TEMBEC Industries Inc.
Canal Flats
Operating Area
PEM
(Predictive Ecosystem Mapping)
A Report and Maps
For:
Marcie Belcher
TEMBEC Industries Inc.
220 Cranbrook Street North
Cranbrook, BC V1C 3R2
(250) 426-6241
By:
Maureen V. Ketcheson M.Sc. R.P. Bio.
Tom Dool B.ES.
Gareth Kernaghan, Research
Keyes Lessard, Research
Grant Burns B.Sc.
JMJ Holdings Inc.
208-507 Baker Street
Nelson, B.C. V1L 4J2
(250) 354-4913
jmj@netidea.com
Steve Wilson
Ecologic Research
PO Box 167
Sayward, B.C. V0P 1R0
(250) 282-3768
Graham Smith B.ES.
Geosense Ltd.
203-507 Baker Street
Nelson, B.C. V1L 4J2
(250) 354-0277
gsmith@geosense.com
March 31, 2001
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
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TABLE OF CONTENTS
AKNOWLEDGEMENTS............................................................................................... 1
1.0 INTRODUCTION..................................................................................................... 2
1.1 OBJECTIVES....................................................................................................................................... 2
1.2 STUDY AREA LOCATION ................................................................................................................ 2
1.3 GEOLOGY, SURFICIAL DEPOSITS AND SOILS ............................................................................ 6
1.4 ECOSECTION AND BIOGEOCLIMATIC CLASSIFICATION OF TEMBEC’S CANAL FLATS
OPERATING AREA.................................................................................................................................. 8
1.5 THE HISTORY OF PEM IN THE CANAL FLATS OPERATING AREA........................................ 14
2.0 METHODS .............................................................................................................. 15
2.1 GIS INPUT DATA ASSEMBLY, ASSESSMENT AND PREPARATION....................................... 15
2.1.1 RASTER DATA FORMAT ........................................................................................................... 15
2.1.2 SOURCE DATA........................................................................................................................... 16
2.1.3 PEM INPUT LAYERS.................................................................................................................. 16
2.1.4 LANDSCAPE LAYERS ................................................................................................................ 18
2.1.5 LANDSAT LAYER........................................................................................................................ 19
2.1.6 TRIM LAYERS ............................................................................................................................. 20
2.1.7 GEOLOGY and SOIL .................................................................................................................. 20
2.1.8 FOREST COVER......................................................................................................................... 20
2.1.9 BEC LAYER................................................................................................................................. 21
2.1.10 OVERLAY .................................................................................................................................. 21
2.2 FIELD DATA COLLECTION, MODEL DEVELOPMENT AND SUMMARIZATION OF FIELD
VARIABLES............................................................................................................................................ 22
2.21 YEAR TWO FIELD DATA COLLECTION................................................................................... 22
2.3 KNOWLEDGE BASE CREATION ................................................................................................... 22
2.3.1 NEURAL NETWORK CLASSIFICATION ................................................................................... 23
2.3.2 SITE SERIES MODIFIERS AND STRUCTURAL STAGE MODEL............................................ 27
2.3.3 ACCURACY AND GOODNESS OF FIT ASSESSMENTS........................................................... 28
2.3.4 PEM MAP PRODUCTION.......................................................................................................... 28
3.0 RESULTS ................................................................................................................ 29
3.1 SITE SERIES MAPPING OF CANAL FLATS OPERATING AREA............................................... 29
3.2 STRUCTURAL STAGE MAPPING OF THE CANAL FLATS OPERATING AREA ..................... 34
3.3 MODEL GOODNESS OF FIT AND ACCURACY ASSESSMENT ................................................. 35
4.0 DISCUSSION .......................................................................................................... 37
4.1 RASTER BASED PEM MODELING ................................................................................................ 37
4.2 ACCURACY OF PEM SITE SERIES MODEL................................................................................ 38
4.4 ACCURACY OF THE STRUCTURAL STAGE MODEL ................................................................ 39
4.5 IMPROVEMENT TO TEMBEC’S CANAL FLATS PEM MODEL ................................................. 40
CONCLUSION.............................................................................................................. 41
REFERENCES CITED ................................................................................................ 42
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March 31, 2001
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LIST OF TABLES
Table 1. TEMBEC PEM map sheet list
Table 2. Input and Derived layers
Table 3. Site Series Aspect Modifiers used in PEM
Table 4. Structural Stage Modifiers Used in PEM
Table 5. Total Area by BEC subzone/variant, Canal Flats Operating Area
Table 6. Total Area By Site Series Mapped by PEM in the Canal Flats Operating Area
Table 7. Structural Stage Distribution in the Canal Flats Operating Area.
Table 8. Canal Flats PEM Neural Network Accuracy Assessment
Table 9. Canal Flats PEM Mean Accuracy of Final Mapping By the Subzone or Variant
Table 10. Canal Flats PEM Accuracy Assessment Final Mapping By the Site Series
Table 11. Canal Flats PEM Structural Stage Accuracy Assessment for Final Mapping
Table 12. Relative Proportions of Site Series Mapped by the PEM Compared to Relative
Proportions of Site Series Randomly Sampled on the Ground
Table 13. Relative Proportions of Structural Stages Mapped by the PEM compared to
Relative Proportions of Structural Stages Randomly Sampled on the Ground
LIST OF FIGURES
Figure 1. Location of TEMBEC’s Canal Flats Operating Area within the Province of
British Columbia
Figure 2. TEMBEC Canal Flats Operating Area PEM map location and Landscape Unit
Boundaries
Figure 3. Ecosections of South Eastern British Columbia
Figure 4. Biogeoclimatic Subzones within TEMBEC’s Canal Flats Operating Area
Figure 5. Edatopic grids for the Dominant Subzones of TEMBEC’s Canal Flats
Operating Area
Figure 6. A Simple Neural Network
Figure 7. Topology of a typical Neural Network
LIST OF APPENDICIES
APPENDIX 1.
APPENDIX 2.
APPENDIX 3.
APPENDIX 4.
APPENDIX 5.
APPENDIX 6.
APPENDIX 7.
APPENDIX 8.
APPENDIX 9.
Field Database
Goodness of Fit Confusion Matrices – site series level
Accuracy Assessment Confusion Matrices and Kappa
Neural Network Training and Verification Matrices
Field Data Cards
Venus Data
Classification Tree Results
Structural Stage Model
Knowledge Bases
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March 31, 2001
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AKNOWLEDGEMENTS
This project was made possible through funding by Forest Renewal British Columbia
(FRBC) in partnership with TEMBEC, Forest Resource Management, British Columbia
Division, Cranbrook B.C.
We would like to thank the following people who assisted with the completion of this
project: Marcie Belcher and Dan Murphy of TEMBEC for coordinating this project.
Tom Braumandl of MOF Nelson Region, Del Meidinger of MOF Research Branch and
Dave Clark of MOELP, Victoria for review comments.
We also thank Steve Wilson, of Ecologic Research, for his inspired assistance and
cheerful undertaking of the neural network analysis. Much credit must be given to Tom
Dool, of JMJ Holdings Inc., for finishing the year two PEM and neural network
combination under unexpected and stressful conditions. Graham Smith of Geosense for
the year one PEM and the early parts of the year two analysis. Finally, none of this
would have been possible without all of the JMJ employees who collected and processed
field data and helped put this project together – Vicky Lipinski, Rayanne McKay, Donna
Ross, Gareth Kernaghan, Keyes Lessard, Grant Burns and Bruce Sinclair.
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JMJ Holdings Inc. suite 208 – 507 Baker Street, Nelson, BC V1L 4J2
(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
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TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
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1.0 INTRODUCTION
Predictive Ecosystem Mapping (PEM) was initiated in TEMBEC’s Canal Flats Operating
Area near Cranbrook B.C. January 1, 2000. TEMBEC engaged an ecological mapping
consultant, JMJ Holdings Inc. to develop a Predictive Ecosystem Mapping (PEM)
mapping model similar to that produced for the Arrow IFPA (Ketcheson and Dool 2001).
This report presents a background to the ecological classification of the Canal Flat’s
Operating Area, and documents the methodology used to produce site series and
structural stage maps and site series summaries.
1.1 OBJECTIVES
We accomplished the following objectives in this project:
1. To produce site series and structural stage maps using PEM methodology over
the entire TEMBEC Canal Flats Operating Area using an ARCINFO based,
raster GIS model.
2. To utilize field data from the Canal Flat’s Operating Area in the refinement
and improvement of the Year One PEM model.
3. To document methodology and results in a report and to provide paper maps,
plot files and seamless digital coverage in a format compatible with
TEMBEC’s GIS requirements.
1.2 STUDY AREA LOCATION
TEMBEC’s Operating Area is approximately 497,000 ha. in size and is located on both
sides of the East Kootenay Trench centred on Canal Flats, in the southeastern corner of
British Columbia (see Figure 1).
The boundary extends from the Purcell Wilderness Conservancy, Greenland, Doctor, and
lower Findley drainages in the west to the White River and Height of the Rockies
Wilderness Area in the east. It extends in the south from Top of the World Park,
Skookumchuck and Buhl Creeks to Dutch Creek, Columbia Lake and Fenwick Creek in
the north.
The TEMBEC Operating Area is represented on 41 - 1:20,000 map sheets, PEM map plot
files consist of 1:40,000 “quads”, and is contained in fifteen Landscape Units within the
Invermere Forest District. The LU’s covered by PEM mapping are shown in Figure 2,
they are:
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•I-1 Findlay
•I-2 Buhl Bradford
•I-3 Skook Torent
•I-4 Premier Diorite*
•I-5 Lussier – Coyote
•I-6 Blackfoot – Thunder
•I-7 East Middle White River
•I-8 North White
•I-9 Grave Creek
•I-10 Moscow – Nine Mile
•I-11 Kootenay River
•I-12 Lavington – Fir
•I-13 Columbia Lake*
•I-14 Dutch Creek
•I-19 Fenwick
*both of these landscape units have also been TEM mapped. These areas were retained in the PEM to
provide consistant seamless, digital site series coverage of the project area. PEM polygons represent a
single site series, whilst TEM polygons can contain up to three site series, which are represented nonspatially.
