and the outputs can be linked to a Geographic Information System for map production. A pilot study was undertaken on the application of this technology to the Alberta Ecological
Land Classification database and the resulting ecosite map for a township located in central Alberta (Tp42R9W5). The range of attributes
used in the program was constrained by the attributes recorded on mapped polygons. Three maps with suitable attributes were available for the township studied: a Digitized Elevation Model map, an Alberta Vegetation Inventory map, and a reconnaissance soil survey map. Attributes of all polygons from all three maps were compiled and seven attributes (humus form, Ah thickness, surface texture, aspect, organic thickness, slope angle, and Alberta Vegetation Inventory moisture regime) were chosen to produce a computerized
program for ecosite identification. Four sets of data were used to calibrate the program, as well as a small-plot data set collected from
the township studied. The computer program was used to analyze the polygon data corresponding to two sets of data collected in the
field and resulted in 72% and 70% similarity between the choices of experts and of the computer program. The quality of the original polygon attributes contributed to errors in identification. In addition, the reconnaissance soil survey map gave only an estimate of
four attributes (Ah horizon thickness, organic thickness, humus form, and surface texture).
Key words: ecosystem classification, site classification, fuzzy logic, fuzzy sets, predictive ecosystem mapping, predictive site mapping
La cartographie prédictive des écosites implique le développement de modèles informatiques qui identifient et cartographient systématiquement les écosystèmes. Cette méthode de prédiction de l’occurrence des écosystèmes dans le paysage utilise l’information tirée des inventaires de base ainsi que les connaissances des experts et constitue un outil efficace de planification intégrée permettant d’obtenir un relevé
de la localisation et de la distribution spatiale des écosystèmes au sein d’une unité d’aménagement. La technologie de logique floue peut
être utilisée pour informatiser les éléments essentiels de l’identification des écosystèmes et les données peuvent être reliées à un système
d’information géographique pour produire une carte. Une étude préliminaire a été entreprise sur l’application de cette technologie à la base
de données de la Classification écologique du territoire de l’Alberta et sur la carte résultante des écosites d’un canton situé dans le centre
de l’Alberta (Tp42R9W5). L’étendue des attributs utilisés dans le programme a été limitée aux attributs relevés dans les polygones cartographiés.
Trois cartes contenant les attributs adéquats étaient disponibles pour le canton étudié : une carte modélisée des élévations numérisées, une
carte de l’Inventaire de la végétation de l’Alberta et une carte d’étude préliminaire des sols. Les attributs de tous les polygones des trois cartes
ont été compilés et sept attributs (forme de l’humus, épaisseur du Ah, texture de la surface, aspect, épaisseur de la matière organique, angle
de la pente et le régime hydrique selon l’Inventaire de la végétation de l’Alberta) ont été choisis dans le but de produire un programme informatique pour l’identification de l’écosite. Quatre ensembles de données ont été utilisés pour calibrer le programme ainsi que les ensembles de données des petites parcelles recueillies dans le canton étudié. Le programme informatique a été utilisé pour analyser les données
des polygones correspondant aux deux ensembles de données recueillies sur le terrain et a enregistré 72% et 70% de similitude entre les
choix des experts et le programme informatique. La qualité des attributs des polygones initiaux a contribué aux erreurs d’identification. De
plus, la carte d’étude préliminaire des sols ne donnait qu’un estimé de quatre des attributs (épaisseur de l’horizon Ah, épaisseur de la matière
organique, forme de l’humus et texture de la surface).
Mots-clés: classification écosystémique, classification des stations, logique floue, ensembles flous, cartographie prédictive des écosystèmes,
cartographie prédictive des stations
Introduction
For biodiversity management, national and international community classification
methods are needed to order
ecosystems into functional
units (Ponomarenko and Alvo
2001). Ecosystems with similar characteristics are expected to respond to disturbances
in a similar manner, and ecological land classification is
generally considered an essen-
L.B. Nadeau
1Northern
Alberta Institute of Technology, 11762 – 106 Street, Edmonton,
Alberta T5G 2R1. E-mail: [email protected] (corresponding author).
