The document summarizes a project that evaluated the connectivity capabilities of the Landscape Modelling Framework (LMF) toolset. The project involved 4 case studies: 1) a basic demonstration of the connectivity tools, 2) analyzing the effect of adjusted resistance weights, 3) comparing outputs from different years, and 4) creating species-specific resistance weights. The case studies illustrated how resistance weights influence connectivity outputs and how the tools can help identify landscape changes and create customized species models.

1. ILLUSTRATING THE CONNECTIVITY
FUNCTIONALITIES OF THE LANDSCAPE
MODELLING FRAMEWORK TOOLSET
Applied Geomatics Research Project
Matthew Cousens

2. CONTENTS
• Introduction
• Background
• Project Goals
• Case Study Summary
• Description of Datasets
• Methodology: Case Studies 1-4
• Conclusions
• Questions

3. INTRODUCTION
• Applied Geomatics Research Group contracted by Nova Scotia Department of
Natural Resources to create Landscape Modelling Framework (2012).
• Goal to evaluate Species/Land Cover relationships.
• Connectivity of an area is one of the metrics calculated by the LMF.
• This project will focus on the connectivity capabilities of the LMF.
• Department of Natural Resources are eventually interested in using these
capabilities to analyze the movements of a species of interest.

4. BACKGROUND
• Connectivity defined as “the capacity of individual species to move between areas of habitat
via corridors and linkage zones” (Lindenmayer and Fischer 2006).
• LMF represents connectivity using a permeability calculation.
• Permeability is defined as "the degree to which regional landscapes will sustain ecological
processes and are conducive to the movement of many types of organisms" (Anderson,
Clark, and Sheldon 2012).
• In order to use the LMF toolset to calculate connectivity, weights must first be assigned to
the elements of a land cover map.

5. CHIGNECTO ISTHMUS
• Initial interest
prompted by the
Mainland Moose (Alces
Alces Americana).

6. PROJECT GOALS
• Use the Landscape Modelling Framework to identify issues in habitat connectivity
and illustrate the capabilities of the toolset.
• Evaluate the effectiveness of assorted land elements when used as weighting factors
to calculate connectivity.
• Use the weighting schemes created, to analyze the suitability of specified regions
with respect to habitat connectivity.
• Document the effective uses of the LMF’s connectivity tools.

7. CONNECTIVITY TOOL SUMMARY
Tool Name Tool Function
Classify Forest Stands
Classifies the dataset using an XML file containing the Forest and ForNon
classes that stands can belong to.
Create Patches Creates patches by merging neighbouring stands of the same class.
Create Resistance Table
Creates a resistance table from patch data. A resistance table designates
how easily a species can move through a landscape.
Calculate Local Connectedness
Using the resistance table, local connectedness is calculated by creating
a large set of resistance kernels - one for each cell in the output raster. A
kernel is created from each cell and the size of the kernel (in pixels) is
measured; the kernel size is then used as the output raster's cell value
(AGRG 2013).

8. Class Resistance Weight
Hardwood 20
Softwood 20
Mixedwood 20
Leaf Thermal 15
Softwood Thermal 10
New Clear Cut 55
Old Clear Cut 45
Treed Bogs 30
Wetlands 30
Open Water 65
Other 70
Urban 85

9. CONNECTIVITY TOOL SUMMARY
Tool Name Tool Function
Classify Forest Stands
Classifies the dataset using an XML file containing the Forest and ForNon
classes that stands can belong to.
Create Patches Creates patches by merging neighbouring stands of the same class.
Create Resistance Table
Creates a resistance table from patch data. A resistance table designates
how easily a species can move through a landscape.
Calculate Local Connectedness
Using the resistance table, local connectedness is calculated by creating
a large set of resistance kernels - one for each cell in the output raster. A
kernel is created from each cell and the size of the kernel (in pixels) is
measured; the kernel size is then used as the output raster's cell value
(AGRG 2013).