The following 1:20,000 map sheets were grouped to make the “quads” presented in the
paper PEM maps.
Table 1. TEMBEC PEM Map Sheet List
Sheet A: 82F.089, 82F.090, 82F.099, 82F.100
Sheet B: 82G.081, 82G.082, 82G.091, 82G.092
Sheet C: 82G.083, 82G.084, 82G.093, 82G.094
Sheet D: 82K.009, 82K.010, 82K.019, 82K.020
Sheet E: 82J.001, 82J.002, 82J.011, 82J.012
Sheet F: 82J.003, 82J.004, 82J.013, 82J.014
Sheet G: 82K.029, 82K.030. 82K.039. 82K.040
Sheet H: 82J.021, 82J.022. 82J.031, 82J.032
Sheet I: 82J.023, 82J0.24, 82J0.33, 82J.034
Sheet J: 82J.041, 82J.042, 82J.051, 82J.052
Sheet K: 82J.043, 82J.044, 82J.053, 82J.054
Sheet L: 82J.045, 82J.055
Sheet M: 82J.025, 82J.035
Sheet N: 82J.005, 82J.015
Sheet O: 82G.071, 82G0.72
Sheet P: 82F.079, 82F.080
Sheet R: 82K.018, 82K.008
Sheet Q: 82F.098
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JMJ Holdings Inc. suite 208 – 507 Baker Street, Nelson, BC V1L 4J2
(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
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The location of these maps sheets relative to the Canal Flats Operating Area is depicted
in Figure 1.
TEMBEC’s
Canal Flats
Operating Area
Figure 1 Location of TEMBEC’s Canal Flats Operating Area, within the Province of
British Columbia.
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JMJ Holdings Inc. suite 208 – 507 Baker Street, Nelson, BC V1L 4J2
(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
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TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
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I19
I8
I13
I9
I11
I7
I10
I6
I12
I5
I3
I1
I4
I2
Figure 2 TEMBEC Canal Flats Operating Area PEM Map Location and Landscape Unit
Boundaries.
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JMJ Holdings Inc. suite 208 – 507 Baker Street, Nelson, BC V1L 4J2
(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
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1.3 GEOLOGY, SURFICIAL DEPOSITS AND SOILS
The bedrock geology and nature of surficial deposits form the landscape shapes upon
which the PEM is based. Although this project did not have bioterrain as an input layer,
consideration of the patterns of deposition and their effect upon soil moisture regime was
given during development of the knowledge bases and input layer variables.
The Canal Flats Operating Area is divided by the East Kootenay Trench (EKT). The
eastern portion of the operating area lies within the Kootenay and Park Ranges of the
Rocky Mountains, and the western portion within the Purcell Mountains.
The EKT acts as a natural divide between bedrock geology found in the Purcell and
Rocky Mountain portions of the operating area. An extensive thrust-fault system that is
oriented NNW to SSE follows the EKT. The geology associated with the Park Range of
the Rockies is dominantly Cambrian to Devonian in age, whereas geology of the Purcell
portion is chiefly middle Proterozoic in age. The Rocky Mountains represent an old
passive continental margin that has been subsequently uplifted. Margin sediments
associated with this area are resistant dolomite, limestone, shale, and pockets of
sandstone interbedded with shale. The Purcell side of the operating area also contains
continental margin sediments, but more associated with the cratonic basin. The main
bedrock types associated with this area are argillite, siltstone, limestone, dolomite, and
quartzite (Wheeler and McFeely 1991).
During the Pleistocene Epoch (2,000,000 to 10,000 years before present (BP)), this area
was subjected to multiple episodes of glaciation. Most of the landscape features visible
today are the result of the most recent (Fraser) glaciation and the subsequent alpine
glaciations. Since the end of the Fraser Glaciation, further alteration of the landscape has
occurred as a result of the ongoing processes that remove, transport, and re-deposit
materials. These include mass movement (slope processes) and fluvial (stream) activity.
The Purcell portion of the TEMBEC operating area can be characterized as being heavily
effected by glaciation. The Findlay and Skookumchuck Valleys are U-shaped in profile,
while smaller side valleys such as Buhl and Doctor have been glacially oversteepened.
Similarly, the Rockies portion of the TEMBEC operating area has been affected by
glaciers, with glacially oversteepened side valleys and glacially scoured ridges. The
alpine and high sub-alpine landscape consists of ice fields, rocky mountain horns, peaks
and ridges, alpine and cirque glaciers, rock glaciers, hanging valleys, and tarns (lakes in
cirque basins). Here the materials are mainly rock, colluvium, and till from the Little Ice
Age advance (from about 550 to 150 BP). At middle elevations, moderate to steep
mountain slopes are blanketed by till and colluvium, with scattered outcrops of rock. In
side valleys, where mountain slopes tend to be steeper, avalanche tracks, gullies, slide
paths, and colluvial cones and fans are abundant. In the larger U-shaped valleys, such as
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North White and the Kootenay, slopes tend to be more uniform from the valley floor to
mountain summits, and thus geomorphologic processes are less active.
Valley bottoms are occupied by river terraces, fans, and floodplains including the wide,
active floodplains of the Lussier and Kootenay valleys, which are made up of fluvial
terraces, meandering channels, back waters, and pockets of organic material. In the
narrow side valleys, colluvial cones and fans comprise a large portion of the valley
deposits with some till of variable thickness and organic deposits.
Soils of the TEMBEC operating area have formed under the influence of several climatic
conditions. These range from the dry, cold Alpine Tundra Biogeoclimatic Zone, to the
dry, cool Engelmann Spruce - Subalpine Fir and Montane Spruce Biogeoclimatic Zones,
to dry, mild Interior Douglas-fir in the Biogeoclimatic Zone, to the dry, hot Ponderosa
Pine Biogeoclimatic Zone. These varied climatic conditions combined with diverse
geomorphic and biological environments, have resulted in the development of a variety
of different soils.
The majority of the TEMBEC operating area is dominated by mid-slope coniferous
forests that overlay medium to fine textured parent materials. Parent materials in lower
slopes and valley bottoms have soil textures that range from coarse to fine. Alpine and
sub-alpine areas are dominated by coarse to medium-textured soils that are moderately
well to rapidly drained.
Moist, cool climatic conditions associated with mid to upper elevations, tend to facilitate
the development of Podzolic soils. These soils are characterized by eluviated Ae horizons
that are light gray and are found overlying enriched Bf horizons that range from orangered to dark brown. This diagnostic Podzolic Bf horizon is enriched with varying amounts
of amorphous aluminum and iron as well as organic material leached from the Ae horizon
above.
Within the the TEMBEC operating area, Humo-Ferric Podzols with their diagnostic Bf
horizon, are found on cooler, more humid aspects at mid-elevations and subalpine areas.
Ferro-Humic Podzols are found in moister toe slope positions and in higher elevation
ridges and cirques. These soils have a Bhf horizon that is enriched with significant
amounts of organic matter as well as Al and Fe (as defined in the Canadian System of
Soil Classification (1998)).
Where soils have had less time to form, they show poor to very poor horizon
development which results in the formation of Regosols. These occur in young materials
such as river gravels, fresh colluvium and recently deglaciated soils, or in disturbed
materials subject to flooding or slope processes.
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Brunisolic soils can be distinguished from Regosolic soils based on their diagnostic Bm
horizon. This horizon exhibits the development of soil structure and leaching, of soluble
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TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
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salts and carbonates from the A horizon. In the field it is recognizable by its browner to
redder colour when compared with the underlying parent material (Lavkulich and
Valentine 1978). Brunisolic soils are undergoing similar processes to Podzolic soils, but
because of relative youth, or because of development in a drier environment, do not meet
the criteria for Podzolic B horizons. Dystric Brunisols are commonly found in complex
with Podzolic soils on steep valley sides and are typical soils on coarser soil parent
materials on drier sites. At lower elevations Eutric Brunisols are extensive and can be
found on coarse parent materials. On permanent grasslands, at lower elevations, isolated
pockets of Chernozems can be found with their diagnostic dark thick Ah horizon.
Where drainage is imperfect to very poor, Gleysolic and Organic soils have developed.
These soils are found at mid-elevations along floodplains and in depressions where
periodic to prolonged saturation occurs. Gleysols can also be found at toe slopes that
receive significant amounts of runoff from the slope above.
1.4 ECOSECTION AND BIOGEOCLIMATIC CLASSIFICATION OF
TEMBEC’S CANAL FLATS OPERATING AREA
The study area was classified within a global ecosystem classification hierarchy that
descends from broad-based units of similar climate and physiography to the detailed site
series, modifiers and structural stage classification. The classifications used are derived
from Demarchi’s (1996) Ecosection system and from the BC Ministry of Forests Site
Series Classification system (Braumandl and Curran 1992).
Ecoregions are large regional-sized, ecological land units that have similar macroclimate,
physiography, vegetation and wildlife potential. Five levels of Ecoregion Classification
are recognized including Ecodomain, Ecodivision, Ecoprovince, Ecoregion and
Ecosection. Following the ecological land classification hierarchy set forth by Demarchi
(1996), the TEMBEC operating area is located within the Humid Temperate Ecodomain,
the Humid Continental Highlands Ecodivision, the Southern Interior Mountains
Ecoprovince, and in the Northern Columbia Mountains, Western Continental Ranges and
Southern Rocky Mountain Trench Ecoregions.
Ecosections are subregional units within ecoregions that are similar in climate,
landforms, bedrock geology, soils, and plant and animal distributions. Demarchi (1996)
classifies the TEMBEC operating area as being located within the Eastern Purcell
Mountains (EPM), the Southern Park Ranges (SPK) and the East Kootenay Trench
(EKT) Ecosections. (see Figure 3).
The Eastern Purcell Mountains (EPM) Ecosection lies in the rainshadow on the east side
of the Purcell Mountains with Montane Spruce forests in the eastern valleys but
otherwise is dominated by rugged subalpine forests and alpine vegetation.