2Canadian Forest Service, Northern Forestry Centre, 5320 – 122 Street,
Edmonton, Alberta, Canada T6H 3S5. E-mail addresses: [email protected],
[email protected]
MAY/JUNE 2004, VOL. 80, NO. 3, THE FORESTRY CHRONICLE
C. Li
H. Hans
tial element for the decision-making process of managers in
facilitating resource stewardship.
In Canada, although the government is striving to produce an ecological classification system (Baldwin 2001),
more than 50 local systems exist (Ponomarenko and Alvo 2001).
359
A national ecological classification for Canada would include
all types of biotic communities (terrestrial, subterranean,
freshwater, and marine), would use some components of
local ecosystem classifications, and would allow the country
to be well represented in the development of a global conventional
classification (Ponomarenko and Alvo 2001).
Local ecosystem classifications have generally been produced to meet specific management needs, but they also
reflect the inherent complexity of ecosystems. Classification depends on deciding which biotic or abiotic factors are
considered the most influential in a given ecosystem. Experts
often have different opinions on the importance and impact
of various soil or plant factors used in classification, which results
in different emphasis and thus different classification systems.
Many approaches have been taken for mapping ecosystems.
In the United States, The National Biological Survey/National Park Service (NBS/NPS) vegetation mapping program is
developing a hierarchical vegetation classification standard to
generate vegetation maps for most of their park units using field
work and imagery interpretation (National Biological Information Infrastructure 2002). Also in the United States, the Environmental Ecology Program is mapping wetlands using
geospatial data sets. Specific data layers of key resources are
combined and provide a holistic image of ecosystems (Remote
Sensing/Geographic Information System Center 2000). Other
approaches involved developing computer models that consistently classify and map ecosystems (Franklin 1995). Such
predictive ecosystem mapping is a method of forecasting
ecosystem occurrence on the landscape using basic inventory information and expert knowledge. It is an effective integrated planning tool that provides a record of the location and
spatial distribution of ecosystems within a management area.
In Canada, a computerized knowledge-based ecosystem identification and predictive mapping system, EcoGen, has been
developed by the government of British Columbia (Meidinger et al. 2000). EcoGen uses geographic and inventory data
to map the ecosystem classification units of British Columbia.
In Alberta, another computerized ecosystem classification,
ELDAR (Ecological Land Data Acquisition Resource), which
stemmed from the Naia program (Mulder and Corns 1993),
was designed to represent the knowledge used by a forest ecologist to infer a forest ecosystem from site, soil, and forest cover
attributes. ELDAR uses a major Ecological Land Classification (ELC) system, originally produced by Corns and Annas
(1986) and later modified by Beckingham et al. (1996). This
particular ELC system has a hierarchical structure of natural
region, subregion, ecosite, ecosite phase, and plant community type. ELDAR identification is made at the ecosite level,
and the outputs are linked to a Geographic Information System (GIS) database for map production.
An alternative to ELDAR is the use of fuzzy logic technology
to computerize essential elements of ecosystem classification,
and the outputs can be linked to GIS to produce maps. Fuzzy
logic was first applied to forest land management by Bare and
Mendoza (1988) and to forest ecology by Roberts (1989). More
recently, a computer program, Sitelogix, was developed by the
Geographic Dynamics Corporation (GDC 1999). This program
includes aspects of fuzzy logic, and some statistical analyses
and mapping abilities. Sitelogix has been used for ecosite identification but has not been described extensively in published
articles (Beckingham et al. 1999, GDC 1999).
360
The major advantage of fuzzy logic is its flexibility: the experience of more than one person can be translated into a program for decision-making purposes. The transition from the
fuzzy logic computer outputs to a map is relatively simple: the
outputs are tabulated in a form accessible to a GIS program,
directly linked to plot locations and then mapped. This report
describes a pilot study on the application of fuzzy logic computer programs to Alberta Ecological Land Classification
databases in the Lower Foothills subregion for classifying ecosites
from map attributes, and its resulting predictive map.
Methods
The area chosen for ecosystem mapping was a township (9328
ha) located in the Weyerhaeuser Drayton Valley Forest Management Area (FMA), south of the O’Chiese and Sunchild
Reserves (Tp42R9W5).
