Delineation of Landcover Boundaries in Areas Utilized or Avoided by Female Caribou during Calving and Post-Calving Using Publicly Available Spatial Datasets

8/9/2019 Delineation of Landcover Boundaries in Areas Utilized or Avoided by Female Caribou during Calving and Post-Calvin

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69EG4218: M.Sc. Dissertation

Delineation of Landcover Boundaries in Areas Utilized or Avoided by Female Caribou

during Calving and Post-Calving Using Publicly Available Spatial Datasets

Paul Warren Saunders

A dissertation submitted in partial fulfilment of the requirements for the degree of Master of

Science in GIS, The Manchester Metropolitan University.

Department of Environmental and Geographical Sciences

The Manchester Metropolitan University

May 2010


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Declaration of originality

This is to certify that the work is entirely my own and not of any other person, unless

explicitly acknowledged (including citation of published and unpublished sources). The work

has not previously been submitted in any form to the Manchester Metropolitan University or

to any other institution for assessment for any other purpose.

Signed _________________________________________________

Date ___________________________________________________


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Abstract

The availability and utility of spatial datasets, at no cost to the end user, directly impacts the

ability of government and non-governmental wildlife management agencies to delineatelandcover utilization or avoidance for targeted wildlife species. The availability and utility of

four datasets; Canada Land Inventory for Ungulates, Earth Observation for Sustainable

Development of Forests, Provincial Forest Inventory for the Island of Newfoundland, and the

Landsat 7 ETM+ were evaluated for their usefulness in delineating landcover boundaries in

areas utilized by caribou during calving and post-calving. To perform this evaluation a

representative sample of landcover features, in both utilized and avoided areas, were selected

through the use of space-time scan statistics and maximum step length calculations, then

landcover boundaries were recorded using spiral transects based on the Fibonacci sequence.

The location of all land cover boundaries intersected during the completion of ground based

transects were recorded and provided a baseline dataset for comparison to those depicted

using the selected datasets. Object-oriented segmentation had to be completed for the

Landsat ETM+ dataset before a comparison could be conducted. Root mean square error

(RMSE) values were calculated for all datasets and compared with the ground based results.

In addition, RMSE values were also calculated for a set of randomly generated boundary

locations for each completed transect. For all datasets errors of omission were taken into

account on an independent basis. Upon completion of the evaluation it was determined that

all datasets, except the Earth Observation for Sustainable Development of Forests, where

both the RMSE for random (r) and actual (a) boundary points (r=22.89, a=14.93, error

25meters (m)) was below the associated positional error of the dataset, would be useful for

the delineation of landcover boundaries. The Canada Land Inventory (r=86.60, a=30.43,

error 35m) was deemed useful only for its ability to provide information on historical location

and permanence of boundaries at the landscape scale. To provide landcover delineation for

the island of Newfoundland a combination of both the forest inventory (r=64.71, a=39.47,

error 35m) and landsat datasets (r=37.02, a=27.92, error 30m) must be utilized along with a

variety of ancillary data sources.


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Word Count

Number of Pages: 86Number of Words: 16,02


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Table of Contents

Table of Contents _______________________________________________________________ iv

CHAPTER 1 ________________________________________________________________ 1

INTRODUCTION ____________________________________________________________ 1

1.1: Study Background ___________________________________________________________ 1

1.2: Study Area _________________________________________________________________ 4

1.3: Aim and Objectives __________________________________________________________ 6

1.4: Dissertation Structure ________________________________________________________ 7

CHAPTER 2 ________________________________________________________________ 8

The Importance of Scale in Ecology and Remote Sensing ___________________________ 8

2.1: Introduction _______________________________________________________________ 8

2.2: Spatial and Temporal Scales and the Delineation of Landcover Features _______________ 9

2.2.1 The Concept of Scale _______________________________________________________________ 9

2.2.2 The Effect of Sensor Resolution on Landcover Identification _______________________________ 11

2.3: Landcover Boundaries, Animal Behaviour and Remote Sensor Resolution _____________ 15

2.4: Woodland Caribou and Scale _________________________________________________ 16

CHAPTER 3 _______________________________________________________________ 20

DATA AND METHODS _______________________________________________________ 20

3.1: Introduction ______________________________________________________________ 20

3.2: Data Used ________________________________________________________________ 21

3.2.1: Female Caribou Telemetry Data _____________________________________________________ 22

3.2.2: Landsat-7 Orthorectified Enhanced Thematic Mapper (ETM+) Imagery _____________________ 22

3.2.3: Earth Observation for Sustainable Development of Forests (EOSD) _________________________ 25

3.2.4: Canada Land Inventory (CLI), Land Capability Classification for Wildlife, Ungulates ____________ 26

3.2.5: Newfoundland and Labrador Provincial Forest Inventory _________________________________ 27

3.3: Methods and Techniques ____________________________________________________ 28

3.3.1: The Selection of Study Parameters and Implementation of a Study Design __________________ 28

3.3.1.1 The Identification of Areas Used or Avoided by Female Caribou __________________________ 28

3.3.1.2 The Selection of Attributes to be recorded During Sampling and Sampling Protocol Design ____ 32


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3.3.1.3 The Selection and Utilization of an Unbiased Sampling Design ___________________________ 38

3.3.2: The Comparison of Spatial Datasets __________________________________________________ 40

3.3.3: The Selection of Boundaries and Boundary Location for the Evaluation of Spatial Datasets _____ 42

3.3.4: Landsat 7 ETM+ Dataset Preparation _________________________________________________ 42

3.3.4.1 ENVI Feature Extraction Module Workflow ___________________________________________ 45

3.3.4: Landsat ETM+ Band Selection and Segmentation _______________________________________ 46

3.3.5: Temporal Currency and the Need for Ancillary Data _____________________________________ 52

3.4: The Evaluation of Selected Datasets. ___________________________________________ 52

CHAPTER 4 _______________________________________________________________ 53

RESULTS and DISCUSSION ___________________________________________________ 53

4.1: Introduction ______________________________________________________________ 53

4.2: Observational and Statistical Evaluation of Individual Datasets _____________________ 53

4.2.1: Canada Land Inventory ____________________________________________________________ 53

4.2.2: Earth Observation for Sustainable Development of Forests _______________________________ 56

4.2.3: Provincial Forest Inventory _________________________________________________________ 60

4.2.4 Landsat ETM+ Segmentation File Evaluation ___________________________________________ 63

4.3: Fulfilment of Aims and Objectives _____________________________________________ 64

CHAPTER 5 _______________________________________________________________ 67

CONCLUSIONS _____________________________________________________________ 67

5.1: Conclusions _______________________________________________________________ 67

5.2: Recommendations _________________________________________________________ 68

APPENDICES ______________________________________________________________ 69

REFERENCES ______________________________________________________________ 72


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List of Tables

Table 2.1: Spatial and temporal scales utilized in previous caribou studies. There has not

been a consistent approach to the study of caribou within the context of scale. Earlier studies

were often confined to a single spatial or temporal scale but more recent studies have started

to look at caribou behaviour and landscape interaction at multiple

scales........................................................................................................................................17

Table 2.2: Caribou landcover associations vary with the scale of the life history trait being

investigated. As the temporal and spatial extent of the trait decreases the resolution of the

data required to determine landcover utilization must become finer.......................................18

Table3.1: Specifications for the Landsat series of Earth observation satellites.......................23

Table 3.2: Bands and bandwidths associated with satellites in the Landsat series..................24

Table 3.3: Characteristics of the Landsat ETM+ image utilized in this study.........................25

Table 3.4: The Canada Land Inventory for Ungulates utilized capability classes to rank the

ability of land to support ungulates. These classes are based on limitations that affect the

quantity and quality of habitat for the target species................................................................27

Table 3.5: Female caribou activity patterns during study period............................................28

Table 3.6: Covariance Matrix for Landsat ETM+ bands 1 - 5 and 7......................................47