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The Southern Park Ranges (SPK) of the Rocky Mountains is a dry mountainous area with
many wide valleys and isolated ridges. The climate is warm in summer and cold in
winter.
The East Kootenay Trench (EKT) is a broad, flat glacial plain with a distinctive
rainshadow and is dominated by Douglas-fir and lodgepole pine forests.
Biogeoclimatic Zones, Subzones and Variants occur within each Ecosection and are
classified using the Ministry of Forests Biogeoclimatic Ecosystem (BEC) system
(Braumandl and Curran 1992). These units represent groups of ecosystems under the
influence of the same regional climate. The TEMBEC Canal Flats Operating Area is
predominatly in the Dry Cimate Region which supports twelve biogeoclimatic subzones
and variants (see Figure 4).
Figure 3 Ecosections of South Eastern British Columbia
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TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
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Figure 4 Biogeoclimatic Subzones within TEMBEC’S Canal Flats Operating Area
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1) IDFun - Undifferentiated Interior Douglas-fir (Windermere Lake) Unit occurs
between 800 - 900m primarily on warm aspects. It is only found along the shores of
Columbia Lake. This zone is characterized by hot, very dry summers and cool winters
with very light snowfall. Mature zonal sites support open stands of Douglas-fir, other tree
species in this zone are rare. Bluebunch wheatgrass and junegrass are the dominant
understory species. This subzone has also been described by Marcoux et al. (1997). The
area supports a wide variety of animal species dependant on a mix of forest and
grassland. It is an especially important winter range for mule deer, white-tailed deer and
elk.
2) PPdh2 - The Kootenay Dry Hot Ponderosa Pine Variant generally occurs between 700
and 900m elevation. It is found in the southern portion of the Canal Flats Operating Area
along the Kootenay River. This zone is characterized by very hot, very dry summers and
mild winters with very light snowfall. Zonal sites (Braumandl and Curran 1992) support
open stands of ponderosa pine and Douglas-fir. Common species in the understory
include bluebunch wheatgrass, saskatoon, prairie rose, and rosy pussytoes. There has
been extensive fire, grazing, and logging disturbance within this subzone. It supports a
wide variety of wildlife species dependent on open forests and is an especially important
winter range for mule deer, white-tailed deer and elk.
3) IDFdm2 - The Kootenay Dry, Mild Interior Douglas-fir Variant occurs generally
between 800 and 1200 m in elevation on warm aspects and between 800 and 1100 m on
cool aspects. It is found in the operating area at the lower elevations in the Columbia and
Kootenay River Valleys plus along valley bottoms in the lower reaches of the
Skookumchuck, Lussier and Findlay Creeks. This zone is characterized by hot, very dry
summers and cool winters with very light snowfall. Mature zonal sites (Braumandl and
Curran 1992) support stands of Douglas-fir; however, due to frequent wildfires, mixed
seral stands of Douglas-fir and lodgepole pine are more common. In northern parts and
areas adjacent to the ICHmk1 in the White River, cool aspect slopes and moister site
series in the IDFdm2 may exibit characteristics of the ICH. The appearence of western
redcedar is one characteristic of this transition. Past frequent wildfires have kept these
species from developing into mature stands. This subzone supports a wide variety of
wildlife species dependent on open forests and is an especially important winter range for
mule deer, white-tailed deer and elk.
4) MSdk -The Dry Cool Montane Spruce Subzone occurs between 1200 and 1650 m in
elevation on warm aspects and between 1100 and 1550 m on cool aspects. It extends up
many lower elevation valleys above the IDFdm2 and below the ESSFdk. This zone is
characterized by warm, dry summers and cold winters with light snowfall (Braumandl
and Curran 1992). Mature zonal sites support stands of hybrid white spruce and
subalpine fir with minor amounts of Douglas-fir; however, due to widespread wildfires,
extensive stands of lodgepole pine exist today. This subzone is important autumn and
early winter range for deer, elk and moose. It is an important habitat for grizzly bear and
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the remaining old-growth pockets are key to the maintenance of insect-feeding, cavitynesting bird populations which, in turn, aid in control of forest insect pests.
5) ESSFdk - The Dry Cool Engelmann Spruce - Subalpine Fir Subzone occurs between
1650 and 2100 m in elevation on warm aspects and between 1550 and 2050 m on cool
aspects. This zone is located above the MSdk and is characterized by cool, moist
summers and very cold winters with moderately heavy snowfall (Braumandl and Curran
1992). Mature zonal sites support stands of subalpine fir and Engelmann spruce. Areas in
the Lower White River at the transition between the ESSFdk and the ESSFwm are
characterized with the appearance of western hemlock, western redcedar and the
dominance of white rhododendron on cool aspect slopes and moist sites. Old growth
stands in this subzone are important for the maintenance of wildlife populations while
seral stages provide highly productive deer, elk and moose summer range. Avalanche
and riparian areas provide good habitat for grizzly bear.
6) ESSFdku - The Upper Dry Cool Engelmann - Spruce Subalpine Fir Subzone occurs
between 2100 and 2350 m in elevation on warm aspects and between 2050 and 2300 m
on cool aspects. It is located above the ESSFdk on the highest forested slopes. This
subzone is characterized by cool, dry summers and very cold winters with heavy
snowfall. Mature zonal sites support stands of subalpine fir, Engelmann spruce and
subalpine larch. Late lying snow and frost pocketing create a mosaic of forest and
permanent meadows. This subzone is not documented in Braumandl and Curran (1992)
and has been described by Kernaghan et al (1997, 1998). Old growth stands in this
subzone are important for the maintenance of wildlife populations, while seral stages
provide highly productive deer, elk and moose summer range. Avalanche and riparian
areas provide good habitat for grizzly bear.
7) ESSFdkp - The Dry Cool Engelmann Spruce - Subalpine Fir Parkland Subzone occurs
between 2350 and 2500 m in elevation on warm aspects and between 2300 and 2500 m
on cool aspects. It is a transition above the continous forest and the alpine tundra of the
high Purcells and Rockies. This zone is characterized by short, cool and dry summers and
very cold winters with heavy snowfall. Mature zonal sites support patchy stands of
krummholtz subalpine fir, Engelmann spruce and subalpine larch. Late lying snow and
frost pocketing create a landscape of scattered tree islands and permanent meadows.
8) AT - Alpine Tundra biogeoclimatic zone occurs at elevations above 2500 m elevation
on both aspects. It encompasses the high, treeless peaks of the Purcells and Rockies.
This zone is characterized by short, cool and dry summers and very cold winters with
heavy snowfall. Much of the subzone is non-vegetated and zonal vegetated sites are
characterized by mountain avens and arctic willow with no conifers.
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Figure 5 Edatopic Grids for the Dominant Subzones of TEMBEC’s Canal Flats Operating
Area
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1.5 THE HISTORY OF PEM IN THE CANAL FLATS OPERATING AREA
In the first year of this project the mapping model was based on plot data collected from
TEM mapping projects in areas adjacent to the PEM mapping area which supported the
same subzones. The TEM projects used were Steamboat Mountain (Kernaghan et al
1998), Brewer Creek (Kernaghan et al 1997), East Columbia Lake (Marcoux et al 1997),
Stoddart Creek (Marcoux et al 1997), Wasa Park (Ketcheson et al 1998) and Premier
Ridge – Diorite (Kernaghan et al, 2000). We used the TEM full and ground plot site
series classifications and related them to plot attributes that could be modeled by the GIS.
Features including slope, aspect, slope position, soil moisture and nutrient regime,
distance to water, stream density, landscape shape, and forest cover attributes were
summarized over plots with the same site series classification. These summaries were
used in conjunction with expert knowledge of the TEM mappers familiar with those
subzones to build the first draft knowledge bases used for initial runs of the PEM model.
These plots were used for model building only in Year One, results of the Year One PEM
are reported in Ketcheson et al (2000). Plot data for model verification and accuracy
assessment were collected during the field season of 2000.
In Year Two of the Canal Flat’s operating area PEM plot data was collected in 600
locations from 200 randomly determined points within 500 m of TRIM roads. The UTM
grid coordinates for each plot were measured using differentially corrected GPS. Plot
field data were summarized by BEC site series and structural stage and GIS input layer
characteristics. This data was used to refine the Year One site series and structural stage
models and to generate a neural network classification that was also used to derive a site
series classification. The PEM was run several times until we were satisfied with its
goodness of fit to the original model. The final PEM accuracy was assessed with 86
randomly chosen plots.
The ecosystem classification of polygons generated by this model follow RIC (1998)
Standards for TEM mapping, Ecological Data Committee (1998) Standards for Digital
Data Capture for databases and spatial files, and recently released Specifications for
Predictive Ecosystem Mapping Standards (Moon et al, 1999)
(http://www.for.gov.bc.ca/research/TEMalt). The database, which accompanies the
mapping, includes BEC subzone and variant, site series and directional exposure
modifiers, as per the PEM standards. A structural stage model, using the TEM structural
stage standards, was generated based on forest cover age class and leading species for the
final mapping. The goodness of fit of the structural stage model was also assessed.
Mapping using PEM methodologies has been initiated in BC over the past 18 months.
This project is one of a few innovative approaches to ecosystem mapping, based on
computer modeling, being investigated in British Columbia. Standards for this approach
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were recently set (PEM Data Committee, 2000) and few final products from similar
approaches exist at this time. The accuracy of this mapping and the efficiency with
which knowledge bases assign site series classifications within the IDF, MS, ICH, and
ESSF zones is not well documented in other Forest Regions. Validation of the mapping
model has to be an integral part of this project. Improvements can be made to the model
as more PEM projects within similar subzones are reported. The groundwork has been
computed, re-running the model is inexpensive and straightforward.
