THIS FILE PEM_5511_IDQ.RTF CONTAINS THE ORIGINAL INPUT DATA QUALITY ASSESSMENT REPORTS FOR THE DRY BELT PEM (KETCHESON ET AL 2002), THE WET BELT PEM (KETCHESON ET AL 2008 AND THE VERY DRY BELT PEM KETCHESON ET AL 2009.

Okanagan IFPA Dry Belt Predictive Ecosystem Mapping
Input Data Layer Assessment Report

Submitted to:

Glen Dick – Manager Okanagan IFPA
Riverside Forest Products Ltd.
Lumby Division
4280 Highway 6
Lumby BC V0E 2G0
(250) 547-1208

Submitted by:

Maureen Ketcheson M. Sc. R.P. Bio.
Tom Dool B.E.S
Lawson Bradley
JMJ Holdings Inc.
208-507 Baker Street
Nelson, B.C.  V1L 4J2
(250)354-4913
jmj@netidea.com

October 23, 2002.

Table of Contents

1.0 Introduction	1
2.0  Project Objectives	1
3.0 Thematic Input Layer Documentation and Quality Assessment	2
3.1 TRIM 1 Data	3
3.1.1 TRIM 1 – TNTL Layer	4
3.1.2 TRIM 1 – TWTR Layer	5
3.1.3 TRIM 1 – TDEM Layer	6
3.2 Forest Inventory Forest Cover Mapping	7
3.3 Okanagan IFPA Plot Data	8
3.4 Biogeoclimatic Subzone Mapping	9
3.5 JMJ Holdings Bioterrain Mapping	10
4.0 Input Processing and Quality Assessment	11
4.1 TRIM 1 Input Layers	13
4.1.1 Input Layers Generated from TRIM TNTL Input Layer	14
4.1.1.1 Mask Grid – MASK	14
4.1.2 Input Layers Generated from TRIM TDEM Input Layer	15
4.1.2.1 Slope - SLP	15
4.1.2.2 Aspect – AS	18
4.1.2.3 Landscape Shape - Shp	20
4.1.2.4 Slope Position	23
4.1.3 Input Layers Generated From TRIM TWTR Input Layer	26
4.1.3.1 Hydrographic Features – TWTR	26
4.1.4 Input Layers Generated From Forest Cover	27
4.1.4.1 Aspen Leading Input Layer – AT_LEAD	27
4.1.4.2 Douglas-fir Leading Input Layer – FD	28
4.1.4.3 Lodgepole Pine Leading Input Layer – PL	30
4.1.4.4 Urban Areas – UR	31
4.1.4.5 Forest Cover Rock Input Layer – FC_ROCK	31
4.1.5 Input Layers Generated From Bioterrain	33
4.1.5.1 Bioterrain Wetlands Input Layer – BIO_WET	33
4.1.5.2 Bioterrain Rock Input Layer – BIO_RO	35
4.1.5.3 Bioterrain Talus Input Layer – BIO_TAL	37
4.1.5.4 Bioterrain Vegetated Rock Input Layer – BIO_VGR	39
5.0 References	42

List of Tables

Table 1. Thematic Input Layers and Input Data Layers	12
Table 2. Slope Classes	15
Table 3. Results of the Chi-Square Test of Proportions for Slope Class	16
Table 4. Slope Input Layer Confusion Matrix	16
Table 5. Statement of Overall Accuracy	16
Table 6. Aspect Classes	18
Table 7. Results of the Chi-Square Test of Proportions for the Aspect Class	18
Table 8. Aspect Input Layer Confusion Matrix	18
Table 9. Statement of Overall Accuracy	19
Table 10. Distribution of Curvature Values	21
Table 11. Landscape Shape Classes	21
Table 12. Results of the Chi-Square Test of Proportions for the Landscape Shape Classes	22
Table 13. Landscape Shape Input Layer Confusion Matrix	22
Table 14. Statement of Overall Accuracy of Landscape Shape	22
Table 15. Distribution of Slope Position Values	24
Table 16. Slope Position Classes	24
Table 17. Results of the Chi-Square Test of Proportions for the Slope Position Class	25
Table 18. Slope Position Input Layer Confusion Matrix	25
Table 19. Statement of Overall Accuracy of Slope Position	25
Table 20. TRIM 1 Feature Codes and Corresponding Hydrographic Features	26
Table 21. Aspen Stand Classes	27
Table 22. Results of the Chi-Square Test of Proportions for the Aspen Leading Input Layer	27
Table 23. Aspen Leading Input Layer Confusion Matrix	27
Table 24. Statement of Overall Accuracy of Aspen Leading	28
Table 25. Douglas-fir Stand Classes	28
Table 26. Results of the Chi-Square Test of Proportions for the Aspen Leading Input Layer	29
Table 27. Douglas-fir Leading Input Layer Confusion Matrix	29
Table 28. Statement of Overall Accuracy of Douglas-fir Leading	29
Table 29. Lodgepole Pine Stand Classes	30
Table 30. Results of the Chi-Square Test of Proportions for the Lodgepole Pine Leading Input Layer	30
Table 31. Lodgepole Pine Leading Input Layer Confusion Matrix	30
Table 32. Statement of Overall Accuracy of Lodgepole Pine Leading	31
Table 33. Urban Areas Classes	31
Table 34. Forest Cover Rock Input Layer Classes	31
Table 35. Bioterrain Wetland Classes	33
Table 36. Results of the Chi-Square Test of Proportions for the Bioterrain Wetlands Input Layer	33
Table 37. Bioterrain Wetlands Input Layer Confusion Matrix	33
Table 38. Statement of Overall Accuracy of Bioterrain Wetlands	34
Table 39. Bioterrain Rock Classes	35
Table 40. Results of the Chi-Square Test of Proportions for the Bioterrain Rock Input Layer	35
Table 41. Bioterrain Rock Input Layer Confusion Matrix	36
Table 42. Bioterrain Talus Classes	37
Table 43. Results of the Chi-Square Test of Proportions for the Bioterrain Talus Input Layer	38
Table 44. Bioterrain Talus Input Layer Confusion Matrix	38
Table 45. Statement of Overall Accuracy of Bioterrain Talus	38
Table 46. Bioterrain Vegetated Rock Classes	39
Table 47. Results of the Chi-Square Test of Proportions for the Bioterrain Vegetated Rock Input Layer	40
Table 48. Bioterrain Vegetated Rock Input Layer Confusion Matrix	40
Table 49. Statement of Overall Accuracy of Bioterrain Vegetated Rock	40

List of Figures

Figure 1. TRIM 1 Mapsheets required for the Project	2

1.0 Introduction

Predictive Ecosystem Mapping in the Okanagan IFPA began June of 2001 with the collection of data and the creation of potential Geographic Information Systems (GIS) input layers. This document reports the quality of these input layers relative to measured values collected in the field.
 
2.0  Project Objectives  

The task required of this phase of the project included 

•	The assessment of thematic input layers for use in the PEM process
•	The creation of GIS input layers
•	The assessment of GIS input layers for use in the PEM process

For purposes of clarification it should be stated here that this phase of the project involved the use of 4 Thematic Input Layers for assessment and collected field data.

•	Ministry of Sustainable Resource Management TRIM 1 mapping
•	Ministry of Forests Forest Cover Data
•	JMJ Holdings Bioterrain Mapping
•	Ministry of Forests Biogeoclimatic Mapping
•	Okanagan IFPA Predictive Ecosystem Mapping Project Field Plot Data

The final PEM used 16 input layers:

•	Mask
•	Biogeoclimatic zones
•	Slope
•	Aspect
•	Landscape Shape
•	Slope Position
•	Hydrography
•	Urban
•	Rock
•	Aspen Leading (40% or more stand composition)
•	Douglas-fir Leading (40% or more stand composition)
•	Lodgepole Pine Leading (40% or more stand composition)
•	Bioterrain wetlands
•	Bioterrain rock
•	Bioterrain talus
•	Bioterrain vegetated rock

The original mapping extents for thematic input layers ranged from localized plot data to provincial data sets. All input layers with the exception of the Okanagan IFPA PEM Plot Data are contiguous layers covering 148 TRIM 1 mapsheets:

Figure 1. TRIM 1 Mapsheets required for the Project

082L.089	
082L.088	
082L.079	
082L.078	
082L.077	
082L.070	
082L.069	
082L.068	
082L.067	
082L.066	
082L.063	
082L.060	
082L.062	
082L.059	
082L.058	
082L.057	
082L.056	
082L.055	
082L.054	
082L.053	
082L.050	
082L.052	
082L.049	
082L.051	
082L.048	
082L.047	
082L.046	
082L.045	
082L.044	
082L.043	
082L.042	
082L.039	
082L.041	
092I.050	
082L.038	
082L.037	
082L.036	
082L.035	
082L.034	
082L.033	
082L.032	
082L.029	
082L.031	
092I.040	
082L.028	
082L.027	
082L.026	
082L.025	
082L.024	
082L.023	
082L.022	
082L.019	
082L.021	
082L.018	
082L.017	
082L.016	
082L.015	
082L.014	
082L.013	
082L.012	
082L.009	
082L.011	
082L.008	
082L.007	
082L.006	
082L.005	
082L.004	
082L.003	
082L.002	
082E.099	
082L.001	
092I.010	
082E.098	
082E.097	
082E.096	
082E.095	
082E.094	
082E.093	
082E.092	
082E.091	
092H.100	
082E.088	
082E.087	
082E.086	
082E.085	
082E.084	
082E.083	
082E.082	
082E.081	
092H.090	
092H.089	
082E.077	
082E.076	
082E.075	
082E.074	
082E.073	
082E.072	
082E.071	
092H.080	
092H.079	
082E.065	
082E.064	
082E.063	
082E.062	
082E.061	
092H.070	
082E.055	
082E.054	
082E.053	
082E.052	
082E.051	
092H.060	
082E.044	
082E.043	
082E.042	
082E.041	
092H.050	
082E.035	
082E.034	
082E.033	
082E.032	
082E.031	
092H.040	
082E.025	
082E.024	
082E.023	
082E.022	
082E.021	
092H.030	
092H.029	
092H.028	
082E.015	
082E.014	
082E.013	
082E.012	
082E.011	
092H.020	
092H.019	
092H.018	
082E.004	
082E.003	
082E.002	
082E.001	
092H.010	
092H.009	
092H.008	

It should be noted that these mapsheets represent the extents of the entire Okanagan IFPA study area. Currently the areas subject to study include only those biogeoclimatic subzones found in the Dry Belt.
They are:
IDF dk 1
IDF dk 2
IDF dm 1
MS xk
MS dm 1
MS dm 2
ESSF xc
ESSF dc 1
ESSF dc 2

3.0 Thematic Input Layer Documentation and Quality Assessment

Consultation with the client and the Ministry of Sustainable Resource Management indicated that there was sufficient data of a quality high enough to carry out a PEM project. These thematic input layers included

•	Ministry of Sustainable Resource Management TRIM 1 mapping
•	Ministry of Forests Forest Cover mapping
•	JMJ Holdings Bioterrain Mapping
•	Ministry of Forests Biogeoclimatic Mapping
•	Okanagan IFPA Predictive Ecosystem Mapping Project Field Plot Data

3.1 TRIM 1 Data

TRIM 1 data was acquired from the client in the spring of 2001.

Several different data layers were used from the TRIM 1 thematic mapping.
•	TDEM – Elevation points and 3D breaklines 
•	TWTR – Hydrographic Features
•	TNTL – 1:20,000 Neatlines

This information was then processed and used to produce
•	Mask
•	Aspect
•	Landscape Shape
•	Slope Position
•	Slope
•	Hydrography

More information on the processing of this information for the generation of input layers can be found in Section 4 “Input Processing” of this document.

3.1.1 TRIM 1 – TNTL Layer

Citation: TRIM 1 Program

Consultant/Department: Geographic Data BC Branch, Ministry of Sustainable Resource Management

Publication scale: 1:20,000

Period of Compilation: 1997

Base Map Projection: Albers, NAD 83

Quality Control: As per Table 1and Section 4.4.1.1. in the Standards for Predictive Ecosystem Mapping in British Columbia, Inventory Standard, (RIC, 1999) the neatline features in the TRIM 1 data were considered to be of adequate quality to complete a Predictive Ecosystem Mapping project at a scale of 1:20,000.

Edge Matching: Edge matching was completed along mapsheet boundaries using the Arc/Info 8.1 APPEND process. No dangles were found along mapsheet boundaries.

Edge Matching Error Minimum: 0 m shift along the X axis, 0 m shift along the Y axis.

Edge Matching Error Average: 0 meters on the X axis. 0 meters along the Y axis. 

Edge Matching Error Max: 0 meters on the X axis. 0 meters along the Y axis.

Attribute/Label Matching: All neatline boundaries contained the appropriate labels and attributes

Raster Sized: NA

Adjusted Control Feature Shift: None

3.1.2 TRIM 1 – TWTR Layer

Citation: TRIM 1 Program

Consultant/Department: Geographic Data BC Branch, Ministry of Sustainable Resource Management

Publication scale: 1:20,000

Period of Compilation: 1997

Base Map Projection: Albers, NAD 83

Quality Control: As per Table 1and Section 4.4.1.1. in the Standards for Predictive Ecosystem Mapping in British Columbia, Inventory Standard, (RIC, 1999) the single line water features in the TRIM 1 data were considered to be of adequate quality to complete a Predictive Ecosystem Mapping project at a scale of 1:20,000.

Edge Matching: Edge matching was completed along mapsheet boundaries using the Arc/Info 8.1 APPEND process. No dangles were found along mapsheet boundaries. Line work spanning map boundaries was joined to create seamless coverage. 100 lines were randomly selected from the input data and compared to the final output seamless coverage. Error was measured in terms of shift along the X axis (Easting) and the Y axis (Northing).  

Edge Matching Error Minimum: 0 m shift along the X axis, 0 m shift along the Y axis.

Edge Matching Error Average: 0 meters on the X axis. 0 meters along the Y axis. 

Edge Matching Error Max: 0 meters on the X axis. 0 meters along the Y axis.

Attribute/Label Matching: All water features were coded with the correct FCODE.

Raster Sized: NA

Adjusted Control Feature Shift: None

3.1.3 TRIM 1 – TDEM Layer

Citation: TRIM 1 Program

Consultant/Department: Geographic Data BC Branch, Ministry of Sustainable Resource Management

Publication scale: 1:20,000

Period of Compilation: 1997

Base Map Projection: Albers, NAD 83

Quality Control: As per Table 1and Section 4.4.1.1. in the Standards for Predictive Ecosystem Mapping in British Columbia, Inventory Standard, (RIC, 1999) the TDEM layer in the TRIM 1 data was considered to be of adequate quality to complete a Predictive Ecosystem Mapping project at a scale of 1:20,000.

Edge Matching: DEM points were merged into one seamless Arc/Info point coverage. No edge-matching was required as there is inherent overlap in the data points for each mapsheet. It should be noted that in the zone of overlap between 2 given mapsheets there were some irregularities in the elevation values. This is to be expected as each mapsheet relies on different control points, which may be slightly higher or lower. These differences were of a low enough proportion that the process of converting to grid would have the effect of “smoothing” the surface and nullifying the differences. It should also me noted that as no 2 TDEM points occupy the same space they cannot be compared in an absolute fashion. 

In an effort to gauge the degree of difference between the respective sheets contours with a 20m interval were created and compared in areas of overlap. 50 different meeting points along map sheet neatlines were selected between contour ‘pairs’ and the degree of difference was measured. Edge matching error is measured on an x-y plane as the contour pairs both represent the same elevation.  

Edge Matching Error Minimum: 2.1 meters

Edge Matching Error Average: 6.3 meters

Edge Matching Error Max: 9.8 meters

Attribute/Label Matching: N/A

Raster Sized: N/A

Adjusted Control Feature Shift: None

3.2 Forest Inventory Forest Cover Mapping

Ministry of Forests Forest Cover Mapping (FC1) was provided by Ministry of Forests (Kamloops Region).

This information was received in as seamless Arc/Info coverage for the Kamloops District and then processed to produce information on:

•	Douglas-fir leading (40% or more stand composition)
•	Aspen leading (40% or more stand composition)
•	Lodgepole Pine leading (40% or more stand composition)
•	Open Range
•	Urban Areas
•	Rock

For Information on the processing of this information for the generation of input layers can be found in Section 5 “Input Processing” of this document.

Citation: Forest Cover/Forest Inventory Plan Data

Consultant/Department: MoF, Kamloops Forest Region

Publication scale: 1:20,000

Period of Compilation: 1997

Base Map Projection: UTM NAD 83 Zone 11

Quality Control: As per Table 1and Section 4.4.1.1. in the Standards for Predictive Ecosystem Mapping in British Columbia, Inventory Standard, (RIC, 1999) the MoF FC1 data was considered to be of adequate quality to complete a Predictive Ecosystem Mapping project at a scale of 1:20,000.

Edge Matching: Data was received in a seamless format. No edge-matching was required.

Edge Matching Error Minimum: N/A

Edge Matching Error Average: N/A 

Edge Matching Error Max: N/A

Attribute/Label Matching: N/A

Raster Sized: N/A

Adjusted Control Feature Shift: None

3.3 Okanagan IFPA Plot Data

This information was gathered from August 9th-October 19th 2001, for use this project by JMJ Holdings Inc

For information on the processing of this data for the generation of input layers can be found in Section 5 “Input Processing” of this document.

Citation: Okanagan IFPA Predictive Ecosystem Mapping Project

Consultant/Department: JMJ Holdings Inc.

Publication scale: 1:20,000

Period of Compilation: August 9th – October 19th, 2001

Base Map Projection: UTM NAD 83 Zone 11

Quality Control: Field cards were filled in on the site and then rechecked as the data was entered into digital format in an Excel spreadsheet. UTM coordinates were collected using a Garmin GPS 2 Plus hand held GPS with a stated accuracy of +/-15m RMS. 

Edge Matching: No edge matching was required

Edge Matching Error Minimum: None

Edge Matching Error Average: None

Edge Matching Error Max: None

Attribute/Label Matching: None

Raster Sized: NA

Adjusted Control Feature Shift: NA

3.4 Biogeoclimatic Subzone Mapping

Biogeoclimatic Subzone mapping was provided by Dennis Lloyd, Ministry of Forests, Kamloops Region.