The choice of attributes used to develop a computer program
was limited to polygon attributes on maps that relate to forest ecosystem function. The final choice was based on
attributes identified by Luttmerding et al. (1990) and Beckingham et al. (1996) as having a major influence on site
moisture and soil nutrient regimes. Three maps with polygon
attributes were available for the area: a Digitized Elevation Model
(DEM) map, an Alberta Vegetation Inventory (AVI) map, and
the Brazeau Dam reconnaissance soil survey map (Agriculture Canada 1981). Both the DEM and AVI maps were
obtained from Weyerhaeuser in 1998. A portion of the
Brazeau Dam map was digitized and polygon attributes were
described. Attributes of all polygons of all three maps were
then compiled and those that would relate the most to ecosystem function were chosen. Luttmerding et al. (1990) and
Beckingham et al. (1996) provided most of the ecological information for developing the fuzzy logic computer program.
Of the plot attributes available, seven attributes were used:
humus form, Ah thickness (Ah is topsoil enriched with organic matter, with 17% or less organic carbon by weight), surface
texture, aspect, organic thickness, slope angle, and moisture
regime (Fig. 1). The nine classes of site moisture regime
(Luttmerding et al. 1990 and Beckingham et al. 1996) were
grouped into four broader classes to resemble the four classes used on AVI maps, thereby matching the moisture attributes
of the map used to produce the ecosite predictive map. Soil
nutrient regime and a second nine-class site moisture regime
were generated from these classes in the first subprogram (first
part of the program). This second nine-class site moisture regime
was needed to identify ecosites. The outputs of this first subprogram were then used to generate ecosite identification in
the second subprogram (second part of the program). The second subprogram’s identification was compared with the identification of experts to calibrate the subprogram. The experts’
identification was obtained from the ecosite designation of each
record in each database.
Program structure
To produce our program, we designed two fuzzy logic
subprograms, which used the FuzzyTECH software package
(Version 5.52a Prof. ed.; Inform Software Corporation, Chicago, IL 2001), to classify ecosites from the Lower Foothills natural subregion in Alberta. The first subprogram used polygon
attributes to obtain site moisture and soil nutrient regimes
and the second subprogram used the resulting site moisture and
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Fig. 1. Structure of the fuzzy logic program. Rule blocks consist of a number of quantitative descriptions of input variables that take the form
of “if-then” statements.
soil nutrient regimes to predict ecosites (Fig. 1). Soil nutrient
regime was estimated from humus form, Ah thickness, and surface texture from the Brazeau Dam reconnaissance map (Agriculture Canada 1981). Site moisture regime was estimated from
AVI moisture, organic thickness, aspect, and slope angle.
A more detailed explanation of the application of fuzzy logic
for ecosite identification is available from Nadeau et al. (2002)
and will be briefly summarized here. To use a fuzzy logic program, measured values of inputs and outputs are translated into
words, and relationships between the two are established by creating functions, referred to as membership functions. For each
attribute of Ah thickness and organic thickness, we used four linguistic variables, representing membership functions, ranging
from absent to very thick. Humus form had three variables
(mor, moder, and mull), surface texture had 12 variables from
clay to sand, ranked following the degree of fineness of the mineral fractions (Beckingham et al. 1996). Aspect had four variables based on the four cardinal points, and slope angle had five
variables, from flat to very steep. AVI moisture classes were based
on four variables: dry, mesic, wet and aquatic.
Similarly, outputs, site moisture and soil nutrient regimes,
for the first subprogram and ecosites of the Lower Foothills
for the second subprogram, were translated into linguistic variables with membership functions. Site moisture regime had
nine variables, from very xeric to hydric, soil nutrient regime
had five variables, from very poor to very rich, and ecosites
had five variables, from a very low to a very high possibility of occurrence.