Table 3.7: Correlation Matrix for Landsat ETM+ bands 1 - 5 and 7.......................................47

Table 3.8: Optimum Factor Index for the 6 highest ranked band combinations......................48

Table 4.1: RMSE values for datasets evaluated in this study. All RMSE values should be

interpreted within the context of the positional error of individual datasets............................65


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List of Figures

Figure 1.1: Adult female caribou and calf (courtesy John Neville)...........................................1

Figure 1.2: Canadian distributions of Woodland and Barren-ground caribou (Rangifer

tarandus) including the various boreal dwelling ecotypes (Map compiled by the Canadian

BEACONs Project, University of Alberta)...............................................................................2

Figure 1.3: Study area, The Topsails, Newfoundland, Canada..................................................5

Figure 1.4: Elevation values for the study area (meters), scale 1:50,000...................................6

Figure 2.1: An hypothetical illustration of caribou neighbourhoods as defined by Addicott et

al. (1987). The different neighbourhoods are a result of the interaction between the spatial

and temporal behaviour of both individual caribou and their actions as a group. (A)

Represents the frame within which the neighbourhoods were measured. (B) Represents the

complete range of the population over its lifetime. (C) Represents the area utilized by caribou

in the winter. (D) Represents the neighbourhood of caribou during calving..........................10

Figure 2.2: An increase in pixel size leads to an increase in the number of landcover features

represented leading to a reduction in pixel variance................................................................13

Figure 2.3: Temporal and spatial scales associated with various ecological processes

influencing caribou in the study area.......................................................................................16

Figure 3.1: Landcover features associated with transect sc2007095h2 as depicted by utilized

spatial datasets..........................................................................................................................22

Figure 3.2: Classification scheme for the EOSD dataset.........................................................26

Figure 3.3: The construction of cylinders during the use of space-time scan statistics involves

the setting of: (a) the spatial extent which dictates the cylinder diameter thus the area over

which points are included, and (b) the temporal extent which is represented by the height of

the cylinder with increasing height signifying the inclusion of points over a larger temporal

period........................................................................................................................................30

Figure 3.4: Transect centered on the maximum step length for caribou sc2006026. Blue dots

indicate the two endpoints that were used to calculate the centroid of the maximum step

length........................................................................................................................................32

Figure 3.5: Transect line based on Fibonacci spiral................................................................39

Figure 3.6: Sampling location identification and transect creation.........................................40

Figure 3.7: Positional Differences between Actual and Mapped Locations............................41


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Figure 3.8: An example of the well defined ecotone that exists between bog and forest in the

study area. Ecotones of this type are often driven by the water content of the soil and tend to

be highly stable over time........................................................................................................44

Figure 3.9: An example of a classification process that could be utilized for the Landsat

imagery evaluated in this study. In this study only the boundaries of features were extracted,

classification of the features contained was not completed.....................................................45

Figure 3.10: The ENVI feature extraction module workflow diagram (ENVI 2008)..............45

Figure 3.11: Landsat bands 1, 4 and 5 represented at: (a) 30m pixel size, (b) pansharpened

15m pixel size and (c) segmented landcover features created using ENVI software. Image is

centered on transect line sc2007096H5....................................................................................50

Figure 3.12: The effects of merge value selection on the number of segments created using

ENVI software: (a) scale factor 1, merge factor 0; (b) scale factor 1, merge factor 85...........51

Figure 3.13: Temporal lags associated with the datasets utilized in this study........................52

Figure 4.1: Landscape limitations associated with sites avoided or used by female caribou

based on the CLI dataset..........................................................................................................54

Figure 4.2: Primary species designation for each polygon intersected by individual transect

lines..........................................................................................................................................55

Figure 4.3: Location of the CLI polygon boundary, detected boundaries, and random points

for transect sc2006082H3.........................................................................................................56

Figure 4.4: The location of ground survey and EOSD boundaries for transect

SC2007051L3_07.....................................................................................................................57

Figure 4.5: The number of boundaries detected for ground-based and EOSD-based datasets

based on individual transects....................................................................................................58

Figure 4.6: The difference between the number of boundaries detected for EOSD data based

on the number of boundaries detected during ground-based surveys for individual transect

lines..........................................................................................................................................59

Figure 4.7: Boundaries detected during a ground based survey of transect SC2007095L1

overlain on the boundaries found in the Provincial Forest Inventory......................................62

Figure 4.8: Forest Inventory coverage of the area included in this study................................63


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Figure 1.2: Canadian distributions of Woodland and Barren-ground caribou ( Rangifer tarandus)

including the various boreal dwelling ecotypes (Map compiled by the Canadian BEACONs Project,

University of Alberta)

Caribou have been present on the island of Newfoundland for approximately 5000 years (Bell

and Renouf 2004). Historical trends in population numbers are not documented however a

drastic decline in numbers was noted in the early 1900s leading to a closure of all caribou

hunting on the island in 1925(Bergerud 1971). In response to high poaching levels, hunting

seasons were reinstated in 1935, albeit at extremely low licence numbers. By mid century,

numbers had started to increase and licence sales were allowed to increase from 33 in 1933 to

IslandofNewfoundland


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7200 in 1950 (Bergerud 1971). In the mid to late 1990s caribou numbers peaked at

approximately 95,000 animals and then declined markedly to an estimated 37,000 by 2008 as

a result of increased adult mortality and reduced calf recruitment (Chris Callahan per.

comm.).

Responding to these dramatic declines, the Government of Newfoundland and Labrador

initiated a caribou strategy in 2006. The objectives were to identify possible causes for the

decline, and development of recommendations to halt or reverse the populations negative

trajectory. A component was the evaluation of existing and historical habitat utilized by

caribou thereby identify habitat attributes deemed important to caribou. This research

represents a sub-component of this habitat evaluation (Environment and Conservation 2008).

The yearly association of Newfoundland caribou with habitat types during specific periods in

their lifecycle has been well documented (Davis 1895, Ware 1903, Sclater 1905, Dugmore

1913). Historical yearly migrations to calving and post calving grounds have been delineated

(Mahoney 2000). It has been postulated that migration to specific calving and post-calving

rearing areas were based on the nutritional needs of the female or an attempt to reduce calf

mortality from predation (Bergerud 1985, Bergerud et al. 1984, Rettie and Messier 2001,

Tamstorfet al. 2005, Servheen and Lyon 1989, Mahoney and Virgl 2003, Barten et al. 2001,

Crete et al. 1990, Stuart-Smith et al. 1997, Johnson et al. 2002, ). This places a critical

importance on areas utilized by female caribou for calving and post-calving rearing. Given

the accumulated evidence regarding the impacts of human development, such as mine

development, forest harvesting, and linear development, on woodland caribou (Schaefer and

Mahoney 2007, Mahoney and Schaefer 2002, Chubbs et al. 1992, Weladji 2002, Vistnes and

Nellemann 2001, Weir et al. 2007, McLoughlin et al. 2003, Courtois et al. 2007, Bergerud

1974a), there is an increased need for baseline data on all areas within the woodland

caribous range. This study has been designed to provide an evaluation of the ability to

delineate landcover boundaries, and their occurrence in areas used or avoided by woodland

caribou on the island of Newfoundland during calving and post-calving using spatial datasets

available at no cost. This need is based on the premise that without the ability to accurately

identify and delineate the boundaries of landcover features it would not be possible to

quantify the relationship between caribou and specific lancover types, or the spatial patterns

exhibited by those landcover features across the landscape. Both, the degree of relationships

between a species and landcover features, and the spatial occurrence of those features in areas


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utilized or avoided, is required to formulate effective habitat management plans (Rolstad

2005, Loveland et al. 2005, Griffith 2004).