This information was received in as seamless Arc/Info coverage for the study area. It was used to produce the Biogeoclimatic Subzone Input Layer.

Citation: Biogeoclimatic Subzone Data, Ministry of Forests

Consultant/Department: MoF; Research Branch; Kamloops Forest Region; Dennis Lloyd

Publication scale: 1:50,000

Period of Compilation: 2001

Base Map Projection: UTM NAD 83 Zone 11

Quality Control: The Biogeoclimatic Subzone mapping was approved by the Regional Ecologist for use in this PEM. It is not yet correlated and approved by Del Meidinger, Research Branch, BC Ministry of Forests, Victoria.

Edge Matching: Data was received in a seamless format. No edge-matching was required.

Edge Matching Error Minimum: N/A

Edge Matching Error Average: N/A 

Edge Matching Error Max: N/A

Attribute/Label Matching: N/A

Raster Sized: N/A

Adjusted Control Feature Shift: None

3.5 JMJ Holdings Bioterrain Mapping

Bioterrain mapping was undertaken by JMJ Holdings for use in the Okanagan IFPA PEM Project.

Bioterrain polygons were delineated by Jen Shypikta P.Geo, Tedd Robertson G.I.T, Grant Burns G.I.T, Betsy Weddington P.Geo, and Anthony Collette P.Geo using the classification system of Howes and Kenk (1997).
 
Spatial data was captured by Kevin Misurak of JMJ Holdings Inc. using a high-resolution scanner and PCI Geomatics v8.0.
 
This information was used to create the following thematic input layers:

•	Bioterrain rock
•	Bioterrain talus
•	Bioterrain vegetated rock
•	Bioterrain wetlands

Citation: Bioterrain Mapping, JMJ Holdings Inc.

Consultant/Department: JMJ Holdings Inc.

Publication scale: 1:15,000

Period of Compilation: 2001/2002

Base Map Projection: UTM NAD 83 Zone 11

Quality Control: Internal quality control was accomplished by Vicky Lipinski, Donna Ross and Jen Shypitka. External quality assurance was undertaken by Deepa Filatow of the Ministry of Sustainable Resource Management, Pentiction BC.

Edge Matching: Data was edge-matched by hand using ARC/Info 8.1.

Edge Matching Error Minimum: N/A

Edge Matching Error Average: N/A 

Edge Matching Error Max: N/A

Attribute/Label Matching: N/A

Raster Sized: N/A

Adjusted Control Feature Shift: None

4.0 Input Processing and Quality Assessment

For the purposes of this report Section 4.0 Input Processing will describe the processes and the rationale to convert the raw input coverages discussed in Section 3.0 Input Data Documentation and Quality Assessment and generate the thematic data layers used in the PEM process. 

Quantifying the success of the overall process is also a part of input processing and quality assessment. This section will also describe the results of the processes in terms of 3 statistical tests. These tests were deemed appropriate and approved for the assessment of spatial data by the Ministry of Sustainable Resource Management (Meidinger, 2000). The methods are:

•	The Chi-Square Test of Proportions
•	The Confusion Matrix Description of Overall Accuracy
•	The Statement of Percent Correct to the Nearest +/-95% Confidence Interval

The Chi-square test of proportions tests the proportions of the of field data attributes against those of the GIS generated input layers. For example, if field data was collected from 100 randomly placed field locations, and slope was one of the criteria collected, the distribution of slope classes from the GIS generated slope layer should approximate the distribution of slope classes found in the field data. This test gives an indication of how well the GIS is modeling the distribution of  classes of data (e.g. slope) across the landscape.  This test does not indicate the spatial accuracy of those classes though. The field data may indicate that 20% of the landscape is slope class 1 (<=5% slope) and the GIS may predict that 19% of the landscape is slope class 1 (which would be a very good fit). This test will not indicate whether or not the GIS is predicting the occurrence of slope class one in the right locations.

To test the locational accuracy of the GIS data a confusion matrix is developed to test each class of the field data against each class of the GIS data.  For instance, how many of the field plots that were classified as slope class 1 actually fall into pixels that the GIS has classified as slope class 1. The results of the matrix include a stated Error of Omission, Error of Commission, and an initial statement of Overall Accuracy. To use the slope attribute as an example, the Error of Commission represents the percentage of occurrences were the plot data (a known thing) has been classified by the GIS as being in a slope class where it does not belong. In this case we have committed the act of getting it wrong. The Error of Omission represents the percentage of occurrences were the plot data has not been classified by the GIS as being in the right slope class. In this case we have omitted the act of getting it right (Meidinger, 2000). The initial Statement of Overall Accuracy is the number of plots where the GIS and the field data agree divided by the number of plots where the GIS data and the field data disagree. The Initial statement of overall accuracy does not indicate how much overall confidence we have in the results of the Confusion Matrix Description of Overall Accuracy it’s result is limited to just the test. 

The Statement of Overall Accuracy defines the results of the Confusion Matrix in terms of a 95% confidence interval. For example, the confusion matrix may state an Initial Overall Accuracy of 62% but if only 8 plots were used for the test then the lower 95% confidence interval would be 25% and the upper 95% confidence interval would be 87%. In testing the same spatial data with the same result of 62% Initial Overall Accuracy but with a sample set of 100 plots the Statement of Overall Accuracy would have a lower 95% confidence interval of 56% and an upper 95% confidence interval of  69%. With more plots (a greater sample population) we can state our results with a higher degree of accuracy.

The Input Data Layers and the Thematic Input Layers which they were derived from are listed in Table 1. Thematic Input Layers and Input Data Layers.

Table 1.  Thematic Input Layers and Input Data Layers
Thematic Input Layer	Data Layer	PEM Data Layer	
TRIM 1	TRIM1 – TNTL	Mask – MASK	
TRIM 1	TRIM1 – TWTR	Hydrography – TWTR	
TRIM 1	TRIM 1 – TDEM	Slope – SLP
Aspect – AS
Landscape Shape – SHP
Slope Position – LNDPOS	
Forest Cover	Forest Cover	Urban – UR
Open Range – OPEN_RA
Rock – FC_ROCK
Douglas-fir leading – FD
Aspen leading – AT
Lodgepole Pine leading – PL	
Bioterrain Mapping	Bioterrain Mapping	Bioterrain rock – BIO_RO
Bioterrain talus – BIO_TA
Bioterrain vegetated rock – BIO_VGR
Bioterrain wetlands – BIO_WET	
Field Data	Field Data	Plot Data	
Biogeoclimatic Subzone Mapping	Biogeoclimatic Subzone Mapping	Biogeoclimatic Subzones – BEC	

It should be noted that an initial data set of 729 plots were compiled and made into a spatial input layers for testing. These plots were then screened for use in tests for each GIS Input Data Layer. For each of these tests plots were excluded when they were not deemed fit for use due to discrepancies in the in the classes.  This is the reason that sample sizes for the testing of each layer varies.

4.1 TRIM 1 Input Layers

The TRIM 1 Input layers included
•	TNTL
•	TDEM

All layers were projected from their original Albers Projection into Universal Transverse Mercator Zone 11. This was done using an AML provided by the Ministry of Sustainable Resource Management and the ArcInfo 8.1 PROJECT command. The parameters of the translation are as follows. 

input
Projection    ALBERS
Datum         NAD83
Zunits        NO
Units         METERS
Xshift        0.0000000000
Yshift        0.0000000000
Parameters
 50  0  0.000 /* 1st standard parallel
 58 30  0.000 /* 2nd standard parallel
-126  0  0.000 /* central meridian
 45  0  0.000 /* latitude of projection's origin
1000000.00000 /* false easting (meters)
0.00000 /* false northing (meters)
output
Projection    UTM
Zone          11
Datum         NAD83
Zunits        NO
Units         METERS
Xshift        0.0000000000
Yshift        0.0000000000

4.1.1 Input Layers Generated from TRIM TNTL Input Layer

4.1.1.1 Mask Grid – MASK

The TRIM1 neatline coverage for the study area was converted into a 50m grid using the ArcInfo 8.1 POLYGRID command. This coverage provides the ‘template’ for all other grids. It is used in the analysis environment to ensure that all subsequent grids and their individual pixels line up so that a given 50m x 50m point in space within the study area is represented by pixels that are congruent despite the fact that they are in different input layers. 

4.1.2 Input Layers Generated from TRIM TDEM Input Layer

4.1.2.1 Slope - SLP

ArcInfo Grid Slope Command was used on the Elevation Grid. The options selected were percent and a z-factor of 1.

Slope identifies the maximum rate of change in value from each cell to its neighbors.  An output slope grid can be calculated as percent slope or degree of slope. 

Conceptually, the slope function fits a plane to the z values of a 3x3 cell neighborhood around the processing or center cell. The direction the plane faces is the aspect for the processing cell. The slope for the cell is calculated from the 3x3 neighborhood using the average maximum technique. If there is a cell location in the neighborhood with a no-data z value the z value of the center cell will be assigned to the location. At the edge of the grid, at least three cells (outside the grid’s extent) will contain no-data as their z values. These cells will be assigned the center cell’s z value. The result is a flattening of the 3x3 plane that is fit to these edge cells, which thus usually leads to a reduction in the slope.

The actual algorithm that is used to calculate slope is (ESRI, 2001):

rise_run = SQRT(SQR(dz/dx)+SQR(dz/dy))degree_slope = ATAN(rise_run) * 57.29578

where the deltas are calculated using a 3x3 roving window.a through i represent the z_values in the window:

a b cd e fg h i

(dz/dx) = ((a + 2d + g) - (c + 2f + i)) / (8 * x_mesh_spacing)(dz/dy) = ((a + 2b + c) - (g + 2h + i)) / (8 * y_mesh_spacing)

Table 2. Slope Classes

Slope 
Class	Description	
1	0 to 5%	
2	5% to 25% (on warm aspect)	
3	5% to 25% (on cool aspect)	
4	25% to 50%	
5	50% to 70%	
6	70% to 100	
7	>= 100%	

The new slope layer was then tested against 638 field plots which were stratified into the same classes as the GIS input layer. The distribution of classes in terms of area were then compared to those of the plot data. 

Table 3. Results of the Chi-Square Test of Proportions for Slope Class
Slope Class	Field Slope (Observed)		GIS Slope (Expected)		(Observed-Expected)2 
/Expected	
	Area	Proportion	Freq	Area	Proportion	Freq			
1	127	0.174	127	88,364.5	0.112	81.7	25.11344677	
2	211	0.290	211	215,658.3	0.274	199.4	0.674828891	
3	241	0.331	241	312,459.5	0.397	288.9	7.942932681	
4	121	0.166	121	134,818.8	0.171	124.7	0.107161184	
5	22	0.030	22	28,994.8	0.037	26.8	0.862593358	
6	5	0.007	5	6,936.0	0.009	6.4	0.311371129	
7	1	0.001	1	126.5	0.000	0.1	6.666655431	
Total	728	1.000	728	787,358.3	1.000	728.0	41.67898944	
									
							chi-sqr =	41.6790	
							df =	6	
							p-value =	0.00000021276	

 The locational accuracy of the GIS layer was also tested against the 638 plots. The results are displayed below in Table 4. Slope Input Layer Confusion Matrix.
 
Table 4. Slope Input Layer Confusion Matrix
Field Slope
GIS Slope	Class 1	Class 2	Class 3	Class 4	Class 5	Class 6	Class 7	Total	Error of Omission	
Class 1	76	10	13	2	0	0	0	101	24.8%	
Class 2	20	178	13	25	5	2	0	243	26.7%	
Class 3	31	18	208	28	2	2	1	290	27.9%	
Class 4	0	5	6	66	11	1	0	89	25.8%	
Class 5	0	0	1	0	4	0	0	5	20.0%	
Class 6	0	0	0	0	0	0	0	0	0.0%	
Class 7	0	0	0	0	0	0	0	0	0.0%	
Total	127	211	241	121	22	5	1	728		
Error of 
Commission	40.2%	15.6%	13.7%	45.5%	81.8%	100.0%	100.0%			

Average Error of Commission		56.7%
Average Error of Omission		17.8%
Overall Error: (196/728)			26.9%

Table 5. Statement of Overall Accuracy
Total Plots		728	
Number Correct	532	
Percent Correct	73.1%	
Lower 95% Confidence Value	70%	
Median 95% Confidence Value	73%	
Upper 95% Confidence Value	76%	

The locational tests used to create the confusion matrix were done using a 50m proximity analysis. Using a 50m proximity analysis is justifiable in the case of the slope layer. In looking at the algorithm used to describe how the delta values were arrived at a 3 X 3 (75m x 75m) window was used which has the effect of “smoothing” the landscape to a degree. The slope described by a given pixel in the Slope Layer is defined by the 75m x 75m area surrounding that pixel. Because of this process a certain amount of shift in classes has to be accounted for. As well, the plot locations describe areas roughly 50m x 50m. This data is stored as point data for simplicity and because no surveying is done for the outer boundaries of the plots. While the point in reality occupies no area in space it is representing an area of about the size of one pixel. The result is that plot locations can fall into the next pixel over from where they are really intended to be. Using a 50m proximity analysis has the effect of compensating for these issues.

The plots were first tested using the 50m proximity analysis to see if they were ‘correct’. If the test found the proper corresponding code in the GIS layer, then the plot is ‘right’. If the 50m proximity analysis failed to locate a match, then an ‘on-point’ method is used to determine what was actually found in that plot location. The ‘on-point’ method uses the spatial location of the plot to determine what the GIS layer modeled beneath it. Because using a 50m proximity analysis may return more than one result when testing a layer, it is not possible to use it to determine exactly what is under each plot, but only if the correct values were found within a certain proximity to the plot.

At the 95% confidence interval (CI) the stated accuracy for this layer is 73% with a lower confidence value of 73% and an upper confidence value of 79%. In examining Table 3. We can see that there is a fairly strong relationship between the field data and the GIS data for slope classes 1, 2, 5, 6. Classes 3 and 4 display a relatively poor relationship.  The results would suggest that the GIS is over-predicting the presence of classes 3 and 4. This is entirely possible in that these are both very broad classes. We should also take into account there is a relatively low sample population for classes 4,5, and 6. This is in part due to the fact the field crews can rarely gather information on extremely steep slopes and that there is very little very steep terrain in this landscape. After examining the confusion matrix this becomes evident.  What the results also suggest is that model should be adjusted to make more of the terrain in slope class 1 into slope class 2. This is supported by the chi-square statistic. The case of slope class three is similar to that of slope class 1. The chi-square statistic suggests that there should be should be less slope class 3 and the confusion matrix suggests that some of the slope class 3 terrain should in fact be classified as slope class 2. 

Overall the results of the tests suggest that the Slope Input Data Layer would be appropriate for use in the PEM but that there is room for improvement . Currently project ecologists could use this data but it is suggested that they apply caution to calls made based on slope classes 4,5, and 6 as there is little field data to support the accuracy of these slope classes. 

4.1.2.2 Aspect – AS

The ArcInfo Grid Aspect command was applied to the Elevation grid. There are no available options for this command. 

Aspect identifies the direction of maximum rate of change in z value from each cell. Aspect is expressed in positive degrees from 0 to 360, measured clockwise from the north.

Aspect identifies the down-slope direction of the maximum rate of change in value from each cell to its neighbors.  Aspect can be thought of as the slope direction.

The Slope grid was then applied to the Aspect grid. All areas with a slope of less then 25% were given an aspect of neutral.

The grid was then reclassified into discrete classes

Table 6. Aspect Classes
Aspect Class	Description	
1	135deg to 285deg (warm)	
2	285deg to 135deg (cool)	
3 	Neutral (< 25% slope)	

The final landscape shape layer was tested against 639 field plots where on the ground aspect data was recorded. 

Table 7. Results of the Chi-Square Test of Proportions for the Aspect Class

Aspect	Field Aspect (Observed)	GIS Aspect (Expected)	(Observed-Expected)2 
/Expected	
	Area	Proportion	Freq	Area	Proportion	Freq			
1	65	0.090	65	64,058.8	0.081	59.1	0.596005286	
2	82	0.113	82	106,836.8	0.136	98.5	2.767349312	
3	579	0.798	579	616,462.8	0.783	568.4	0.19684058	
Total	726	1	726	787,358.3	1	726.0	3.560195178	
									
							chi-sqr =	3.560195	
							df =	2	
							p-value =	0.168621	

Table 8. Aspect Input Layer Confusion Matrix
Field Aspect
GIS Aspect	Class 1	Class 2	Class 3	Total	Error of Omission	
Class 1	30	1	6	37	18.9%	
Class 2	1	54	4	59	8.5%	
Class 3	34	27	569	630	9.7%	
Total	65	82	579	726		
Error of 
Commission	53.8%	34.1%	1.7%			

Average Error of Commission: 		29.9%
Average Error of Omission:		12.4%
Overall error (73/726):			10.1%

Table 9. Statement of Overall Accuracy
Total Plots		639	
Number Correct	577	
Percent Correct	90%	
Lower 95% Confidence Value	88%	
Median 95% Confidence Value	90%	
Upper 95% Confidence Value	92%	

The distribution of Aspect Class across the landscape as described by the Chi-square statistic suggests that there is a reasonable relationship between the GIS data and the field data. This is in turn supported by the results of the confusion matrix and the overall statement of accuracy. Much of this landscape is made of gently undulating terrain where aspect calls are difficult to predict with a 50m pixel size. Much of landscape also falls into the neutral aspect class (class 3) which indicates that aspect, as it is currently defined, may not necessarily be as significant as it would be in more mountainous terrain. 

Most of the locational errors made are between aspect class 3 and 1 and between slope class 3 and 2. This may suggest problems of resolution. Because the slope cut-off for the neutral aspect class is 25% and much of the landscape falls into gentler grades and most of the calls for aspect fall into the neutral class it could be that some areas that the GIS called 26% slope the field staff called 24% slope. 

With an overall accuracy of 90% the Aspect input layer can be considered for use but project ecologists should be advised that the GIS input layers tends to underpredict the existence of neutral classes in favor of warm and cool aspects.  Scoring within knowledge table should take this into account. 

 4.1.2.3 Landscape Shape - Shp

Landscape Shape was modeled using the grid CURVATURE function. Before calculating curvature, the elevation grid was smoothed twice using a 3x3 focal filter. 

