176-2375-2-pbdelineation of landcover boundaries in areas used or avoided by female woodland caribou...

8/13/2019 176-2375-2-PBDelineation of Landcover Boundaries in Areas Used or Avoided by Female Woodland Caribou (Rangi

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ORIGINAL PAPER

Wildl. Biol. Pract., 2013 December 9(2): 40-62doi:10.2461/wbp.2013.7

Copyright 2013P.W. Saunders

This is an open access article distributed under the terms of theCreative Commons Attribution License, which permits unrestricted use, distri-

bution, and reproduction in any medium, provided the original work is properly cited. Published by: Portuguese Wildlife Society.

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Spata Dataet

P.W. Saunders

Department of Environment and Conservation, Wildlife Division, P.O. Box 2007 Corner Brook,

Newfoundland and Labrador, Canada, A2H 7S1; e-mail: [email protected].

Keywords

Spiral transect;

Boundary delineation;Fibonacci sequence;

Land cover;

Scale;

Landscape;

Caribou.

Abstract

The availability and utility of spatial datasets, at no cost through web-

based data services or government agencies, directly impacts the abilityof government and non-governmental wildlife management agencies

to delineate land cover use 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 land cover

boundaries in areas used by caribou during calving and post-calving. 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 land cover 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 land cover 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 used along with a variety of ancillary data sources.

Introduction

Responding to dramatic declines in caribou populations on the island of

Newfoundland, 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 used by

caribou, thereby, identify habitat attributes deemed important to caribou. Researchpresented in this paper is a sub-component of this habitat evaluation.

The yearly association of Newfoundland caribou with habitat types during specic

periods in their lifecycle has been well documented [1,2,3,4]. Historical yearly

migrations to calving and post calving grounds have also been delineated [5]. It has
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been postulated that migration to specic calving and post-calving rearing areas were

based on the nutritional needs of the female or an attempt to reduce calf mortality from

predation [6,7,8,9,10,11,12,13]. This places a critical importance on areas used by

female caribou for calving and post-calving rearing. Given the accumulated evidenceregarding the impacts of human development, such as mine development, forest

harvesting, and linear development, on woodland caribou [14,15,16,17,18,19,20],

there is an increased need for data on the attributes found and their spatial associations

for all areas within the woodland caribous range.

The occurrence of landcover boundaries in areas used or avoided by woodland

caribou on the island of Newfoundland during calving and post-calving were delineated

using selected spatial datasets. The aims and objectives of this study were centered on

three main tasks, the identication and selection of spatial datasets, the selection of

methods for the evaluation of selected datasets that will allow for the identication oflandcover boundaries and the evaluation of these datasets utilizing a representative

sample of boundaries occurring in areas used or avoided by caribou. The issue of

used or avoided sites and the ability to identify boundaries can be problematic in

areas occupied by caribou due to the heterogeneous nature of existing landcover,

representing some of the most difcult areas for landcover classications. Without

the ability to accurately identify and delineate landcover boundaries it is not possible

to quantify the relationship between caribou GPS locations and specic landcover

types. Data on species-habitat relationships and the spatial associations of features in

areas used or avoided, is required to formulate effective habitat management plans

[21,22,23].

Methods

Study area

Based on GPS locations for individual caribou included in the analysis (N=11,

location frequency=2hrs), the study area is comprised of three separate sites totalling

2394 km2 located in the Central Newfoundland Forest Ecoregion, North-central

subregion (Fig. 1). A complex of coniferous forests and wetlands characterizes thisarea [24]. Wetlands are represented by mire complexes, as dened by Rydin and

Jeglum [25], 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 specic 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) [24]. Alder (Alnus rugosa) is

also abundant along the edges of waterways and waterbodies or the transition zones

between mires and forests.

The region experiences a more continental climate than other areas of the island

with an average yearly temperature of 3.5C 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.
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42 || P.W. Saunders | Delineation of Landcover Boundaries in Areas Used or Avoided by FemaleWoodland Caribou (Rangifer tarandus caribou) Using Publicly AvailableSpatial Datasets.