1.2: Study Area

The study area is comprised of three separate sites totalling 2394km2

located in the Central

Newfoundland Forest Ecoregion, North-central subregion as shown in Figure 1.3 (Parks

Division 2000). A complex of coniferous forests and wetlands characterizes this area (Parks

2000, Damman 1964). Wetlands are represented by mire complexes, as defined by Rydin

and Jeglum (2006), often as mixture of bogs and fens. Raised bogs are a common feature in

this area. Forests are predominately coniferous and represented by Black Spruce (Picea

marianna) and Balsam Fir (Abies balsamea). Fire plays an important role in the occurrence

of specific forest types allowing for the establishment of Black Spruce forest in areas

previously dominated by Balsam Fir, as well as the establishment of localized stands of

White Birch ( Betula papyrifera), Trembling Aspen (Populus tremuloides) and Pin Cherry

(Prunus pensylcanica) (Damman 1964). Alder ( Alnus rugosa) is also abundant along the

edges of waterways and waterbodies or the transition zones between mires and forests.

Based on data from Environment Canada (2004) this region experiences a more continental

climate than other areas of the island with an average yearly temperature of 3.5 degrees

Celsius and 1200 mm of annual precipitation, approximately 30 percent which falls as snow.

Warmest temperatures are in July, average 16.2C and the coldest month is February, average

-9.1C. The region experiences 140 -160 growing days with green up beginning around mid-

May. Evapotranspiration rates range from 450 500 mm leading to a moisture surplus of

380630 mm per year (Damman 1964).

The study area contains approximately 800 km of manmade linear features comprised of

roadways, transmission corridors and an old railway bed, all of which have the potential to

negatively affect caribou (Forestry 2008, Vors 2006 et al.). The majority of these features are

unpaved forest access roads used for pulpwood harvesting activities. Forest harvesting has

occurred in this area on a regular basis since the 1980s and has resulted in a mosaic of

cutovers in various stages of regeneration (Forestry 2000). Three large forest fires (over 200

ha) have been recorded in the study area occurring in 1964 (393 ha, location 49.0 -56.07),


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1986 (1399 ha, location 49.031 -56.095), and 1999 (3675 ha, location 49.3 -56.23) (Canadian

Forest Service 2002).

Figure 1.3: Study area, The Topsails, Newfoundland, Canada.

Topography of the area is characterized by rolling terrain with elevation ranging from 76 -

647meters (Wildlife Division 2007, Figure 1.4). Extreme values are represented by river

valleys and rock outcrops, and forests are restricted to higher terrain or areas where the

terrain rises above the surrounding mires.


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Figure 1.4: Elevation values for the study area (meters), scale 1:50,000.

1.3: Aim and Objectives

The aim of this investigation is to evaluate freely available spatial datasets (available at no

cost) for the identification of boundaries between landcover features in areas either utilized or

avoided by female caribou during calving and post calving periods.

To reach this aim the objective will be to:

Identify publicly available (cost free) spatial data sets covering the study area.

Apply a sampling protocol for the identification of areas utilized or avoided by

caribou.

Delineate boundaries between landcover features and record associated attributes

occurring along transects representing areas utilized and avoided by caribou.


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Quantify the accuracy of selected spatial datasets, when compared to recorded

transect data, using RMSE calculations.

Evaluate spatial error rates associated with selected spatial datasets and compare to

RMSE values obtained for each dataset to determine suitability of those datasets forcaribou landcover utilization studies.

Determine if a publicly available spatial dataset (available at no cost) can be used for

the identification and delineation of landcover features associated with areas utilized

or avoided by caribou.

The evaluation of selected datasets will rely on an understanding of scale as it applies to

animal ecology, the recording and utilization of spatial data, and the variability inherent in the

spatial and temporal scales selected for the study of woodland caribou. These topics will be

discussed in the following chapter.

1.4: Dissertation Structure

The following section outlines the aims and objectives of this study. Chapter Two presents

the concept of scale, and its relationship to remote sensing, ecology and caribou behaviour is

explored. This exploration includes recognition of the interplay between scale, caribou, and

boundaries. Chapter Three begins with the outline of selected datasets. This is followed by

methods for the unbiased selection of sample sites used for the collection of ground truth

data. Sampling design and the selection of attributes to be recorded was discussed next. The

use of root mean square error as a means for the comparison of datasets is then introduced.

Chapter 4 is a combined results and discussion section since this provided the best

mechanism to present graphical results and interpretative information in a sequence that was

easy to follow. This chapter ends with conclusions based on the presented results and

associated discussion. Chapter 5 highlights important components of the dissertation and re-

emphasizes conclusions based on the results obtained in the study. The final section outlines

recommendations that will allow continuation of this work and methods for utilization of the

datasets included in this study.


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CHAPTER 2

The Importance of Scale in Ecology and Remote Sensing

2.1: Introduction

The use of remotely sensed data for the classification and delineation of wildlife habitat has a

long and varied history of adaptation to advances in technology and techniques used in its

collection (Glenn and Ripple 2004). The extent of these advances and methods of adaption

have been extensively reviewed during the past three decades (Gustafson 1998, Tueller 1980,

Gottschalket al. 2005, Glenn and Ripple 2004). The development of desktop computing

systems allowed for the effective and efficient incorporation of remote sensing data intoresearch studies aimed at determining landcover utilization by wildlife (George et al. 1978).

Additionally, enabling technologies, such as Global Positioning and Geographical

Information systems, provided a cost effective means for management agencies to monitor

changes in wildlife habitat (Rogan and Chen 2004).

Tueller (1980) recognized the potential of remote sensing for use in the delineation of

wildlife habitat over large areas. This potential was reemphasized by Leyequien et al. (2007)

who concluded that remote sensing products, in conjugation with ancillary data, is the most

promising approach for the monitoring and management of biodiversity provided validation

is conducted using traditional observation techniques. Compared to traditional ground based

surveys of wildlife habitat, remote sensing products facilitate the use of more economical

techniques for habitat delineation (Kushwaha and Roy 2002).

Classification and delineation of landcover features using remotely sensed data has to be

conducted within the bounds set by the available data, and the ecology of the species to

which the results of the classification and delineation are to apply. All remotely sensed data

have inherent properties that influence the final use to which it can be applied. These

properties take the form of positional uncertainty, radiometric and spatial resolution of

sensors, and categorical uncertainty resulting from inadequate training data or generalization

procedures used in classification (Castilla and Hay 2006, Frank and Tweddale 2006).


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The concept of scale, from both the temporal and spatial perspective, provides the primary

link between available remotely sensed data and its ability to delineate landcover utilized for

a specific species. If animals are interacting with landcover features on scales below the

resolution of the remotely sensed data then the resulting classification may actually

underestimate the amount of habitat available. Ritters et al. (1997) found a 50% reduction

for habitat delineated as the resolution of scanning windows became coarser. The issue of

scale permeates the complete process of the classification and delineation of landcover

utilization. It is influenced by; (1)the scale at which species locations are collected; (2) the

scale at which used and unused sites are indentified, (3) the scale at which ground truthing

data is collected; and (4)the minimum mappable area possible based on sensors used in the

collection of selected spatial datasets.

2.2: Spatial and Temporal Scales and the Delineation of Landcover

Features

2.2.1 The Concept of Scale

The concept of scale has been in existence for half a century with its roots traceable to work

conducted in the field of plant ecology. Pioneers in the field, such as Watt (1947), Greig-

Smith (1952) and Hutchinson (1953), started to realize that methods for the detection of

random or non-random distributions of plants provided conflicting or erroneous results

depending on the size and distribution of sample plots used. It was noted that the comparison

of ecological features across varying scales could pose problems (Turrill 1954). Over the

next three decades, the concept of scale became prominent in ecological literature reaching a

peak in importance by the mid to late 1980s. Throughout this period researchers attempted

to define scale in an ecological context and establish methods for selecting or setting

associated boundaries (Elton and Miller 1954, Goodall 1963, Van Dyne et al. 1963, Meade1974, Mack and Harper 1977, Delcourt et al. 1983, Addicott et al. 1987)

Two overarching concepts prevailed during this period; the presence of patterns in the

distribution of an organism and the existence of a neighbourhood in which an organism

operates (Figure 2.1). These concepts were expanded to incorporate not only the spatial

aspects in intra and inter species relationships but also interactions across temporal spans.