Linguistic rules joined inputs to outputs and were translated
by means of “if, then” expressions. Rules were then evaluated by the program using the Min/Max algorithm (Nadeau et
al. 2002). For example, if a site faced south, with a steep slope,
Ah absent, the organic horizon shallow, and had a dry AVI moisture designation, then the site probably had a xeric moisture
MAY/JUNE 2004, VOL. 80, NO. 3, THE FORESTRY CHRONICLE
regime, a poor nutrient regime, and belonged to an ecosite typical of the grassland ecosite. The resulting linguistic value for
output was then translated to a numeric value. The greater the
numeric value, the greater the probability that the ecosite
chosen by the computer was representative of the site.
Fig. 1 represents an example of the fuzzy logic process. Input
variables such as Ah thickness or organic thickness for a
plot were fed into the first fuzzy logic subprogram as ASCII
text. The subprogram then processed the information. The lefthand column in Fig. 1 represents the input variable (the
attribute) and membership function of each attribute. The membership values (not shown) of each attribute were run through
rules (rule blocks 1 and 2 in Fig. 1) where the computer subprogram assigned linguistic values to outputs. Linguistic values were then translated back to numeric values of site moisture and soil nutrient regimes using membership functions and
the Min/Max algorithm (not shown). The process was then applied
using the numeric values of site moisture and soil nutrient regimes
as inputs in the second subprogram. These inputs were run through
a set of rules (rule block a in Fig. 1), producing ecosite identification as output.
Data set description
Five plot data sets were used to calibrate the fuzzy logic program (Table 1). The term “plot” refers to a field record that
may be of an area much smaller than a map polygon and represent only point data. The five plot data sets were all in the
Lower Foothills subregion, scattered throughout the western
portion of Alberta.
The first data set contained 21 plots from a chronosequence study in which soil, site, and plant species information
was recorded. Humus form, Ah thickness, surface texture, aspect,
organic thickness, slope angle, and site moisture regime were
used to calibrate both subprograms.
361
Table 1. Data sets used to calibrated the fuzzy logic program
Version 1a
Sample
Sizec
Similarityd
(%)
Version 2b
Mean similarity
across ecositese (%)
Subprogam 1. Derivation of site moisture and soil nutrient regimes
Chronosequence
21
81
Productivity
90
69
Weyerhaeuser
207
77
Collected 1
45
58
Collected 2
20
63
56
71
48
SEf
19
14
9
9
Subprogram 2. Derivation of ecosites from site moisture and soil nutrient regimes
ESIS
866
aThe computer program calibrated with the chronosequence, productivity
bThe computer program after calibration with the “Collected 1” data set.
cNumber of plots analyzed.
d“Goodness of fit” between experts’ and computer choices.
eMean percentage similarity across ecosites
fStandard error of the mean.
Mean similarity
across ecositese (%)
SEf
81
70
64
87
68
67
61
60
90
67
18
9
6
8
18
79
69
6
and Weyerhaeuser data sets.
The second data set contained 90 plots from a productivity study. As for the previous plot data, soil, site and plant species
information was included. Humus form, surface texture,
aspect, organic thickness, slope angle and site moisture
regime grouped into AVI moisture classes were used to calibrate the subprograms. The Ah attribute was not well defined
and categorized only as present or absent. Thus, when present,
it was assumed to be 15 cm thick so that the fuzzy logic program would consider it as present without any doubt.
The third data set, containing 207 plots from the Drayton
Valley Weyerhaeuser FMA covering Lower Foothills plots also
contained soil, site, and plant species information. All the plot
attributes mentioned for the first data set were available
except for texture. Thus, texture was assumed to be a silty loam
for the entire FMA (Agriculture Canada 1981).
The fourth data set contained 866 ESIS (Ecological Site Information System) plots from the Lower Foothills Natural subregion. The data covered soil, parent material, site, plant
species, and plant community information, and tree measurement and tree regeneration characteristics. Three of the seven
plot attributes could not be determined from the information
given; thus, only site moisture and soil nutrient regimes listed in the database were used to calibrate the second subprogram.
A fifth data set, referred as “Collected 1” in Table 1, was
used to calibrate the program after the first version of the map
was produced. More information on this data set is provided
in the “Map calibration” section.
