TEMBEC Industries Inc. Canal Flats Operating Area PEM ...a100.gov.bc.ca/appsdata/acat/documents/r1501/pem...Geosense Ltd. 203-507 Baker Street Nelson, B.C. V1L 4J2 (250) 354-0277 [email protected]

By:

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

Graham Smith B.ES. Geosense Ltd.

203-507 Baker Street Nelson, B.C. V1L 4J2

(250) 354-0277 [email protected]

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 [email protected]

Steve Wilson Ecologic Research

PO Box 167 Sayward, B.C. V0P 1R0

(250) 282-3768

March 31, 2001

TEMBEC Canal Flats Operating Area Predictive Ecosystem Mapping (PEM) ______________________________________________________

_______________________________________________________________________________ JMJ Holdings Inc. suite 208 – 507 Baker Street, Nelson, BC V1L 4J2

(250) 354-4913 fax (250) 354-1162 [email protected] March 31, 2001

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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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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. Field Database APPENDIX 2. Goodness of Fit Confusion Matrices – site series level APPENDIX 3. Accuracy Assessment Confusion Matrices and Kappa APPENDIX 4. Neural Network Training and Verification Matrices APPENDIX 5. Field Data Cards APPENDIX 6. Venus Data APPENDIX 7. Classification Tree Results APPENDIX 8. Structural Stage Model APPENDIX 9. Knowledge Bases




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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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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 non-spatially. 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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The location of these maps sheets relative to the Canal Flats Operating Area is depicted in Figure 1.

Figure 1 Location of TEMBEC’s Canal Flats Operating Area, within the Province of British Columbia.

TEMBEC’s Canal Flats

Operating Area




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I1I12

I3

I2I4

I5

I6

I7

I8I19

I9

I10

I13

I11

Figure 2 TEMBEC Canal Flats Operating Area PEM Map Location and Landscape Unit Boundaries.




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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 orange-red 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. 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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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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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, cavity-nesting 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 LAYER GIS NAME CLASSES TRIM DEM SLOPE SLP 6 classes TRIM DEM ASPECT AS 2 classes TRIM DEM SOLAR RADIATION SRD 2 classes TRIM DEM SHAPE SHP 4 classes TRIM DEM TOE SLOPE TOE 1 class LANDSAT SATELLITE CLASSIFICATION SAT 4 classes




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LANDSAT AND TRIM Slp12_vro Slp12_vro 1 class TRIM WETLAND

INTERMITTANT LAKE TRIM 2 classes

FOREST COVER ALPINE FOREST B 1 class FOREST COVER INVENTORY TYPE GROUP ITG 4 classes

FOREST COVER FOREST HEIGHT HT 2 classes FOREST COVER CROWN CLOSURE Cc 2 classes FOREST COVER SPECIES, PERCENTAGE

COMBINATIONS various 11 classes

FOREST COVER AND TRIM

CROWN CLOSURE AND SLOPE CLASSES

Cc10_slp56 4 classes

GEOLOGY MATERIAL TEXTURE GEO 4 classes SOIL QUATERNARY DEPOSIT SOIL 1 class TRIM STREAM DENSITY STRMS 2 classes TRIM SLOPE CLASS AND DISTANCE

FROM WATER Slp1_50w Slp12_50w Slp2_100w

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 135 to 285 degrees azimuth Cool 285 to 135 degrees azimuth Neutral 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 -100 to –5 straight -5 to 5 convex 5 – 15 convex-ridge 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 non-productive 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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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.

Input0.625

Activation0.5

Output

1

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.




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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 “over-learned” 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).

Input layerHidden layer

Output layer

GIS values:•Slope•Aspect•Etc.

Site seriesclassifications

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-of-squares 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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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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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.




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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

Area in hectares Percent of Canal Flats Operating Area

AT 17,915 3.6% ESSFdkp 41,158 8.3% ESSFdku 86,173 17.4% ESSFdk 166,313 33.5% ESSFwmp 332 0.1% ESSFwmu 2,053 0.4% ESSFwm 3,240 0.7% MSdk 96,529 19.4% ICHmk1 5,905 1.2% IDFdm2 58,405 11.8% IDFun 6,229 1.3% PPdh2 12,320 2.5%




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Table 6. Total Area By Site Series Mapped by PEM in the Canal Flats Operating Area BEC_code Subzone /variant and site series number