2.0 METHODS
2.1 GIS INPUT DATA ASSEMBLY, ASSESSMENT AND PREPARATION
2.1.1 RASTER DATA FORMAT
A raster data model was selected as the processing format over a vector (polygon) based
approach. There are several advantages to using raster data for predictive ecosystem
modeling. The raster format provides more efficient processing, especially in
multivariate analysis, over vector data since it does not have the topological overhead to
maintain. Raster layers are analyzed with numeric calculations on a pixel-by-pixel basis,
whereas vector analysis is based more on the geometry of polygons. Raster data
maintains a high level of spatial resolution since the landscape at its largest scale is a
collection of individual pixels of relatively small size. A 25 meter pixel size was chosen
as the standard cell size for all the PEM input layers.
PEM layers portraying landscape character, including, slope, aspect, and shape, raster
data are represented by a continuous surface. Digital values will increase and decrease in
gradients from pixel to pixel. Neighborhood analysis (moving window) is used to smooth
and filter input layers and analyze the gradients between pixel values. Filters are used to
reduce minor noise and smoothing with different window sizes is used to adjust layers to
an appropriate scale for the landscape model. A majority filter was used for removing
noise and a mean filter was used for smoothing. The use of filters is discussed below.
A raster model permits flexibility for assigning and adjusting class breaks since the raw
data will remain in a continuous form. Non-linear changes in gradient on a surface can
be measured. For example, the rate of change of elevation is measured to extract profile
morphology and derive toe slopes and terrain shape.
The software environment used for the raster processing was Arc/Info GRID version 7
and PCI Image Analysis version 6.
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2.1.2 SOURCE DATA
The GIS inputs for the PEM model were derived from the following five source layers:
•
•
•
•
TRIM - 1:20,000
Forest Cover - 1:20,000 pre-VRI
Landsat 7 - 30 meter multi-spectral satellite imagery
Geology - 1:250,000
2.1.2.1 DIGITAL ELEVATION MODEL
A Digital Elevation Model (DEM) was the primary layer used to produce landscape
layers. From the TRIM contour, elevation, and break-line layers a TIN (Triangular
Irregular Network) was built. There was minimal weeding of TIN nodes in order to
preserve the elevation detail of TRIM data. The TIN was sampled on a 25 meter pixel
grid to create a raster DEM. Two DEM’s were used for deriving landscape layers. The
first was the raw output from the TIN to raster DEM conversion. This DEM represents
the highest level of terrain complexity. A second DEM was produced from a 3x3 mean
filter applied to the raw DEM to provide a low pass smoothing of the elevation model.
The DEM smoothed out micro variations in the terrain and produced smoother derivative
output.
2.1.3 PEM INPUT LAYERS
There were thirty-one input layers created with 58 different attributes overall. Layers had
a range of one to eleven classes. Each class was assigned a numeric value, which in turn
was assigned to the pixel values for the raster layer. The layer’s names and the numeric
values of each layer relate to the knowledge tables. A zero value was the NULL class.
For single class layers, such as wetland, a value of one represented the presence of
wetland, and zero represented no wetland was present that pixel location.
Table 2 Input and Derived Layers
SOURCE
TRIM DEM
TRIM DEM
TRIM DEM
TRIM DEM
TRIM DEM
LANDSAT
LAYER
SLOPE
ASPECT
SOLAR RADIATION
SHAPE
TOE SLOPE
SATELLITE CLASSIFICATION
GIS NAME
SLP
AS
SRD
SHP
TOE
SAT
CLASSES
6 classes
2 classes
2 classes
4 classes
1 class
4 classes
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LANDSAT AND TRIM
TRIM
FOREST COVER
FOREST COVER
FOREST COVER
FOREST COVER
FOREST COVER
FOREST COVER AND
TRIM
GEOLOGY
SOIL
TRIM
TRIM
Slp12_vro
WETLAND
INTERMITTANT LAKE
ALPINE FOREST
INVENTORY TYPE GROUP
FOREST HEIGHT
CROWN CLOSURE
SPECIES, PERCENTAGE
COMBINATIONS
CROWN CLOSURE AND SLOPE
CLASSES
MATERIAL TEXTURE
QUATERNARY DEPOSIT
STREAM DENSITY
SLOPE CLASS AND DISTANCE
FROM WATER
Slp12_vro
TRIM
1 class
2 classes
B
ITG
1 class
4 classes
HT
Cc
various
2 classes
2 classes
11 classes
Cc10_slp56
4 classes
GEO
SOIL
STRMS
Slp1_50w
Slp12_50w
Slp2_100w
4 classes
1 class
2 classes
4 classes
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2.1.4 LANDSCAPE LAYERS
From the 50 meter DEM the following layers were produced:
Slope
- percent slope
Slope was classified into the following six classes:
0-5%
6-25%
26-50%
51-70%
71-100%
over 100%
Aspect – warm/cool/neutral
The DEM with a 3x3 mean filter was used to produce the aspect layer. This
helped to reduce small amounts of noise and speckle in the output. Aspect was
classified into the following three classes:
Warm
Cool
Neutral
135 to 285 degrees azimuth
285 to 135 degrees azimuth
Any aspect with a slope of 25% or less
Solar Radiation
Solar radiation was calculated for Julian days 120, 171, and 273, the start, middle
and end of the growing season. An average was taken for the three dates. The
model (Kumar et al 1997) calculates Kilojoules of energy per square meter per
day. The model accounts for the solar azimuth, and elevation variation by the
solar calendar. Latitude in decimal degrees is input to adjust sun elevation. The
model is useful because it identifies regions with a cool aspect that receive sun
due to exposed terrain position, and also identifies regions of warm aspect that are
cool because of cast shadows and terrain blockage, such as in deep valley
bottoms. These two classes were used as an adjustment layer for aspect.
Shape
Landscape curvature was classified into four categories, concave, straight,
convex, and convex-ridge. The DEM with a 3x3 mean filter was used to smooth
out micro variations. Pixel values representing curvature range from negative
values for concave to positive values for convex. A lookup table with the values
was used to classify terrain shape:
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concave
straight
convex
convex-ridge
-100 to –5
-5 to 5
5 – 15
15-200
A 3x3 majority filter was run on the classification to reduce noise and speckle and
produce more homogenous units.
Toe Slope
The change in slope perpendicular to the direction of the slope was measured
from the smoothed DEM. This measure represents regions of increasing and
decreasing slope values. Pixel values represent the rate of decreasing slope from
steeper to less steep slope values. Through an iterative process and comparison to
field plots, a ranges of values were identified as toe slopes areas. A range of
values was extracted that represent the flattening out inflection point of the
landscape profile. The values used were 80-350.
A 3x3 majority filter was run on the classification to reduce noise and speckle and
produce more homogenous units.
2.1.5 LANDSAT LAYER
A Landsat 7 scene from September 9, 1999 was ortho-rectified to TRIM. Thematic
bands 3,4,5 representing red, near-infrared and mid-infrared were the source image data
for the satellite classification. Digital orthophoto imagery was used as the primary source
of ground control training. The overall classification accuracy, based on the training
samples, was 81%. There was no field verification of the classification layer. A
maximum likelihood classification was trained with the following land cover classes:
Rock
Talus
Avalanche chute
Vegetated rock/soil
The avalanche chute class required post-classification processing since its spectral
signature occurred in non-avalanche areas. Logged areas and the area below the
operability line were masked out. From forest cover mapping polygons with nonproductive code NPBR (non-productive brush) were added. Rock, talus, and operable
land were masked out from the NPBR layer.
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2.1.6 TRIM LAYERS
•
Wetland
Wetland boundaries were extracted from the TRIM water layer and
wetland polygons were created. The polygons were then rasterized to a 25
meter pixel size.
•
Stream Density
A circular moving window with a 200 meter radius was used to measure the
density of streams. Each pixel in the output was assigned a value representing the
length in meters of stream within the moving window. The following two classes
were created:
1 – 20 meters of stream/hectare
Greater than 20 meters of stream/hectare
2.1.7 GEOLOGY and SOIL
•
Bedrock geology polygons were reduced to three classes representing the
following material texture:
Fine
Coarse
Mix of fine and coarse
•
Quaternary deposits were extracted from geology data and put into a separate
layer
2.1.8 FOREST COVER
From the Ministry of Forests, Forest Inventory Data, the following layers were extracted:
•
Alpine forest – B leading
•
Inventory Type Groups
1 - Douglas-fir Leading
32 – Yellow Pine Leading
21 – Spruce Leading
35 – Cottonwood Leading
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• Species Presence Groups
At – Trembling Aspen in top two species
Pa – White-barked pine leading
La – Alpine Larch anywhere in stand
Fd_S – Douglas-fir and spruce in top four species
Py – Yellow Pine anywhere in stand in any layer
S – spruce anywhere in stand in any layer
• Non-forested types
OR – open range
•
Forest Height Class
Class 1 – 0.1 to 10.4 meters
Class 2 – 10.5 to 19.4 meters
• Crown Closure Class
Class 5 – crown closure less than 5%
Class 10 – crown closure between 5 and 10%
• Combination Classes
CC10_slp56 – crown closure less than 10% and slope class 5 or 6
2.1.9 BEC LAYER
Biogeoclimatic subzone and variant lines from the Invermere Forest District were
reviewed and adjusted based on observations from field sampling. The ESSFdku line was
modeled into that coverage based on elevation criteria from previous TEM projects in
nearby areas and field observations within the Canal Flats study area. Although
commonly mapped in the East Kootenay area, the ESSFdku has yet to appear on updated
provincial or regional BEC mapping.
2.1.10 OVERLAY
The thirty-one input GIS layers were combined into a single raster layer. Each pixel
value in the combined grid was assigned a unique number representing the combination
of the class values of all the input layers. While there were over ten million possible
permutations, in actual number of combinations for the entire TEMBEC Canal Flats
Operating Area was approximately 100,000. Each record in the combined attribute
database contained the attribute value for each input layer. This database was the input
for applying the knowledge tables for the PEM model.