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

380-630 mm per year [31]. The study area contains approximately 800 km of human-made linear features

comprised of roadways, transmission corridors and an old railway bed, all of which

have the potential to negatively affect caribou [26]. 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. Three large forest res (over

200 ha) have been recorded in the study area occurring in 1964 (393 ha, location

49.0-56.07), 1986 (1399 ha, location 49.031-56.095), and 1999 (3675 ha, location

49.3-56.23) [27]. Topography of the area is characterized by rolling terrain with elevation ranging

from 76 - 647 meters. Extreme values are represented by river valleys and rock

outcrops, with forests being restricted to higher terrain or areas where the terrain rises

above the surrounding mires.

Datasets Used

One caribou telemetry dataset and ve spatial datasets were used in this study. Four

spatial datasets were selected based on their accessibility, being available through web

data services or from government agencies, and their potential for use in completinglandcover boundary delineation for the island of Newfoundland. The selected datasets

are represented by, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery,

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

and Labrador Forest Inventory Dataset, and the Canada Land Inventory for Ungulates

(CLI). In addition, landcover boundary data were collected via ground based transects

in areas used or unused by collared caribou.

Fig. 1: Study area. The Topsails, Newfoundland, Canada.


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Caribou Locational Data and Study Durations

Lotek Wireless Inc. (Newmarket, Ontario) GPS collars, Model 4400, were used in

this study being programed to record a location every two hours. The positional error

rate associated with recorded locations was5 m. All locations for the period May15 to September 10, 2007 were selected which covered three activity periods included

in the reproductive and rearing cycle of female caribou (Table 1). It is recognized

that post-calving rearing could and does extend beyond September 10, but the study

period was terminated due to the opening of the annual caribou hunting season which

could have a direct effect on caribou use or avoidance behavior.

Table 1: Female caribou activity patterns during the temporal period covered in this study. Activity cluster

identication was designed to provide a sample of landcover boundaries in areas used or avoided during

the listed life history periods.

The Identication of Areas Used or Avoided by Female Caribou

Evaluation of landcover use requires the identication of a study area from which a

sample of used or 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 [28,29]. Bias occurs because of inuences from

factors outside the boundaries of the study area, or areas being identied as avoided

because the temporal duration of animal telemetry was too short to allow for the

complete delineation of the range. To eliminate bias in this study used and avoided

areas were selected based on known locations of individual caribou. Used areas are

identied by employing the space-time permutation scan statistic (STPSS) [30,31].

Developed for the detection of disease outbreaks, the STPSS has seen only limited use

in the eld of ecology [32]. The statistic involves the use of millions of overlapping

cylinders to dene the spatial extent of the study, and whose maximum diameter the user

has dened. Cylinder size varies via a set of concentric circles, since we do not know

the size of existing clusters a priori, allowing for the identication of multiple sizedclusters. Cylinder sizes range from 0 to the maximum size specied, 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, with an increase in height translating into the inclusion of points over a longer

time period. This leads to the use of cylinders that can vary from short and wide to tall

and narrow, 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 [33]. The output of the statistic is a value that ranks the importance of

the identied cluster found at a specic location and bounded by a given spatial extentand time period. p-values for identied clusters can then be compared using Monte

Carlo testing methods [34]. Use of the STPSS allows for the temporal identication of

spatial clusters that can then be linked to animal activity or part of their annual lifecycle

requirements.

An alternate method was developed to identify avoided sites. This was accomplished


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

called Hawth Tools v3.27, an extension available for Esri ArcGis 9.2 [35,36]. Avoidedareas were then represented by calculating centroids using an ArcGis script created by

Pete, based on the endpoints for each of the three maximum step length pairs calculated

for each individual caribou Aniello [37] (Fig. 3). This method was based on the

assumption that individual caribou would spend the minimum amount of time in areas

they perceived as unsuitable and these areas would be represented by the longest step

lengths which correspond to the fastest caribou movement rates through the landscape.

Indirect conrmation of this assumption was supported by the observation that the

maximum step length for some individuals often occurred in the same area.

The Selection of Attributes to be recorded during Sampling and Sampling Protocol

Design

Data collected will be used to determine the accuracy of boundary delineation of

the provincial forestry inventory database and other selected datasets, with the goal of

evaluating their usefulness for identication of suitable caribou habitat on the island of

Fig. 2 (left): 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.

Fig. 3 (right): Sampling 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.


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Newfoundland. 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, with additional attributes added based on

landcover observations in areas where female caribou were known to have occupiedduring the study period [38].

Attributes of land cover types intersected along transect lines was recorded (Table 2).

Forest composition, height, age and canopy cover was measured as per existing forest

inventory guidelines for forest 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 (Table 3).