11. CASE STUDY SUMMARY
Case Study 1:
• Basic representation of the toolset’s functionality with respect to connectivity.
Case Study 2:
• Representation of the effect an adjustment to the weighting scheme can have on the connectivity raster output.
Case Study 3:
• Run data from a different year through the LMF connectivity process.
• Demonstrate the toolset’s ability to provide an appropriate output for functional analytic comparison.
Case Study 4:
• Use moose point data to create the resistance weighting scheme.
• Illustration of the quality of a weighting scheme created using species data rather than a subjective scheme.

12. DESCRIPTION OF DATASETS
Historical Forest Resource Inventory (HFRI):
• Compilation of DNR forest data and LandSat
imagery from 1985-2013 provided by the
AGRG.
• Time-enabled functionality will allow for
more extensive characteristics of the LMF to
be displayed.
• Primary dataset for all case studies.
Nova Scotia 20m Digital Elevation Model
(DEM):
• Retrieved from the Nova Scotia Geomatics
Centre website.
• Used to add topographic information into the
habitat connectivity calculation.
• Slope layer merged with the forest layer prior
to the connectivity calculation.
Nova Scotia roads layer:
• Retrieved from the Nova Scotia Geomatics
Centre website.
• Roads layer also merged with forest layer.
Moose Point Data:
• Provided by the Department of Natural
Resources via the AGRG.
• Collected from GPS collars.
• Used as the basis for the resistance weighting
scheme in Case Study 4.

13. CASE STUDY 1
• Investigation into the connectivity processing capabilities of the LMF.
• Demonstrate the basic connectivity functionality of the toolset and the output it
creates.
• Secondary investigation into the effect the size of the dataset has on the tool
processing time.
• Intention is to aid in future project planning that will involve the connectivity
features in the LMF by providing an accurate representation of the connectivity
toolset’s processing time.

15. CASE STUDY 1
• Looking to demonstrate that the Local Connectedness tool’s representation of the
connectivity of the area, is directly correlated to the resistance weighting.
• Red: Low connectivity values and high resistance weights: Other (70), Urban (85),
New Clear Cut (55), and Old Clear Cut (45).
• Green: High connectivity values and low resistance weights: Leaf Thermal (15),
Softwood Thermal (10), Hardwood (20), Softwood (20), and Mixedwood (20).

16. CASE STUDY 1
• The tool processing time increased at a consistent rate as the size of the
study area increased.
• The largest study area (2,500km2) processed a Local Connectedness output
in 15 hours and 44 minutes.
• The smallest study area (25km2) processed a Local Connectedness output in
only 9 minutes.

17. 25
100
225
400
625
2500
0
500
1000
1500
2000
2500
3000
0 200 400 600 800 1000
StudyAreaSize(Sq.Km)
Tool Processing Time (Minutes)
Calculate Local
Connectedness Tool
25
100
625
2500
0
500
1000
1500
2000
2500
3000
0 5 10 15 20 25 30
StudyAreaSize(Sq.Km)
Tool Processing Time (Minutes)
Create Patches Tool

18. CASE STUDY 2
• Show the effect that different resistance weighting schemes can have on how the
connectivity of an area is represented.
• Include the Nova Scotia roads layer.
• Include slope data derived from a subset of the Nova Scotia DEM.
• New output rasters will be compared to the outputs created from the original
resistance weights to depict the differences in the representation of the connectivity
of the area.

19. CASE STUDY 2
• Multiple Resistance Weighting Schemes:
• Urban: 0
• Urban: 100
• Urban: 99
• Urban: 90
• Urban: 85 & Leaf-On Thermal: 1
• Urban: 85 & Leaf-On Thermal: 10 with interval alterations amongst other land cover
classes

22. CASE STUDY 2
• Road polygons were combined with the forest patches using ArcMap’s Update tool.
• Subtle difference in outputs
• Roads have created barriers of relatively low connectivity in areas that were
previously highly connected.
• All roads were grouped into one class and given the same resistance weight.