Site Series Name Site series two letter code

Area_ha Total

AT 01 Alpine heath AH 1,805 AT 02 Saxicolous lichen SL 544 AT 03 Black alpine sedge - Woolly pussytoes BP 51 AT 44 Talus TA 2,798 AT 99 Rock RO 12,717

Total AT 17,915ESSFdk 01 Bl - Azalea - Foamflower FA 9,845 ESSFdk 02 Fd - Douglas maple - Soopolallie DM 1,808 ESSFdk 03 Bl - False azalea - Grouseberry FG 20,813 ESSFdk 04 Bl - Azalea - Soopolallie FS 114,476 ESSFdk 05 Bl - Azalea - Step moss FM 607 ESSFdk 06 Bl - Azalea - Horsetail FH 107 ESSFdk 07 Willow - Sedge WS 42 ESSFdk 101 Water WA 134 ESSFdk 44 Talus TA 2,920 ESSFdk 75 Avalanche runout AR 2,644 ESSFdk 77 Avalanche chute AC 9,780 ESSFdk 99 Rock RO 3,137

Total ESSFdk 166,313ESSFdkp 01 SeBl - White mountain-heather YW 5,924 ESSFdkp 02 Mountain-avens - Snow willow AW 1,037 ESSFdkp 03 PaBl WF 4,695 ESSFdkp 04 Yellow mountain-heather - Woolly pussytoes EM 1,948 ESSFdkp 05 Bl - Subalpine larch - White mountain-heather LM 412 ESSFdkp 06 Subalpine daisy - Sitka valerian DV 171 ESSFdkp 101 Water WA 112 ESSFdkp 44 Talus TA 3,796 ESSFdkp 77 Avalanche chute AC 4,389 ESSFdkp 99 Rock RO 18,674

Total ESSFdkp 41,158




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Area_ha Total

ESSFdku 101 Water WA 267 ESSFdku 44 Talus TA 4,806 ESSFdku 61 Subalpine larch - Mixed herb LG 7,320 ESSFdku 62 Pa - Common juniper PJ 7,119 ESSFdku 63 Subalpine larch - Moss LM 2,238 ESSFdku 64 Bl - Grouseberry FG 37,661 ESSFdku 65 Alpine Larch - Mountain-heather LH 3,530 ESSFdku 66 Western pasqueflower - Arctic willow PW 9 ESSFdku 67 Bl - Pink mountain-heather HG 142 ESSFdku 68 Bl - Horsetail FH 60 ESSFdku 69 Willow - Sedge WS 123 ESSFdku 75 Avalanche runout AR 1,676 ESSFdku 77 Avalanche chute AC 9,299 ESSFdku 99 Rock outcrop RO 11,923

Total ESSFdku 86,173ESSFwm 01 Bl - Black huckleberry - Red stemmed feathermoss FP 581 ESSFwm 02 BlPa - Grouseberry FG 561 ESSFwm 03 Bl - Rhododendron - Black huckleberry FV 1,243 ESSFwm 04 Bl - False azalea - Horsetail FH 92 ESSFwm 05 Bl - Sedge - Sphagnum FS 2 ESSFwm 101 Water Wa 0 ESSFwm 44 Talus TA 74 ESSFwm 75 Avalanche runout AR 71 ESSFwm 77 Avalanche chute AC 544 ESSFwm 99 Rock RO 72

Total ESSFwm 3,240ESSFwmp 02 Bl - White mountain-heather - Sitka valerian FM 56 ESSFwmp 44 Talus TA 40 ESSFwmp 77 Avalanche chute AC 25 ESSFwmp 99 Rock outcrop RO 210 Total ESSFwmp 331




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Area_ha Total

ESSFwmu 01 Bl - Black huckleberry - Mountain arnica FB 83 ESSFwmu 02 Pa - Black huckleberry WH 513 ESSFwmu 06 Willow - Horsetail WE 4 ESSFwmu 101 Water Wa 1 ESSFwmu 44 Talus TA 120 ESSFwmu 75 Avalanche runout AR 33 ESSFwmu 77 Avalanche chute AC 952 ESSFwmu 99 Rock RO 347 Total ESSFwmu 2,053ICHmk1 01 CwSxw - Falsebox RF 2,683 ICHmk1 02 Fd - Juniper - Penstemon DP 37 ICHmk1 03 FdPl - Pinegrass - Twinflower DT 259 ICHmk1 04 FdPl - Sitka alder - Pinegrass DA 708 ICHmk1 05 SxwFd - Gooseberry - Sarsaparilla SG 2,045 ICHmk1 06 Sxw - Oak fern SO 106 ICHmk1 07 Sxw - Horsetail SH 0 ICHmk1 101 Water Wa 1 ICHmk1 44 Talus TA 1 ICHmk1 77 Avalanche chute AC 54 ICHmk1 99 Rock outcrop RO 11