Table 2: Landcover types recorded during the completion of transect lines. Landcover types were adopted

from the forest inventory database with variables pertaining to water and alternate landcover types being

added.

Sampling of attributes along a transect line was conducted using the line intercept

(intersect) method [29,39,40,41,42,43,44,45] with only those features that weredirectly intersected by the line being recorded. Site classication codes were adopted

from the data dictionary with the required addition of codes related to water bodies

and waterways, and alternate cover types, such as grasses. When the landcover type

intercepted was a forest stand standard classication codes for species, age class,

height class and canopy cover were used with additional codes for site disturbance

and understory being added.


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The Selection and Use of an Unbiased Sampling Design After identication 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) [45,46]. 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 sampling design was selected to eliminate or reduce bias that could

be introduced by spatial distribution of landcover features.

Fortin and Dale [46] describe the use of Fibonacci spirals as a means of avoiding

error that may be introduced by directionality and trend. A modied version of theFibonacci spiral was constructed from straight line segments, with segment lengths

based on the Fibonacci number sequence, for use in this study (Fig. 4). The adequacy

of a spiral sampling design was emphasized by Kalikhman [45] 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 was

completed using an application markup language (AML) script developed by Carl

Marks (unpublished) for use in ArcInfo 9.1 [47]. This method allowed for the creation

of sampling transects that could be completed in half a day and were representative of

the three behavioral periods listed in Table 1.

Table 3: Forest stand characteristics recorded for forest stands intersected by transect lines. The variables

selected matched those included in the forest inventory database with additions that were deemed important

to caribou.

Fig. 4: Transect line based on Fibonacci spiral.


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The Comparison of Spatial Datasets

Line intersect sampling was used for the compilation of a ground truth dataset to

allow for the evaluation of all spatial datasets used. Given the diverse techniques

used in the creation of each of the selected spatial datasets and differences in the nalproduct, they were evaluated on an individual basis. Evaluation was based on the

ability to identify or quantify positional accuracy of boundaries between landcover

features identied during the completion of the ground based transects and those

shown on selected spatial datasets. The statistical method for the quantication of

positional accuracy was the calculation of the root mean square error (RSME) [48].

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

(as measured on the ground) and the location specied by a given spatial dataset

(Fig. 5). Differences between the x and y coordinates are calculated, then squared

and summed giving a combined (error radius)2value. All (error radius)2values aresummed and divided by the number of point pair comparisons with the square root of

this value representing the RMSE of the spatial dataset being evaluated.

Fig. 5: Positional differences between actual and mapped locations. These differences are measurable andform the basis for comparisons between datasets using RMSE calculations. All comparisons rely on a

dataset of known boundary locations collected with high accuracy. This was achieved in this study by the

completion of ground based transects.

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

accuracy of a given dataset and the Z score for a selected condence interval, therefore

the allowable RMSE is dependent on the spatial dataset being evaluated at the level of

condence required by the user.

The Selection of Boundaries and Boundary Locations for the Evaluation of SpatialDatasets

All currently existing landcover boundaries were identied and delineated using

ground based transects. Boundary identication was based on an observed change from

one landcover type to another, provided that the new landcover feature intersected by

the transect line was > 10m in width (as measured along the transect line), otherwise


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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 on datasets used due to their limited resolution. All boundary locationsobserved 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.

Recorded ground transect points were overlaid on the each of the spatial datasets

and the boundary closes to the recorded ground point was selected for inclusion in the

analysis. Boundaries consisted of either vector based entities or the edge of raster cells

that depicted different landcover features. Both types of boundaries were extracted

and saved in new shapeles using ArcGIS. The extraction of boundaries from the

Landsat 7 data required additional steps before extraction consisting of pansharpening

(increasing image resolution), segmentation (demarcation of land feature boundaries)

and vector extraction (exportation of boundaries to a new le). RMSE values were

then calculated using distances between the actual boundary location (from ground

based surveys) and the location depicted by each dataset.

Landsat 7 ETM+ Dataset Preparations

The study area is represented by landcover features from three distinct ecosystems,

boreal forest, bog and fen complexes, and alpine/upland barrens. This creates the need

for the selection of a band, band combination, or combination of band composites,

that provides the best separation of landcover features. Boundary delineation of

landcover features requires the demarcation of ecotones recorded by landsat imagery.

Ecotones can be dened as an identiable transition between landscape features

where one feature changes to another due to underlying biotic or abiotic factors [49].

