MODELING URBAN GROWTH IMPACT ON GRIZZLY BEAR

HABITAT

Morgan BakerOlivia Ma

Marilyn RomaoJacqueline Tourand

TOPICModeling Urban Growth along the Sunshine Coast and its

impact on Grizzly Bear Habitat

Motivation for Choosing TopicWhy are grizzly bears important?• Umbrella Species

• Protecting Grizzly Bear habitat protects the habitat of many animals

• Endangered Species• British Columbia is our shared

home

Habitat Loss

ClimateChange

Urban Growth

Study Area

Objectives

1Creating a model that captures the decrease in viable grizzly bear habitat due to urban growth in the regions surrounding the Sunshine Coast based upon known data.

2Forecasting the grizzly bear habitat for the following possible scenarios:• if the rate of urban growth

decreases• if the rate of urban growth

continues as is• if the rate of urban growth

increases

The Bear Essentials: Theoretical Background

Using Cellular Automata (CA) for Urban Growth

• Explicitly takes neighbourhood effects/interaction into account models spatially auto-correlated patterns

• Effectively represents complex phenomenon with simple rules

• Discrete representation of space and time

(Mustafa, Saadi, Cools, & Teller, 2014)

The Bear Essentials: Theoretical BackgroundUsing Multi-Criteria Evaluation (MCE)

• Assists assessment of a region based upon multiple factors

• Supports decision making process

(Mohammed, Elhadarry, & Samat, 2016)

Image Source (Wu & Webster, 1998)

The Bear Essentials: Great Bear Rainforest

Great Bear Rainforest• Forested and mountainous coastal

region of BC home of the densest population of Grizzly Bears in the world

Resource Exploitation• Forestry and mining operated by

provincial government• Land contested by numerous First

NationsEven in GBR, Grizzlies are endangered(Dempsey, 2010)

The Bear Essentials: Theoretical BackgroundWhy use three scenarios?

• Allows for comparison of outputs and calibration of model

• Demonstrates significance of rate of change

Data Sets• Grizzly Bear Population Areas• Digital Elevation Model• Landuse Classification• Parks, Ecological Reserves,

and Protected Areas• Road Network• Study Area

METHODOLOGY

Methodology: Flowchart Overview

Methodology: Urban Growth MCE

Methodology: Urban Growth CATransition Rules• Slow Grow

• Rule 1: If the cell is urban, it stays urban.• Rule 2: If there are 13 or more cells urban in the 5x5

neighborhood, non-urban cell becomes urban.• Status Quo Grow

• Rule 1: If the cell is urban, it stays urban.• Rule 2: If there are 8 or more cells urban in the 5x5

neighborhood, non-urban cell becomes urban.• Fast Grow

• Rule 1: If the cell is urban, it stays urban.• Rule 2: If there are 5 or more cells urban in the 5x5

neighborhood, non-urban cell becomes urban.

Methodology: Grizzly Bear MCE

RESULTS

Results: Maps

Results: Slow Grow

Results: Status Quo Grow

Results: Fast Grow

Results: Quantitative Analysis• Loss of Habitat per Scenario

• Slow Grow <1% loss = 1,282.5 Km2

• 119 Football Fields

• Status Quo Grow 2% loss = 90,257.4 Km2

• 8,358 Football Fields

• Fast Grow 4% loss = 210,726.9 Km2

• 19,512 Football Fields

Slow Grow Status Quo Grow

Fast Grow00.0050.01

0.0150.02

0.0250.03

0.0350.04

0.045

<1%

2%

4%

Loss of Habitat

Loss

of H

abita

t (%

)

Challenges• Validation• Data not lining up• Figuring out/justifying

transition rules for CA model• DEM model in Geographic

Coordinate System and not in Projected Coordinate System• Unable to get sensible slope

data

Us!

Our challenges!

Did We Achieve Our Objectives?

1Creating a model that captures the decrease in viable grizzly bear habitat due to urban growth in the regions surrounding the Sunshine Coast based upon known data.

2Forecasting the grizzly bear habitat for the following possible scenarios: if the rate of urban growth decreases, if the rate of urban growth continues as is, and if the rate of urban growth increases.YES!

References & Acknowledgments• Suzana Dragicevic and Taylor

Anderson for their guidance.• Justin Song and SFU SIS Labs

for technological assistance and use of equipment.

Dempsey, J. (2010). Tracking Grizzly Bears in British Columbia's Environmental Politics. Environment and Planning, 1138-1156.

Michel, C. (2012). Wolves attack a grizzly mother & cubs in Alaska. She escapes. Retrieved from https://www.flickr.com/photos/cmichel67/7761841618/

Mohammed, K. S., Elhadarry, Y. A., & Samat, N. (2016). Identifying Potential Areas for Future Urban Development Using GIS-Based Multi Criteria Evaluation Technique. SHS Web of Conferences.

Mustafa, A., Saadi, I., Cools, M., & Teller, J. (2014). Measuring the Effect of Stochastic Perturbation Component in Cellular Automata Urban Growth Model. Procedia Environmental Sciences, 156-168.

Vernon, A. (2007). Alaskan Coastal Brown bear.....11. Hyder, Alaska. Retrieved from https://www.flickr.com/photos/32541690@N02/3200789230Wu, F., & Webster, C. J. (1998). Simulation of Land Development through the Integration of Cellular Automata and Multicriteria Evaluation. Environment and Planning, 103-126.

THANK YOU!