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2.2 FIELD DATA COLLECTION, MODEL DEVELOPMENT AND
SUMMARIZATION OF FIELD VARIABLES
2.21 YEAR TWO FIELD DATA COLLECTION
We needed spatially accurate data from the Canal Flats Operating Area to modify and
check the accuracy of the PEM model. A sampling plan was derived where 200 random
locations were determined within 500 metres of mapped road systems. At each of these
locations a transect consisting of three plots was established with 30 metres perpendicular
to the slope between each plot. At each plot location the following data were collected:
•
•
•
•
•
•
•
•
•
•
•
•
•
UTM grid coordinates
BEC subzone and variant
Site series
Structural stage
Elevation
Aspect
Slope
Soil Texture, parent materials and coarse fragments
Species lists with percentage cover and layer
Distance from water
Plot shape
Leading tree species
Landsat classification group
All of the field data collected is housed in Appendix 1, copies of field data cards are
housed in Appendix 5 and the VENUS data base that contains all the field data in a
format approved by RIC standards is on the CD in the back of the report labeled as
Appendix 6. The field data collection was biased towards the operable area of the Canal
Flats Operating Area. This was a practical implementation, as budgets for helicopter
access were limited and working off road systems maximized the number of samples to
be collected in a field day.
2.3 KNOWLEDGE BASE CREATION
Within the PPdh2, IDFdm2, MSdk, ICHmk1 and ESSFdk, subzones, each site series was
initially assigned values based on environmental attributes documented in Braumandl and
Curran (1992). The ESSFwm, ESSFdku, ESSFdkp, ESSFwmu, ESSFwmp and AT
values were derived from TEM mapping in TFL 14 (Kernaghan et al 1999), Premier
Ridge - Diorite (Kernaghan et al 2000), Steamboat Mountain (Kernaghan et al 1998),
Brewer Creek (Kernaghan et al 1997) and East Columbia Lake Marcoux et al (1997).
This was a subjective process based on expert knowledge, existing plot data and the
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experience of TEM mappers Gareth Kernaghan, Keyes Lessard and Maureen V.
Ketcheson who approached the task with detailed knowledge and experience in those site
series and subzones. The goal was to document and weight the individual input layers in
a fashion which imitated the logic a traditional TEM mapper uses via the TEM working
legend and consistent application over the working legend over an entire mapping area.
Input data, which were strongly associated with site series, were weighted as high as 100
(in this case satellite imagery classification of rock, talus, avalanche chutes, some forest
cover categories and TRIM wetlands) when directly associated with a single input data
layer. GIS input layers, which were consistently associated with site series, were
weighted as high as 30 when expert knowledge and extensive TEM mapping experience
identified them as important for differentiating between site series within a subzone.
The final version of the knowledge bases can be found in Appendix 9.
2.3.1 NEURAL NETWORK CLASSIFICATION
Year Two field plot locations, based on UTM grid coordinates, were given attributes for
each of the thirty-two input layers and those attributes were used in an objective
classification procedure known as a neural network. The results of the neural network
classification can be found in Appendix 4.
Neural network analysis is a powerful technique used to model complex functions. A
network is composed of an interconnected series of artificial neurons, which function in a
way similar to their biological counterparts. A neuron (also known as a “node” or “unit”)
accepts a series of inputs (with specific input strengths, or “weights”) from input data or
from other neurons. Each neuron has a threshold value, and if the weighted sum of the
inputs exceeds the threshold value, the neuron “fires”, and the output value is passed on
to the next series of neurons in the network (Bishop 1995). Figure 6 illustrates a very
simple network consisting of one decision, or “hidden” node. In this example, the input
node of the network accepts a value (0.625) from a single variable, and passes it to a node
with a simple threshold activation of 0.5. The threshold is subtracted from the sum of the
inputs, and because the result (0.125) is >0, the output node “1” is triggered. As a result,
this simple network can accept inputs of numbers between 0 and 1 and classify them into
binary categories.
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Output
Input
0.625
1
Activation
0.5
0
Figure 6 A simple neural network that accepts a single input value between 0 and 1 and
classifies it into 1 of 2 categories.
More complex classification problems involving additional input variables and additional
output classes can be handled by adding more hidden units. “Training” is the process
whereby a network with optimal activations and signal weightings is iterated from a set
of “known” cases of input and output data. The result is a very flexible, non-linear
classification technique that can model very complex functions.
Typically, a network has an input layer with one node for each input variable, a hidden
layer of several nodes, and an output layer with one node for each output class. The nodes
of each layer are connected to every node in the preceding and subsequent layers. For this
project, the input layer passed the values of GIS variables (e.g. slope, aspect classes) to
the network for processing, and the output was a set of probabilities that represented the
probability that a case belonged to one of the sites series being modeled in the analysis
(Figure 7).
There were several steps involved in developing and applying the neural networks. First,
network models were “trained” on the GIS data and site series calls associated with
ground plots. Data were divided into 3 sets: training, verification, and test sets. The
training set was used to train an initial network to associate GIS input values with site
series classifications. Weights and thresholds were adjusted iteratively to minimize the
sum-of-squares errors between the output activations of the network and the expected
activations based on the known site series classifications of the data. The verification data
set was then used to test the fit of the model on independent data.
Next, the network topology was changed to include a different set of input variables and a
different number of hidden units. Again, weights and thresholds were adjusted to
minimize sum-of-square errors in the training set, and then tested independently with the
verification set. The process was repeated many times (typically >100) and the network
with the lowest error (i.e. the closest fit between the predicted and actual site series calls)
was selected as the best.
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Because the training and verification sets were used repeatedly in developing the model,
a test data set was run on the final network to determine whether the network had “overlearned” the data. Over-learning occurs when a model generates a good fit to training and
verification data, but generalizes poorly to independent data. Because neural network
analysis is a very flexible, non-linear modeling technique, over-learning is a common
problem.
Models that fit training and verification sets well, and generalize adequately to an
independent test set, can be used to classify novel sets of data. In this project, results from
the plot data were generalized to the entire map by running GIS data from each pixel
through the models.
Figure 7 Topology of a typical neural network. The network accepts data from 10 input
variables (derived from GIS coverages) and classifies cases into 3 output classes (sites
series).
Hidden layer
Input layer
Output layer
GIS values:
•Slope
•Aspect
•Etc.
Site series
classifications
2.3.1.1 Model Building
Analyses were stratified by BEC subzone, and site series were included in analyses only
where there were >10 ground plots. Occasionally, site series with more plots were
excluded from model building because they could not be classified correctly with any
certainty by the neural network analysis. Plot data were divided between training,
verification, and test sets in roughly a 3:1:1 ratio, although we also varied the ratio in
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attempts to achieve a better fit. Model fit was assessed first by sum-of-squares errors and
then by classification frequencies.
All analyses were conducted with Statistica Neural Networks software (Statsoft Inc.,
Tulsa, OK). Data were fitted to 3-layer perceptron networks using a second-order
conjugate gradient decent training algorithm. The softmax activation and entropy
(multiple) error transformations were applied to final models to allow the interpretation
of output activations as probabilities (Statistica 2000). Probabilities for each case
summed to one.
We used sensitivity analyses to determine the contribution of each variable to the final
networks. Models were run in which each variable was excluded in turn, and sum-ofsquares errors were calculated for the subset models. Variables were ranked according to
the fit of the subset models from which the variable had been excluded.
We summarized classification data with confusion matrices. The goal of the modeling
was to minimize classification errors among site series calls in all 3 data subsets (training,
verification, and test sets). Similar results among subsets suggested that models
generalized well. Significantly higher errors in test sets suggested that over-learning had
occurred and models might not generalize well. In practice, over-fitting can be difficult to
avoid, particularly with small sample sizes.
2.3.1.2 Model Application
Final models were applied to map data by subzone. We ran GIS data for each pixel on the
corresponding subzone model and then mapped the resulting activations. Pixels were
assigned to a site series if an activation was >0.75. If no probability was >0.75, the pixel
was classified as “unknown.” The assessment of the accuracy of the neural network can
be found in Appendix 4. There are three reports of accuracy via the confusion matrix
methodology recommended in Meidinger 2000. They are for the model building plots, for
a validation set of plots and for a set of randomly chosen plots not used in the
development of the neural network.
The neural network was used to assign pixels to the following subzone and site series
combinations.
ESSFdk – site series 01, 03 and 04
MSdk – site series 01, 03, 04 and 05
IDFdm2 – site series 01, 03 and 04
If the neural network did not assign a pixel to any of those units, if it was classified as
“unknown”, then the knowledge base classification of that pixel was used to assign site
series. In this manner, the PEM allocated site series based on a combination of neural
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Field Code Changed
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
______________________________________________________
network and knowledge bases. The “goodness of fit” of the overall PEM model can be
found in Appendix 2.
2.3.1.3 Classification Tree Analysis
Plot field variables were subjected to a classification tree analysis, (Loh and Shih 1997),
that derived the most important field variables for distinguishing between site series. The
field plot variables were ranked between 1 and 100, depending on their importance to the
classification. The results of that analysis can be found in Appendix 7. The classification
tree results were used to assist in the subjective ranking of variables in the knowledge
bases.
2.3.2 SITE SERIES MODIFIERS AND STRUCTURAL STAGE
MODEL
Four aspect modifers were reported in the PEM data base to be used, where appropriate,
with each site series classification. These modifiers are:
Table 3. Site Series Aspect Modifiers Used in PEM
•w – warm aspects >25% slope 135 to 285 degrees
•k – cool aspects >25% slope 286- 134 degrees
•q – very steep cool > 100% slope
•z – very steep warm >100% slope
Evolving PEM standards also require structural stages be modeled as a separate layer.
We modeled structural stage based on site series, forest cover age class, height class,
leading species, and non-productive type class. It is presented as a separate layer in the
PEM. Resultants form homogenous site series/structural stage polygons. The structural
stage model can be found in Appendix 8. Expanded definitions of each structural stage
can be found in RIC (1998) TEM standards. A copy of these definitions is housed in
Appendix 8 as well.