While completing transects it was noted that most ecotones fell below the 10m limit

for feature recording, a factor that could be benecial for completion of the boundary

delineation exercises (Fig. 6).

Fig. 6: An example of the well-dened ecotones 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.


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Boundary demarcation was completed using the image analysis software ENVI.

The ENVI Feature Extraction Model is based on object-orientated (OO) segmentation

for which additional information can be found in the associated users guide [50].

Segmentation is dened as a process of dividing an image into segments by groupingadjacent pixels with similar feature values (brightness, texture, colour, etc).

Landsat 7 ETM+ Band Selection and Segmentation

Landsat ETM+ imagery is composed of 8 bands, each of which can be used for the

identication and delineation of landcover features based of their ability to reect

or emit energy at a specic band width. Landsat spectral bands have been used

individually or in combination, as composite images, for landcover classication of

forested and other vegetated areas throughout the world [51,52,53,54]. The selection

of specic bands for the completion of a landcover segmentation and\or classicationexercise is dependent on the landcover features in the target area. The study area

is composed of a mosaic of boreal forest, scrub and shrub combinations, mires,

and barren alpine\tundra\taiga landscapes and the reective properties of each of

these landcover types varies, creating the need for the selection of a band or band

combination that will provide the highest degree of separation. The resulting image

was pansharpened based on an increased usefulness in delineating landcover features

used by wildlife [55].

Fig. 7: 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.Colour shifts in the pansharpened image are a result of the pansharpening process.

Segmentation involves the selection of the appropriate scale at which pixels are to

be viewed during the aggregation process and the degree to which resulting segments

are to be merged. Scale selection for landscape analysis was addressed by Burnett

and Blaschke [56] who proposed reducing landscape objects to their smallest most

basic unit called a holon which were comprised of contiguous landcover features.

Segementation at a scale above the holon size would result in objects containing

multiple features. Segmentation was conducted at a scale level of 1 based on a range

of 0 to 100, 100 being the largest. This was done to ensure that all landcover featuresdetectable within the constraints imposed by the image pixel size, were actually

detected.

Within the ENVI program a level of segment merging must be selected from a scale

of 0 to 100, with 0 representing the least amount of merging. Determination the best

merge level for the utilized Landsat imagery, resulted from the visual comparison of


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actual transect boundary locations to the results of multiple segmentation exercises.

This resulted in the selection of a merge level of 85 which gave the best agreement

with boundaries identied in selected transects (Fig. 7(c)). Results from this exercise

were exported to a vector le for use in ArcGIS.

Temporal Currency and the Need for Ancillary Data

All used datasets were prone to some degree of temporal incongruence. This

incongruence had to be taken into account during the calculation of RMSE values for

individual datasets failure to do so would introduce a source of bias. Ancillary datasets,

such as forest cutover and road layers, were used to allow for the identication of

boundaries that may or may not have been present during the creation of a specic

dataset.

Results and Discussion

There is a great cost differential between coarse, medium and high resolution

remotely sensed data, which can differ by an order of magnitude in price [57]. This

has restricted many government agencies, or nongovernmental organizations, to the

use of low resolution, or dated datasets that can be obtained free of cost, and often

means using information that was developed for alternate purposes. The use of these

datasets often requires a preliminary analysis of their appropriateness for use in agiven task. Work presented in this paper involved the evaluation of four datasets for

use in the identication and/or delineation of landcover boundaries in areas used or

avoided by female caribou. Success in delineating landcover boundaries was used as

a proxy to determine the suitability of the dataset for subsequent future classication

of landcover features.

Observational and Statistical Evaluation of Individual Datasets

Canada Land Inventory

Five primary landscape limitations were extracted from the CLI data associatedwith sites avoided or used by female caribou and have been displayed in Figure 8.

There was no signicant difference between primary landscape variables occurring

at used or avoided sites (X2=6.392, df=4,p=0.172).

Only two ungulate species, moose and caribou, exist on the island of Newfoundland

with data on both species being incorporated into the CLI dataset. All areas of the

island were designated as both moose and caribou habitat with an interchange

between primary species occurring on a polygon-by-polygon basis. The occurrence

of either moose or caribou as the primary species had no effect on whether a site

was avoided or used (X2=2.922, df=1,p=0.087). A review of Figure 9 does show adifference between primary species designations and site use, which is signicant at

the 10% interval, and indicates the need for continued investigation.