Scale was seen as important due to conflicting results being obtained when research was

conducted that utilized different size plots or study areas. It was not until the late 1980s that


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the concept of scale had matured and entered mainstream research as an overarching axiom

(Levin 1992, Fortin and Dale 2005).

Figure 2.1: A hypothetical illustration of caribou neighbourhoods as defined by Addicott et al. (1987).

The different neighbourhoods are a result of the interaction between the spatial and temporal behaviour

of both individual caribou and their actions as a group. (A) Represents the frame within which the

neighbourhoods were measured. (B) Represents the complete range of the population over its lifetime. (C)

Represents the area utilized by caribou in the winter. (D) Represents the neighbourhood of caribou

during calving.

During the later part of the 1980s the issues surrounding scale had reached a point whereGolley (1989) announced that a paradigm shift, as defined by Kuhn (1980), had occurred in

ecology (Schneider 2001). This narrowing of focus was evident with the creation of a branch

of ecology concerned with landscapes and the appearance of journals and papers dealing

specifically with the subject. It was at this time that landscape ecology was defined and its

place in Holistic theory described (Weins and Milne 1989, Zonneveld 1989). Research began

to address space and time issues that were identified in the previous decades, in an attempt to

develop methods and procedures to mitigate their affect on experimental results.


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As research progressed it seemed that no sector of ecology was immune from the effects of

scale. This led to the formulation of a series of propositions that could be used to guide

research in the field:

1. Scale is species specific (Wiens 1989, Wiens and Milne 1989, Angelstam et al. 2003,

Baldi and McCollin 2003, Nams 2005).

2. The scale selected for a study influences the results obtained (Miline et al. 1989,

Jentsch et al 2002, Denny et al. 2004).

3. There is no set scale at which a study can be performed (Levin 1992, Denny et al.

2004).

4. A change in the temporal or spatial scale of a study causes a corresponding change in

the variance of variables being measured (Wiens 1989).

5. The level of heterogeneity observed for a given landscape depends on the scale at

which it is viewed (Chen et al. 2002).

6. Results obtained at one scale are not readily transferrable to another scale (Edmunds

and Bruno 1996).

Work in these areas is still progressing within the context of two paradigms; the scale-

invariance inherent in ecological patterns, and the hierarchical and distinct nature of

ecological patterns at various scales; both, which still have a role to play in individualized

studies (McMahon and Diez 2007). With the advent of studies at a broadening range of both

spatial and temporal scales and the increasing use of satellite obtained imagery, a call has

been made for the establishment of new interdisciplinary approach to the study of the

landscape at multiple scales named Satellite Ecology (Muraoka and Koizumi 2009, Table

2.2).

2.2.2 The Effect of Sensor Resolution on Landcover IdentificationThe issue of scale has been a part of remote sensing since the first camera was used to get an

aerial view of the landscape (Coops et al. 2007). Photography continued to be the standard

until 1959 saw the first satellite launched to provide coverage of the Earths surface. In 1972,

the first member of the Landsat series was launched, dedicated to the delineation and

monitoring of landcover (Cohen and Goward 2004). Additional satellites have been

launched, some to take the place of aging precursors, and others targeted at the collection of

data at other locations along the spectral spectrum (Rogan and Chen 2004). Consistent


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throughout this period was the launch of satellites with the capability to provide images at a

finer spatial and/or spectral scale.

Scale is interchangeable with resolution, which in turn dictates the size of objects that can be

delineated using remote sensing imagery. Several aspects of remote sensing are affected by

scale, such as spatial, spectral, radiometric or temporal resolution and the extent of coverage.

All can vary in their implication for feature identification, dependent on their interaction

with, and the size of target objects (Coops et al. 2007, Renick and Grebner 2002). The

influences of these aspects are outlined below:

1. Spatial Scale

The spatial scale is most often representative of the size of the instantaneous field of

view of the imaging sensor (Tueller 1989). This is often stated as the effective pixel

or grain size in produced images and is sensor dependent (Innes and Koch 1998).

Currently available remote sensing data is available with a range in pixel size from

several centimetres to several kilometres. Pixel size has a direct impact on the

smallest land based object visible, with resolution decreasing as pixel size increases

(Liam et al. 2004). This is referred to as the smallest object mappable and it imposes

strict limitations on the use of satellite imagery (Anderson et al. 1976). Resolution of

satellite imagery has a direct effect on the area occupied by a specific feature (Saura

2004, Figure 2.2). Topan et al. (2009) found that the effective resolution of a given

satellite system may be greater than the stated nominal size of objects that can be

identified. It was found that as resolution got coarser the size of contiguous features

increased and small isolated features tended to disappear (Benson and MacKenzie

1995, Ponzoni et al. 2002). In addition, a disparity between the spatial resolution of

the image and the size of specific landcover features could lead to a reduction in pixel

variance reducing the ability to distinguish unique landcover features (Woodcock

1987). This leads to the occurrence of mixed-pixels which could pose a problem for

conventional classification techniques (Tiwari et al. 1999). Tawari et al. (1999)

provided an overview of the four prevailing techniques in use at the time for dealing

with this issue:

1. Maximum Likelihood Classifier

2. Linear Mixture Modelling


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3. Fuzzy Sets

4. Neural Networks

New algorithms and techniques are still being developed to address this issue (Uttam

et al. 2008, Ge et al. 2006).

Figure 2.2: An increase in pixel size leads to an increase in the number of landcover features represented

leading to a reduction in pixel variance.

2. Spectral Scale

Spectral scale refers to the portion of the electromagnetic spectrum from which the

satellite-based sensors are able to collect data. These portions are commonly referred

to as bands and can encompass radiation from the ultraviolet to the infrared range.

The first satellites launched were restricted in the number of bands they could record

but as technology advanced hyperspectral based receivers, capable of recording

hundreds of bands, were launched (Coops et al. 2007). The addition of these bands

allowed for the separation of landcover features where there existed only small

differences in reflective profiles. This allowed for the mediation of spectral


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14

swamping of adjacent bands caused by highly reflective landcover features which is

important in areas with high levels of landcover heterogeneity. (Ferguson 1990).

3. Temporal Scale

The identification of landcover features can benefit from remotely sensed data that

has been collected at various times of the year (Lefsky et al. 2001). Seasonal

availability of imagery for a given location is controlled by the repeat period of the

satellite and prevailing weather conditions, especially cloud and haze. As an

example, the Landsat 7 satellite has a repeat period of 16 days but it could take from

3-5 years to obtain a follow-up image for a specific location (Cihlar et al. 2003,

Ranson et al. 2003). Temporal scale become even more important when applied to

the activity patterns of the species under investigation (Boyce 2006). Landcover

utilization can vary seasonally resulting in the requirement for imagery from several

periods throughout the year which is not available for the datasets included in this

study.

4. Radiometric Scale

Coops et al. (2007) refers to radiometric resolution as the information contained in an

image expressed as a number representing the intensity recorded by the sensor.

Information is stored in the form of bits where a higher bit count corresponds to an

increasing number of recorded intensity levels. Current satellites, such as Landsat-7,

record intensities with 8 bits or 28

=256 intensity levels. Newer satellites are being

launched that use 11 bits. This is deemed as the least critical factor in selecting

satellite image data because the 8 bit level used by most satellites is more than

adequate for forest cover identification (Coops et al. 2007). Of important note, is the

impact that increasing temporal and spatial scales can have on the radiometric

properties of remotely sensed images. The scale of temporal impacts are influenced

by changes in the ambient conditions at the various times that images are obtained,

whereas, the scale of spatial impacts are dictated by the spatial separation of

landcover features and the effective pixel size of the recorded image (Tuominen and

Pekkarinen 2004, Chen et al. 2005).