Table 4. Structural Stage Modifiers Used in PEM
•
•
•
•
•
•
•
1 sparsely vegetated
2 herb dominated
3 shrub/herb dominated
4 pole sapling
5 young forest
6 mature forest
7 old forest
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(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
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Field Code Changed
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
______________________________________________________
2.3.3 ACCURACY AND GOODNESS OF FIT ASSESSMENTS
The accuracy assessment was conducted using a level 4 accuracy assessment as
recommended by Meidinger (2000). It was conducted twice, once on the neural network
results and once on the final PEM. Eighty-six plots were randomly removed from the
field plot data base. These were held out of other analyses and used to assess the
accuracy of the final PEM. The results of this exercise can be found in Appendix 3. The
number of plots selected is based on criteria in Meidinger’s accuracy assessment
protocol. It is based on a 0.5 probability that a pixel is correctly classified on the map,
with a confidence level of 0.80 and a maximum error of 0.07. The results of this
assessment are reported in a confusion matrix and a Kappa statistic was calculated, as
suggested by Meidinger’s protocol.
Site series classified by the neural network were subjected to an independent test of
accuracy and the results reported in a confusion matrix. This can be found in Appendix
4.
The remainder of the plot data were subjected to a “goodness of fit” assessment. This
demonstrates the proportion of correctly classified plots overall in a confusion matrix
with a Kappa statistic. The results of these assessments can be found in Appendix 2.
2.3.4 PEM MAP PRODUCTION
Seamless digital coverage of the Canal Flats operating area was produced where site
series, modifiers and structural stage were reported for each 25 x 25 m pixel. The
following attributes are presented in an ARCINFO grid data base (.vat file).
•
•
•
•
Biogeoclimatic subzone and variant
Site series
Directional exposure modifiers
Structural stage
Plot files have been created depicting the groupings of 1:20,000 maps reported in Table
1. A set of paper maps accompanies this report. The digital coverage and plot files can be
found on the CD attached to the back cover of this report.
Field Code Changed
_______________________________________________________________________________
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(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
page 28.
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
______________________________________________________
3.0 RESULTS
3.1 SITE SERIES MAPPING OF CANAL FLATS OPERATING AREA
Mapping is presented on paper accompanying this report, as well as on the CD in both a
seamless coverage and as plot files at 1:40,000.
A total of 12 subzones and variants were mapped to the site series level with 119
different site series units. Table 5 shows the percentage of total area of the Canal Flats
operating area occupied by each subzone variant. Table 6 lists the site series mapped and
the area over which they were modeled.
The PEM was run a total of four times with various iterations of the knowledge bases and
the neural network. The objective of each run was to increase the magnitude the goodness
of fit of the plot data to the model. Once we were satisfied with the goodness of fit scores,
we utilized the 86 independent, randomly selected, non-model building plots to assess the
overall accuracy of the PEM model.
The largest subzone in the Canal Flats operating area is the ESSFdk, within that subzone
the 04 site series (Bl - Azalea - Soopolallie) is the most commonly mapped site series.
The next largest subzone is the MSdk with site series 01 (Sxw - Soopolallie Grouseberry) and 04 (Pl - Oregon-grape - Pinegrass) being the most commonly mapped
site series The ESSFdku is the next most widespread subzone variant. Site series 64 (Bl Grouseberry) and 99 (Rock Outcrop) were the most commonly mapped site series. The
IDFdm2 was the next largest subzone with site series 01 (FdPl - Pinegrass - Twinflower),
03 (Fd - Snowberry - Balsamroot) and 04 (FdLw - Spruce - Pinegrass) being the most
commonly mapped units. The remaining subzones occupy approximately 16% of the
study area.
Table 5. Total Area by BEC subzone/ variant, Canal Flats Operating Area
BEC subzone
and/or variant
AT
ESSFdkp
ESSFdku
ESSFdk
ESSFwmp
ESSFwmu
ESSFwm
MSdk
ICHmk1
IDFdm2
IDFun
PPdh2
Area in hectares
Percent of Canal
Flats Operating Area
17,915
41,158
86,173
166,313
332
2,053
3,240
96,529
5,905
58,405
6,229
12,320
3.6%
8.3%
17.4%
33.5%
0.1%
0.4%
0.7%
19.4%
1.2%
11.8%
1.3%
2.5%
_______________________________________________________________________________
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(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
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page 29.
Field Code Changed
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
______________________________________________________
Table 6. Total Area By Site Series Mapped by PEM in the Canal Flats Operating Area
BEC_code
Site Series Name
Subzone
/variant and site
series number
AT 01
Alpine heath
AT 02
Saxicolous lichen
AT 03
Black alpine sedge - Woolly pussytoes
AT 44
Talus
AT 99
Rock
Total AT
ESSFdk 01
Bl - Azalea - Foamflower
ESSFdk 02
Fd - Douglas maple - Soopolallie
ESSFdk 03
Bl - False azalea - Grouseberry
ESSFdk 04
Bl - Azalea - Soopolallie
ESSFdk 05
Bl - Azalea - Step moss
ESSFdk 06
Bl - Azalea - Horsetail
ESSFdk 07
Willow - Sedge
ESSFdk 101
Water
ESSFdk 44
Talus
ESSFdk 75
Avalanche runout
ESSFdk 77
Avalanche chute
ESSFdk 99
Rock
Total ESSFdk
ESSFdkp 01
SeBl - White mountain-heather
ESSFdkp 02
Mountain-avens - Snow willow
ESSFdkp 03
PaBl
ESSFdkp 04
Yellow mountain-heather - Woolly pussytoes
ESSFdkp 05
Bl - Subalpine larch - White mountain-heather
ESSFdkp 06
Subalpine daisy - Sitka valerian
ESSFdkp 101
Water
ESSFdkp 44
Talus
ESSFdkp 77
Avalanche chute
ESSFdkp 99
Rock
Total ESSFdkp
Site
Area_ha Total
series
two
letter
code
AH
1,805
SL
544
BP
51
TA
2,798
RO
12,717
17,915
FA
9,845
DM
1,808
FG
20,813
FS
114,476
FM
607
FH
107
WS
42
WA
134
TA
2,920
AR
2,644
AC
9,780
RO
3,137
166,313
YW
5,924
AW
1,037
WF
4,695
EM
1,948
LM
412
DV
171
WA
112
TA
3,796
AC
4,389
RO
18,674
41,158
Field Code Changed
_______________________________________________________________________________
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(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
page 30.
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
______________________________________________________
Table 6. Total Area By Site Series Mapped by PEM in the Canal Flats Operating Area
BEC_code
Site Series Name
Subzone
/variant and site
series number
ESSFdku 101
Water
ESSFdku 44
Talus
ESSFdku 61
Subalpine larch - Mixed herb
ESSFdku 62
Pa - Common juniper
ESSFdku 63
Subalpine larch - Moss
ESSFdku 64
Bl - Grouseberry
ESSFdku 65
Alpine Larch - Mountain-heather
ESSFdku 66
Western pasqueflower - Arctic willow
ESSFdku 67
Bl - Pink mountain-heather
ESSFdku 68
Bl - Horsetail
ESSFdku 69
Willow - Sedge
ESSFdku 75
Avalanche runout
ESSFdku 77
Avalanche chute
ESSFdku 99
Rock outcrop
Total ESSFdku
ESSFwm 01
Bl - Black huckleberry - Red stemmed feathermoss
ESSFwm 02
BlPa - Grouseberry
ESSFwm 03
Bl - Rhododendron - Black huckleberry
ESSFwm 04
Bl - False azalea - Horsetail
ESSFwm 05
Bl - Sedge - Sphagnum
ESSFwm 101
Water
ESSFwm 44
Talus
ESSFwm 75
Avalanche runout
ESSFwm 77
Avalanche chute
ESSFwm 99
Rock
Total ESSFwm
ESSFwmp 02
Bl - White mountain-heather - Sitka valerian
ESSFwmp 44
Talus
ESSFwmp 77
Avalanche chute
ESSFwmp 99
Rock outcrop
Total ESSFwmp
Site
Area_ha Total
series
two
letter
code
WA
267
TA
4,806
LG
7,320
PJ
7,119
LM
2,238
FG
37,661
LH
3,530
PW
9
HG
142
FH
60
WS
123
AR
1,676
AC
9,299
RO
11,923
86,173
FP
581
FG
561
FV
1,243
FH
92
FS
2
Wa
0
TA
74
AR
71
AC
544
RO
72
3,240
FM
56
TA
40
AC
25
RO
210
331
Field Code Changed
_______________________________________________________________________________
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(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
page 31.
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
______________________________________________________
Table 6. Total Area By Site Series Mapped by PEM in the Canal Flats Operating Area
BEC_code
Site Series Name
Subzone
/variant and site
series number
ESSFwmu 01
Bl - Black huckleberry - Mountain arnica
ESSFwmu 02
Pa - Black huckleberry
ESSFwmu 06
Willow - Horsetail
ESSFwmu 101 Water
ESSFwmu 44
Talus
ESSFwmu 75
Avalanche runout
ESSFwmu 77
Avalanche chute
ESSFwmu 99
Rock
Total ESSFwmu
ICHmk1 01
CwSxw - Falsebox
ICHmk1 02
Fd - Juniper - Penstemon
ICHmk1 03
FdPl - Pinegrass - Twinflower
ICHmk1 04
FdPl - Sitka alder - Pinegrass
ICHmk1 05
SxwFd - Gooseberry - Sarsaparilla
ICHmk1 06
Sxw - Oak fern
ICHmk1 07
Sxw - Horsetail
ICHmk1 101
Water
ICHmk1 44
Talus
ICHmk1 77
Avalanche chute
ICHmk1 99
Rock outcrop
Total ICHmk1
IDFdm2 01
FdPl - Pinegrass - Twinflower
IDFdm2 02
Antelope-brush - Bluebunch wheatgrass
IDFdm2 03
Fd - Snowberry - Balsamroot
IDFdm2 04
FdLw - Spruce - Pinegrass
IDFdm2 05
SxwAt - Sarsaparilla
IDFdm2 06
Scrub birch - Horsetail
IDFdm2 07
Sxw - Horsetail
IDFdm2 101
Water
IDFdm2 66
Great bulrush marsh
IDFdm2 77
Avalanche chute
IDFdm2 88
Beaked sedge - Water sedge marsh
IDFdm2 99
Rock outcrop
Total IDFdm2
Site
Area_ha Total
series
two
letter
code
FB
83
WH
513
WE
4
Wa
1
TA
120
AR
33
AC
952
RO
347
2,053
RF
2,683
DP
37
DT
259
DA
708
SG
2,045
SO
106
SH
0
Wa
1
TA
1
AC
54
RO
11
5,905
DT
36,336
AW
1,183
DS
8,489
SP
8,393
SS
90
BH
55
SH
167
Wa
2,230
BU
93
AC
10
SM
554
RO
805
58,405
Field Code Changed
_______________________________________________________________________________
JMJ Holdings Inc. suite 208 – 507 Baker Street, Nelson, BC V1L 4J2
(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
page 32.