24. CASE STUDY 2
• Include slope as an attribute of the resistance
weighting scheme.
• Slope layer created using the Slope tool in
ArcMap.
• All slope values converted to integers using
ArcMap’s Raster Calculator.
• Slope raster binned into 3 categories using
Reclassify tool.
• Slope values 0-5 degrees = 1, 5-15 degrees = 2,
>15 degrees = 3.
• Binned slope polygons were created from Raster
to Polygon tool and combined with forest patches
using Update tool.
• Patch classes manually updated using Attribute
Selection and the Field Calculator.
• New resistance weighting scheme created.
Class Resistance Weight
Hardwood1 20
Hardwood2 35
Hardwood3 50
Softwood1 20
Softwood2 35
Softwood3 50
Mixedwood1 20
Mixedwood2 35
Mixedwood3 50
Leaf Thermal1 15
Leaf Thermal2 30
Leaf Thermal3 45
Softwood Thermal1 10
Softwood Thermal2 25
Softwood Thermal3 40
New Clear Cut1 55
New Clear Cut2 70
New Clear Cut3 85
Old Clear Cut1 45
Old Clear Cut2 60
Old Clear Cut3 75
Treed Bogs 30
Wetlands 30
Open Water 65
Other 60
Road 70
Urban 85

25. • Inclusion of the slope values had a far less noticeable effect.
• Few occurrences of high slope in the Chignecto Isthmus.

26. CASE STUDY 3
• Potential usages of the LMF connectivity output for historical change identification and
change detection assessments.
• Time slider tool in ArcGIS allows the user to isolate data for a particular time frame.
• Data extracted from the HFRI in 2004 and 2013 to illustrate the potential for drastic change
to the connectivity of the area.
• Change Detection tool within the ERDAS IMAGINE software used to depict the change
detection ability of the LMF connectivity tools.
• Change Detection image from ERDAS Imagine compared to each forest patch.

28. Legend
High : 65535
Low : -65535
No Data
Decreased Connectivity
Increased Connectivity
IMAGE DIFFERENCE WITH HIGHLIGHT
CHANGE
IMAGE DIFFERENCE

29. CASE STUDY 3
• Land cover change that caused the most significant change in permeability is one of
5 forested land covers being clear cut and vice versa.
• Resistance weighting scheme: Forested areas are weighted between 10 and 20, while
the two clear cut areas are weighted at 45 and 55.

31. CASE STUDY 4
• Using alternative sources of data may help to create a more realistic, species specific
resistance scheme.
• A weighting scheme will be devised based on the frequency with which the
Mainland Moose is found within each respective land cover type.
• Moose points were spatially joined to the forest patches and the number of points
that were found in each respective class were recorded.
• Moose points totals calculated as relative values rather than absolute.
• Resistance weighting scheme created based on the prevalence of Mainland Moose.

33. CASE STUDY 4
• Very different
representation of land cover
impedance than had been
originally created.
• Most significant differences
found in: Softwood Thermal,
Wetlands, Open Water, Leaf
Thermal, and Treed Bogs.
Open Water and Treed Bogs
were less resistant than
originally valued, while the
three remaining classes were
considered more resistant.
Class
Resistance
Weight
Hardwood 20
Softwood 20
Mixedwood 20
Leaf Thermal 15
Softwood
Thermal
10
New Clear
Cut
55
Old Clear Cut 45
Treed Bogs 30
Wetlands 30
Open Water 65
Other 70
Urban 85
Class
Resistance
Weight
Hardwood 20
Softwood 10
Mixedwood 30
Leaf Thermal 35
Softwood
Thermal
70
New Clear Cut 50
Old Clear Cut 35
Treed Bogs 10
Wetlands 60
Open Water 40
Other 80
Urban 90

34. CASE STUDY 4
• Inconsistencies:
• Decrease in resistance of Open Water.
• Differentiation between Treed Bogs
and Wetlands.
• Avoidance of Softwood Thermal.

37. CONCLUSIONS
• The Local Connectedness output is directly correlated to the resistance
weighting scheme.
• An understanding of the parameters required for resistance weighting was
developed.
• A basic representation of tool processing time was created.
• Potential applications for future use involving change detection or the use of
weighting schemes created from species prevalence data were identified.

38. ACKNOWLEDGEMENTS/PARTNERS
David Colville and Michael Gemmell (AGRG)
LMF Development Team (AGRG)
Nova Scotia Department of Natural Resources