Map production
When the program was calibrated, a map of ecosites for the
township area Tp42R9W5 was produced by combining sections of Digitized Elevation Map (DEM) (1-km resolution) and
an AVI map (approximately 1:20 000) for that township
with digitized portions of the Brazeau Dam soil map (1:126 720).
The resolution of the DEM map was considered adequate for
a relatively flat terrain. A GIS overlay was used to produce this
new map and attribute data set. The Brazeau Dam soil map was
obtained from the Brazeau Dam reconnaissance soil survey
with polygons representing soil units, for which dominant (>
40% of the area) and significant (15% to 40% of the area) soils,
usually developed on one parent material, and their proportions were available. Normally, only one or two soil subgroups
were considered significant. An estimate of soil characteris-
362
Similarityd
(%)
tics (Ah thickness, organic thickness, and surface texture) was
calculated for each polygon and weighted by proportion of dominant and significant soils in each polygon. It was understood at that time that the map produced would represent averages of soil characteristics, which could affect the accuracy
of the predictive map.
The polygons were considered too large to be represented
by only one ecosite; thus, an ecosite association was used to
label each polygon. For example, a polygon could be identified as “de,” implying that the most probable ecosite designation for that polygon was “d” (Labrador tea – mesic ecosite),
but the presence of “e” (Low-bush cranberry ecosite) was also
possible. When the program output assigned the same numeric value to two ecosites, three ecosite labels were used for a
polygon ecosite designation. For example, an identification of
“ed = c” indicates that ecosite “e” is the most possible, with
ecosites “d” and “c” (Hairy wild rye) being equally possible
but less likely than “e.” Alternatively, an identification of “e = dc”
indicates that ecosite “e” and “d” are equally possible and ecosite
“c” is slightly less likely. It is important to recognize that this
identification system may have slightly inflated the similarity between computer choices and experts’ opinion. A more
detailed explanation of the fuzzy logic process of ecosite
classification is available in Nadeau et al. (2002).
Map calibration
After program calibration and map production, sites on the
map were chosen 30 to 500 m away from major roads to calibrate the map. This implies that the sampling was not totally random and does not represent well inaccessible areas. Care
was taken to choose sites located in polygons of different ecosite
associations. A total of 24 sites were chosen. Two areas per
site were measured for humus form, Ah thickness, surface texture, aspect, organic thickness and slope angle, except for three
sites where only one area was measured. In all, a total of 45
areas were measured. Areas were approximately 1 m2 and sites
were 10 m2. A clinometer was used to measure slope angle,
and a compass to measure aspect. The data set collected was
referred to as “Collected 1” in Table 1.
The data collected in the field for the 24 sites measured differed from data obtained from maps of the same sites. Thus,
we felt that the lower quality of the attribute data from the maps
of township Tp42R9W5 used to predict ecosites affected the
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accuracy of ecosite identification on the first version of the predictive map. We therefore used the data set collected from the
45 areas as an additional data set to calibrate the computer program and to optimize the similarity between the computer program and experts’ choices for all data sets. When a greater similarity between the computer program output and experts’
prediction was attained, polygon data were re-analyzed by the
revised computer program, and a second version of the map
was generated. Ecosites before and after calibration were
also compared with the ecosites of the mapped polygons
(Table 2).
Map validation
On the second version of the predictive map (Fig. 2), 10 sites
were chosen in polygons of different ecosite associations. Unfortunately, not all 20 ecosite associations of Fig. 2 were sampled.
We did not have access to the wet ecosites such as bogs, marshes, fens, and subhygric ecosites (“kh” to “nm” ecosites of Alberta) (Beckingham et al. 1996). Two areas per site were measured for humus form, Ah thickness, surface texture, aspect,
organic thickness, and slope angle to produce the “Collected
2” data set of Table 1 and keyed to ecosite using the field guide
to ecosites of west-central Alberta (Beckingham et al. 1996).
The computer program was then used to analyze the data set
for the new sites and compared to the keyed ecosites. As for
calibration, ecosites were also compared to the ecosites of the
mapped polygons (Table 2). The data set “Collected 2” was
not used to calibrate the program or the map, but was used only
for validation.