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5. Extent

Extent refers to the aerial coverage of the selected satellite imagery data. A

relationship exists between the spatial scale of the imagery and the extent covered,

whereby, as resolution increases the area covered by the image is reduced (Edwards et

al. 2001). This relationship also applies to the temporal resolution whereby an

increase in extent leads to increased temporal resolution (Cihlar 2000). Increases in

spectral and radiometric resolution have an indirect affect on extent through the

increase in data file sizes. The file size for a static extent would increase as the

spectral and/or radiometric resolution is increased. Any increase in extent would

compound the increase in file size limiting possible users. This limitation is being

overcome by the introduction of quad core computers and the lifting of random access

memory restrictions through the increased adoption of 64 bit based operating systems.

Issues related to scale are often project specific and vary depending on the scale at which

landscape features exist and the patterns they exhibit. This variability in landscape features

led Ju et al. (2005) to conclude that no single scale could represent a feature or classification.

Bocket al. (2005) and Andrefouet et al. (2003) extended on this concept by stating that the

observer and methods employed contribute to the selection of scale. It can therefore be stated

that the selection of the appropriate scale is dependent on the geographical phenomenon

under study, the scale at which it interacts with its surroundings and the type of analysis to be

performed (Makido and Shortridge 2005). Given the fine scale at which some species

interact with their habitat a mismatch currently exists between readily available remotely

sensed data and the scale required to evaluate these interactions (Kerr and Ostrovsky 2003).

2.3: Landcover Boundaries, Animal Behaviour and Remote SensorResolution

Advances in animal telemetry technology allowed the collection of animal movement data at

temporal and spatial resolutions far finer than the readily available remotely sensed imagery

(Ropert-Coudert and Wilson 2005). When this telemetry data is plotted on coarse resolution

remotely sensed imagery it could show an exaggerated affinity, by the species being studied,

for larger landcover features. This is a direct consequence of the relationship between the

apparent size of landcover features and the resolution at which they were recorded (Benson


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and MacKenzie 1995, and Ponzoni et al. 2002). This issue limits the ability to determine the

relationship between landcover features (especially those covering a small extent) and the

utilization or avoidance of those landcover features by a specific species.

2.4: Woodland Caribou and Scale

Woodland caribou are large, highly mobile ungulates that interact with the landscape at

multiple scales (Johnson et al. 2001, Figure 2.3). These scales are the result of life history

traits, such as fall and spring migration, selection of calving and post-calving areas, the fall

rut, occupation of a winter range, and the inherent heterogeneity of the landscape they

inhabit. The degree of landscape utilization various by an order of magnitude across its

range, from the selection of individual plants to historical shifts in distributions and has

necessitated the selection of remotely sensed datasets that are compatible with the scale under

investigation (Table 2.2). This has led to the completion of studies that vary widely in scale

in both the spatial and temporal domain (Table 2.1).

Figure 2.3: Temporal and spatial scales associated with various ecological processes influencing caribou

in the study area


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Table 2.1: Spatial and temporal scales utilized in previous caribou studies. There has not been a

consistent approach to the study of caribou within the context of scale. Earlier studies were often

confined to a single spatial or temporal scale but more recent studies have started to look at caribou

behaviour and landscape interaction at multiple scales.

There has not been a consistent approach developed for the study of woodland caribou (Table

2.1). The spatial extent of completed studies varies from 600700,000 km

2

, with a mean of

Study

Extent

(km2)

Spatial

Scale(s)Data Type

Resolution

Collar Fix

Period

Location

Fix

Error

(meters)

Temporal

Periods

Temporal

Span

(years)

References

16,000 1landsat

field plots80

N/A+N/A N/A 1 2 George et al. 1978

25,000

700,0001 N/A N/A 3 or 4 days


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32,000 km2

(with the 700,000 km2

study removed). Several factors have contributed to the

variability in extents between studies including; population size, temporal period selected,

and the number of years over which the study was completed. It has been recognized that the

mapping scale can have a large impact on the results obtained in wildlife habitat or

behavioural studies (Anderson et al. 1999). This has led to the completion of studies at

multiple spatial scales with two distinct scales being used for most studies, these being the

population and the individual. A similar situation exists for temporal scales and has resulted

in the division of caribou life history into 6 temporal periods; calving, post-calving, summer,

fall, winter, spring/pre-calving with some subdivisions occurring in these catagories

(Bergerud 1974b, Rettie and Messier 2000). Although some studies have addressed all

periods, the majority have been concentrated on only a select portion of the complete yearly

life cycle. This requires researchers to be cognizant of the domain(s), as described by Ford

(2004), to which the results apply and the fact that it is inappropriate to apply findings outside

those domains without proper justification.

Table 2.2: Caribou landcover associations vary with the scale of the life history trait being investigated.

As the temporal and spatial extent of the trait decreases the resolution of the data required to determine

landcover utilization must become finer.

Caribou EcologicalPeriod

RequiredResolution

(meters)

MissionSatelliteSensor

Resolution(meters)

Historical Distribution 1000

Spot-4

Spot-5

Envisat

Vegetation

AATSR

1150

1000

Range Shifts

Historical< 1000

Aqua

Terra

Envisat

Modis

MERIS

250

300

Generational Home

Ranges


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Data on landcover below this limit has, in most cases, been collected through the use of plots

or transect and later digitized for use in a GIS. The use of higher resolution satellite imagery

was possibly limited by the high cost of data per km2

and the larger areas covered by

migratory ungulates like caribou (Table 2.1).

Also important is the high level of variability, both spatially and temporally, associated with

recorded caribou telemetry locations. The cause for most of this variability can be attributed

to the improvements in technology which translated into more frequent locations with a lower

positional error rate. The quality of mammal collar technology has matured to a stage where

limitations involved in obtaining fine scale data on movements are now only limited by

logistical and financial considerations

It was only in the last decade that works on the relationship between caribou and the

landscape in which they inhabit has received direct attention. This was the result of advances

in computer and satellite technology, which allowed for the processing of the large datasets

required for the study of a highly mobile and migratory species. Most of the newer satellite

datasets are only available at a substantial cost limiting most scientists to the use of publicly

available data. This study will evaluate several of these datasets an attempt to determine their

usefulness in providing a province wide map of landcover resources available to woodland

caribou. The next section will provide a brief overview of these selected datasets.


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CHAPTER 3

DATA AND METHODS

3.1: Introduction

This research will be guided by two postulates:

1. Female caribou are responding to small scale (small aerial extent) landcover features due

to increased nutritional requirements and the need to reduce the chance of losing a calf

through predation, therefore there should be a significant difference between used and

unused areas (Gustine et al. 2006, Rettie and Messier 2000).

2. Landcover selection is determined by the ability of adult female caribou and calves to

move through the landscape, therefore mobility should be restricted in areas avoided by

female caribou (Recovery Implementation Group, Hart and Cariboo Mountains 2005,

Luick and White 1986).

Landcover selection has been evaluated using three distinct experimental designs (Garshelis

2000 p. 115). These designs are characterized as:

1. Use/Availability: compares the time an animal spends in each landcover type to the

percentage of availability.

2. Use/Demographic: compares the demographic response (recruitment, survival, etc.) for

populations utilizing different landcover types.

3. Site Attribute: compares a selection of site attributes in selected landcover types to

random or avoided sites.

Given the postulate that female caribou are reacting to fine scale landcover features, the site

attribute experimental design was utilized because of its ability to provide more detailed

information about landcover (Garshelis 2000 p.150) This allowed for greater precision in areas

where landcover features are clumped or rare (Buckland et al. 2007). The study design is based

on two fundamental assumptions: used sites are in appropriate habitat and unused sites are in

inappropriate habitat (Garshelis 2000 p. 143).