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
______________________________________________________
Table 6. Total Area By Site Series Mapped by PEM in the Canal Flats Operating Area
BEC_code
Site Series Name
Subzone
/variant and site
series number
IDFun 01
Fd - Rocky Mountain juniper - Bluebunch wheatgrass
IDFun 02a
Pasture sage - Bluebunch wheatgrass: moderate slope phase
IDFun 02b
Pasture sage - Bluebunch wheatgrass: gentle slope phase
IDFun 03
Fd - Pinegrass - Step moss
IDFun 04
SxwAt - Sarsaparilla
IDFun 05
ActSxw - Red-Osier dogwood
IDFun 101
Water
IDFun 44
Talus
IDFun 66
Great bulrush marsh
IDFun 88
Beaked Sedge - Water sedge marsh
IDFun 99
Rock outcrop
Total IDFun
MSdk 01
Sxw - Soopolallie - Grouseberry
MSdk 02
Saskatoon - Bluebunch wheatgrass
MSdk 03
Pl - Juniper - Pinegrass
MSdk 04
Pl - Oregon-grape - Pinegrass
MSdk 05
Sxw - Soopolallie - Snowberry
MSdk 06
Sxw - Dogwood - Horsetail
MSdk 07
Sxw - Scrub birch - Sedge
MSdk 101
Water
MSdk 44
Talus
MSdk 75
Avalanche runout
MSdk 77
Avalanche chute
MSdk 99
Rock outcrop
Total MSdk
PPdh2 01
Py - Bluebunch wheatgrass - Junegrass
PPdh2 02a
Bluebunch wheatgrass - Junegrass: steep
PPdh2 02b
Bluebunch wheatgrass - Junegrass: gentle to moderate
PPdh2 03
PyAt - Rose - Solomon's-seal
PPdh2 04
Act - Dogwood - Nootka rose
PPdh2 101
Water
PPdh2 44
Talus
PPdh2 69
Saltgrass - Foxtail
PPdh2 77
Avalanche chute
PPdh2 99
Rock outcrop
Total PPdh2
Site
Area_ha Total
series
two
letter
code
DJ
1,625
SWa
169
SWb
1,073
DP
213
SS
421
CD
53
Wa
2,054
TA
5
BU
339
SM
39
RO
238
6,229
SG
32,717
SW
221
LJ
13,080
LP
34,833
SS
11,937
SH
99
SB
1,270
Wa
1,180
TA
273
AR
43
AC
104
RO
772
96,529
PW
7,869
WJa
392
WJb
1,593
AR
950
CD
159
Wa
453
TA
47
SF
367
AC
2
RO
488
12,320
Total Area
Canal Flats
_______________________________________________________________________________
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(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
page 33.
Field Code Changed
496,571
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
______________________________________________________
The data bases linked to the site series mapping also include directional exposure
modifiers on warm (135 to 285 degrees on slopes >25%), cool (285 to 135 degrees on
slopes >25%), very steep warm and very steep cool aspects (greater than 100% slope).
These are not reported on the map labels, but are housed in the .vat data base. These are
required by PEM standards. The mapping presented in this report is colour coded by site
series, with each 25 metre pixel depicted as a homogenous, single, site series. The data
base is found on the CD accompanying this report, it is presented as a .vat file, the
exposure modifiers are found in the data base, but not on the plotted mapping. The
combinations of directional exposure modifier and site series are not reported, but can be
derived from the data base.
3.2 STRUCTURAL STAGE MAPPING OF THE CANAL FLATS
OPERATING AREA
The structural stages modeled by the PEM are presented as an acetate overlay registered
to the site series paper mapping. The total area by structural stage is presented in Table 7.
Table 7. Structural Stage Distribution in the Canal Flats Operating Area.
Structural Stage No.
Description*
Area in hectares
Percentage of Canal
Flats Operating Area
1
Sparsely vegetated
72,236
14.5%
2
Herb
93,496
18.8%
3
Shrub – Herb
41,920
8.4%
4
Pole Sapling
44,407
8.9%
5
Young Forest
78,614
15.8%
6
Mature Forest
87,410
17.6%
7
Old Forest
78,484
15.8%
*for a full description of structural stages and the algorithms used to generate the classifications please see
Appendix 8.
Based on the forest cover derived algorithm, the model mapped approximately 50% of
the study area into either young, mature or old forested types. Herb dominated sites
occupy 19% of the study area, this would include both herb dominated alpine, parkland,
grassland and herb dominated cut blocks. Eight percent of the study area is classified as
shrub – herb dominated and about the same area is in a pole – sapling dominated
structure. Approximately 15% of the study area is mapped as sparsely vegetated.
The .vat data base found on the CD accompanying this report reports the structural stage
of each pixel, this is presented as an overlay to the site series mapping. We choose not to
report the area of each site series by structural stage as there are potentially 700
combinations of site series and structural stage. The data housed on the CD, which
accompanies this report, is there to summarize if TEMBEC needs to derive that level of
detail from this mapping.
_______________________________________________________________________________
JMJ Holdings Inc. suite 208 – 507 Baker Street, Nelson, BC V1L 4J2
(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
page 34.
Field Code Changed
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
______________________________________________________
3.3 MODEL GOODNESS OF FIT AND ACCURACY ASSESSMENT
The neural network model training, verification and test results from 86 randomly
selected plots not used on the model building are reported in Appendix 4. Summaries of
this information can be found in Table 8. An assessment of both the site series and
structural stage levels of accuracy in the overall PEM model from can be found in
Appendix 3 and is summarized in Tables 9, 10 and 11.
The neural network model was developed for the ESSFdk, MSdk and IDFdm2 subzone
variants only. These BGC units have sufficient plot data to undertake that analysis, other
subzones and variants found in the study area did not have enough plot data to use in a
neural network. Table 8 summarizes the percentage of correctly allocated plots in three
categories; the training data set which was used to develop the neural network
classification, the verification data set which was used to prove the training set, and
finally, 86 randomly selected plots which were not used to develop the neural network,
only used to test its accuracy.
Table 8. Canal Flats PEM Neural Network Accuracy Assessment
Training set
Percentage Correct Neural Network Call Compared to Field Calls
ESSFdk
N
MSdk
N
IDFdm2
01, 03, 04 only
01, 03, 04, 05
01, 03, 04
only
only
93.3%
60
90.8%
76
86.5%
37
Verification set
86.2%
29
78.9%
38
72.2%
18
Independent
test set
83.2%
30
67.6%
37
78.9%
19
N
In Table 8 we report the percentage of correctly classified pixels from a second set of
randomly derived, independent field plots when the field site series call is compared to
the 25 metre pixel PEM call at the field determined UTM grid coordinate using
differentially corrected GPS. We also report the percentage of correct plots where if the
site series call by the PEM is within one site series of the field call according to the
edatopic grid in Braumandl and Curran (1992), the edatopic grids for the IDFdm2, MSdk
and ESSFdk are presented in Figure 5. A full point is allocated for a correct PEM call and
half a point is allocated for a PEM call that is within one site series of the field call on the
edatopic grid.
Field Code Changed
_______________________________________________________________________________
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(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
page 35.
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
______________________________________________________
Table 9. Canal Flats PEM Mean Accuracy of Final Mapping By the Subzone or
Variant
BGC subzone or
variant
Point Accuracy within
“Close” Accuracy
Number of
the single 25 m pixel of within one site series
independent
the field UTM call
assessment plots
PPdh2
100%
N/A
3
IDFdm2
58.3%
79.1%
12
MSdk
37.5%
51.6%
32
ESSFdk
55.9%
73.5%
34
ESSFdku*
0%
50.0%
2
ESSFdkp
33.3%
66.6%
3
*this assessment is based on the PEM confusing talus with rock, a plot field call of rock was interpreted as
talus by the PEM
Table 10. Canal Flats PEM Accuracy Assessment Final Mapping By the Site Series
BEC Unit
PPdh2 01
IDFdm2 01
IDFdm2 03
IDFdm2 04
IDFdm2 05
MSdk 01
MSdk 03
MSdk 04
MSdk05
MSdk06
ESSFdk 01
ESSFdk 03
ESSFdk 04
ESSFdk 05
ESSFdku 44
ESSFdkp 01
ESSFdkp 02
Point Accuracy within
the single 25 m pixel of
the field UTM call
100%
100%
33%
0%
0%
50%
0%
40%
0%
0%
0%
0%
73.1%
0%
0%
0%
50%
“Close” Accuracy
within one site series
Number of Plots
N/A
N/A
66.6%
50%
50%
71.9%
50%
55%
33%
50%
50%
50%
82.7%
0%
50%
50%
75%
3
6
3
2
1
16
2
10
3
1
4
2
26
1
2
1
2
The result of the assessment of the accuracy of the structural stage model is reported in
Table 11. In this table we allocated a half point for a “close” structural stage call that is
within one stage of the field call.
Field Code Changed
_______________________________________________________________________________
JMJ Holdings Inc. suite 208 – 507 Baker Street, Nelson, BC V1L 4J2
(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
page 36.