 Curvature describes the curvature of the surface at each cell center. A positive curvature indicates that the surface is upwardly convex at that cell.  A negative curvature indicates that the surface is upwardly concave at that cell.  A value of zero indicates that the surface is flat. Units of the CURVATURE output grid (CURVGRID) are one over 100 z-units, or 1/100(z-units). The curvature of a surface is calculated on a cell-by-cell basis.  For each cell, a fourth-order polynomial of the form 

Z = Ax2y2 + Bx2y + Cxy2 + Dx2 + Ey2 + Fxy + Gx + Hy + I

is fit to a surface composed of a 3x3 window.  The coefficients a, b, c, and so on, are calculated from this surface.

The relationships between the coefficients and the nine values of elevation for every cell numbered as shown on the diagram are as follows:

Diagram 1. Calculation of Curvature

A = [(Z1 + Z3 + Z7 + Z9) /4  - (Z2 + Z4 + Z6 + Z8) /2 + Z5] /L4B = [(Z1 + Z3  - Z7 - Z9) /4  -  (Z2 - Z8) /2] /L3C = [(-Z1 + Z3 - Z7 + Z9) /4 + (Z4 - Z6)] /2] /L3D = [(Z4 + Z6) /2 - Z5] /L2E = [(Z2 + Z8) /2 - Z5] /L2F = (-Z1 + Z3 + Z7 - Z9) /4L2G = (-Z4 +Z6) /2LH = (Z2 - Z8) /2LI = Z5

The output of the CURVATURE function is the second derivative of the surface (i.e., the slope of the slope), such that 

Curvature = -2(D + E) * 100

The outputs of the initial output curvature grid were from –41.76 to +39.39. The distribution of the values was then examined in ¼ standard deviations:

Table 10. Distribution of Curvature Values
Max Value	Min Value	Standard Dev	
-0.67	-41.76	< -3 Std. Dev	
-0.61	-0.67	-3.0 to -2.75 Std. Dev.	
-0.56	-0.61	-2.75 to -2.5 Std. Dev.	
-0.5	-0.56	-2.5 to -2.25 Std. Dev.	
-0.45	-0.5	-2.25 to -2.0 Std. Dev.	
-0.39	-0.45	-2.0 to -1.75 Std. Dev.	
-0.34	-0.39	-1.75 to -1.5 Std. Dev.	
-0.28	-0.34	-1.5 to -1.25 Std. Dev.	
-0.22	-0.28	-1.25 to -1.0 Std. Dev.	
-0.17	-0.22	-1.0 to -0.75 Std. Dev.	
-0.11	-0.17	-0.75 to –0.5 Std. Dev.	
-0.06	-0.11	-0.5 to –0.25 Std. Dev.	
0.0	-0.06	-0.25 to 0.0 Std. Dev.	
0.0	0.0	Mean	
0.06	0.0	0.0 to 0.25 Std. Dev.	
0.11	0.06	0.25 to 0.5 Std. Dev.	
0.17	0.11	0.5 to 0.75 Std. Dev.	
0.22	0.17	0.75 to 1.0 Std. Dev.	
0.28	0.22	1.0 to 1.25 Std. Dev.	
0.34	0.28	1.25 to 1.5 Std. Dev.	
0.39	0.34	1.5 to 1.75 Std. Dev.	
0.45	0.39	1.75 to 2.0 Std. Dev.	
0.5	0.45	2.0 to 2.25 Std. Dev.	
0.56	0.5	2.25 to 2.5 Std. Dev.	
0.61	0.56	2.5 to 2.75 Std. Dev.	
0.67	0.61	2.75 to 3.0 Std. Dev.	
39.39	0.67	> 3.0 Std. Dev	

After consulting the field staff and field ecologist values were then grouped to describe the shape of the landscape in more general terms.

Table 11. Landscape Shape Classes
Min Value	Max Value	Curvature	Curvature Code	
-41.76	-0.17	Concave	1	
-0.17	0.06	Flat	2	
0.06	39.39	Convex	3	

A Majority Filter was applied twice to the Curvature grid to reduce ‘noise’ or ‘speckling’ in the flatter areas of the landscape. This process replaces the value of each cell in grid based on the majority value of its contiguous neighboring cell. A neighborhood of the eight nearest cells was used. A clear majority needed to be obtained before the center cell value could be replaced with the surrounding value. This may have masked some of the more subtle micro features in the landscape but it was felt that at a 50m pixel size and at a 1:20,000 scale that was acceptable.

Table 12. Results of the Chi-Square Test of Proportions for the Landscape Shape Classes

Landscape Shape	Field Slope (Observed)		GIS Slope (Expected)		(Observed-Expected)2 
/Expected	
	Area	Proportion	Freq	Area	Proportion	Freq			
1	40	0.076	40	59913	0.076	40.1	0.000255197	
2	390	0.740	390	518572.5	0.659	347.1	5.304186233	
3	97	0.184	97	208877	0.265	139.8	13.10651879	
Total	527	1	527	787362.5	1	527.0	18.41096022	
									
							chi-sqr =	18.4109602	
							df =	2	
							p-value =	0.0001005	

Table 13. Landscape Shape Input Layer Confusion Matrix
Field Shape
GIS Shape	Class 1	Class 2	Class 3	Total	Error of Omission	
Class 1	7	0	0	7	0.0%	
Class 2	26	372	3	401	7.2%	
Class 3	7	18	94	119	21.0%	
Total	40	390	97	527		
Error of 
Commission	82.5%	4.6%	3.1%			

Average Error of Commission:	30.1%
Average Error of Omission:	9.4%
Overall Error (54/527):		10.2%

Table 14. Statement of Overall Accuracy of Landscape Shape
Total Plots		527	
Number Correct	473	
Percent Correct	94%	
Lower 95% Confidence Value	87%	
Median 95% Confidence Value	90%	
Upper 95% Confidence Value	92%	

The distribution of Landscape Shape across the landscape as described by the Chi-square statistic suggests that there is a good relationship between the GIS data and the field data. This is in turn supported by the results of the confusion matrix and the overall statement of accuracy.  

4.1.2.4 Slope Position

The Slope Position layer describes the position of a slope relative to the overall landscape shape. This process was designed to characterize the landscape into six classes: Crest (ridge), Upper Slope, Mid Slope, Lower Slope, Toe Slope, and Valley Bottom. 

This layer was modeled using an AML downloaded from the Swiss Federal Research Institute WSL website, and was written by Niklaus E. Zimmermann.

This simple AML is used to identify topographic exposure (ridge, slope, toe slope, etc) at various spatial scales, and to hierarchically integrate these features into a single grid. Topographic position, necessary e.g. to classify soil properties over large areas, can be calculated using a hierarchically nested approach. In this AML, circular moving-windows with increasing radii are applied to a DEM, and the difference between the average elevation of the window and the center cell of the window is calculated and written to temporary output grids. The user is prompted for the number search radii, for which the temporary grids are generated. The resulting (temporary) maps are interpreted as relative topographic exposure at different spatial scales. The exposure can be interpreted as a ridge or peak if the center cell in the mowing window has a higher elevation than the average elevation of the cells in the window. Contrarily if the center cell is of lower elevation than the average elevation of the window, then the center pixel can be interpreted as "toe slope" or "valley bottom". 

A hierarchical integration into a single map is achieved by starting with the standardized exposure values of the largest window, then adding standardized values from smaller windows where the (absolute) values of the smaller (search) scale grids exceed the values of the larger scale map. 

When starting the AML, the user is prompted for the name (&path, if different from the directory the AML was started) of the DEM the topographic position has to be derived from. Further, the minimum and maximum radius of the search window has to be entered, as well as the increment to enlarge the search radius stepwise. The AML does increment the search radius (and repeat the exposure calculation for each radius) until the incremented radius is bigger than the max. radius. It then starts integrating hierarchically the generated temporary grids.

Two versions of relative topographic exposure are available as a grid, one containing the “raw” values, and a second containing a smoothed version of the topographic exposure. The smoothed version has an "s" added to the output file name. 

Arguments for the toposcale.aml:

Name of the DEM of the study site
Name of the output grid 
Number of pixels for smallest search radius 
Number of pixels for largest search radius
Number of pixels to increment search radius 

The DEM was smoothed twice using a low pass filter using a focal mean with a 3 by 3 kernel. This generalized the overall values of the DEM, effectively reducing the elevation value range. 

After the smoothing process, toposcale.aml was run on the DEM.

The number of pixels for the smallest search radius was set to 2, with increments of 2 up to the maximum of 10 pixels for the largest search radius.
The user has the option of keeping individual exposure grids. Each exposure grids is sampled with a different radii (from the smallest to largest, following the increment path). Each exposure grid was examined for suitability, and the 4 pixel exposure grid was selected.

Table 15. Distribution of Slope Position Values
Min Value	Max Value	Standard Dev	
-2814	-299	< -3 Std. Dev	
-299	-275	-3.00  to –2.75 Std. Dev.	
-274	-250	-2.75 to –2.50 Std. Dev	
-249	-225	-2.50 to –2.25 Std. Dev	
-224	-200	-2.25 to –2.00 Std. Dev	
-199	-175	-2.00 to –1.75 Std. Dev	
-174	-150	-1.75 to –1.50 Std. Dev	
-149	-125	-1.5 to –1.25 Std. Dev	
-124	-100	-1.25 to –1.00 Std. Dev	
-99	-75	-1.00 to –0.75 Std. Dev	
-74	-50	-0.75 to –0.50 Std. Dev	
-49	-25	-.50 to –0.25 Std. Dev	
-24	0	-0.25 to 0 Std. Dev	
0	0	Mean	
1	24	0.00 to 0.25 Std. Dev	
25	49	0.25 to 0.50 Std. Dev	
50	74	0.50 to 0.75 Std. Dev	
75	99	0.75 to 1.00 Std. Dev	
100	124	1.00 to 1.25 Std. Dev	
125	149	1.25 to 1.50 Std. Dev	
150	174	1.50 to 1.75 Std. Dev	
175	199	1.75 to 2.00 Std. Dev	
200	224	2.00 to 2.25 Std. Dev	
225	249	2.25 to 2.50 Std. Dev	
250	274	2.50 to 2.75 Std. Dev	
275	299	2.75 to 3.00 Std. Dev	
299	10894	> 3.00 Std. Dev	

After consultation with the field ecologists, values from -2814 to -30 were selected for lower slopes, values from –30 to 30 were classified as mid-slope, values from –30 to 10894 were delineated as upper slope. 

The slope grid was then applied, and areas with a slope of 5% or less were classified as ‘flat’ or ‘valley bottoms’.
Table 16. Slope Position Classes
Slope Position Classes	Description	
1	Lower Slope	
2	Mid Slope	
3	Upper Slope	
4	Flat or Valley Bottom	

The Slope Position grid was tested against 615 field plots. 

Table 17. Results of the Chi-Square Test of Proportions for the Slope Position Class

Slope Position	Field Slope Position (Observed)	GIS Slope Position (Expected)	(Observed-Expected)2 
/Expected	
	Area	Proportion	Freq	Area	Proportion	Freq			
1	68	0.093	68	152,257.5	0.194	140.9	37.69559158	
2	403	0.554	403	367,021.8	0.466	339.6	11.84638843	
3	126	0.173	126	167,755.3	0.213	155.2	5.49725634	
4	131	0.180	131	99,807.5	0.127	92.3	16.18209442	
Total	728	1.000	728	786,842.0	1.000	728.0	71.22133077	
									
							chi-sqr =	71.2213	
							Df =	3	
							p-value =	2.33724E-15	

Table 18. Slope Position Input Layer Confusion Matrix
Field Slope Position
GIS Slope Position	Class 1	Class 2	Class 3	Class 4	Total	Error of Omission	
Class 1	39	1	3	7	50	22.0%	
Class 2	20	381	5	23	429	11.2%	
Class 3	6	16	112	1	135	17.0%	
Class 4	3	5	6	100	114	12.3%	
Total	68	403	126	131	728		
Error of Commission	42.6%	5.5%	11.1%	23.7%			

Average Error of Commission:	20.7%
Average Error of Omission:	15.6%
Overall Error (96/728):		13.2%

Table 19. Statement of Overall Accuracy of Slope Position
Total Plots		728	
Number Correct	632	
Percent Correct	86.8%	
Lower 95% Confidence Value	84%	
Median 95% Confidence Value	87%	
Upper 95% Confidence Value	89%	

The distribution of Slope Position across the landscape as described by the Chi-square statistic suggests that there is a moderate relationship between the GIS data and the field data. This is in turn supported by the results of the confusion matrix and the overall statement of accuracy. There is room for improvement between classes 1 and 2, but the overall accuracy for this layer is high, and is suitable for use in the PEM.

4.1.3 Input Layers Generated From TRIM TWTR Input Layer

4.1.3.1 Hydrographic Features – TWTR

A query was run on the TWTR coverage to select any double line water features by feature code (fcode). The fcodes used for the query were

Table 20. TRIM 1 Feature Codes and Corresponding Hydrographic Features
Feature Line Code	TRIM 1
Description	Polygon Feature	Polygon
Code	
GA90000110	Stream/River - Left Bank	River	2	
GA90000120	Stream/River - Right Bank	River	2	
GB15300000	Lake – Definite	Lake >= 50 ha.	1	
GB15300000	Lake – Definite	Pond <= 50 ha.	9	
GB15300130	Lake – Indefinite	Shallow Open Water	10	
GB15300140	Lake – Intermittent	Shallow Open Water	10	
GB24300000	Reservoir – Definite	Reservoir	3	
GE25850000	Sand Bar	Sand Bar	5	
GC17100000	Marsh	Marsh	6	
GC30050000	Swamp	Swamp	7	
GD12300000	Glacier	Glacier	8	
GE14850000	Island – Definite	Island	4	

The features were then converted to polygons using the ArcInfo 8.1 CLEAN procedure. Line work was then compared to the originals to check to insure that no large shifts had taken place.
 
The command used to clean the coverage was

CLEAN <in_cover> {out_cover} {dangle_length} {fuzzy_tolerance} {POLY | LINE}

In_cover was the cover selected for cleaning, out_cover was the output coverage. The dangle length was the acceptable tolerance for dangles. This was set to 25. Fuzzy tolerance was set to 2.5m. The polygon option was selected to convert the data from line to polygon.

The resulting polygons were then given a numeric value associated with the feature described.

This polygon coverage was then converted to a 50m grid using the ArcInfo 8.1 GRIDPOLY command (see section 5.1.1) with the MASK grid defined as the Mask. Grid values were defined using the code value assigned to the polygons.

4.1.4 Input Layers Generated From Forest Cover

4.1.4.1 Aspen Leading Input Layer – AT_LEAD

Using ArcInfo 8.1, the spatial forest cover database was queried for Aspen (and all sub-species) with a stand composition percentage of 40% or more over the first four species columns. (SPECIES1, SPECIES2, SPECIES3, SPECIES4; PERCENTAGE1, PERCENTAGE2, PERCENTAGE3, PERCENTAGE4).
An attribute called ‘AT_LEAD’ was added to the forest cover database, and all selected polygons were coded with a ‘1’.
Table 21. Aspen Stand Classes
Description	Code	
Aspen Leading	1	
Not Aspen Leading	0	

The ArcInfo 8.1 DISSOLVE command was applied to the forest cover data using the AT_LEAD attribute.  The resulting polygon coverage describing presence or absence of Aspen within the study area. This coverage was then converted to Grid using the same classes.

POLYGRID <in_cover> <out_grid> {value_item} {lookup_table} {weight_table}

A 50m proximity analysis was run using 670 plot locations from the field and the reclassified Aspen Leading Input Layer. From the available 729 plots, only 670 had species data filled in. Because there was no comparable ‘stand composition’ column in the field data, the first two species columns (SPC1, SPC2) were queried for the presence of Aspen. After consultation with the project ecologist, this was deemed an acceptable way of comparing the two datasets, as an entry of Aspen in SPC1 or SPC2 was unlikely to be less than 40% of the plot area. The results of the comparison between the plot data and the input layer are portrayed in the confusion matrix below 

Table 22. Results of the Chi-Square Test of Proportions for the Aspen Leading Input Layer

Aspen Leading	Field Aspen (Observed)	GIS Aspen (Expected)	(Observed-Expected)2 
/Expected	
	Area	Proportion	Freq	Area	Proportion	Freq			
1	27	0.037	27	9,550.3	0.012	8.5	39.94738762	
2	702	0.963	702	806,158.8	0.988	720.5	0.473241205	
Total	527	1	527	787362.5	1	527.0	18.41096022	
								
							chi-sqr =	40.42062883	
							df =	1	
							p-value =	0.00000000020	

Table 23. Aspen Leading Input Layer Confusion Matrix
Field Aspen Leading

GIS Aspen Leading	AT Leading	Not AT Leading	Total	Error of Omission	
AT Leading	5	1	6	16.7%	
Not AT Leading	22	642	664	3.3%	
Total	27	643	670		
Error of 
Commission	81.5%	0.2%			

Average Error of Commission	40.8%
Average Error of Omission	10.0%
Overall Error (24/670)		3.4%

Table 24. Statement of Overall Accuracy of Aspen Leading
Total Plots		670	
Number Correct	647	
Percent Correct	96.6%	
Lower 95% Confidence Value	95%	
Median 95% Confidence Value	97%	
Upper 95% Confidence Value	98%	

The confusion matrix above describes the accuracy of the Aspen Leading Input Layer. The overall accuracy is high, suggesting that the layer is suitable for use in the PEM process. However, with a high error of commission when  predicting aspen-dominated stands, it should be used with caution. This suggests that the forest cover data is under-predicting the aspen in the area.

4.1.4.2 Douglas-fir Leading Input Layer – FD

Using ArcInfo 8.1, the spatial forest cover database was queried for Douglas-fir (any species) with a stand composition percentage of 40% or more over the first four species columns. (SPECIES1, SPECIES2, SPECIES3, SPECIES4; PERCENTAGE1, PERCENTAGE2, PERCENTAGE3, PERCENTAGE4).
An attribute called ‘FD_LEAD’ was added to the forest cover database, and all selected polygons were coded with a ‘1’.
Table 25. Douglas-fir Stand Classes
Description	Code	
Douglas-fir Leading	1	
Not Douglas-fir Leading	0	

The ArcInfo 8.1 DISSOLVE command was applied to the forest cover data using the FD_LEAD attribute.  The resulting polygon coverage describing presence or absence of Douglas-fir within the study area. This coverage was then converted to Grid using the same classes.