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Data selected for the completion of this study is outlined below followed by a description of

methods for the selection of sites used or avoided by caribou. This is followed by a description

of methods used to determine the suitability of selected datasets. Suitability can be interpreted as

being the ability to use the selected datasets for the identification and delineation of landcover

boundaries in areas identified as being utilized or avoided by female caribou. To increase

validity and eliminate or reduce bias, the study was designed such that individual caribou

dictated the study area boundaries and the areas utilized or avoided. This was coupled with a

ground based landcover boundary detection protocol that eliminated or reduced bias that could

be introduced by any inherent directionality of landcover features.

3.2: Data Used

One caribou telemetry dataset and five spatial datasets were utilized in this study. The caribou

telemetry dataset was collected under the Newfoundland and Labrador Caribou Strategy, a 5-

year research program initiated by the Government of Newfoundland and Labrador. Four spatial

datasets were selected based on their accessibility, being available on the internet for download

by the public, and their potential for use in completing landcover delineation for the island of

Newfoundland (Figure 3.1). The selected datasets are represented by; Landsat 7 ETM+ imagery,

Earth Observation for Sustainable Development of Forests (EOSD), Newfoundland and

Labrador Forest Inventory Dataset, and the Canada Land Inventory for Ungulates. In addition,

landcover data was collected via ground transects in areas used or unused by collared caribou.

The characteristics of each of these datasets are outlined below.


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Figure 3.1: Landcover features associated with transect sc2007095H2 as depicted by utilized spatial datasets.

(all images are displayed at the same scale)

3.2.1: Female Caribou Telemetry Data

A dataset was obtained from the Wildlife Division, Government of Newfoundland and Labrador,

containing telemetry data obtained from GPS collars on 11 individual caribou. Locations were

recorded at 2-hour intervals for the complete study period from May 15 to September 10, 2007.

Collars were comprised of both differentially corrected and uncorrected units with a

corresponding location error of


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government to enact the Land Remote Sensing Policy which was design to make imagery

available based solely on delivery cost. With the advent of the internet and geoportals this cost

has been reduced to nil.

Table3.1: Specifications for the Landsat series of Earth observation satellites.

SystemLaunch Date

(Decommissioned) Sensors*

Resolution

Range

(meters)

Altitude

(kilometers)

Return

Period

(days)

Landsat

1

23-Jul-72

6-Jan-78RBV

MSS79 - 80 917 18

Landsat

2

22-Jan-75

25-Feb-82RBV

MSS79 - 80 917 18

Landsat3

5-Mar-7831-Mar-83

RBVMSS

40 - 240 917 18

Landsat

416-Jul-83

MSS

TM30 - 120 705 16

Landsat

51-Mar-84

MSS

TM30 - 120 705 16

Landsat

6

5-Oct-93

5-Oct-93 ETM 15 - 60 705 16

Landsat

715-Apr-99 ETM+ 15 - 60 705 16

*Sensor details can be found in Table 3.2

Imagery was made available to the public through a variety of internet based geoportals such as

the Canadian Geogratis website. Documentation supplied with this imagery describes

orthorectified as An image derived from a conventional image by simple or differential

rectification to remove image displacements caused by sensor tilt and relief of terrain. (GeoBase

2008). The result of orthorectification is an image with a planimetric accuracy of 20 30

meters. A secondary consequence is the reduction in feature mismatch during the mosaicing of

multiple images. Characterics of the Landsat image used in this study can be found in figure

Table 3.3.


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Table 3.2: Bands and bandwidths associated with satellites in the Landsat series.

Satellite SensorBandwidths

(micrometers)

Resolution

(meters)

Landsats 1 and 2

RBV

MSS

0.480.57

0.580.68

0.700.83

0.5 - 0.6

0.60.7

0.70.8

0.81.1

80

80

80

79

79

79

79

Landsat 3

RBV

MSS

0.5050.75

0.5 - 0.6

0.60.7

0.70.8

0.81.1

10.412.6

40

79

79

79

79

240

Landsat 4 and 5

MSS

TM

0.50.6

0.60.70.70.8

0.81.1

0.450.52

0.520.60

0.630.69

0.760.90

1.551.75

10.412.5

2.082.35

82

8282

82

30

30

30

30

30

120

30

Landsat 7

ETM+ 0.450.52

0.520.60

0.630.690.760.90

1.551.75

10.412.5

2.082.35

0.500.90*

30

30

3030

30

60

30

15*Panchromatic


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Figure 3.2: Classification scheme for the EOSD dataset

3.2.4: Canada Land Inventory (CLI), Land Capability Classification for Wildlife,

Ungulates

The compilation of a digital classification of the suitability of selected sections of the Canadian

landmass for ungulates was initiated in 1961 (Environment Canada 1970). By 1965 a

classification system was agreed upon by the federal, provincial and territorial governments.

26.5% of the Canadian landmass was classified which included 100% of the island of

Newfoundland (Environment Canada 1980). Areas were classified at a scale of 1:50,000 and

classifications were based on the best available data derived from multiple sources and in

multiple formats. Errors associated with the location of polygonal features were not specified in

the documentation. Primary land classification scheme is outlined in Table 3.4. It should be

noted that these primary classes are used in conjunction with a series of subclasses related to

climate and land categories plus identification of the target species. The reader is directed

towards the referenced documentation for more information on the classification scheme and

selected categories.


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Table 3.4: The Canada Land Inventory for Ungulates utilized capability classes to rank the ability of land to

support ungulates. These classes are based on limitations that affect the quantity and quality of habitat for

the target species.

Landscape

Classification

Capability Description

CLASS 1Lands having no significant limitations to the production of ungulates. Capability onthese lands is very high. They provide a wide variety and abundance of food plants and

other habitat elements.

CLASS 1W Lands in this special class are CLASS 1 areas which are winter ranges upon whichanimals from surrounding areas depend.

CLASS 2Lands having very slight limitations to the production of ungulates. Capability on these

lands is high but less than CLASS 1. Slight limitations are due to climatic or other

factors which have a slight adverse effect on the habitat.

CLASS 2W These are CLASS 2 lands which serve as necessary winter ranges for animals fromsurrounding areas.

CLASS 3

Lands having slight limitations to the production of ungulates. Capability on these

lands is moderately high, although productivity may be reduced in some years. Slightlimitations are due to characteristics of the land which affect the quantity and quality of

habitat or to climatic factors which limit the mobility of ungulates or the availability of

food and cover.

CLASS 3W These are CLASS 3 lands which serve as necessary winter ranges for animals fromsurrounding areas.

CLASS 4Lands having moderate limitations to the production of ungulates. Capability on these

lands is moderate. Limitations are similar to those in CLASS 3, but the degree oflimitation is greater.

CLASS 5

Lands having moderately severe limitations to the production of ungulates. Capability

on these lands is moderately low. Limitations are usually a combination of two or more

of climate, soil moisture, fertility, soil depth to bedrock or other impervious layer,

topography, flooding, exposure, or adverse soil characteristics.

CLASS 6

Lands having severe limitations to the production of ungulates. Capability on theselands is very low. Limitations are so severe that they are easily recognized. For

example, soil depth may be negligible or climatic factors so extreme that ungulatepopulations are severely reduced.

CLASS 7Lands having limitations so severe that there is little or no ungulate production.

Capability on these lands is negligible or non-existent. Limitations are so severe that

ungulate production is precluded or nearly precluded.