The CLI for ungulates was developed from a variety of ancillary data, which

included ground surveys and air photo interpretation and includes land classication

for moose and caribou, the only two ungulates that occur in the study area. Given the


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use of ne resolution data in the development of the CLI, a positional error rate of

30m was selected as a buffer around ground based GPS locations. Map boundaries

(polygon perimeters) were considered as matching ground based survey data if they

fell within a 30m buffer zone around plotted ground-based landcover boundaries.

17 of the transects completed were crossed (one transect passed within 5m of the

boundary) by polygon boundaries in the CLI dataset. Boundary detection was

deemed successful for 16 of these transects.

To conrm boundary detection the RMSE was calculated for all transects that crossed

a CLI feature boundary. The accuracy of detection was evaluated through comparison

of the obtained RMSE with that obtained from a set of randomly distributed points

(Fig. 10). The calculated RMSE values were 30.4322 (C.I. 2.75m, 95%) for transect

boundary locations and 86.6044 (C.I. 8.49m, 95%) for randomly selected boundary

locations along individual transect lines. Mapping of the CLI data was conducted at a

scale of 1:50,000, based on data obtained from multiple sources for which positional

error rates have not been stated, leading to the adoption of an arbitrary positional

error rate of 33 38m which takes into account GPS based locational error. TheRMSE of 30.4322 is reective of this rate of positional error.

Earth Observation for Sustainable Development of Forests

The number of feature boundaries was on average 67% greater for the EOSD data

when compared to ground based transect surveys and was signicant at the .01 level

t=-8.08, df=34,P


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of ground based boundaries identied was compared to the number of boundaries

indicated in the EOSD data for individual transects. Using the Pearson product-

moment correlation it was indicated that there is a signicant positive association

between the number of ground-based and EOSD-based boundaries identied along

transect lines (r=0.64, df=35,P


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such as, forest-bog, bog-water, or shrub-lichen. The combination of high variability

in both landcover features and their spectral signatures results a high number of

pixels with distinct pixel values over short distances leading to the classication of

landcover features often restricted to one pixel in size. Both of these conditions existsin the EOSD data and are evident in Figure 11. With the existence of such features

the degree of data smoothing becomes critical, especially where single or small sets

of pixels, often referred to noise, are created through misclassication [60].

Provincial Forest Inventory

The Provincial forest inventory represents the most temporally current dataset

available for the island of Newfoundland. It is maintained by the Department of

Natural Resources and was compiled from air photo interpretation, air and ground

based surveys and available ancillary data. Classication is based on the DataDictionary for District Library as published by the Provincial Department of Natural

Resources [38].

There was a signicant difference between the number of landcover boundaries

detected during the ground based surveys and those occurring in the forest inventory

dataset (t=-2.344, df=27,P


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the inventory dataset is 30m plus a positional error of 5m for handheld GPS

units thus it would not be uncommon to have a boundary positional error of 35m.

This number corresponds to the error of 39.47m calculated based on the ground based

transect data. The error of 64.71m derived from the evaluation of randomly placedpoints along the transect represents a 64% increase in positional error over that

achieved using the transect dataset. Thus it can be concluded that the forest inventory

dataset can be used for the delineation and identication of landcover features used

by female caribou.

A review of the provincial forest inventory dataset completed by McLaren and

Mahoney [61] identied limitations in the delineation of landcover features in areas

that have a non-commercial potential. These limitations involve the inclusion of

features in a specic classication even though it may form a substantial component

of another landcover type. Issues of this nature were also noted for the classicationof scrub, where bogs had a tendency to be placed within this category in areas with

a large ericaceous cover. This was conrmed during a visual assessment directed

at identifying areas, along with the underlying landcover classications, where the

ground truth data and the forest inventory dataset differed. The evaluation suggested

that landcover features, other than commercial forests, were not delineated on the

scale used for forest stands during creation of the inventory resulting in a blending of

features in these areas. The opposite effect was seen in areas comprised of commercial

stands where delineation resulted in the differentiation of individual components of

contiguous forest stands based on composition, size, density or age. Ground truth

data would have produced a listing of fewer landcover features for these areas since

the differences in forest stand type was not recorded with this level of detail.