POLYGRID <in_cover> <out_grid> {value_item} {lookup_table} {weight_table}

A 50m proximity analysis was run using 670 plot locations from the field and the reclassified Douglas-fir Leading Input Layer. From the available 724 plots, only 670 had species data filled in. Because there was no comparable ‘stand composition’ column in the field data, the first two species columns (SPC1, SPC2) were queried for the presence of Douglas-fir. After consultation with the project ecologist, this was deemed an acceptable way of comparing the two datasets, as an entry of Douglas-fir in SPC1 or SPC2 was unlikely to be less than 40% of the plot area. The results of the comparison between the plot data and the input layer are portrayed in the confusion matrix below 

Table 26. Results of the Chi-Square Test of Proportions for the Aspen Leading Input Layer

Douglas-fir Leading	Field Douglas-fir (Observed)	GIS Douglas-fir (Expected)	(Observed-Expected)2 
/Expected	
	Area	Proportion	Freq	Area	Proportion	Freq			
1	163	0.243	163	126,031.5	0.155	103.5	34.17769669	
2	507	0.757	507	689,677.5	0.845	566.5	6.245624049	
Total	670	1	670	815,709.0	1	670.0	40.42332074	
								
							chi-sqr =	40.42332074	
							df =	1	
							p-value =	0.00000000020	

Table 27. Douglas-fir Leading Input Layer Confusion Matrix
Field Douglas-fir Leading

GIS Douglas-fir Leading	FD Leading	Not FD Leading	Total	Error of Omission	
FD Leading	118	4	122	3.3%	
Not FD Leading	45	503	548	8.2%	
Total	163	507	670		
Error of 
Commission	27.6%	0.8%			

Average Error of Commission	14.2%
Average Error of Omission	5.7%
Overall Error (52/670)		7.3%

Table 28. Statement of Overall Accuracy of Douglas-fir Leading

Total Plots		670	
Number Correct	621	
Percent Correct	92.7%	
Lower 95% Confidence Value	91%	
Median 95% Confidence Value	93%	
Upper 95% Confidence Value	95%	

The confusion matrix above describes the accuracy of the Douglas-fir Leading Input Layer. The overall accuracy is high, suggesting that the layer is suitable for use in the PEM process. The low error of both omission and commission means that the model is not over or under predicting Douglas-fir in this area.
4.1.4.3 Lodgepole Pine Leading Input Layer – PL

Using ArcInfo 8.1, the spatial forest cover database was queried for Lodgepole Pine (any species) with a stand composition percentage of 40% or more over the first four species columns. (SPECIES1, SPECIES2, SPECIES3, SPECIES4; PERCENTAGE1, PERCENTAGE2, PERCENTAGE3, PERCENTAGE4).
An attribute called ‘PL_LEAD’ was added to the forest cover database, and all selected polygons were coded with a ‘1’.
Table 29. Lodgepole Pine Stand Classes
Description	Code	
Lodgepole Pine Leading	1	
Not Lodgepole Pine Leading	0	

The ArcInfo 8.1 DISSOLVE command was applied to the forest cover data using the PL_LEAD attribute.  The resulting polygon coverage describing presence or absence of Lodgepole Pine within the study area. This coverage was then converted to Grid using the same classes.

POLYGRID <in_cover> <out_grid> {value_item} {lookup_table} {weight_table}

A 50m proximity analysis was run using 670 plot locations from the field and the reclassified Lodgepole Pine Leading Input Layer. From the available 724 plots, only 670 had species data filled in. Because there was no comparable ‘stand composition’ column in the field data, the first two species columns (SPC1, SPC2) were queried for the presence of Lodgepole Pine. After consultation with the project ecologist, this was deemed an acceptable way of comparing the two datasets, as an entry of Lodgepole Pine in SPC1 or SPC2 was unlikely to be less than 40% of the plot area. The results of the comparison between the plot data and the input layer are portrayed in the confusion matrix below 

Table 30. Results of the Chi-Square Test of Proportions for the Lodgepole Pine Leading Input Layer

Douglas-fir Leading	Field Douglas-fir (Observed)	GIS Douglas-fir (Expected)	(Observed-Expected)2 
/Expected	
	Area	Proportion	Freq	Area	Proportion	Freq			
1	87	0.130	87	126,031.5	0.155	103.5	2.635913337	
2	583	0.870	583	689,677.5	0.845	566.5	0.481686167	
Total	670	1	670	815,709.0	1	670.0	3.117599504	
								
							chi-sqr =	3.117599504	
							df =	1	
							p-value =	0.07745081954	

Table 31. Lodgepole Pine Leading Input Layer Confusion Matrix
Field Lodgepole Pine Leading

GIS Lodgepole Pine Leading	PL Leading	Not PL Leading	Total	Error of Omission	
PL Leading	68	308	376	81.9%	
Not PL Leading	19	275	294	6.5%	
Total	87	583	670		
Error of 
Commission	21.8%	52.8%			

Average Error of Commission	37.3%
Average Error of Omission	44.2%
Overall Error (327/670)		48.8%

Table 32. Statement of Overall Accuracy of Lodgepole Pine Leading

Total Plots		670	
Number Correct	343	
Percent Correct	51.2%	
Lower 95% Confidence Value	47%	
Median 95% Confidence Value	51%	
Upper 95% Confidence Value	55%	

The results indicate that the presence or absence of Lodgepole Pine is not reliably predicted by forest cover data when it is said to compose >40% of the stand. These results showed that the Lodgepole Pine leading input layer should not be emphasized in the PEM model for differentiating between map entities.

4.1.4.4 Urban Areas – UR

Using ArcInfo 8.1, the spatial forest cover database was queried for any non-productive polygons with a NPFORESTCODE of 50 or 54. These areas represent urban settlements and large road corridors. An attribute called ‘UR’ was added to the database, and all selected polygons were coded with a ‘1’.

Table 33. Urban Areas Classes
Description	Code	
Urban Area	1	
Not Urban Area	0	

The ArcInfo 8.1 DISSOLVE command was applied to the forest cover data using the UR attribute.  The resulting polygonal coverage describes the presence of open range areas.

The ArcInfo 8.1 POLYGRID command was used to convert the coverage to a grid.

POLYGRID <in_cover> <out_grid> {value_item} {lookup_table} {weight_table}

The available field data did not contain any non-productive forest information, so a statistical analysis was not carried out.

4.1.4.5 Forest Cover Rock Input Layer – FC_ROCK

Using ArcInfo 8.1, the spatial forest cover database was queried for any polygon with an NPFORESTCODE of 3 (rock). An attribute called FC_ROCK was added to the forest cover database, and all selected polygons were coded with a ‘1’.

Table 34. Forest Cover Rock Input Layer Classes
Description	Code	
Forest Cover Rock	1	
No Forest Cover Rock	0	

The ArcInfo 8.1 DISSOLVE command was applied to the forest cover data using the FC_ROCK attribute.  The resulting polygonal coverage describes the presence of rock within the forest cover data.

The ArcInfo 8.1 POLYGRID command was used to convert the coverage to a grid.

POLYGRID <in_cover> <out_grid> {value_item} {lookup_table} {weight_table}

The available field data did not contain any non-forest information, so a statistical analysis was not carried out.

4.1.5 Input Layers Generated From Bioterrain

4.1.5.1 Bioterrain Wetlands Input Layer – BIO_WET

Using ArcInfo 8.1, the spatial bioterrain database was queried for organic material (‘O’) in the primary surficial material column (SURF_M1) that also had a primary terrain decile of 10. An attribute called ‘BIO_WET’ was added to the database, and all selected polygons were coded with a 1.
Table 35. Bioterrain Wetland Classes
Description	Code	
Bioterrain Wetland	1	
Not Bioterrain Wetland	2	

The resulting polygon coverage describing presence or absence of bioterrain wetlands within the study area. This coverage was then converted to Grid using the same classes.

POLYGRID <in_cover> <out_grid> {value_item} {lookup_table} {weight_table}

A 50m proximity analysis was run using 729 plot locations from the field and the reclassified bioterrain wetlands input layer. The field data was queried for organic material, and an attribute was added to the database with code comparable to the input layer. However, because the field data only contains one surficial material attribute and no decile attributtes (the spatial bioterrain has three), the comparison is not exact.  The results of the proximity analysis are portrayed in the confusion matrix below 

Table 36. Results of the Chi-Square Test of Proportions for the Bioterrain Wetlands Input Layer

Bioterrain Wetlands 	Field Bioterrain Wetlands (Observed)	GIS Bioterrain Wetlands (Expected)	(Observed-Expected)2 
/Expected	
	Area	Proportion	Freq	Area	Proportion	Freq			
1	23	0.032	23	5,392.5	0.009	6.8	38.90128937	
2	706	0.968	706	575,215.5	0.991	722.2	0.364689761	
Total	729	1	729	580,608.0	1	729.0	39.26597913	
								
							chi-sqr =	39.26597913	
							df =	1	
							p-value =	0.00000000037	

Table 37. Bioterrain Wetlands Input Layer Confusion Matrix
Field Bioterrain Wetland

GIS Bioterrain Wetland	Bioterrain Wetland	Not Bioterrain Wetland	Total	Error of Omission	
Bioterrain Wetland	9	0	9	0.0%	
Not Bioterrain Wetland	14	706	720	1.9%	
Total	23	696	729		
Error of 
Commission	60.9%	0.0%			

Average Error of Commission	30.4%
Average Error of Omission	1.0%
Overall Error (24/670)		1.9%

Table 38. Statement of Overall Accuracy of Bioterrain Wetlands
Total Plots		729	
Number Correct	715	
Percent Correct	98.1%	
Lower 95% Confidence Value	97%	
Median 95% Confidence Value	98%	
Upper 95% Confidence Value	99%	

The confusion matrix above describes the accuracy of the Bioterrain Wetlands Input Layer. The overall accuracy is high, suggesting that the layer is suitable for use in the PEM process. However, with a high error of commission when  predicting wetlands, it should be used with caution. This illustrates the compatibility problems when comparing the field data (no decile) and the GIS data  (deciles). This layer should receive less emphasis in the raster PEM knowledge bases.

4.1.5.2 Bioterrain Rock Input Layer – BIO_RO

Using ArcInfo 8.1, the spatial bioterrain database was queried for the presence of rock using the query in the table below. An attribute called ‘BIO_RO’ was added to the database, and all selected polygons were coded with a 1. All other polygons were coded with a 2. The layer is further stratified using aspect.

SURF_M1	SDEC_1	SSURFM_1	SURF_M2	SURF_M3	
R	10	Any	Any	Any	

Table 39. Bioterrain Rock Classes
Description	Code	
Bioterrain rock on warm aspect	3	
Bioterrain rock on cool aspect	2	
Bioterrain rock on gentle slopes	1	
Not Bioterrain rock	0	

The resulting polygon coverage describing presence or absence of bioterrain rock within the study area. This coverage was then converted to grid using the same classes.

POLYGRID <in_cover> <out_grid> {value_item} {lookup_table} {weight_table}

A 50m proximity analysis was run using 729 plot locations from the field and the reclassified bioterrain wetlands input layer. The field data was queried for rocky material, and an attribute was added to the database with code comparable to the input layer. However, because the field data only contains one surficial material attribute and no decile attributtes (the spatial bioterrain has three), the comparison is not exact.  The results of the proximity analysis are portrayed in the confusion matrix below 

Table 40. Results of the Chi-Square Test of Proportions for the Bioterrain Rock Input Layer

Bioterrain Rock	Field Bioterrain Rock (Observed)	GIS Bioterrain Rock (Expected)	(Observed-Expected)2 
/Expected	
	Area	Proportion	Freq	Area	Proportion	Freq			
1	1	0.001	1	16.8	0.00003	0.0	45.5698507	
2	0	0.000	0	27.3	0.00005	0.0	0.034214638	
3	0	0.000	0	22.3	0.00004	0.0	0.027936723	
0	728	0.999	728	580,540.5	0.99989	728.9	0.001153156	
Total	729	1.000	729	580,606.8	1.000	729.0	45.63315522	
									
							chi-sqr =	45.6332	
							Df =	3	
							p-value =	6.78717E-10	

Table 41. Bioterrain Rock Input Layer Confusion Matrix
Field Bioterrain Rock

GIS Bioterrain Rock	Class 1	Class 2	Class 3	Class 0	Total	Error of Omission	
Class 1	0	0	0	0	0	0.0%	
Class 2	0	0	0	0	0	0.0%	
Class 3	0	0	0	0	0	0.0%	
Class 0	1	0	0	728	729	0.1%	
Total	1	0	0	728	729		
Error of 
Commission	100.0%	0.0%	0.0%	0.0%			

Average Error of Commission	25.0%
Average Error of Omission	0.0%
Overall Error (1/729)		0.1%

The confusion matrix above describes the accuracy of the Bioterrain Rock Input Layer. The overall accuracy is high, suggesting that the layer is suitable for use in the PEM process. However, with a high error of commission when  predicting rock formations, it should be used with caution. The lack of field points sampled in rocky areas contributes to the high errors of omission and commission. This explains the lack of available plots containing rock, to test this input layer.
As rock outcrops are relatively reare on the landscape, the random sampling grid was unlikely to hit them. Bioterrain mappers recognize rock outcroppings as smaller proportions of larger polygons, while pure rock outcrop polygons were mapped relatively rarely. The second-run PEM process subsequently recognizes rock outcrops which occupy small areas within larger polygons.

4.1.5.3 Bioterrain Talus Input Layer – BIO_TAL

Using ArcInfo 8.1, the spatial bioterrain database was queried for talus slopes using the queries in the table below. 

Query #	TTEXT1A	TTEX1B	TTEX1c	
1	r			
2	r	x		
3	r	a		
4	r	x	a	
5	r	a	x 	
6	x			
7	x	r		
8	x	a		
9	x	r	a	
10	x	a	r	
11	a			
12	a	x		
13	a	r		
14	a	x	r	
15	a	r	x	

The same set of queries are applied to the second (TTEXT2A, TTEX2B and TTEX2C) and third (TTEXT3A, TTEX3B, TTEX3C) terrain texture descriptors. An attribute called ‘BIO_TAL’ was added to the database, and all selected polygons were coded with a ‘1’. All other polygons were coded with a ‘2’.

Table 42. Bioterrain Talus Classes
Description	Code	
Bioterrain Talus	1	
Not Bioterrain Talus	2	

The resulting polygon coverage describing presence or absence of talus slopes within the study area. This coverage was then converted to Grid using the same classes.

POLYGRID <in_cover> <out_grid> {value_item} {lookup_table} {weight_table}

A 50m proximity analysis was run using 729 plot locations from the field and the reclassified Bioterrain Talus input layer. The query set was run on the field data, and an attribute was added to the database with a code comparable to the input layer. However, because the field data only contains one set of terrain texture attributes (the spatial bioterrain has three), the comparison is not exact.  The results of the proximity analysis are portrayed in the confusion matrix below 

Table 43. Results of the Chi-Square Test of Proportions for the Bioterrain Talus Input Layer

Bioterrain Talus	Field Bioterrain Talus (Observed)	GIS Bioterrain Talus (Expected)	(Observed-Expected)2 
/Expected	
	Area	Proportion	Freq	Area	Proportion	Freq			
1	1	0.001	1	2,039.5	0.004	2.6	0.951265795	
2	728	0.999	728	578,568.5	0.996	726.4	0.003353288	
Total	729	1	729	580,608.0	1	729.0	0.954619083	
								
							chi-sqr =	0.954619083	
							df =	1	
							p-value =	0.32854634013	

Table 44. Bioterrain Talus Input Layer Confusion Matrix
Field Bioterrain Talus

GIS Bioterrain Talus	Bioterrain Talus	Not Bioterrain Talus	Total	Error of Omission	
Bioterrain Talus	1	0	1	0.0%	
Not Bioterrain Talus	0	728	728	0.0%	
Total	1	728	729		
Error of 
Commission	0.0%	0.0%			

Average Error of Commission	0.0%
Average Error of Omission	0.0%
Overall Error (0/728)		0.0%

Table 45. Statement of Overall Accuracy of Bioterrain Talus
Total Plots		729	
Number Correct	729	
Percent Correct	100.0%	
Lower 95% Confidence Value	100%	
Median 95% Confidence Value	100%	
Upper 95% Confidence Value	100%	

The confusion matrix above describes the accuracy of the Bioterrain Talus Input Layer. This layer scored extremely well in all tests, showing that it is suitable for use in the PEM process. However, it should be noted that with only 1 plot out of 729 (0.1%) was a ‘talus’ plot; This is not a large enough sample population for reliable testing, and more field data should be collected to remedy this (small) problem.

4.1.5.4 Bioterrain Vegetated Rock Input Layer – BIO_VGR

Using ArcInfo 8.1, the spatial bioterrain database was queried for vegetated rock areas using the queries in the table below. 

Query #	SURF_M1	SURF_E1A	SSURFM_1	
1	M	x	R	
2	C	x	R	
3	M	x	nothing	
4	C	x	nothing	

An attribute called ‘BIO_VGR’ was added to the database, and all selected polygons were coded with a ‘1’. All other polygons were coded with a ‘2’. This layer is further stratified using aspect.

Table 46. Bioterrain Vegetated Rock Classes
Description	Code	
Bioterrain  vegetated rock on warm aspect	3	
Bioterrain vegetated rock on cool aspect	2	
Bioterrain vegetated rock on gentle slopes	1	
Not Bioterrain vegetated  rock	0	

The resulting polygon coverage describing presence or absence of vegetated rock within the study area. This coverage was then converted to Grid using the same classes.