Results and Discussion
The program was first calibrated using plot data from
three sources: chronosequence, productivity, and Weyerhaeuser data, providing 318 observations. The calibration
was considered adequate even though the chronosequence, Weyerhaeuser, and productivity data sets resulted in 81%, 77%, and
69%, respectively, “goodness-of-fit” or similarity to the
experts’ opinion (Table 1). These values represented the
optimum similarities between computer choices and experts’
choices of ecosite designation, or the optimum number of times
the computer was able to get the same answer as the experts.
The program could have been modified further, but would have
favoured more drastically one data set over another.
Some ecosites were not tested as often as others if their occurrence was less frequent than others in any plot data sets. The
most common ecosites were mesic, with poor to rich soil nutrient regimes, and the least common were the hydric to hygric
and xeric to subxeric ecosites, independently of soil nutrient
regime. The “goodness-of-fit” or similarity was therefore a percentage that put more emphasis on the mesic, poor to rich ecosites.
Thus, two other calculations were added: mean percentage similarity across ecosites, which weighted all ecosites similarly,
and the associated standard errors of the mean (Table 1). In
most cases, mean percentage similarities across ecosites were
lower than the similarities, mainly because few plot data
were available for calibrating the less frequent ecosites,
which resulted in low similarities between computer choices
and expert choices.
Each of the three data sets had drawbacks. The chronosequence data set represented plots of varying stand ages, and,
for young stands, a change in shading, transpiration, and
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evaporation; thus, a change in site moisture regime might have
affected the computerized site identification.
Ecosite identification for the Weyerhaeuser and the productivity
data sets was based on only six site and soil characteristics rather
than the seven used for the chronosequence data set. Considering
the number of factors that can affect soil nutrient and site moisture regimes (Luttmerding et al. 1990, Beckingham et al. 1996),
it is interesting that similarities of 64% and 70% between experts
and the computer program identification were still obtained.
The Ecological Site Information System (ESIS) data set was
used to calibrate only the second subprogram, using site
moisture and soil nutrient regimes to classify ecosites. Edatopic
grids (i.e., site moisture regime versus soil nutrient regime grids)
are available for each subregion (Beckingham et al. 1996), and
the translation of the grid for the Lower Foothills Subregion
into fuzzy logic inputs was straightforward. Ecosites on the
Lower Foothills grid often overlap and, therefore, the similarity
between prediction and actual ecosite identification was 79%,
even though a gradient in site moisture and soil nutrient
regimes were produced by the program (e.g., a moisture
regime of 5.5). Experts often resort to more attributes and weight
them differently. The computer program averaged the opinions of experts, resulting in overlapping ecosites.
When calibrating the computer program, the “Collected 1”
data set differed from the polygon data set. Soil unit boundaries in the Brazeau Dam soil survey were not accurate, and
an estimate of soil attributes was made from soil units of the
Brazeau Dam reconnaissance soil survey. Thus, soil attributes
assigned to some map polygons were erroneous. In addition, the different scales of maps used to produce the predictive map may also have contributed to a reduced similarity
between expert identification and polygon identification.
Another problem involved organic thickness. In the “Collected 1” and “Collected 2” data sets, ecosites “c” (Hairy wild
rye), “d” (Labrador tea), and “e” (Low-bush cranberry) were
found to have more organic matter accumulation than had been
previously recorded in the data sets, resulting in only a 58%
similarity between field data computer identification and
experts’ identification. The computer program identified
many of these sites as an “f” (Bracted honeysuckle) ecosite.
We therefore adjusted the program by altering the existing membership functions for organic thickness, and by adding a
function, very thick. For the chronosequence and productivity data sets, the revised computer program (Version 2) did not
affect the similarities between the computer program and
experts’ identification and it improved the mean similarities
across ecosites. However, for the Weyerhaeuser data set, the
revised computer program resulted in reduced similarity and
mean similarity across ecosites between the computer program
and the experts’ identification. Despite this drawback, the similarity between computer and experts’ identification was
improved from 58% to 87% for the first set of collected data
(Collected 1), and the mean similarity across ecosites was
improved from 48% to 90%.