Another limitation involved in using Provincial forest inventory for the identication

and delineation of caribou landcover usage is shown in Figure 13 and concerns the

spatial coverage of the dataset. The lack of coverage is not conned to the study area

but occurs at various sites across the province. The areas excluded are often void of

commercial forests but nonetheless represent important areas for caribou. This has

led to the need for the identication of a spatial data set that could be used to ll

the gaps inherent in the forest inventory, thus the inclusion of the Landsat 7 ETM+

dataset in this study.

Fig. 13: Forest Inventory coverage of the area included in this study.


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Landsat ETM+ Segmentation File Evaluation

A representative band combination was selected through an evaluation of the

covariance and correlation matrices for all bands in the landsat image except bands

6a and 6b. Both matrices were calculated using ArcGIS (Tables 4 and 5).

Table 4: Covariance Matrix for Landsat ETM+ bands 1 - 5 and 7.

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

Utilizing the covariance and correlation matrix, and the standard deviation

associated with each band, the Optimum Index Factor (OIF) was calculated for all

band combinations. The OIF calculation is dependent of the standard deviation of the

pixel values for individual bands and the value of the correlation between band pairs,

being developed to identify the 3 band combination that provides the highest amountof information with the lowest amount of overlap [75,76]. OIF values are calculated

using the following equation:

Where:

Stdi standard deviation of band i; Stdj standard deviation of band j; Stdk standard

deviation of band k; Corrij correlation coefcient of band i and band j;Corrikcorrelation coefcient of band i and band k; Corrjkcorrelation coefcient of band j

and band k.

For the six bands included in this study, the band combination 1, 4, 5 was ranked as

the highest for use in classication activities (Table 6).

Table 6: Optimum Factor Index for the 6 highest ranked band combinations. For the Landsat imagery used

in this study the band combination of 1,4,5 provides the best spectral separation of landcover features in

the study area.

The number of feature boundaries was on average 40% greater for the segmented

landsat image when compared to ground based transect data and was signicant

at the .01 level (t=-9.05, df=27, P


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boundaries within a distance less than the effective pixel size was counted as only one

boundary. For the pan-sharpened landsat imagery the effective pixel size was 15m. A

portion of the boundary discrepancy was a result of the difculty to create adequate

segmentation in areas covered by bogs and mires which required a reduction in thesize of the scaling factor and a small reduction in the merge value used. A consequence

of this was the creation of additional segments in forested areas leading to an increase

in the number delineated boundaries. Most of these additional segments would be

eliminated upon completion of either the rule or supervised based classication of the

objects identied. Given that the aim of this study was the identication of boundaries,

originally identied during ground-based surveys, using selected datasets, the nal

step of classication was not required.

After accounting for temporal inconsistencies and errors of omission through the

use of ancillary datasets, the RMSE using ground based transect data was 27.92m(C.I. 1.14m, 95%) and 37.02m (C.I. 1.52m, 95%) for a set of randomly generated

boundary points. The landsat image had a positional error of 20m and when

combined with the GPS error of5m creates an error rate comparable to the RMSE

error obtained for the segmented image. The RMSE obtained during the comparison

with ground based survey results is within the25m combined positional error rate

thus represents a conrmation that landsat imagery and segmentation can be used to

delineate existing and former landscape boundaries. This fact is further supported by

the 24.6% difference between the RMSE obtained for ground based survey points

and the set of randomly generated boundary points.

The implementation of a rule or supervised classication scheme would reduce

the number of boundaries in the segmented Landsat image. This would increase the

difference between the ground based and random RMSE results, since most of the

segments occurring inside contiguous landcover features would be eliminated, thereby

increasing the discrepancy between the location of random point and segments.

Given these results, segmented, pansharpened, Landsat ETM+ imagery can be used

to complement the Provincial Forestry Inventory dataset for the delineation and

classication of caribou habitat on the island of Newfoundland.

Conclusions

Fulllment of Aims and Objectives

As outlined above, the aims and objectives of this study were centered on three main

tasks; the identication and selection of spatial datasets, the selection of methods for

the evaluation of selected datasets that will allow for the identication of landcover

boundaries, and the evaluation of these datasets utilizing a representative sample

of boundaries occurring in areas used or avoided by caribou. This analysis was

conducted on datasets that were available for free through web-based data sharingsites or from various federal or provincial agencies.