Total ICHmk1 5,905IDFdm2 01 FdPl - Pinegrass - Twinflower DT 36,336 IDFdm2 02 Antelope-brush - Bluebunch wheatgrass AW 1,183 IDFdm2 03 Fd - Snowberry - Balsamroot DS 8,489 IDFdm2 04 FdLw - Spruce - Pinegrass SP 8,393 IDFdm2 05 SxwAt - Sarsaparilla SS 90 IDFdm2 06 Scrub birch - Horsetail BH 55 IDFdm2 07 Sxw - Horsetail SH 167 IDFdm2 101 Water Wa 2,230 IDFdm2 66 Great bulrush marsh BU 93 IDFdm2 77 Avalanche chute AC 10 IDFdm2 88 Beaked sedge - Water sedge marsh SM 554 IDFdm2 99 Rock outcrop RO 805

Total IDFdm2 58,405




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Area_ha Total

IDFun 01 Fd - Rocky Mountain juniper - Bluebunch wheatgrass DJ 1,625 IDFun 02a Pasture sage - Bluebunch wheatgrass: moderate slope phase SWa 169 IDFun 02b Pasture sage - Bluebunch wheatgrass: gentle slope phase SWb 1,073 IDFun 03 Fd - Pinegrass - Step moss DP 213 IDFun 04 SxwAt - Sarsaparilla SS 421 IDFun 05 ActSxw - Red-Osier dogwood CD 53 IDFun 101 Water Wa 2,054 IDFun 44 Talus TA 5 IDFun 66 Great bulrush marsh BU 339 IDFun 88 Beaked Sedge - Water sedge marsh SM 39 IDFun 99 Rock outcrop RO 238

Total IDFun 6,229MSdk 01 Sxw - Soopolallie - Grouseberry SG 32,717 MSdk 02 Saskatoon - Bluebunch wheatgrass SW 221 MSdk 03 Pl - Juniper - Pinegrass LJ 13,080 MSdk 04 Pl - Oregon-grape - Pinegrass LP 34,833 MSdk 05 Sxw - Soopolallie - Snowberry SS 11,937 MSdk 06 Sxw - Dogwood - Horsetail SH 99 MSdk 07 Sxw - Scrub birch - Sedge SB 1,270 MSdk 101 Water Wa 1,180 MSdk 44 Talus TA 273 MSdk 75 Avalanche runout AR 43 MSdk 77 Avalanche chute AC 104 MSdk 99 Rock outcrop RO 772

Total MSdk 96,529PPdh2 01 Py - Bluebunch wheatgrass - Junegrass PW 7,869 PPdh2 02a Bluebunch wheatgrass - Junegrass: steep WJa 392 PPdh2 02b Bluebunch wheatgrass - Junegrass: gentle to moderate WJb 1,593 PPdh2 03 PyAt - Rose - Solomon's-seal AR 950 PPdh2 04 Act - Dogwood - Nootka rose CD 159 PPdh2 101 Water Wa 453 PPdh2 44 Talus TA 47 PPdh2 69 Saltgrass - Foxtail SF 367 PPdh2 77 Avalanche chute AC 2 PPdh2 99 Rock outcrop RO 488

Total PPdh2 12,320

Total Area Canal Flats

496,571




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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.




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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

Percentage Correct Neural Network Call Compared to Field Calls ESSFdk 01, 03, 04 only

N MSdk 01, 03, 04, 05 only

N IDFdm2 01, 03, 04 only

N

Training set

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

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.