POLYGRID <in_cover> <out_grid> {value_item} {lookup_table} {weight_table}

A 50m proximity analysis was run using 729 plot locations from the field and the reclassified Bioterrain Vegetated Rock input layer. The query set was run on the field data, and an attribute was added to the database with a code comparable to the input layer. The results of the proximity analysis are portrayed in the confusion matrix below 

Table 47. Results of the Chi-Square Test of Proportions for the Bioterrain Vegetated Rock Input Layer

Bioterrain Vegetated Rock	Field Bioterrain Vegetated Rock (Observed)	GIS Bioterrain Vegetated Rock (Expected)	(Observed-Expected)2 
/Expected	
	Area	Proportion	Freq	Area	Proportion	Freq			
1	10	0.014	10	6,725.0	0.01158	8.4	0.28681065	
2	4	0.005	4	3,482.5	0.00600	4.4	0.031744943	
3	4	0.005	4	5,411.8	0.00932	6.8	1.14960827	
0	711	0.975	711	564,987.5	0.97310	709.4	0.003659738	
Total	729	1.000	729	580,606.8	1.000	729.0	1.4718236	
								
							chi-sqr =	1.4718	
							df =	3	
							p-value =	0.68878853	

Table 48. Bioterrain Vegetated Rock Input Layer Confusion Matrix

Field Bioterrain Vegetated Rock

GIS Bioterrain Vegetated Rock	Class 1	Class 2	Class 3	Class 0	Total	Error of Omission	
Class 1	3	0	0	0	3	0.0%	
Class 2	0	3	0	0	3	0.0%	
Class 3	0	0	2	0	2	0.0%	
Class 0	7	1	2	711	721	0.0%	
Total	10	4	4	711	729		
Error of 
Commission	70.0%	25.0%	50.0%	0.0%			

Average Error of Commission	48.3%
Average Error of Omission	0.0%
Overall Error (10/729)		1.4%

Table 49. Statement of Overall Accuracy of Bioterrain Vegetated Rock
Total Plots		729	
Number Correct	719	
Percent Correct	98.6%	
Lower 95% Confidence Value	98%	
Median 95% Confidence Value	99%	
Upper 95% Confidence Value	99%	

The confusion matrix above describes the accuracy of the Bioterrain Vegetated Rock Input Layer. Every plot that failed the proximity test contained either ‘M’ or ‘C’ in the SURFM_1 attribute; the difference was in SURF_E1A, where each failing plot contained a ‘v’ instead of the requisite ‘x’. This can be attributed to the micro/macro issues when comparing field data (micro) to a larger-scale form of analysis (airphoto interp of bioterrain polygons, macro); field crews may not have the benefit of the ‘larger picture’, and such may call sites somewhat differently from the airphoto interpreter. 
Overall, this layer scored well and is suitable for use in the PEM process.

5.0 References

Meidinger, D. 2000. Protocol for Quality Assurance and Accuracy Assessment of Ecosystem Maps. Prepared for the TEM Alternatives Task Force. July 2000. 

Moon, D. Problem Analysis on Data Quality Assessment Issues. ITQ#CRS 9062. Submitted to the TEM Alternatives Task Force. 2000.

Moore, I. D., Grayson, R. B., and Landson, A. R., 1991.  ‘Digital Terrain Modeling: A Review of Hydrological, Geomorphological, and Biological Applications’ , Hydrological Processes. Vol. 5.3-30.

Silverman, B.W. Density Estimation for Statistics and Data Analysis. New York: Chapman and Hall, 1986.

Zeverbergen, L. W., and C. R. Thorne, 1987.  ‘Quantitative Analysis of Land Surface Topography’, Earth Surface Processes and Landforms, Vol. 12, pp 47 - 56.

Howes, D.E & E. Kenk (eds.) 1997. ‘Terrain Classification System for British Columbia’

Version 2 MOE Manual 10. Updated by Resources Inventory Branch, BC Ministry of Environment, Lands & Parks, Victoria BC

Okanagan TSA Wet Belt
Predictive Ecosystem Mapping 
(PEM) 

Input Data Quality Assessment Report

Submitted to:

Glen Dick RPF
Manager Okanagan IFPA
Terrafor Resources Ltd.
9304 Aberdeen Road
Coldstream BC V1B 2K5

By:

Maureen V. Ketcheson M.Sc. R.P.Bio
Brian Calder
Kim Everett M.Sc.
Vicky Lipinski
Lawson Bradley

of

JMJ Holdings Inc. 
PO Box 8367 Yates Street Station
Victoria, BC V8W 3R9
250 217 7671
and 

Timberline Forest Inventory Consultants Ltd
#601 – 1207 Douglas Street
Victoria BC V8W 2E7
250 480 3309

March 31, 2006

Table of Contents

1.0 Introduction

2.0  Project Objectives

3.0 Thematic Input Layer Documentation and Quality Assessment
3.1 TRIM 1 Data
3.1.1 TRIM 1 – TNTL Layer
3.1.2 TRIM 1 – TWTR Layer
3.1.3 TRIM 1 – TDEM Layer
3.2 Forest Inventory Forest Cover Mapping
3.3 Okanagan IFPA Plot Data
3.4 Biogeoclimatic Subzone Mapping
3.5 JMJ Holdings Bioterrain Mapping

4.0 Input Processing and Quality Assessment
4.1 TRIM 1 Input Layers
4.1.1 Input Layers Generated from TRIM TNTL Input Layer
4.1.2 Input Layers Generated from TRIM TDEM Input Layer
4.1.4  Input Layers Generated From Forest Cover

5.0 References

List of Tables

Table 1. TRIM 1 Mapsheets required for the Project
Table 2.  Thematic Input Layers and Input Data Layers
Table 3. Slope Classes
Table 4. Results of the Chi-Square Test of Proportions for Slope Class
Table 5. Slope Input Layer Confusion Matrix
Table 6. Slope Input Layer Statement of Overall Accuracy
Table 7. Aspect Classes
Table 8. Results of the Chi-Square Test of Proportions for the Aspect Class
Table 9. Aspect Input Layer Confusion Matrix
Table 10. Aspect Input Layer Statement of Overall Accuracy
Table 11. Distribution of Slope Position Values
Table 12. Slope Position Classes
Table 13. Results of the Chi-Square Test of Proportions for the Slope Position Class
Table 14. Slope Position Input Layer Confusion Matrix
Table 15. Slope Position Statement of Overall Accuracy
Table 16. Douglas-fir Stand Classes
Table 17. Results of the Chi-Square Test of Proportions for the Douglas-fir Leading Input Layer
Table 18. Douglas-fir Leading Input Layer Confusion Matrix
Table 19. Statement of Overall Accuracy of Douglas-fir Leading
Table 20. Spruce Stand Classes
Table 21. Results of the Chi-Square Test of Proportions for the Spruce Leading Input Layer
Table 22. Spruce Leading Input Layer Confusion Matrix
Table 23. Statement of Overall Accuracy of Spruce Leading
Table 24. Spruce Stand Classes
Table 25. Results of the Chi-Square Test of Proportions for the Spruce Top Three Input Layer
Table 26. Spruce Top Three Input Layer Confusion Matrix
Table 27. Statement of Overall Accuracy of Spruce Top Three
Table 28. Western Red Cedar Stand Classes
Table 29. Results of the Chi-Square Test of Proportions for the Western Red Cedar Top Three Input Layer
Table 30. Western Red Cedar Top Three Input Layer Confusion Matrix
Table 31. Statement of Overall Accuracy of Western Red Cedar Top Three
Table 32. Douglas-fir Stand Classes
Table 33. Results of the Chi-Square Test of Proportions for the Douglas-fir Mix Input Layer
Table 34. Douglas-fir Mix Input Layer Confusion Matrix
Table 35. Statement of Overall Accuracy of Douglas-fir Mix
Table 36. Trembling Aspen Stand Classes
Table 37. Results of the Chi-Square Test of Proportions for the Trembling Aspen Top Three Input Layer
Table 38. Trembling Aspen Top Three Input Layer Confusion Matrix

1.0 Introduction

Predictive Ecosystem Mapping in the entire Okanagan TSA IFPA area began in June of 2001, and more specifically in the Wet Belt portion in 2005, with the collection of data and the creation of potential Geographic Information Systems (GIS) input layers. This document reports the quality of these input layers relative to measured values collected in the field.
 
2.0  Project Objectives  

The task required of this phase of the project included 

•	The assessment of thematic input layers for use in the PEM process
•	The creation of GIS input layers
•	The assessment of GIS input layers for use in the PEM process

For purposes of clarification it should be stated here that this phase of the project involved the use of 4 Thematic Input Layers for assessment and collected field data.

•	Ministry of Sustainable Resource Management TRIM 1 mapping
•	Ministry of Forests Forest Cover Data
•	JMJ Holdings Bioterrain Mapping
•	Ministry of Forests Biogeoclimatic Mapping
•	Okanagan IFPA Predictive Ecosystem Mapping Project Field Plot Data

The final PEM used 16 input layers:

•	Mask
•	Biogeoclimatic zones
•	Slope
•	Aspect
•	Landscape Shape
•	Slope Position
•	Hydrography
•	Urban
•	Rock
•	Aspen Leading (40% or more stand composition)
•	Douglas-fir Leading (40% or more stand composition)
•	Lodgepole Pine Leading (40% or more stand composition)
•	Bioterrain wetlands
•	Bioterrain rock
•	Bioterrain talus
•	Bioterrain vegetated rock

The original mapping extents for thematic input layers ranged from localized plot data to provincial data sets. All input layers with the exception of the Okanagan IFPA PEM Plot Data are contiguous layers covering 122 TRIM 1 mapsheets:

Table 1. TRIM 1 Mapsheets required for the Project
082M.066
082M.056
082M.057
082M.045
082M.046
082M.047
082M.034
082M.035
082M.036
082M.037
082M.023
082M.024
082M.025
082M.026
082M.027
082M.028
082M.013
082M.014
082M.015
082M.016
082M.017
082M.018
082M.003
082M.004
082M.005
082M.006
082M.007
082M.008
082L.092
082L.093
082L.094
082L.095
082L.096
082L.097
082L.098
082L.099
082L.082
082L.083
082L.084
082L.085
082L.086
082L.087
082L.088
	082L.089
082L.072
082L.073
082L.074
082L.075
082L.076
082L.077
082L.078
082L.079
082L.062
082L.063
082L.064
082L.065
082L.066
082L.067
082L.068
082L.069
082L.070
082L.052
082L.053
082L.054
082L.055
082L.056
082L.057
082L.058
082L.059
082L.060
082L.043
082L.044
082L.045
082L.046
082L.047
082L.048
082L.049
082L.032
082L.033
082L.034
082L.035
082L.036
082L.037
082L.038
082L.039
082L.023
	082L.024
082L.025
082L.026
082L.027
082L.028
082L.029
082L.013
082L.014
082L.015
082L.016
082L.017
082L.018
082L.019
082L.003
082L.004
082L.005
082L.006
082L.007
082L.008
082L.009
082E.094
082E.095
082E.096
082E.097
082E.098
082E.099
082E.084
082E.085
082E.086
082E.087
082E.073
082E.074
082E.075
082E.076
082E.077
082E.065	

It should be noted that these mapsheets represent the extents of the entire Okanagan IFPA study area. Currently the areas subject to study include only the following biogeoclimatic subzone variants found in the Wet belt. They are:

ESSFvc
ESSFvwc
ESSFwc1
ESSFwc2
ESSFwc4
ESSFwcw
ICHmk1
ICHmk2
ICHmw1
ICHmw2
ICHmw3
ICHvk1
ICHwk1
ICHwk1c
IDFmw1
IDFmw2
3.0 Thematic Input Layer Documentation and Quality Assessment

Consultation with the client and the Ministry of Sustainable Resource Management indicated that there was sufficient data of a quality high enough to carry out a PEM project. These thematic input layers included

•	Ministry of Sustainable Resource Management TRIM 1 mapping
•	Ministry of Forests Forest Cover mapping
•	JMJ Holdings Bioterrain Mapping
•	Ministry of Forests Biogeoclimatic Mapping
•	Okanagan IFPA Predictive Ecosystem Mapping Project Field Plot Data

3.1 TRIM 1 Data

TRIM 1 data was acquired from the client in the spring of 2004.

Several different data layers were used from the TRIM 1 thematic mapping.
•	TDEM – Elevation points and 3D breaklines 
•	TWTR – Hydrographic Features
•	TNTL – 1:20,000 Neatlines

This information was then processed and used to produce
•	Mask
•	Aspect
•	Landscape Shape
•	Slope Position
•	Slope
•	Hydrography

More information on the processing of this information for the generation of input layers can be found in Section 4 “Input Processing” of this document.

3.1.1 TRIM 1 – TNTL Layer

Citation: TRIM 1 Program

Consultant/Department: Geographic Data BC Branch, Ministry of Sustainable Resource Management

Publication scale: 1:20,000

Period of Compilation: 1997

Base Map Projection: Albers, NAD 83

Quality Control: As per Table 1and Section 4.4.1.1. in the Standards for Predictive Ecosystem Mapping in British Columbia, Inventory Standard, (RIC, 1999) the neatline features in the TRIM 1 data were considered to be of adequate quality to complete a Predictive Ecosystem Mapping project at a scale of 1:20,000.

Edge Matching: Edge matching was completed along mapsheet boundaries using the Arc/Info 8.1 APPEND process. No dangles were found along mapsheet boundaries.

Edge Matching Error Minimum: 0 m shift along the X axis, 0 m shift along the Y axis.

Edge Matching Error Average: 0 meters on the X axis. 0 meters along the Y axis. 

Edge Matching Error Max: 0 meters on the X axis. 0 meters along the Y axis.

Attribute/Label Matching: All neatline boundaries contained the appropriate labels and attributes

Raster Sized: NA

Adjusted Control Feature Shift: None

3.1.2 TRIM 1 – TWTR Layer

Citation: TRIM 1 Program

Consultant/Department: Geographic Data BC Branch, Ministry of Sustainable Resource Management

Publication scale: 1:20,000

Period of Compilation: 1997

Base Map Projection: Albers, NAD 83

Quality Control: As per Table 1and Section 4.4.1.1. in the Standards for Predictive Ecosystem Mapping in British Columbia, Inventory Standard, (RIC, 1999) the single line water features in the TRIM 1 data were considered to be of adequate quality to complete a Predictive Ecosystem Mapping project at a scale of 1:20,000.

Edge Matching: Edge matching was completed along mapsheet boundaries using the Arc/Info 8.1 APPEND process. No dangles were found along mapsheet boundaries. Line work spanning map boundaries was joined to create seamless coverage. 100 lines were randomly selected from the input data and compared to the final output seamless coverage. Error was measured in terms of shift along the X axis (Easting) and the Y axis (Northing).  

Edge Matching Error Minimum: 0 m shift along the X axis, 0 m shift along the Y axis.

Edge Matching Error Average: 0 meters on the X axis. 0 meters along the Y axis. 

Edge Matching Error Max: 0 meters on the X axis. 0 meters along the Y axis.

Attribute/Label Matching: All water features were coded with the correct FCODE.

Raster Sized: NA

Adjusted Control Feature Shift: None

3.1.3 TRIM 1 – TDEM Layer

Citation: TRIM 1 Program

Consultant/Department: Geographic Data BC Branch, Ministry of Sustainable Resource Management

Publication scale: 1:20,000

Period of Compilation: 1997

Base Map Projection: Albers, NAD 83

Quality Control: As per Table 1and Section 4.4.1.1. in the Standards for Predictive Ecosystem Mapping in British Columbia, Inventory Standard, (RIC, 1999) the TDEM layer in the TRIM 1 data was considered to be of adequate quality to complete a Predictive Ecosystem Mapping project at a scale of 1:20,000.

Edge Matching: DEM points were merged into one seamless Arc/Info point coverage. No edge-matching was required as there is inherent overlap in the data points for each mapsheet. It should be noted that in the zone of overlap between 2 given mapsheets there were some irregularities in the elevation values. This is to be expected as each mapsheet relies on different control points, which may be slightly higher or lower. These differences were of a low enough proportion that the process of converting to grid would have the effect of “smoothing” the surface and nullifying the differences. It should also me noted that as no 2 TDEM points occupy the same space they cannot be compared in an absolute fashion. 

In an effort to gauge the degree of difference between the respective sheets contours with a 20m interval were created and compared in areas of overlap. 50 different meeting points along map sheet neatlines were selected between contour ‘pairs’ and the degree of difference was measured. Edge matching error is measured on an x-y plane as the contour pairs both represent the same elevation.  

Edge Matching Error Minimum: 2.1 meters

Edge Matching Error Average: 6.3 meters

Edge Matching Error Max: 9.8 meters

Attribute/Label Matching: N/A

Raster Sized: N/A

Adjusted Control Feature Shift: None

3.2 Forest Inventory Forest Cover Mapping

Ministry of Forests Forest Cover Mapping (FC1) was provided by Ministry of Forests (Kamloops Region).

This information was received in as seamless Arc/Info coverage for the Kamloops District and then processed to produce information on:

•	Douglas-fir leading 
•	Douglas-fir mix
•	Spruce leading 
•	Spruce in the top three species
•	Western Red Cedar in the top three species
•	Aspen in the top three species 

For Information on the processing of this information for the generation of input layers can be found in Section 5 “Input Processing” of this document.

Citation: Forest Cover/Forest Inventory Plan Data

Consultant/Department: MoF, Kamloops Forest Region

Publication scale: 1:20,000

Period of Compilation: 1997

Base Map Projection: UTM NAD 83 Zone 11

Quality Control: As per Table 1and Section 4.4.1.1. in the Standards for Predictive Ecosystem Mapping in British Columbia, Inventory Standard, (RIC, 1999) the MoF FC1 data was considered to be of adequate quality to complete a Predictive Ecosystem Mapping project at a scale of 1:20,000.

Edge Matching: Data was received in a seamless format. No edge-matching was required.

Edge Matching Error Minimum: N/A

Edge Matching Error Average: N/A 

Edge Matching Error Max: N/A

Attribute/Label Matching: N/A

Raster Sized: N/A

Adjusted Control Feature Shift: None

3.3 Okanagan IFPA Plot Data

This information was collected between July 2002 and October 2004, for use in this project by JMJ Holdings Inc.

For information on the processing of this data for the generation of input layers can be found in Section 5 “Input Processing” of this document.

Citation: Okanagan IFPA Predictive Ecosystem Mapping Project

Consultant/Department: JMJ Holdings Inc.

Publication scale: 1:20,000

Period of Compilation:  July 2002 – October, 2004

Base Map Projection: UTM NAD 83 Zone 11

Quality Control: Field cards were filled in on the site and then rechecked as the data was entered into digital format in VENUS 4.2 for GIS plots and an Excel spreadsheet for transect data and notes. UTM coordinates were collected using a Garmin GPS 2 Plus hand held GPS with a stated accuracy of +/-15m RMS. 