“Collected 1” and “Collected 2” data sets were often different from the map polygon data, probably because of the way
the Brazeau Dam reconnaissance soil survey obtained polygon data, that is, the boundaries of polygons are imprecise and
the soil polygon data was estimated. Thus, when the computer
program was used to analyze the polygon data corresponding
to “Collected 1” and “Collected 2” data sets, we obtained 72%
363
Table 2. Number of map polygons sampled from Tp42R9W5 and percentage similarity referring to “goodness of fit” between experts and computer
choices, and mean percent similarity across ecosites.
Version 1a
Collected 1
Collected 2
Version 2b
Number of
polygonsc
Similarityd
(%)
Mean similarity
across ecositese (%)
SEf
(%)
Mean similarity
across ecositese (%)
SEf
23
10
55
52
29
72
70
86
75
14
14
aThe computer program calibrated with the chronosequence, productivity
bThe computer program after calibration with the “Collected 1” data set.
cNumber of polygons analyzed.
d“Goodness of fit” between experts’ and computer choices.
eMean percentage similarity across ecosites
fStandard error of the mean
Similarityd
and Weyerhaeuser data sets.
Fig. 2. Ecosite associations for the township T42R9W5 of the Lower Foothills subregion of Alberta by the fuzzy logic program and Geographic Information System (GIS). Individual letters refer to the ecosite designation of Beckingham et al. (1996) in order of equal or decreasing proportion. For example, a polygon mapped as “ba” is identified as an association where “b” refers to the bearberry/hairy wild rye ecosites
and “a” refers to the bearberry ecosites. Scale: 1:9346.
and 70% similarities between experts and the computer program choices, respectively, as well as 86% and 75% mean similarities across ecosites, respectively (Table 2). Similarities of
70 to 80% are generally expected (GDC 1999).
Given that there are only 14 (“a” to “n”) ecosites in the Lower
Foothills subregion (Beckingham et al. 1996), the predictive
map represents six more ecosite labels. The ecosite most
364
likely to be identified with other ecosites in this pilot study was
“j” (Labrador tea/horsetail).
Little time was spent on gathering field information because
few parameters were measured. The entire project, developing the fuzzy logic program through its map production and
field validation, took less than three months. Producing this
pilot study map could be considered inexpensive since it did
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not require extensive field work and the data were provided
at no cost. However, greater similarity between computer-based
and expert designation of ecosites could be achieved by using
larger sample sizes. Ecosite classification in the field could also
be improved by identifying a more appropriate suite of
attributes, provided that the attributes are available from
mapped polygons.
The opportunity costs of neglecting to register the appropriate attributes with sufficient accuracy during original mapping projects can be evaluated from the results reported here.
The trade-offs between loss of accuracy versus the cost of improving ecosite designation could be evaluated in terms of mismanagement of resources.
Conclusion
Fuzzy logic programs can be easily tailored to any data set.
The main challenge is to obtain the “best fit” for more than one
data set. Membership functions are flexible enough that estimated data can still be used in developing fuzzy logic programs.
One main advantage of computerized classification is standardization. For example, if and when a national ecological
community classification is produced (Ponomarenko and
Alvo 2001), it could easily be translated into a fuzzy logic program to standardize ecosystem designation. The program
could then be used with GIS if polygon attributes related to
ecosystem function were available, and could generate predictive
maps. Even with the limitations we encountered (i.e., soil polygon boundaries were imprecise and soil attributes were estimated) and although our inputs were limited to seven attributes,
we were still able to get more than 70% similarity between the
choices of experts and the fuzzy logic program.
Acknowledgements
We thank D. Allan, T. Little, R. Yang, Weyerhaeuser
Canada Ltd. and the Data Management Section of the Resource
Data Branch of Alberta Sustainable Resource Development
for providing the data used in this project. We are grateful to
B. Lee and R. Hall for their support, to Mike Smith for digitizing portions of the Brazeau reconnaissance survey map, to
D. Allan and X. Li for assisting in field work, and to B.
Amiro, M. McLaughlan, D. Pluth, and J. Volney for reviewing the manuscript and providing insights on the final documents. Financial support was provided by Weyerhaeuser
Canada Ltd.
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