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Table 9. Canal Flats PEM Mean Accuracy of Final Mapping By the Subzone or Variant BGC subzone or variant

Point Accuracy within the single 25 m pixel of the field UTM call

“Close” Accuracy within one site series

Number of independent 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 Point Accuracy within

the single 25 m pixel of the field UTM call

“Close” Accuracy within one site series

Number of Plots

PPdh2 01 100% N/A 3 IDFdm2 01 100% N/A 6 IDFdm2 03 33% 66.6% 3 IDFdm2 04 0% 50% 2 IDFdm2 05 0% 50% 1 MSdk 01 50% 71.9% 16 MSdk 03 0% 50% 2 MSdk 04 40% 55% 10 MSdk05 0% 33% 3 MSdk06 0% 50% 1 ESSFdk 01 0% 50% 4 ESSFdk 03 0% 50% 2 ESSFdk 04 73.1% 82.7% 26 ESSFdk 05 0% 0% 1 ESSFdku 44 0% 50% 2 ESSFdkp 01 0% 50% 1 ESSFdkp 02 50% 75% 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.




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Table 11. Canal Flats PEM Structural Stage Accuracy Assessment for Final Mapping BGC subzone or variant

Point Accuracy within the single 25 m pixel of the field UTM call

“Close” Accuracy within one structural stage of the field call

Number of independent assessment plots

PPdh2 33.3% 50.0% 3 IDFdm2* 8.3% 25.0% 12 MSdk 46.9% 59.3% 32 ESSFdk 41.7% 54.2% 36 ESSFdku* 100% N/A 2 ESSFdkp 100% N/A 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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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.




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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 Proportion of Total Model Building Samples

Proportion of Total Independent Samples

PPdh2 01 1.6% 1.1% 3.5% IDFdm2 01 7.3% 7.4% 6.9% IDFdm2 03 1.7% 4.5% 3.4% IDFdm2 04 1.7% 4.1% 2.3% IDFdm2 05 0.02% 0.7% 1.2% MSdk 01 6.6% 12.1% 18.0% MSdk 03 2.6% 3.4% 2.3% MSdk 04 7.0% 11.5% 11.6% MSdk05 2.4% 5.9% 3.4% MSdk 06 0.01% 0.7% 1.2% ESSFdk 01 2.0% 4.5% 4.5% ESSFdk 03 4.2% 3.4% 2.3% ESSFdk 04 23.1% 21.8% 30.2% ESSFdk 05 0.1% 3.4% 1.2% ESSFdku 44 1.0% 0.2% 2.3% ESSFdkp 01 1.26% 0.2% 1.2% ESSFdkp 02 0.2% 0.0% 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 Proportion of Total Model Building Samples

Proportion of Total Independent Samples

1 – sparsely vegetated 14.5% 4.5% 3.4% 2- herb 18.8% 9.2% 9.3% 3 – shrub/herb 8.4% 34.8% 36.2% 4 – pole sapling 8.9% 5.0% 9.3% 5 – young forest 15.8% 26.9% 23.2% 6 – mature forest 17.6% 13.3% 16.3% 7 – old forest 15.8% 6.3% 2.3%




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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.




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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.




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REFERENCES CITED Agriculture Canada Expert Committee on Soil Survey. 1998. The Canadian

System of Soil Classification. 3rd ed. Agriculture Canada Publishing. Akaike, H. 1983. Information measures and model selection. Bulletin of the international

statistical institute: proceedings of the 44th session. 1:277-290. Bishop, C. 1995. Neural networks for pattern recognition. Oxford University Press,

Oxford, UK. Braumandl, T.F. and Curran, M. 1992. A field Guide for Site Identification and

Interpretation for the Nelson Forest Region. Land Management Handbook NUMBER 20. BC Ministry of Forests.

British Columbia Ministry of Environment, Lands and Parks, BC Ministry of Forests.

1998 Field Manual for Describing Terrestrial Ecosystems. Land Management Handbook NUMBER 25.

Demarchi, D.A. 1996. An Introduction to the Ecosections of British Columbia.

Pamphlet for BC Ministry of Environment Lands and Parks. Victoria, BC. Ecological Data Committee, Ecosystems Working Group/ Terrestrial Ecosystems Task

Force For: Resources Inventory Committee. Standards for Digital Terrestrial Ecosystem Mapping (TEM) Data Capture in British Columbia. Ecosystem Technical Standards and Data Base Manual Version 2.0, October 1998.

Kernaghan, G. B.A. Sinclair, K. Lessard, D. Spathe, M.V.Ketcheson,. 1998. Steamboat

Mountain, Terrestrial Ecosystem Mapping (TEM) Project. Unpublished Report and Maps to Slocan Forest Products Ltd. Radium Division.