Edge Matching: No edge matching was required

Edge Matching Error Minimum: None

Edge Matching Error Average: None

Edge Matching Error Max: None

Attribute/Label Matching: None

Raster Sized: NA

Adjusted Control Feature Shift: NA

3.4 Biogeoclimatic Subzone Mapping

Biogeoclimatic Subzone mapping was provided by Dennis Lloyd, Ministry of Forests, Kamloops Region.

This information was received in as seamless Arc/Info coverage for the study area. It was used to produce the Biogeoclimatic Subzone Input Layer.

Citation: Biogeoclimatic Subzone Data, Ministry of Forests

Consultant/Department: MoF; Research Branch; Kamloops Forest Region; Dennis Lloyd

Publication scale: 1:50,000

Period of Compilation: 2004

Base Map Projection: UTM NAD 83 Zone 11

Quality Control: The Biogeoclimatic Subzone mapping was approved by the Regional Ecologist for use in this PEM. It is not yet correlated and approved by Del Meidinger, Research Branch, BC Ministry of Forests, Victoria.

Edge Matching: Data was received in a seamless format. No edge-matching was required.

Edge Matching Error Minimum: N/A

Edge Matching Error Average: N/A 

Edge Matching Error Max: N/A

Attribute/Label Matching: N/A

Raster Sized: N/A

Adjusted Control Feature Shift: None

3.5 JMJ Holdings Bioterrain Mapping

Bioterrain mapping was undertaken by JMJ Holdings for use in the Okanagan IFPA PEM Project.  Bioterrain polygons were delineated digitally using Diap Viewers by Jen Shypikta P.Geo, Tedd Robertson G.I.T, Grant Burns G.I.T, , and Matt Kalmakov G.I.T. using the classification system of Howes and Kenk (1997). External Quality Assurance was completed by Betsy Waddington, P.Geo.  Spatial data was prepared by Kevin Misurak of JMJ Holdings Inc. in ArcInfo and PCI Geomatics v8.0.
 
This information was used to create the following thematic input layers:

•	Bioterrain rock
•	Bioterrain talus
•	Bioterrain vegetated rock
•	Bioterrain wetlands
•	Well drained polygons
•	Rapidly drained polygons
•	Moderately well drained
•	Imperfectly drained polygons
•	Poorly drained polygons
•	Active Fluvial polygons
•	Fluvial polygons
•	Lacustrine polygons
•	Avalanche paths on well to moderately well drained
•	Avalanche paths on imperfect to poorly drained

Citation: Bioterrain Mapping, JMJ Holdings Inc.

Consultant/Department: JMJ Holdings Inc.

Publication scale: 1:30,000

Period of Compilation: 2003/2006

Base Map Projection: UTM NAD 83 Zone 11

Quality Control: Internal quality control was accomplished by Vicky Lipinski, Donna Ross and Jen Shypitka. External quality assurance was undertaken by Betsy Waddington P. Geo. Of Betsy Waddington Geoscience.

Edge Matching: Data was edge-matched by hand using ARC/Info 8.1.

Edge Matching Error Minimum: N/A

Edge Matching Error Average: N/A 

Edge Matching Error Max: N/A

Attribute/Label Matching: N/A

Raster Sized: N/A

Adjusted Control Feature Shift: None

4.0 Input Processing and Quality Assessment

For the purposes of this report Section 4.0 Input Processing will describe the processes and the rationale to convert the raw input coverages discussed in Section 3.0 Input Data Documentation and Quality Assessment and generate the thematic data layers used in the PEM process. 

Quantifying the success of the overall process is also a part of input processing and quality assessment. This section will also describe the results of the processes in terms of 3 statistical tests. These tests were deemed appropriate and approved for the assessment of spatial data by the Ministry of Sustainable Resource Management (Meidinger, 2000). The methods are:

•	The Chi-Square Test of Proportions
•	The Confusion Matrix Description of Overall Accuracy
•	The Statement of Percent Correct to the Nearest +/-95% Confidence Interval

The Chi-square test of proportions tests the proportions of the of field data attributes against those of the GIS generated input layers. For example, if field data was collected from 100 randomly placed field locations, and slope was one of the criteria collected, the distribution of slope classes from the GIS generated slope layer should approximate the distribution of slope classes found in the field data. This test gives an indication of how well the GIS is modeling the distribution of  classes of data (e.g. slope) across the landscape.  This test does not indicate the spatial accuracy of those classes though. The field data may indicate that 20% of the landscape is slope class 1 (<=5% slope) and the GIS may predict that 19% of the landscape is slope class 1 (which would be a very good fit). This test will not indicate whether or not the GIS is predicting the occurrence of slope class one in the right locations.

To test the locational accuracy of the GIS data a confusion matrix is developed to test each class of the field data against each class of the GIS data.  For instance, how many of the field plots that were classified as slope class 1 actually fall into pixels that the GIS has classified as slope class 1. The results of the matrix include a stated Error of Omission, Error of Commission, and an initial statement of Overall Accuracy. To use the slope attribute as an example, the Error of Commission represents the percentage of occurrences were the plot data (a known thing) has been classified by the GIS as being in a slope class where it does not belong. In this case we have committed the act of getting it wrong. The Error of Omission represents the percentage of occurrences were the plot data has not been classified by the GIS as being in the right slope class. In this case we have omitted the act of getting it right (Meidinger, 2000). The initial Statement of Overall Accuracy is the number of plots where the GIS and the field data agree divided by the number of plots where the GIS data and the field data disagree. The Initial statement of overall accuracy does not indicate how much overall confidence we have in the results of the Confusion Matrix Description of Overall Accuracy it’s result is limited to just the test. 

The Statement of Overall Accuracy defines the results of the Confusion Matrix in terms of a 95% confidence interval. For example, the confusion matrix may state an Initial Overall Accuracy of 62% but if only 8 plots were used for the test then the lower 95% confidence interval would be 25% and the upper 95% confidence interval would be 87%. In testing the same spatial data with the same result of 62% Initial Overall Accuracy but with a sample set of 100 plots the Statement of Overall Accuracy would have a lower 95% confidence interval of 56% and an upper 95% confidence interval of  69%. With more plots (a greater sample population) we can state our results with a higher degree of accuracy.

The Input Data Layers and the Thematic Input Layers which they were derived from are listed in Table 2. Thematic Input Layers and Input Data Layers.

Table 2.  Thematic Input Layers and Input Data Layers
Thematic Input Layer	Data Layer	PEM Data Layer	
TRIM 1	TRIM1 – TNTL	Mask – MASK	
TRIM 1	TRIM1 – TWTR	Hydrography – TWTR	
TRIM 1	TRIM 1 – TDEM	Slope – SLP
Aspect – AS
Landscape Shape – SHP
Slope Position – LNDPOS	
Forest Cover	Forest Cover	Urban – UR
Open Range – OPEN_RA
Rock – FC_ROCK
Douglas-fir leading – FD
Aspen leading – AT
Lodgepole Pine leading – PL	
Bioterrain Mapping	Bioterrain Mapping	Bioterrain rock – BIOROCK
Bioterrain talus – BIOTA
Bioterrain vegetated rock – BIOVEGROCK
Bioterrain wetlands – BIOWET
Bioterrain rapid drained – BIORAPID
Bioterrain well drained – BIOWELL
Bioterrain mod well drained-BIOWELL
Bioterrain imperfectly drained – BIOIMP
Bioterrain poorly drained – BIOPOOR
Bioterrain active fluvial – BIOACTFLUV
Bioterrain fluvial – BIOFLUV
Bioterrain avalanches well to mod well-BIOAVFREQUMODWELL
Bioterrain avalanches imp to poor
BIOAVFREQIMP	
Field Data	Field Data	Plot Data	
Biogeoclimatic Subzone Mapping	Biogeoclimatic Subzone Mapping	Biogeoclimatic Subzones – BEC	

It should be noted that an initial data set of 678 plots were compiled and made into a spatial input layers for testing. These plots were then screened for use in tests for each GIS Input Data Layer. For each of these tests plots were excluded when they were not deemed fit for use due to discrepancies in the in the classes.  This is the reason that sample sizes for the testing of each layer varies.

4.1 TRIM 1 Input Layers

The TRIM 1 Input layers included
•	TNTL
•	TDEM

All layers were projected from their original Albers Projection into Universal Transverse Mercator Zone 11. This was done using an AML provided by the Ministry of Sustainable Resource Management and the ArcInfo 8.1 PROJECT command. The parameters of the translation are as follows. 

input
Projection    ALBERS
Datum         NAD83
Zunits        NO
Units         METERS
Xshift        0.0000000000
Yshift        0.0000000000
Parameters
 50  0  0.000 /* 1st standard parallel
 58 30  0.000 /* 2nd standard parallel
-126  0  0.000 /* central meridian
 45  0  0.000 /* latitude of projection's origin
1000000.00000 /* false easting (meters)
0.00000 /* false northing (meters)
output
Projection    UTM
Zone          11
Datum         NAD83
Zunits        NO
Units         METERS
Xshift        0.0000000000
Yshift        0.0000000000

4.1.1 Input Layers Generated from TRIM TNTL Input Layer

4.1.1.1 Mask Grid – MASK

The TRIM1 neatline coverage for the study area was converted into a 50m grid using the ArcInfo 8.1 POLYGRID command. This coverage provides the ‘template’ for all other grids. It is used in the analysis environment to ensure that all subsequent grids and their individual pixels line up so that a given 50m x 50m point in space within the study area is represented by pixels that are congruent despite the fact that they are in different input layers. 

4.1.2 Input Layers Generated from TRIM TDEM Input Layer

4.1.2.1 Slope - SLP

ArcInfo Grid Slope Command was used on the Elevation Grid. The options selected were percent and a z-factor of 1.

Slope identifies the maximum rate of change in value from each cell to its neighbors.  An output slope grid can be calculated as percent slope or degree of slope. 

Conceptually, the slope function fits a plane to the z values of a 3x3 cell neighborhood around the processing or center cell. The direction the plane faces is the aspect for the processing cell. The slope for the cell is calculated from the 3x3 neighborhood using the average maximum technique. If there is a cell location in the neighborhood with a no-data z value the z value of the center cell will be assigned to the location. At the edge of the grid, at least three cells (outside the grid’s extent) will contain no-data as their z values. These cells will be assigned the center cell’s z value. The result is a flattening of the 3x3 plane that is fit to these edge cells, which thus usually leads to a reduction in the slope.

The actual algorithm that is used to calculate slope is (ESRI, 2001):

rise_run = SQRT(SQR(dz/dx)+SQR(dz/dy))degree_slope = ATAN(rise_run) * 57.29578

where the deltas are calculated using a 3x3 roving window.a through i represent the z_values in the window:

a b cd e fg h i

(dz/dx) = ((a + 2d + g) - (c + 2f + i)) / (8 * x_mesh_spacing)(dz/dy) = ((a + 2b + c) - (g + 2h + i)) / (8 * y_mesh_spacing)

Table 3. Slope Classes
Slope 
Class	Description	
1	0-10%	
2	11-25%	
3	26-50%	
4	51-70%	
5	70% +	

The new slope layer was then tested against 678 field plots which were stratified into the same classes as the GIS input layer. The distribution of classes, in terms of area, was then compared to those of the plot data. 

Table 4. Results of the Chi-Square Test of Proportions for Slope Class
Slope Class	Field Slope (Observed)		GIS Slope (Expected)		(Observed-Expected)2 
/Expected	
	Area	Proportion	Freq	Area	Proportion	Freq			
1	183	0.269	183	487938.3	0.12421	84.2	115.93	
2	208	0.307	208	1099049	0.279775	189.7	1.77	
3	193	0.285	193	1479060	0.376511	255.3	15.20	
4	71	0.105	71	544813.3	0.138688	94.0	5.63	
5	23	0.034	23	210853.3	0.053675	36.4	4.93	
Total	678	1.0	678		1.0	678	143.46	
									
							chi-sqr =	143.46	
							df =	4	
							p-value =	0.000	

 The locational accuracy of the GIS layer was also tested against the 678 plots. The results are displayed below in Table 4. Slope Input Layer Confusion Matrix.
 
Table 5. Slope Input Layer Confusion Matrix
Field Slope
GIS Slope	Class 1	Class 2	Class 3	Class 4	Class 5	Total	Error of Omission	
Class 1	80	25	3	1	0	109	26.6%	
Class 2	82	115	43	5	1	246	53.3%	
Class 3	17	65	137	36	7	262	47.7%	
Class 4	2	3	10	27	14	56	51.8%	
Class 5	2	0	0	2	1	5	80.0%	
Total	183	208	193	71	23	678		
Error of 
Commission	56.3%	44.7%	29.0%	62.0%	95.7%			

Average Error of Commission		57.5%
Average Error of Omission		51.9%
Overall Error: (289/678)			42.6%

Table 6. Slope Input Layer Statement of Overall Accuracy
Total Plots		678	
Number Correct	389	
Percent Correct	57.4%	
Lower 95% Confidence Value	53.6%	
Median 95% Confidence Value	57.4%	
Upper 95% Confidence Value	61.0%	

Although the overall accuracy of the GIS data when comparing slope class to the PEM field data was relatively low, the greatest amount of error appears to have occurred between adjacent slope classes.  This may suggest problems of resolution, as a large number of plots may occur near slope class breaks.  Furthermore, the GIS smoothes the landscape to a degree, not resolving changes in slope smaller than the pixel size of 25m X 25m.  Overall the results of the tests suggest that the Slope Input Data Layer would be appropriate for use in the PEM but that there is room for improvement. 

4.1.2.2  Aspect – AS

The ArcInfo Grid Aspect command was applied to the Elevation grid. There are no available options for this command. 

Aspect identifies the direction of maximum rate of change in z value from each cell. Aspect is expressed in positive degrees from 0 to 360, measured clockwise from the north.

Aspect identifies the down-slope direction of the maximum rate of change in value from each cell to its neighbors.  Aspect can be thought of as the slope direction.

The Slope grid was then applied to the Aspect grid. All areas with a slope of less then 10% were given an aspect of neutral.

The grid was then reclassified into discrete classes

Table 7. Aspect Classes
Aspect Class	Description	
1	135deg to 285deg (warm)	
2	285deg to 135deg (cool)	
3 	Neutral (< 10% slope)	

The final landscape shape layer was tested against 678 field plots where on the ground aspect data was recorded. 

Table 8. Results of the Chi-Square Test of Proportions for the Aspect Class
Aspect	Field Aspect (Observed)	GIS Aspect (Expected)	(Observed-Expected)2 
/Expected	
	Area	Proportion	Freq	Area	Proportion	Freq			
1	230	0.339	230	1402152	0.357	242.0	0.60	
2	266	0.393	266	2038240	0.519	351.8	20.93	
3	182	0.268	182	487938.3	0.124	84.2	113.60	
Total	678	1.0	678	3928330	1.0	678	135.12	
									
							chi-sqr =	135.12	
							df =	2	
							p-value =	0.000	

Table 9. Aspect Input Layer Confusion Matrix
Field Aspect
GIS Aspect	Class 1	Class 2	Class 3	Total	Error of Omission	
Class 1	179	25	50	254	29.5%	
Class 2	40	222	53	315	29.5%	
Class 3	11	19	79	109	27.5%	
Total	230	266	182	678		
Error of 
Commission	22.2%	16.5%	56.6%			

Average Error of Commission:	31.8%
Average Error of Omission:	28.8%
Overall Error (198/678):		29.2%

Table 10. Aspect Input Layer Statement of Overall Accuracy
Total Plots		678	
Number Correct	480	
Percent Correct	70.8%	
Lower 95% Confidence Value	67.3%	
Median 95% Confidence Value	70.8%	
Upper 95% Confidence Value	74.1%	

The distribution of Aspect Class across the landscape as described by the Chi-square statistic suggests that there is a reasonable relationship between the GIS data and the field data. This is in turn supported by the results of the confusion matrix and the overall statement of accuracy. Much of this landscape is made of gently undulating terrain where aspect calls are difficult to predict with a 25 m pixel size. 

Most of the locational errors occur in aspect class 3 and are being called aspect class 1, 2 or 3 by the model. This may suggest problems of resolution, as a large number of plots may occur near aspect class breaks.

4.1.2.3  Slope Position

The Slope Position layer describes the position of a slope relative to the overall landscape shape. This process was designed to characterize the landscape into six classes: Crest (ridge), Upper Slope, Mid Slope, Lower Slope, Toe Slope, and Valley Bottom. 

This layer was modeled using an AML downloaded from the Swiss Federal Research Institute WSL website, and was written by Niklaus E. Zimmermann.

This simple AML is used to identify topographic exposure (ridge, slope, toe slope, etc) at various spatial scales, and to hierarchically integrate these features into a single grid. Topographic position, necessary e.g. to classify soil properties over large areas, can be calculated using a hierarchically nested approach. In this AML, circular moving-windows with increasing radii are applied to a DEM, and the difference between the average elevation of the window and the center cell of the window is calculated and written to temporary output grids. The user is prompted for the number search radii, for which the temporary grids are generated. The resulting (temporary) maps are interpreted as relative topographic exposure at different spatial scales. The exposure can be interpreted as a ridge or peak if the center cell in the moving window has a higher elevation than the average elevation of the cells in the window. Contrarily if the center cell is of lower elevation than the average elevation of the window, then the center pixel can be interpreted as "toe slope" or "valley bottom". 

A hierarchical integration into a single map is achieved by starting with the standardized exposure values of the largest window, then adding standardized values from smaller windows where the (absolute) values of the smaller (search) scale grids exceed the values of the larger scale map. 

When starting the AML, the user is prompted for the name (&path, if different from the directory the AML was started) of the DEM the topographic position has to be derived from. Further, the minimum and maximum radius of the search window has to be entered, as well as the increment to enlarge the search radius stepwise. The AML does increment the search radius (and repeat the exposure calculation for each radius) until the incremented radius is bigger than the max. radius. It then starts integrating hierarchically the generated temporary grids.

Two versions of relative topographic exposure are available as a grid, one containing the “raw” values, and a second containing a smoothed version of the topographic exposure. The smoothed version has an "s" added to the output file name. 

Arguments for the toposcale.aml:

Name of the DEM of the study site
Name of the output grid 
Number of pixels for smallest search radius 
Number of pixels for largest search radius
Number of pixels to increment search radius 

The DEM was smoothed twice using a low pass filter using a focal mean with a 3 by 3 kernel. This generalized the overall values of the DEM, effectively reducing the elevation value range. 