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
______________________________________________________
Table 11. Canal Flats PEM Structural Stage Accuracy Assessment for Final
Mapping
BGC subzone or
variant
PPdh2
IDFdm2*
MSdk
ESSFdk
ESSFdku*
ESSFdkp
Point Accuracy within
the single 25 m pixel of
the field UTM call
33.3%
8.3%
46.9%
41.7%
100%
100%
“Close” Accuracy
within one structural
stage of the field call
50.0%
25.0%
59.3%
54.2%
N/A
N/A
Number of
independent
assessment plots
3
12
32
36
2
3
4.0 DISCUSSION
4.1 RASTER BASED PEM MODELING
The raster approach facilitates the development of input layers in a grid format. The
output of a raster map retains detail to the 0.25 ha size pixel size, whereas “vectorization”
looses detail of less than one hectare in size. These small units can be important elements
in the landscape. The downside of the raster-based approach is the “blocky” look of the
resultant map and the high standards of precision in plot GPS data required toachieve
acceptable pixel based accuracy scores at such a fine resolution. Time did not permit
assessing accuracy within more than the pixel at the plot UTM coordinates, nor is it
acceptable to existing QA standards (Meidinger, 2000). However, our experience in the
Arrow TSA PEM was that accuracy increases within 50 and 100 metres of the field UTM
coordinate.
A reduction in pixel size would smooth the output polygons. The pixel size of 25 meters
could be dropped to 10 meters. This would create better buffering around features and
capture more detail along the boundaries of the input layers. However, it would still be
important to use 50 m. pixels to capture some layers, as 25 m shapes would create too
much noise. For example, terrain shape is better represented at a coarser resolution,
however, it would be better to capture small wetlands at a finer resolution. Using a
combination of different input pixel sizes to accommodate variables at different scales is
feasible.
Given more time to perfect this PEM model we would like to explore more detailed use
of the Landsat imagery. More classification training is need and could be used to identify
some age classes and species combinations. Landsat, in combination with scanned aerial
photography, could be used to create a more in-depth classification of avalanche chutes,
forest structural stages, grasslands and wetlands. This would require using existing plot
data to ground truth in the field to capture attributes that can be related both to the
spectral response of Landsat and the structural pattern of digital airphotos.
_______________________________________________________________________________
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March 31, 2001
page 37.
Field Code Changed
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
______________________________________________________
4.2 ACCURACY OF PEM SITE SERIES MODEL
The point accuracy of the final PEM model is low, relative to the accuracy of the neural
network classification. The neural network was used to allocate pixels to selected site
series where the probability of that site series determined by the neural network was 75%
or greater. Pixels that did not achieve that score, or were in units not classified by the
neural network, were classified by the knowledge base. Some site series are better
classified than others. The best classified units are also the most widespread. The neural
network may be over classifying some units. The UTM coordinates of field plots may be
suspect, this is evident from the assessment of accuracy relative to the field UTM.
Another way of looking at the accuracy of the PEM model is to determine is the
proportions of site series allocated over the entire study area differ from the proportions
of site series sampled in the field. The random sampling design within 500 metres of the
road systems should provide us with an estimate of the proportion of site series on the
ground, at least in the areas with road access. This is biased to the operable portions of
the Canal Flats Operating Area, but those are probably the areas where the most use will
be made of the mapping.
The proportion of independent field calls and model building plots by site series is
compared to the proportion of the area of each site series allocated by the final PEM in
Table 12. We see that for some site series there is a notable difference between the
proportion of that unit sampled on the ground and the amount mapped overall by the
PEM. This way of looking at the data helps to assess whether the amount of each unit
mapped on the ground is comparable to the randomly sampled model building plots and
independent accuracy assessment plots. At the moment we think that we do not have
enough data to use the chi-square test suggested by Meidinger 2000 to assess "true
proportions” versus “map proportions” by using only 86 independent plots. This
qualitative look at the results still indicates where the model is under allocating a site
series, relative to the proportions observed on the ground. These site series are:
•
•
•
MSdk 01
MSdk 04
ESSFdk 04
The other units, where there were independent samples, seem to have been allocated by
the PEM in a manner proportional to what was seen on the ground.
Field Code Changed
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JMJ Holdings Inc. suite 208 – 507 Baker Street, Nelson, BC V1L 4J2
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March 31, 2001
page 38.
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
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Table 12. Relative Proportions of Site Series Mapped by the PEM Compared to
Relative Proportions of Site Series Randomly Sampled on the Ground.
BEC Unit
Proportion of the Total
Area Mapped by PEM
PPdh2 01
IDFdm2 01
IDFdm2 03
IDFdm2 04
IDFdm2 05
MSdk 01
MSdk 03
MSdk 04
MSdk05
MSdk 06
ESSFdk 01
ESSFdk 03
ESSFdk 04
ESSFdk 05
ESSFdku 44
ESSFdkp 01
ESSFdkp 02
1.6%
7.3%
1.7%
1.7%
0.02%
6.6%
2.6%
7.0%
2.4%
0.01%
2.0%
4.2%
23.1%
0.1%
1.0%
1.26%
0.2%
Proportion of Total
Model Building
Samples
1.1%
7.4%
4.5%
4.1%
0.7%
12.1%
3.4%
11.5%
5.9%
0.7%
4.5%
3.4%
21.8%
3.4%
0.2%
0.2%
0.0%
Proportion of Total
Independent Samples
3.5%
6.9%
3.4%
2.3%
1.2%
18.0%
2.3%
11.6%
3.4%
1.2%
4.5%
2.3%
30.2%
1.2%
2.3%
1.2%
2.3%
Based on an examination of the proportions in Table 11, we feel that the PEM model is
doing a good job at representing the site series distribution over the landscape.
4.4 ACCURACY OF THE STRUCTURAL STAGE MODEL
The point accuracy of the structural stage model is low, we would like to also assess the
relative proportions of each structural stage mapped by the model over the project area
and compare that to the proportions noted by the model building and independent sample
plots. This is accomplished in Table 13.
Table 13. Relative Proportions of Structural Stages Mapped by the PEM Compared
to Relative Proportions of Structural Stages Randomly Sampled on the Ground.
Structural Stage
Proportion of the Total
Area Mapped by PEM
1 – sparsely vegetated
2- herb
3 – shrub/herb
4 – pole sapling
5 – young forest
6 – mature forest
7 – old forest
14.5%
18.8%
8.4%
8.9%
15.8%
17.6%
15.8%
Proportion of Total
Model Building
Samples
4.5%
9.2%
34.8%
5.0%
26.9%
13.3%
6.3%
Proportion of Total
Independent Samples
3.4%
9.3%
36.2%
9.3%
23.2%
16.3%
2.3%
_______________________________________________________________________________
JMJ Holdings Inc. suite 208 – 507 Baker Street, Nelson, BC V1L 4J2
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page 39.
Field Code Changed
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
______________________________________________________
When looking at relative proportions of site series mapped by the model, and noted on
the ground in the model building and accuracy assessment plots, we see that, overall, the
structural stage model is not doing a good job. The mature forest and pole sapling types
(6 and 4) mapped by the PEM model seem to be close to the proportion of those
structures observed by field calls, but all the others are not. This is not unexpected
because the structural stage model is driven by forest cover age class data, which may not
be accurate in the older age classes. It is also understandable that the model over
estimates sparsely vegetated and herb dominated sites compared to plot data. The plots
were biased to areas within 500 m of roads and definitely under represented high
elevation and inoperable sites, which can be rocky, sparsely vegetated or dominated by
herbs in alpine or parkland ecosystems. The model appears to underestimate the shrub
herb structural stage (3) by a substantial amount, however, this may also be an effect of
the sampling design being biased towards the road systems. It is inevitable that cut blocks in structural stage 3 be over sampled. The amount of shrub/herb structure could be
checked using the satellite imagery proportions, relative to the PEM model proportions,
this was not done because our time for data analysis was finite.
The structural stage model is not doing a good job of depicting the study area, however,
the sampling design also complicates the assessment of plot proportions relative to model
proportions. This part of the model could use some more sophisticated input data from
something like a digital ortho-photo.
4.5 IMPROVEMENT TO TEMBEC’S CANAL FLATS PEM MODEL
This is the culmination of a two- year project. The mapping model adequately depicts the
proportion of site series on the ground. The accuracy of plot data when compared to
pixel results is not high. This is the result of UTM coordinates determined from GPS
coordinates that may be inaccurate in some situations. We would like to assess the site
series within one, two and four pixels from the field determined UTM coordinate. This
will probably improve the measured accuracy of the model.
The structural stage model is not good. We could improve it in the higher elevation areas
and in recent cut blocks by using the Landsat imagery to re-assess the shrub/herb
structural stages. Using forest cover age class, in combination with leading species data,
to distinguish between old, mature and young forest types may not be the best way to
assess structure. There is the potential to use digital ortho-photos, if they exist for the
Canal Flats Operating Area, and image analysis to improve the structural stage model.
There is enough field data to accomplish this without more sampling.
Field Code Changed
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JMJ Holdings Inc. suite 208 – 507 Baker Street, Nelson, BC V1L 4J2
(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
page 40.
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
______________________________________________________
CONCLUSION
The Canal Flats Operating Area PEM model is an important building block and an
extremely innovative approach to computer modeled ecosystem mapping. The results of
this model could be improved, as with any modeling process, it could go on through
several additional iterations until high levels of accuracy are achieved. As this is one of
the first PEM projects to be completed within the Province of British Columbia, the
knowledge bases and results form the building blocks for other mapping in the same BEC
units. The quality assurance process for this type of mapping is in its developmental
stage, and a bench -mark for acceptable levels of accuracy have not been proposed. This
project will assist in the development of accuracy criteria that determine whether or not a
PEM model is accepted for use for other types of analysis like SIBEC
It has been an extremely innovative and interesting project.
Field Code Changed
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(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
page 41.
TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM)
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(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
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JMJ Holdings Inc. suite 208 – 507 Baker Street, Nelson, BC V1L 4J2
(250) 354-4913 fax (250) 354-1162 jmj@netidea.com
March 31, 2001
page 43.
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