Kernaghan, G., B.A. Sinclair, J. Riddell, M.V. Ketcheson. 1997. Brewer Creek

Terrestrial Ecosystem Mapping. Unpublished Report and Maps to Peter Holmes, BC Ministry of Environment, Lands and Parks, Invermere, BC.

Kernaghan, G., K. Lessard, B.A. Sinclair, R. McKay, G. Burns and M.V. Ketcheson.

1999. TFL 14 Terrestrial Ecosytem Mapping. Unpublished Report and Maps for Brenda Hopkin for Crestbrook Forest Industries, Cranbrook, BC.

Kernaghan, G. , K. Lessard and G. Burns. 2000. Premier Ridge – Diorite Terrestrial

Ecosystem Mapping. Unpublished Maps for Marcie Belcher, TEMBEC Cranbrook, BC.




page 43.

Field Code Changed

Ketcheson, M.V., R. McKay, R. Wege, and J Shepard. 1998. Wasa Provincial Park Ecosystem Map, Report, Expanded Map Legend and Management Treatment Options. Unpublished Map and Report to Mike Gall, BC Parks, Wasa, BC.

Ketcheson, M.V. and T. Dool. 2001. Arrow TSA Site Index Adjustment Project,

Predictive Ecosystem Mapping, Year Two. An unpublished report to the Arrow IFPA Committee, Nelson, BC

Ketcheson, M.V.,G. Smith, S. Wilson. 2001. Arrow TSA Site Index Adjustment Project,

Predictive Ecosystem Mapping, Year One. An unpublished report to the Arrow IFPA Committee, Nelson, BC

Kumar, L., Skidmore, A.K.,Knowles, E., 1997. Modeling Topographic variation in Solar

Radiation in a GIS Environment. International Journal of Geographical Information Science, 11(5): 475-497

http://www.for.gov.bc.ca/research/TEMalt). Laukulich, L.M., and K.W.G. Valentine. 1978. The Soil Landscapes of British

Columbia: The Canadian System of Soil and Soil Climate Clasification. Ministry of Environment.

Loh, W. –Y., and Y. –S. Shih. 1997. Split selection methods for classification trees.

Statistica Sinica 7:815–840. Marcoux, D., M., G. Kernaghan, U. Lowrey, M. Mathers and M. V. Ketcheson. 1997.

Stoddart Creek Terrestrial Ecosystem Mapping (TEM) Project. 1997. Unpublished report and maps to Larry Ingham, Columbia Basin Fish and Wildlife Compensation Program, Invermere, BC

Marcoux, D.M., M.V. Ketcheson, D. Spathe, G. Kernaghan, B. Sinclair. 1997. East

Columbia Lake Terrestrial Ecosystem Mapping (TEM) Project. Unpublished Report and Maps to Larry Inghan. Columbia Basin Fish and Wildlife Compensation Program, Invermere, BC.

Meidinger, D. 2000. Personal communication. Meidinger, D. Protocol for Quality Assurance and Model assessment of Ecosystem

Maps. July 2000. Research Branch, BC Ministry of Forests, Victoria, BC for the TEM Alternatives Task Force.

Menard, S. 1995. Applied logistic regression analysis. Series: quantitative applications in

the social sciences 106. Sage University Press, Thousand Oaks, CA.

Field Code Changed




page 44.

Field Code Changed

Moon, D., D. Clark, D. Meidinger, and K. Jones. 1999. Standards for Predictive Ecosystem Mapping – Inventory Standard – Review Draft. Version 1.0 November 1999.

PEM Data Committee. April 2000. Standards for Predictive Ecosystem Mapping (PEM)

Digital Data Capture. Predictive Ecosystem Technical Standards and Database Manual. Version 1.0. Prepared by the PEM data cp,,ottee fpr the TEM Alternatives Task Force Resources Inventory Committee.

Statistica. 1999. Statistica for Windows. Statsoft, Inc. Tulsa, OK. Statistica. 2000. Statistica neural networks. Statsoft, Inc. Tulsa, OK. Wheeler, J.O. and McFeely, P, 1991. Tectonic Assemblage Map of the Canadian Cordillera and adjacent parts of the United States of America. Geological Survey of

Canada, Map 1712A

Documents

TEMBEC Industries Inc. Canal Flats Operating Area PEM ...a100.gov.bc.ca/appsdata/acat/documents/r1501/pem...Geosense Ltd. 203-507 Baker Street Nelson, B.C. V1L 4J2 (250) 354-0277 [email protected]