After the smoothing process, toposcale.aml was run on the DEM.

The number of pixels for the smallest search radius was set to 2, with increments of 2 up to the maximum of 10 pixels for the largest search radius.
The user has the option of keeping individual exposure grids. Each exposure grids is sampled with a different radii (from the smallest to largest, following the increment path). Each exposure grid was examined for suitability, and the 4 pixel exposure grid was selected.

Table 11. Distribution of Slope Position Values
Min Value	Max Value	Standard Dev	
-2814	-299	< -3 Std. Dev	
-299	-275	-3.00  to –2.75 Std. Dev.	
-274	-250	-2.75 to –2.50 Std. Dev	
-249	-225	-2.50 to –2.25 Std. Dev	
-224	-200	-2.25 to –2.00 Std. Dev	
-199	-175	-2.00 to –1.75 Std. Dev	
-174	-150	-1.75 to –1.50 Std. Dev	
-149	-125	-1.5 to –1.25 Std. Dev	
-124	-100	-1.25 to –1.00 Std. Dev	
-99	-75	-1.00 to –0.75 Std. Dev	
-74	-50	-0.75 to –0.50 Std. Dev	
-49	-25	-.50 to –0.25 Std. Dev	
-24	0	-0.25 to 0 Std. Dev	
0	0	Mean	
1	24	0.00 to 0.25 Std. Dev	
25	49	0.25 to 0.50 Std. Dev	
50	74	0.50 to 0.75 Std. Dev	
75	99	0.75 to 1.00 Std. Dev	
100	124	1.00 to 1.25 Std. Dev	
125	149	1.25 to 1.50 Std. Dev	
150	174	1.50 to 1.75 Std. Dev	
175	199	1.75 to 2.00 Std. Dev	
200	224	2.00 to 2.25 Std. Dev	
225	249	2.25 to 2.50 Std. Dev	
250	274	2.50 to 2.75 Std. Dev	
275	299	2.75 to 3.00 Std. Dev	
299	10894	> 3.00 Std. Dev	

After consultation with the field ecologists, values from -2814 to -30 were selected for lower slopes, values from –30 to 30 were classified as mid-slope, values from –30 to 10894 were delineated as upper slope. 

The slope grid was then applied, and areas with a slope of 5% or less were classified as ‘flat’ or ‘valley bottoms’.

Table 12. Slope Position Classes
Slope Position Classes	Description	
1	Toe Slope	
2	Lower Slope	
3	Mid Slope	
4	Upper Slope	
5	Crest	
6	Valley floor / Level	
7	Plateau	
8	Bench	

The Slope Position grid was tested against 678 field plots. 

Table 13. Results of the Chi-Square Test of Proportions for the Slope Position Class

Slope Position	Field Slope Position (Observed)	GIS Slope Position (Expected)	(Observed-Expected)2 
/Expected	
	Area	Proportion	Freq	Area	Proportion	Freq			
1	56	0.083	56	271918.5	0.06922	46.9	1.77	
2	74	0.109	74	1012833	0.257828	174.8	58.13	
3	424	0.625	424	1045796	0.266219	180.5	328.49	
4	44	0.065	44	701313.5	0.178527	121.0	49.00	
5	13	0.019	13	408531.8	0.103996	70.5	46.90	
6	67	0.099	67	265325.3	0.067541	45.8	9.81	
7	0	0	0	82870.25	0.021096	14.3	14.30	
8	0	0	0	139742.8	0.035573	24.1	24.10	
Total	678	1.0	678	3928330	1.0	678	532.49	
									
							chi-sqr =	532.49	
							df =	7	
							p-value =	0.000	

Table 14. Slope Position Input Layer Confusion Matrix
Field Slope Position
GIS Slope Position	Class 1	Class 2	Class 3	Class 4	Class 5	Class 6	Class 7	Class 8	Total	Error of Omission	
Class 1	6	14	10	0	0	1	0	0	31	80.60%	
Class 2	31	29	114	3	2	14	0	0	193	85%	
Class 3	9	14	143	9	3	8	0	0	186	23.10%	
Class 4	2	4	89	9	4	3	0	0	111	91.90%	
Class 5	0	0	28	14	2	4	0	0	48	95.80%	
Class 6	6	5	14	1	0	26	0	0	52	50%	
Class 7	0	5	11	6	2	6	0	0	30	100%	
Class 8	2	3	15	2	0	5	0	0	27	100%	
Total	56	74	424	44	13	67	0	0	678		
Error of Commission	89.30%	60.80%	66.30%	79.50%	84.60%	61.20%	0%	0%			

Average Error of Commission:	73.6%
Average Error of Omission:	78.3%
Overall Error (463/678):		68.3%

Table 15. Slope Position Statement of Overall Accuracy 
Total Plots		678	
Number Correct	215	
Percent Correct	31.7%	
Lower 95% Confidence Value	28.3%	
Median 95% Confidence Value	31.7%	
Upper 95% Confidence Value	35.3%	

A fairly significant difference existed between slope position as classified in the field and as classified by the GIS data.  A much greater proportion of plots was found to be classified as mid-slope position in the field and were classified as other slope positions by the GIS.  In the field it is often difficult to view macroslope position as classified by the GIS, resulting in the often incorrect assumption of being in a mid-slope position.  We could expect the large scale model that is used for the GIS may provide a better indication of overall slope position that could be classified in the field.

4.1.4  Input Layers Generated From Forest Cover

4.1.4.1. Douglas-fir Leading Input Layer – FD

Using ArcInfo 8.1, the spatial forest cover database was queried for Douglas-fir (any species) with a stand composition percentage of 40% or more over the first four species columns. (SPECIES1, SPECIES2, SPECIES3, SPECIES4; PERCENTAGE1, PERCENTAGE2, PERCENTAGE3, PERCENTAGE4).
An attribute called ‘FD_LEAD’ was added to the forest cover database, and all selected polygons were coded with a ‘1’.
Table 16. Douglas-fir Stand Classes
Description	Code	
Douglas-fir Leading	1	
Not Douglas-fir Leading	0	

The ArcInfo 8.1 DISSOLVE command was applied to the forest cover data using the FD_LEAD attribute.  The resulting polygon coverage described presence or absence of Douglas-fir leading stands within the study area. This coverage was then converted to Grid using the same classes.

Table 17. Results of the Chi-Square Test of Proportions for the Douglas-fir Leading Input Layer
Douglas-fir Leading	Field Douglas-fir (Observed)	GIS Douglas-fir (Expected)	(Observed-Expected)2 
/Expected	
	Proportion	Freq	Proportion	Freq			
1	0.090	40	0.022	10	90	
0	0.910	406	0.088	436	2.064	
Total	1.0	446	1.0	446	92.064	
							
						chi-sqr =	92.064	
						df =	1	
						p-value =	0.000	

Table 18. Douglas-fir Leading Input Layer Confusion Matrix
Field Douglas-fir Leading

GIS Douglas-fir Leading	Not FD Leading	FD Leading	Total	Error of Omission	
Not FD Leading	398	38	436	8.7%	
FD Leading	8	2	10	20.0%	
Total	406	40	446		
Error of 
Commission	1.9%	5.0%			

Average Error of Commission	3.5%
Average Error of Omission	14.4%
Overall Error (46/446)		10.3%

Table 19. Statement of Overall Accuracy of Douglas-fir Leading

Total Plots		446	
Number Correct	401	
Percent Correct	89.9%	
Lower 95% Confidence Value	86.8%	
Median 95% Confidence Value	89.9%	
Upper 95% Confidence Value	92.4%	

The confusion matrix above describes the accuracy of the Douglas-fir Leading Input Layer. The overall accuracy is high, suggesting that the layer is suitable for use in the PEM process. The low error of both omission and commission means that the model is not over or under predicting Douglas-fir in this area.

4.1.4.2  Spruce Leading Input Layer – SX

Using ArcInfo 8.1, the spatial forest cover database was queried for Spruce (any species) with a stand composition percentage of 40% or more over the first four species columns. (SPECIES1, SPECIES2, SPECIES3, SPECIES4; PERCENTAGE1, PERCENTAGE2, PERCENTAGE3, PERCENTAGE4).
An attribute called ‘SX_LEAD’ was added to the forest cover database, and all selected polygons were coded with a ‘1’.
Table 20. Spruce Stand Classes
Description	Code	
Spruce Leading	1	
Not Spruce Leading	0	

The ArcInfo 8.1 DISSOLVE command was applied to the forest cover data using the SX_LEAD attribute.  The resulting polygon coverage describing presence or absence of Spruce within the study area. This coverage was then converted to Grid using the same classes.

Table 21. Results of the Chi-Square Test of Proportions for the Spruce Leading Input Layer
Spruce Leading	Field Spruce (Observed)	GIS Spruce (Expected)	(Observed-Expected)2 
/Expected	
	Proportion	Freq	Proportion	Freq			
1	0.121076	54	0.118834	53	0.018868	
0	0.878924	392	0.881166	393	0.002545	
Total	1.0	446	1.0	446	0.021412	
							
						chi-sqr =	0.021412	
						df =	1	
						p-value =	0.97	

Table 22. Spruce Leading Input Layer Confusion Matrix
Field Spruce Leading

GIS Spruce Leading	Not SX Leading	SX Leading	Total	Error of Omission	
Not SX Leading	352	28	380	7.4%	
SX Leading	40	26	66	39.3%	
Total	392	54	446		
Error of 
Commission	10.2%	48.1%			

Average Error of Commission	29.2%
Average Error of Omission	23.4%
Overall Error (68/446)		15.2%

Table 23. Statement of Overall Accuracy of Spruce Leading
Total Plots		446	
Number Correct	379	
Percent Correct	85.0%	
Lower 95% Confidence Value	81.4%	
Median 95% Confidence Value	85.0%	
Upper 95% Confidence Value	88.0%	

The confusion matrix above describes the accuracy of the Spruce Leading Input Layer. The overall accuracy is high, suggesting that the layer is suitable for use in the PEM process. The low error of both omission and commission means that the model is not over or under predicting Spruce in this area.

4.1.4.3  Spruce Top Three Input Layer – SX

Using ArcInfo 8.1, the spatial forest cover database was queried for Spruce (any species) with an occurrence in stand composition over the first three species columns (SPECIES1, SPECIES2, SPECIES3).
An attribute called ‘SX_TOP3’ was added to the forest cover database, and all selected polygons were coded with a ‘1’.

Table 24. Spruce Stand Classes
Description	Code	
Spruce Top Three	1	
Not Spruce Top Three	0	

The ArcInfo 8.1 DISSOLVE command was applied to the forest cover data using the SX_TOP3 attribute.  The resulting polygon coverage described the presence or absence of Spruce within the top three species of the forest cover throughout study area. This coverage was then converted to Grid using the same classes.

Table 25. Results of the Chi-Square Test of Proportions for the Spruce Top Three Input Layer
Spruce Top Three	Field Spruce (Observed)	GIS Spruce (Expected)	(Observed-Expected)2 
/Expected	
	Proportion	Freq	Proportion	Freq			
1	0.663677	296	0.643498	287	0.28223	
0	0.336323	150	0.356502	159	0.509434	
Total	1.0	446	1.0	446	0.791664	
							
						chi-sqr =	0.791664	
						df =	1	
						p-value =	0.50	

Table 26. Spruce Top Three Input Layer Confusion Matrix
Field Spruce Leading

GIS Spruce Top Three	Not SX Top Three	SX Top Three	Total	Error of Omission	
Not SX Top Three	248	38	286	13.3%	
SX Top Three	47	113	160	70.6%	
Total	295	151	446		
Error of 
Commission	15.9%	74.8%			

Average Error of Commission	45.4%
Average Error of Omission	42.0%
Overall Error (85/446)		19.1%

Table 27. Statement of Overall Accuracy of Spruce Top Three
Total Plots		446	
Number Correct	362	
Percent Correct	81.1%	
Lower 95% Confidence Value	77.3%	
Median 95% Confidence Value	81.1%	
Upper 95% Confidence Value	84.5%	

The confusion matrix above describes the accuracy of the Spruce Top Three Input Layer. The overall accuracy is high, suggesting that the layer is suitable for use in the PEM process. The low error of both omission and commission means that the model is not over or under predicting Spruce in this area.

4.1.4.4  Western Red CedarTop Three Input Layer – CW

Using ArcInfo 8.1, the spatial forest cover database was queried for Western Red Cedar in the top three species of the forest cover (SPECIES1, SPECIES2, SPECIES3).  An attribute called ‘CW_LEAD’ was added to the forest cover database, and all selected polygons were coded with a ‘1’.

Table 28. Western Red Cedar Stand Classes
Description	Code	
Western Red Cedar Top Three	1	
Not Western Red Cedar Top Three	0	

The ArcInfo 8.1 DISSOLVE command was applied to the forest cover data using the CW_LEAD attribute.  The resulting polygon coverage describing presence or absence of Western Red Cedar within the study area. This coverage was then converted to Grid using the same classes.

Table 29. Results of the Chi-Square Test of Proportions for the Western Red Cedar Top Three Input Layer
Western Red Cedar Top Three	Field Western Red Cedar (Observed)	GIS Western Red Cedar (Expected)	(Observed-Expected)2 
/Expected	
	Proportion	Freq	Proportion	Freq			
1	0.64574	288	0.529148	236	11.45763	
0	0.35426	158	0.470852	210	12.87619	
Total	1.0	446	1.0	446	24.33382	
							
						chi-sqr =	24.33382	
						df =	1	
						p-value =	0.0000	

Table 30. Western Red Cedar Top Three Input Layer Confusion Matrix
Field Western Red Cedar Leading

GIS Western Red Cedar Top Three	Not CW Top Three	CW Top Three	Total	Error of Omission	
CW Not Top three	113	96	209	45.9%	
 CW Top Three	44	193	237	81.4%	
Total	157	289	446		
Error of 
Commission	28.0%	66.8%			

Average Error of Commission	47.4%
Average Error of Omission	63.7%
Overall Error (140/446)		31.4%

Table 31. Statement of Overall Accuracy of Western Red Cedar Top Three
Total Plots		446	
Number Correct	307	
Percent Correct	68.8%	
Lower 95% Confidence Value	64.4%	
Median 95% Confidence Value	68.8%	
Upper 95% Confidence Value	73.0%	

The confusion matrix above describes the accuracy of the Western Red Cedar Top Three Input Layer. The overall accuracy is low, suggesting that the layer is not suitable for use in the PEM process. The high error of both omission and commission means that the model is over and under predicting Western Red Cedar in this area.

4.1.4.5 Douglas-fir Mix Input Layer – FD

Using ArcInfo 8.1, the spatial forest cover database was queried for Douglas-fir (any species).
An attribute called ‘FD_Mix’ was added to the forest cover database, and all selected polygons were coded with a ‘1’.

Table 32. Douglas-fir Stand Classes
Description	Code	
Douglas-fir Mix	1	
Not Douglas-fir Mix	0	

The ArcInfo 8.1 DISSOLVE command was applied to the forest cover data using the FD_Mix attribute.  The resulting polygon coverage described the presence or absence of Douglas-fir within the study area. This coverage was then converted to Grid using the same classes.

Table 33. Results of the Chi-Square Test of Proportions for the Douglas-fir Mix Input Layer
Douglas-fir Mix	Field Douglas-fir (Observed)	GIS Douglas-fir (Expected)	(Observed-Expected)2 
/Expected	
	Proportion	Freq	Proportion	Freq			
1	0.316143	141	0.219731	98	18.86735	
0	0.683857	305	0.780269	348	5.313218	
Total	1.0	446	1.0	446	24.18057	
							
						chi-sqr =	24.18057	
						df =	1	
						p-value =	0.000	

Table 34. Douglas-fir Mix Input Layer Confusion Matrix
Field Douglas-fir Mix

GIS Douglas-fir Mix	Not FD Mix	 FD Mix	Total	Error of Omission	
Not FD Mix	271	76	347	21.9%	
FD Mix	32	67	99	67.7%	
Total	303	143	446		
Error of 
Commission	10.6%	46.9%			

Average Error of Commission	28.8%
Average Error of Omission	44.8%
Overall Error (108/446)		24.2%

Table 35. Statement of Overall Accuracy of Douglas-fir Mix
Total Plots		446	
Number Correct	339	
Percent Correct	76.0%	
Lower 95% Confidence Value	71.8%	
Median 95% Confidence Value	76.0%	
Upper 95% Confidence Value	79.7%	

The confusion matrix above describes the accuracy of the Douglas-fir Leading Input Layer. The overall accuracy is high, suggesting that the layer is suitable for use in the PEM process. The low error of both omission and commission means that the model is not over or under predicting Douglas-fir in this area.

4.1.4.6  Trembling aspen Top Three Input Layer – AT

Using ArcInfo 8.1, the spatial forest cover database was queried for Trembling Aspen over the first three species columns (SPECIES1, SPECIES2, SPECIES3).  An attribute called ‘AT_TOP3’ was added to the forest cover database, and all selected polygons were coded with a ‘1’.

Table 36. Trembling Aspen Stand Classes
Description	Code	
Trembling Aspen Top Three	1	
Not Trembling Aspen Top Three	0	

The ArcInfo 8.1 DISSOLVE command was applied to the forest cover data using the AT_TOP3 attribute.  The resulting polygon coverage describing presence or absence of trembling aspen within the study area. This coverage was then converted to Grid using the same classes.

Table 37. Results of the Chi-Square Test of Proportions for the Trembling Aspen Top Three Input Layer
Trembling Aspen Top Three	Field Trembling Aspen (Observed)	GIS Trembling Aspen (Expected)	(Observed-Expected)2 
/Expected	
	Proportion	Freq	Proportion	Freq			
1	0.038117	17	0.058296	26	3.115385	
0	0.961883	429	0.941704	420	0.192857	
Total	1.0	446	1.0	446	3.308242	
							
						chi-sqr =	3.308242	
						df =	1	
						p-value =	0.055	

Table 38. Trembling Aspen Top Three Input Layer Confusion Matrix
Field Trembling Aspen Top Three

GIS Trembling Aspen Top Three	Not AT Top Three	 AT Top Three	Total	Error of Omission	
Not AT Top Three	407	12	419	2.9%	
AT Top Three	21	6	27	22.2%	
Total	428	18	446		
Error of 
Commission	4.9%	33.3%			

Average Error of Commission	19.1%
Average Error of Omission	12.6%
Overall Error (/446)		7.4%

Table 39. Statement of Overall Accuracy of Trembling Aspen Top Three
Total Plots		446	
Number Correct	415	
Percent Correct	93.0%	
Lower 95% Confidence Value	90.3%	
Median 95% Confidence Value	93.0%	
Upper 95% Confidence Value	95.1%	

The confusion matrix above describes the accuracy of the trembling aspen Leading Input Layer. The overall accuracy is high, suggesting that the layer is suitable for use in the PEM process. The low error of both omission and commission means that the model is not over or under predicting trembling aspen in this area.