3.2.5: Newfoundland and Labrador Provincial Forest Inventory

The digital Provincial Forest Inventory is updated and maintained by the Newfoundland andLabrador Forest Service. The databases extent covers most of the island of Newfoundland with

the exception of areas deemed as having little or no timber of any commercial value. The vector

based dataset is a compilation of spatial data form a wide variety of sources; including, ground

and helicopter based surveys and aerial photography interpretation. Positional error rates are


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below 30 meters (Carl Marks, GIS Analyst, Wildlife Division, Newfoundland and Labrador,

personal communications 2008). Attribute classification is per the Data Dictionary for District

Library as published by the Provincial Department ofNatural Resources (2007).

3.3: Methods and Techniques

Wulder et al. (2006) outlined the steps necessary to establish an effective accuracy assessment

framework for the evaluation of large-area landcover classifications. Emphasized in this paper

was the need to select the proper sampling unit and sampling design. Given the life history

traits of woodland caribou and the unique landcover associations related to these traits, this study

was bounded by both temporal and spatial constraints. Given these constraints, completion of

this study involves:

1. The identification of areas utilized and avoided by female caribou during the

specified time period (Table 3.5)

2. The selection of attributes to be recorded for selected areas.

3. The selection of a study design that will allow for the recording of attribute values

and the elimination or reduction of bias.

Table 3.5: Female caribou activity patterns during study period.

Activity Period From To

Calving Area Selection and Calving 15-May 14-Jun

Post-Calving Migration 15-Jun 7-Jul

Post-Calving Rearing 8-Jul 10-Sep

Methods used in the identification and selection of the above noted study components are

outlined below.

3.3.1: The Selection of Study Parameters and Implementation of a Study Design

3.3.1.1 The Identification of Areas Used or Avoided by Female Caribou

The delineation of used areas was conducted with a space-time permutation scan statistic

(STPSS) (Kulldorffet al. 2005). Developed for the detection of disease outbreaks, the STPSS

has seen only limited use in the field of ecology (Marj et al. 2006).


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Uwt= cwt xtcwt / C (1)

Equation 1 represents the general form of the statistic where Uwt is the expected number of

caribou locations at location w and time t. Cwt is the number of caribou locations at location w at

time t , and C is the total number of caribou locations. The value ofUwt is used to calculate the

test statistic via equation 2.

Twt = [cwt /Uwt ]cwt X [(C-cwt)/(C- Uwt)]

C- cwt if cwt >Uwt = 1 (2)

otherwise the test statistic is

T = maxwt Twt (3)

The statistic involves the use of millions of overlapping cylinders to define the spatial extent of

the study, and whose maximum diameter the user has defined (figure 3.3). Cylinder size varies

via a set of concentric circles, since we do not know the size of existing clusters a priori,

allowing for the identification of multiple sized clusters. Cylinder sizes range from 0 to the

maximum size specified and are centered on each of the points contained in the sample. The

temporal component of the statistics is represented by the height of the cylinder, which again

varies up to a maximum set by the user, and an increase in height translates to the inclusion of

points over a longer time period. This leads to the utilization of cylinders that can vary from

short and wide or tall and skinny, depending on the unique space-time variable combination of

spatial extent and time period used in their creation. Use of the statistic requires only

information on the location of the events and the time of occurrence (Kulldorff 2009). The

output of the statistic is a value that ranks the importance of the identified cluster for a specific

location bounded by a given spatial extent and time period. p-values for identified clusters can

then be compared using Monte Carlo testing methods (Dwass 1957) as per the following

equation:

p = rank(Tcluster) / (1 + # replicants) (4)


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Figure 3.3: The construction of cylinders during the use of space-time scan statistics involves the setting of:

(a) the spatial extent which dictates the cylinder diameter thus the area over which points are included, and

(b) the temporal extent which is represented by the height of the cylinder with increasing height signifying the

inclusion of points over a larger temporal period

The STPSS is included as an extension in the software program SatScan that was originally

designed for the identification of disease clusters or outbreaks (Kulldorff and Information

Management Services, Inc 2006). Telemetry locations for collared caribou, collected under the

Newfoundland and Labrador Caribou Strategy, was utilized for the identification of used sites.


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The use of the STPSS required input of both the spatial and temporal scale at which clusters

were to be identified. Based on visual analysis of locations, plus calculation of average step

length and daily dispersion, a maximum scan window of 0.5 km was used for the spatial

component. When selecting a temporal window the occurrence of three distinct behavior

regimes had been taken into account for the study period (Table 3.5). Through trial and error, it

was determined that a temporal window of 30 days gave an acceptable distribution of identified

clusters. The use of STPSS is warranted when the location of individual clusters is of concern as

opposed to a global measure of clustering within the study area.

Evaluation of landcover utilization also requires the identification of a study area from which a

sample of avoided sites can be drawn. Delineation of the study area has been recognized as a

possible source of bias in habitat related studies, especially when unused or avoided areas have

to be delineated (Manly et al. 2004 p. 5, Morrison et al. 2001 pp. 103-104). Bias occurs because

of influences from factors outside the boundaries of the study area, or selection of an area that

was identified as avoided because the temporal duration of animal telemetry was too short to

allow for the complete delineation of the utilized range. Bias was eliminated by allowing the

individual caribou to select both used and unused areas. While used areas are indentified using

the STPSS, an alternate method was developed to identify unused sites. This was accomplished

by calculating the three largest step lengths, for each caribou, with a software program called

Hawth Tools v3.27, an extension available for Esri ArcGis 9.2 (Beyer 2004, ESRI 2006).

Avoided areas were represented by calculating centroids, based on the endpoints of the three step

lengths selected above, using an ArcGis script created by Pete Aniello (2003) (Figure 3.4).


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Figure 3.4: Transect centered on the maximum step length for caribou sc2006026. Blue dots indicate the two

endpoints that were used to calculate the centroid of the maximum steplength.

3.3.1.2 The Selection of Attributes to be recorded During Sampling and Sampling

Protocol Design

The sampling protocol is designed to complement habitat evaluations conducted at the landscape

scale using GIS with information on mid and micro scale features of habitat utilized by female

caribou during calving and post calving. Data collected will also be used to determine the

accuracy of the provincial forestry inventory database and other selected dataset with the goal of

evaluating its usefulness for the identification of suitable caribou habitat on the island of

Newfoundland.

The landscape scale describes the spatial distribution of habitat features within the general area

of the calving or post calving telemetry location clusters. This will be used to identify broad

scale relationships of habitat features at selected telemetry clusters with the overall


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characteristics of the study area. At this scale topography, site coordinates, and heterogeneity of

the landscape are deemed important. This analysis will be conducted through the use of existing

Landsat and forest inventory data.

This data will be complemented by measurement of variables at the mid scale level. At this level

parasitic fly relief, terrain roughness, presence of escape areas and the variability of habitat

features are deemed important. A categorical measurement of terrain roughness, as it relates to

the movement of calves, will be conducted. One measure of topography is the terrain profile (as

outlined in II a below) which has a major impact on the moisture content of the soil and directly

and indirectly influences vegetation cover. This data will allow for the development of a

categorical scale for the measurement of habitat variability.

At the micro scale (the scale at which a species interacts with the environment on a daily basis to

fulfil the need for food, water, and shelter) an evaluation of forest stands and other habitat types

intersected along Fibonacci spiral transect lines will be carried out. Forest composition, height,

age and canopy cover will be measured as per existing forest inventory guidelines. Forest stand

evaluation will be conducted for stands intersected by transect lines and measurements taken

50m from the stand edge or at the center of the stand if stand size does not permit this distance.

Micro scale data will be used to evaluate the suitability of the four spatial datasets included in

this study.

The following list identifies the variables to be measured and variables are placed according to

their hierarchical level. Variables are located under one classification only.