5.0 References

Meidinger, D. 2000. Protocol for Quality Assurance and Accuracy Assessment of Ecosystem Maps. Prepared for the TEM Alternatives Task Force. July 2000. 

Moon, D. Problem Analysis on Data Quality Assessment Issues. ITQ#CRS 9062. Submitted to the TEM Alternatives Task Force. 2000.

Moore, I. D., Grayson, R. B., and Landson, A. R., 1991.  ‘Digital Terrain Modeling: A Review of Hydrological, Geomorphological, and Biological Applications’ , Hydrological Processes. Vol. 5.3-30.

Silverman, B.W. Density Estimation for Statistics and Data Analysis. New York: Chapman and Hall, 1986.

Howes, D.E & E. Kenk (eds.) 1997. ‘Terrain Classification System for British Columbia’

Version 2 MOE Manual 10. Updated by Resources Inventory Branch, BC Ministry of Environment, Lands & Parks, Victoria BC

Okanagan TSA Very Dry Belt BEC Variants Predictive Ecosystem Mapping (PEM) Input Data Quality Assessment Report March 2009

1.0 Introduction
Input data quality assessments were derived from two main methods. First, when possible, input data layers were compared against field plot data that had been collected over a number of years.  When this data was incomplete or missing critical spatial information or potential inconsistent data was present data, a qualitative review through visual analysis was assessed.

1.1	Input layer processing and quality assurance
The quality of the input layers was assessed by three statistical tests. These tests were deemed appropriate and approved for the assessment of spatial data by the Ministry of Sustainable Resource Management (Meidinger, 2003). The methods are:

•	The Chi-Square Test of Proportions
•	The Confusion Matrix Description of Overall Accuracy
•	The Statement of Percent Correct to the Nearest +/-95% Confidence Interval

The Chi-square test of proportions analyzes the distribution of the field data attributes against those of the GIS generated input layers. For example, if field data was collected from 100 randomly placed field locations, and slope was one of the criteria collected, the distribution of slope classes from the GIS generated slope layer should approximate the distribution of slope classes found in the field data. This test gives an indication of how well the PEM is modelling the distribution of classes of data (e.g. slope, aspect, soil moisture) across the landscape.  This test does not indicate the spatial accuracy of those classes though. The field data may indicate that 20% of the landscape is slope class 1 (<=5% slope) and the GIS may predict that 19% of the landscape is slope class 1. This test will not indicate whether or not the GIS is predicting the occurrence of slope class one in the right locations.

To test the locational accuracy of the PEM, a confusion matrix is developed to test each class of the field data against each class of the PEM output.  For instance, how many of the field observations that were classified as slope class 1 actually fall into pixels that the PEM has modelled as slope class 1. The results of the matrix include a stated Error of Omission, Error of Commission, and an initial statement of Overall Accuracy. To use the slope attribute as an example, the Error of Commission represents the percentage of occurrences were the plot data (a known thing) has been classified by the PEM as being in a slope class where it does not belong. In this case we have committed the act of getting it wrong. The Error of Omission represents the percentage of occurrences where the plot data has not been classified by the PEM as being in the right slope class.  In this case we have omitted the act of getting it right (Meidinger, 2003). The initial Statement of Overall Accuracy is the number of plots where the PEM and the field data agree divided by the number of plots where the PEM output and the field data disagree. The initial statement of overall accuracy does not indicate how much overall confidence we have in the results of the Confusion Matrix Description of Overall Accuracy-- its result is limited to just the test. 

The Statement of Overall Accuracy defines the results of the Confusion Matrix in terms of a 95% confidence interval. For example, the confusion matrix may state an Initial Overall Accuracy of 62% but if only 8 plots were used for the test then the lower 95% confidence interval would be 25% and the upper 95% confidence interval would be 87%. In testing the same spatial data with the same result of 62% Initial Overall Accuracy but with a sample set of 100 plots, the Statement of Overall Accuracy would have a lower 95% confidence interval of 56% and an upper 95% confidence interval of  69%. With more plots (a greater sample population) we can state our results with a higher degree of accuracy.
It is important to remember that the field observations used in this analysis were not collected randomly or consistently.  The data for these plots was collected for a variety of purposes, over a broad timeframe and most commonly on a non-random and inconsistent basis.  The purpose of these tests of proportions was to provide a baseline idea of how well the input data layers reflect the on-ground site attributes prior to the season of field data collection and not a conclusive statement of the validity if the input layers. 

1.2	TRIM
The accuracy (positional and thematic) of TRIM has been well documented in the context of PEM in numerous previous projects and has always been highly recommended for use in ecosystem mapping. A qualitative review of TRIM wetland and water features polygons was completed for the Okanagan TSA land base.  The following represents the findings:

There were no issues of positional accuracy in the TRIM revealed during the quality assessment process; however there are a few minor findings that may warrant attention for the use of TRIM features in PEM:

1)	TRIM wetland size and shape sometimes need modification to account for the changes since the TRIM inventory; a general trend is that TRIM wetland underestimates the size of wetland in real world.  This trend was also found in other projects of a similar nature.  For example, Timberline has completed an exceptional ecology/material mapping project on approximately 5,000,000 ha over the land base of the former Cariboo Region (i.e., to support a Cariboo PEM) and found a similar trend of TRIM wetland features.
2)	A good portion of TRIM water features such as shallow open water or small ponds became wetland ecosystems (i.e., dried out to certain extent).  On the other hand, there is also a number of new water features (presumably caused on beaver dams) that TRIM did not account for. 

Since the map accuracy assessment of PEM is based on the ground verification, rather than features indicated on TRIM, inaccurate wetland boundary placement may potentially cause negative impact on the resultant map accuracy.

1.2.1	TauDEM Wetness Index
The TauDEM wetness index was developed to provide a measure of soil moisture across landscapes (Tarboton 2005).  Accuracy of the TauDEM wetness index input layer was determined through visual analysis, as well as proportions analysis with plot data collected in the summer of 2007 and fall of 2008.  Because soil moisture can vary metre by metre due to microscale site attributes a point to point analysis was not appropriate.  Some of the plot data collected in the Okanagan TSA was not collected randomly and presents a bias distribution of soil moisture across the landbase.  For the purpose of input quality assessments wetness index classifications were viewed against ortho images and contour lines.  The following represents the findings:
•	The TauDEM wetness index provided a very in-depth model of moisture flow across the landscape both in mountainous terrain and on more subdued landscapes.  Drainages and moisture accumulation areas were well modelled and corresponded well with TRIM data.
•	The TauDEM wetness index may fail to identify areas of unexpected seepage (ie- where bedrock comes uncharacteristically close to the surface of a slope).  By combining the TauDEM wetness index with areas of seepage identified in the generalized materials mapping this issue should be resolved.
•	In areas of glaciofluvial or glaciolacustrine deposits moisture may be overestimated or underestimated, respectively.  By combining the TauDEM moisture index with areas of coarse or fine textured soils identified in the generalized materials mapping this issue should be resolved. 
Overall the TauDEM wetness index was deemed to be very useful as a PEM input layer.  Table A1 compares the input data layer with field data collected in the 2007 and 2008 field seasons.

Table A1	Overall TauDEM Wetness Index and Field Data Proportions
Class	Description	Field data (%)	Model (%)	
1	Subhydric - hygric 	0.54	1.72	
2	Subhygric	9.69	9.16	
3	Mesic	31.63	38.26	
4	Submesic - Subxeric	41.09	35.16	
5	Very Xerix - Subxeric	17.06	15.69	

1.2.2	Slope Position 
Accuracy of the slope position input layer was determined through visual analysis rather than using point to point analysis with plot data.  Plot data collected in the Okanagan TSA tended to classify slope position more so on a microsite scale, as well the ground slope position calls differ in definition then the classes used by the model. This resulted in difficulty accurately describing this input layer using the field data.  Significant time was spent adjusting classification of the slope position input layer to make sure it best represented slope position on the landscape. The slope position classifications were viewed against ortho images and contour lines.  The following represents the findings:
•	Midslope-straight classifications were assigned to broad valley bottoms.  This is a characteristic that is well documented with the Toposcale AML that was used to create this layer.  To resolve this problem, the slope position layer must be combined with gentle slope classes (slope gradient classes 1 and 2) to better identify broad valley bottoms compared to more mountainous terrain.
•	The output from the Toposcale AML has been smoothed, when combined with the already smoothed DEM some microsite features can be lost.  As a result, it is necessary to couple the slope position layer with another model that has greater definition that can indicate small pockets of changing soil moisture.  In this circumstance the TauDEM wetness index appears to have provided a valuable input.  
•	The slope position layer is intended to provide macro scale input information rather than more specific site level information. In general, the slope position layer provides very valuable input for the PEM at a macro scale level.  It is important to acknowledge that the PEM requires more detail than this layer provides and this layer must be coupled with more site specific layers to reach a quality PEM output.  Examples of additional layers to couple the slope position layer are: wetness index, sensitive ecosystem inventory data, slope, aspect, and forest cover variables.
Overall the slope position layer was deemed to be very useful as a PEM input layer.  Table A2 represents a direct field point to PEM input layer pixel analysis.  Field mesoslope calls are tallied under the slope position classes attributed by the model.  Matches between field calls and model output are highlighted in yellow.

Table A2	Percentage of field Calls Corresponding to Slope Position Model Output
 	 	Class 1	Class 2	Class 3	Class 4	Class 5	Class 6	
 	 	Depression	Lower	Mid-concave	Mid-straight	Upper/Mid-convex	Crest	
 	LV	1	3	0	8	3	2	
 	TO	0	2	0	2	0	0	
Field	LW	0	10	5	6	1	1	
Data	MD	1	4	22	38	21	4	
 	UP	0	1	2	9	47	1	
 	CR	0	0	1	8	8	2	
Total calls	2	20	30	71	80	10	
% correct	0.00	60.00	90.00	64.79	85.00	20.00	

1.2.3	Aspect
The new aspect layer was tested against 906 field observations which were stratified into the same four classes as the PEM input layer. The proportional distribution of the aspect classes were then compared to those of the plot data using Chi-squared analysis. Spatial variability exists in the input and field data, therefore a 40m buffer was placed on the plot location.  If the PEM input layer included the corresponding slope class as the field data within the 40 m buffer the corresponding value was accepted.  Both the chi-squared analysis and the confusion matrix represent a direct field point to PEM input layer pixel analysis, due to the structure of these analyses, it would be difficult to look at the data with a 40m buffer. 

Table A3	Statement of Overall Accuracy
Total Observations		906	
Number Correct	667	
Percent Correct	73.6%	
Lower 95% Confidence Value	70.8%	
Upper 95% Confidence Value	76.5%	

Table A4	Results of the Chi-Square Test of Proportions for the Aspect Class
PEM Aspect class	Observed - Field	Expected – Input layers	Observed-Expected	(O-E)**2/E	
1	125	134	-9	.6	
2	318	357	-39	4.26	
3	301	379	-78	16.05	
4	162	36	126	441.00	
Total	906	906		461.92	
Chi-Square = 461.92, df = 3, p < 0.000001			

Table A5	Aspect Input Layer Confusion Matrix
GIS Input Aspect	Field Class 1	Field Class 2	Field Class 3	Field Class 4	Total	Error of Omission	
Class 1	82	4	29	19	134	38.8%	
Class 2	6	239	54	58	357	33.1%	
Class 3	34	75	212	58	379	44.1%	
Class 4	3	0	6	27	36	25.0%	
Total	125	318	301	162	906		
Error of 
Commission	34.4%	24.8%	29.6%	83.3%			

Average Error of Commission: 		43.0%
Average Error of Omission:		35.2%
Overall error (340/906):			37.5%

The overall accuracy (73.6%) of the aspect layer indicates it is valid for use in the PEM.  The confusion matrix revealed some discrepancies in aspect. Several reasons may explain these errors.  It is necessary to smooth the DEM to a minor degree to remove small pits and sinks, in addition a mean filter which averages pixel values is applied to the DEM.  These processes add a level of generalization to the model.  When mapped at a 1:20,000 scale, this level of generalization is appropriate, however when compared to point specific samples on the ground variations due to microsite conditions can be expected.

1.2.4	Slope gradient
The new slope layer was tested against 906 field observations which were stratified into the same five classes as the PEM input layer. The proportional distribution of the slope classes were then compared to those of the plot data using Chi-squared analysis. Spatial variability exists in the field data, therefore a 40 m buffer was placed on the pixel.  If the PEM input layer included the corresponding slope class as the field data within the 40 m buffer the corresponding value was accepted.  Both the chi-squared analysis and the confusion matrix represent a direct field point to PEM input layer pixel analysis, due to the structure of these analyses it would be difficult to look at the data with a 40 m buffer. The tables below display the results of the chi-squared analysis.

Table A6	Statement of overall accuracy for slope gradient
Total Observations		906	
Number Correct	574	
Percent Correct	63.4%	
Lower 95% Confidence Value	60.22%	
Upper 95% Confidence Value	66.5%	

Table A7	Results of the Chi-square test of proportions for slope class
Slope class	Observed - Field	Expected – GIS Input layers	Observed-Expected	(O-E)**2/E	
1	156	35	121	418.3	
2	225	306	-81	21.4	
3	347	468	-121	31.3	
4	104	71	33	15.3	
5	74	26	48	88.6	
	906	906		575.0	
Chi-Square = 575.0, df = 4, p < 0.000001 			

 In order to clarify places where the PEM input layer was differing in value from the field data a confusion matrix was developed.  For the purpose of the confusion matrix a direct point to point analysis was used rather than the 40m buffer (Table A8).

Table A8	Slope Input Layer Confusion Matrix
GIS Input Slope	Field Class 1	Field Class 2	Field Class 3	Field Class 4	Field Class 5	Total	Error of Omission	
Class 1	26	7	1	1	0	35	25.7%	
Class 2	96	136	67	8	2	306	55.6%	
Class 3	32	79	258	56	43	468	44.9%	
Class 4	4	1	16	29	21	71	59.2%	
Class 5	1	2	5	10	8	26	69.2%	
Total	156	225	347	104	74	906		
Error of 
Commission	83.3%	39.6%	25.7%	72.1%	89.2%			
Average Error of Commission		62.0% 
Average Error of Omission		50.9%
Overall Error (449/906)			49.6%

When developing the DEM and the subsequent slope gradient PEM input layer the landscape slope was smoothed to a certain degree to remove small pits and sinks in the surface.  When doing this it affects the accuracy of the slope gradient model, however it is necessary to create the input layers. This smoothing may account for a certain amount of shift in the classes. In addition, the majority of errors in the slope class occurred with adjacent slope gradient classes.  This may be an indication of a slight spatial shift in the data.  The overall accuracy of the data was 63.4% and deemed acceptable for use in the PEM.

11.3	VRI/FC1
The PEM standards supports the use of VRI forest cover maps where available, but recommends that the layers are based more so on the presence/absence of the tree species in the top three species rather than relying on the proportion of the VRI polygons that are dominated by specific tree species (RIC 1999).  

VRI/FC1 may be potentially considered as one of major PEM input layers.  For this reason, the quality assessment is conducted against ortho images.  The following is a summary of the assessment results:
 
Positional accuracy of the VRI is typically evaluated through the comparison of the position of VRI hydrological features to the corresponding TRIM features.  Since VRI/FC1 is TRIM based, there are no issues revealed in terms of positional accuracy for the areas assessed.

A comprehensive quality assessment was also conducted on thematic content of the VRI/FC1.  The thematic content of the VRI/FC1 in the Okanagan TSA is in reasonably good condition for the purpose of PEM use.

Non-forested and non-productive polygons: It is generally believed that this layer of information is useful for PEM. Many of the previous PEMs in the region and elsewhere successfully used this layer of information.  Based on this review, information contained in the Okanagan TSA is considered reasonably accurate for the purpose of PEM.  The following is a summary of review findings: 

1)	The delineation and classification of non-forested and non-productive areas are considered reasonably accurate both thematically and spatially.
2)	Some major tree species type group such as “Py” and “Act” leading stands etc. are also reasonably accurate and will be useful for PEM.
3)	Most of the VRI “Rock” polygons are in fact rock dominating polygons.
4)	Most of the “NPBR” polygons reviewed are true “NPBR” polygons. However, since the VRI is rolled over from FC1 and there is no soil moisture attribute it may be difficult for a PEM to distinguish between moist or wet “NPBR” and dry “NPBR”. A similar situation also occurred with “NP” polygons reviewed.

The VRI/FC1 of Okanagan TSA is recommended for use in future PEM.  Cautions are advised on the uses of certain attributes as described above. 

11.4	Terrain and Soil Mapping
Terrain data collected in the field observations was inconsistent in collection methods and quality.  Therefore, this layer of information is recommended for reference use only due to the concerns of the mapping scale (1:50,000) and unknown thematic accuracy.  Positional accuracy is not a primary concern due to its intended use (i.e., not to be used directly in GIS overlay process). 

Most sources of the mapping were, in fact, TRIM based and there are no concerns of the positional accuracy.  For thematic accuracy, certain selected feature polygons (e.g., esker complex, glacial fluvial terrace, active fluvial plains, rock and talus slopes, lacustrine etc.) were assessed.  In general, the information contained within the selected terrain feature polygons are considered accurate but overly generalised.  This is mainly caused by the mapping scale (i.e., 1:50,000). 

Using the Terrain and Soil Mapping as a reference layer for generalized/exceptional materials mapping, as opposed to a direct GRID input layer provides useful guidance for identification of important site features for the PEM.  This mapping can provide indication of coarse textured glaciofluvial parent materials, or alternatively fine textured lacustrine materials that would otherwise be difficult to model in the PEM.