I. Landscape Scale (measured for study area comparison)

a. Geographic relationships

a. Coordinates (WGS 1984)

b. Elevation

c. Slope (%)

d. Aspect (degrees)


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II. Mid Scale (measured at the sample site level)

a. Terrain Profile

1. Concavesurface profile is mainly hollow in one or severaldirections

2. Convexsurface profile is mainly rounded like the exteriorof a sphere

3. Straightsurface profile is linear, either flat or sloping in onedirection

b. Site Classification (category)1. Rock Barrenbarren rock land without sufficient soil for trees

(


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III. Micro Scale (measured at the forest stand level)

a. Transition Zones (measured for intersection with habitat changes)

1. For each intersection

1. Transect lag (section)

2. Distance from origin

3. Coordinates of intersection

4. As per site classification (category)

5. Terrain Roughness

1.microlow relief features (< 0.3 m high) withminimal effect on calf movements

2.slightlyprominent features (0.31m high)

spaced > 7 m apart

3.moderatelyprominent features (0.31m high)spaced 37 m apart

4.stronglyprominent features (0.31m high)

spaced 13 m apart5.severelyprominent features (0.31m high)

spaced < 1 m apart

6.extremelyvery prominent features 100%

ground cover, movement possible with largeeffort

7.ultravery prominent features, movement

impossible or at an extremely low rate

Note: the inverse of these criteria are to be used to evaluate

water based features.

6. Repeat

b. Forest Stand Evaluation

a. Species Composition

1. Balsam Fir (bF)

2. Black Spruce (bS)

3. Balsam Popular (bP)


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4. Englemann Spruce (eS)

5. European Larch (eL)

6. Jack Pine (jP)

7. Japanese Birch (jB)

8. Lodgepole Pine (iP)

9. Norway Spruce (nS)

10.Red Maple (rM)

11.Red Pine (rP)

12.Scots Pine (sP)

13.Sitka Spruce (sP)

14.Tamarack/Larch (tL)

15.Trembling Aspen (tA)

16.White Birch (wB)

17.White Pine (wP)

18.White Spruce (wS)

19.Yellow Birch (yB)

20.Alder Species (aS)

Note: > 75% coverage one species listed

Two species listed one >50% other makes up remainder

Three species 40% -30%-30%

c. Age Class

1. 1-20 (Regenerating)

2. 21-40 (Immature)

3. 41-60 (Semi-mature)

4. 61-80 (Mature)


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5. 81-100 (Overmature)

6. 101-120 (Over-mature)

7. 121+ (Over-mature)

9. All age stands

Note: Age to be determined by core sampling of over story, visual

observation or reference to forest inventory data.

d. Height Class

1. 0-3.5 meters

2. 3.6-6.5 meters

3. 6.6-9.5 meters

4. 9.6-12.5 meters

5. 12.6-15.5 meters

6. 15.6-18.5 meters

7. 18.6-21.5 meters

8. 21.6+ meters

e. Crown Closure

1. >75%

2. 51-75%

3. 26-50%

4.


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4. Wind

5. Vegetation

6. Other

g. Understory

1. Absent (moss/lichen/grasses only)

2. Herbs 1m high

Sampling of attributes (variables) along transect line was conducted using the line intercept

(intersect) method (Newton 2007 p. 95, Morrison et al. 2001 pp. 69-72, 109, Dale 2004 pp. 41-

42, Van Wagner 1982, Canfield 1941, Gregoire et al. 2004, Lutes 2002, Buckland et al. 2007,

Afflecket al. 2005, Manly 2002). Attributes were selected based on landcover observations in

areas where female caribou were known to have occupied during the study period. To make data

compatible with existing data on forest cover, attribute selection and associated categories

followed the Data Dictionary used by Provincial Forestry personnel as closely as possible (Dept.

of Natural Resources 2007). Site classification codes were adopted from the data dictionary with

the required addition of codes related to water bodies and waterways, as well as codes pertaining

to alternate cover types, such as grasses. When the landcover type intercepted was a forest stand

standard classification codes for species, age class, height class and canopy cover were used with

additional codes for site disturbance and understory being added. All data was recorded on the

field sheets in Appendix A.

3.3.1.3 The Selection and Utilization of an Unbiased Sampling Design

After identification of used and avoided sites, selection of an unbiased sampling design was

required. Bias in sampling can be introduced by the spatial orientation (directionality) and

distribution of features (trend or heterogeneity) (Kalikham I. 2007, Fortin and Dale 2005). It was

noted during caribou collaring work and while conducting a point sampling pilot project in 2006

that landcover features exhibit both a high degree of directionality and heterogeneity. The


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sampling design was thus selected to eliminate or reduce bias that could be introduced by spatial

distribution of landcover features in the study area.

Fortin and Dale (2005) describe the use of Fibonacci spirals as a means of avoiding error that

may be introduced by directionality and trend. A modified version of the Fibonacci spiral was

constructed from straight line segments for use in this study(Figure 3.5). The adequacy of a

spiral sampling design was emphasized by Kalikhman (2007) who also noted that shorter spirals

provided the same results as straight or zig-zag lines. Construction of spirals centered on the

centriods of location clusters and maximum step lengths utilized an AML script developed by

Carl Marks (2008) for use in ArcInfo 9.1 (ESRI 2005).

Figure 3.5: Transect line based on Fibonacci spiral.

A flow chart representing all steps taken in the creation of a sampling transect is represented in

Figure 3.6. The methods outlined allowed for the creation of sampling transects that could be

completed in half a day and, for most caribou, resulted in the creation of transects that

represented the three behavioural periods listed in Table 3.5.


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Figure 3.6: Sampling location identification and transect creation.

3.3.2: The Comparison of Spatial Datasets

To allow the evaluation of all spatial datasets utilized in this study, line intersect sampling was

used for the compilation of a ground truth dataset. Given the diverse techniques used in thecreation of each spatial dataset and differences in the final product, they were evaluated on an

individual basis. This evaluation was based on the ability to identify or quantify positional

accuracy of boundaries between landcover features identified during collection of the ground

truth dataset. A statistical method for the quantification of positional accuracy is the calculation

of the root mean square error (RSME) (Worboys and Duckham 2004).

The RMSE provides a measure of the error between the actual location of a feature and the map

location (Figure 3.7). Differences between the x and y coordinates are calculated as per equation

5 and 6:

x (actual)x (mapped) = difference in x (5)


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y (actual)y (mapped) = difference in y (6)

Equations 5 and 6 then allow for the calculation of the (error radius)2

as per equation 7:

(difference in x)2 + (difference in y)2 = (error radius)2 (7)

All (error radius)2 values are summed and divided by the number of point pair comparisons (n):

( )2 + ( )2 =()2 (8)

The RMSE is then calculated per equation 9:

()2 = (9)

Figure 3.7: Positional differences between actual and mapped locations.


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The allowable RMSE is based on a relationship between the absolute positional accuracy of a

given dataset and the Z score at a selected confidence interval, therefore the allowable RMSE is

dependent on the spatial dataset being evaluated and the level of confidence the users requires.

3.3.3: The Selection of Boundaries and Boundary Location for the Evaluation of Spatial

Datasets

All currently existing landcover boundaries were identified and delineated using on the ground,

transect based, surveys. Boundary identification was based on an observed change from one

landcover type to another, provided that the new landcover type encountered along the transect

was > 10m in width, otherwise the transition to an alternate landcover feature was not recorded.

This was required to avoid recording small patches included in otherwise contiguous landcover,

or the transitions zones between distinct landcover features, both of which would not be

discernable from utilized datasets due to their limited resolution. All boundary locations

observed during the completion of survey transects were recorded as a point using a handheld

GPS. GPS units used in this study had an associated positional error of 3-10 m.

Boundaries for an evaluated spatial dataset were selected based on those identified during

completion of ground based survey lines. Boundaries consisted of either vector based entities or

the edge of raster cells which depicted different landcover features. Both types of boundaries

were extracted and saved in new shapefiles using ArcGIS. The extraction of boundaries from the

Landsat 7 data required additional steps before ex

Documents

Delineation of Landcover Boundaries in Areas Utilized or Avoided by Female Caribou during Calving and Post-Calving Using Publicly Available Spatial Datasets