Methods for dataset evaluation were selected based on the requirement to obtain a

representative sample of landcover boundaries in areas used or avoided by caribou

and a mechanism to compare individual datasets. The use of the space-time scan

statistics eliminated the need to designate a study boundary providing an unbiased

means of identifying representative areas used by caribou, with maximum step length


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W B P2013, 9(2) ||57

being used to indicate avoidance. The comparison of the representative sample and

selected datasets was completed through the calculation of RMSE values.

The CLI, forest inventory and Landsat datasets have been shown as useful for the

identication and delineation of landcover boundaries associated with areas used bycaribou. Each of these data sources must be used within the constraints inherent in

each of the datasets. All datasets suffer from temporal inconsistency and as a result

errors of omission or commission. This leads to the need for the use of ancillary data

to insure a complete delineation of all features in a specic area.

The CLI dataset is prone to a high degree of generalization and as such should

only be used for delineation of areas used by caribou at the provincial scale and only

if other datasets are not available. One important result obtained was that landcover

boundaries were detected during ground based surveys that were also present in the

CLI dataset. This outcome indicates the permanence of landcover boundaries andcould provide a baseline dataset for landscape change studies relating to caribou

habitat.

Results of the RMSE evaluation for the forest inventory dataset indicate that it can

and should be used for the delineation of landcover features. In some cases, such as

the delineation of forest stands, some level of generalization may be warranted. The

incompleteness of the forest inventory dataset precludes its usage across the province

and demonstrates the need for a complementary data source such as classied Landsat

ETM+ imagery.

The Landsat ETM+ dataset can be used to complement the forest inventorydataset in those areas currently not covered by the inventory. Although RMSE values

indicate that the landsat data can be used to identify existing landcover boundaries

at an acceptable level of accuracy, the issue of a 40% difference in the number

boundaries when compared to the ground based survey must be addressed. During

the segmentation exercise it was noted that scaling and merging levels that produce

the best results for forested areas often failed to produce adequate segmentation in

areas represented by bogs and mires. In this study a compromise was made during the

selection of scaling and merging values such that the level of accuracy within forested

areas was sacriced for a better delineation in bogs and mires. It is recognized that

the completion of either rule based of supervised based classication will be able to

compensate for this discrepancy.

It is not possible to recommend the use of the EOSD dataset for the delineation of

caribou habitat unless it is subjected to reclassication or generalization. Since the

dataset is supplied only as a platted geotiff le, reclassication will not be possible

without access to the original pre-classied les. Generalization of the existing les

may produce acceptable results for restricted areas Newfoundland.

Summation Through the use of space-time scan statistics, ground transect sampling,

segmentation and RMSE evaluations it was determined that the Provincial Forest

Inventory and Landsat ETM+ data can be used to delineate boundaries associated

with landcover features in areas used or avoided during calving and post-calving

on the island of Newfoundland. The CLI dataset can be used to provide information


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on the location of permanent boundaries occurring at the landscape scale within the

range used by individual caribou. Use of the EOSD dataset cannot be recommended

based on the inability to delineate ground based boundaries at the scale used by this

study. An attempt was made to generalize the dataset such that it would better reectthe ground based location of boundaries but satisfactory results were not achieved.

The methods developed to provide representative sampling have eliminated bias

that could be introduced by both the selection of a study area, and the inuence of

directionality inherent in some landcover features. This was accomplished by the

selection of both used and avoided areas through the use of space-time scan statistics

based on telemetry data for individual caribou, maximum step length calculations,

and the use of non-linear transect sampling.

This study represents only a preliminary step in the identication and delineation

of landcover features important to caribou. The results of this study and data obtainedduring the ground based transect survey must now be used to provide a habitat based

landcover map for caribou on the island of Newfoundland. This can be achieved

through completion of the items outlined in the next section.

Recommendations

To ensure the completion of knowledge based habitat mapping for caribou during

calving and post-calving the following activities are recommended:

1. A combination of both forest inventory and landsat data should be used to

provide complete coverage of the island.

2. Ancillary data (roads, waterways, water bodies, rightaways, etc) must be

incorporated in all delineation activities

3. Logistic regression must be completed for data collected during the ground

based survey to identify landcover features used or avoided by caribou.

4. Reclassication of the forest inventory and classication of the segmented

landsat datasets must be based on the logistic regression results.

5. The completed knowledge based habitat map is to be used for theidentication of landcover features used by caribou during other periods in the yearly

caribou lifecycle.

6. The spatial relationship between landcover features in areas used or avoided

by caribou must be quantied and the results applied to classication activities

conducted on spatial datasets representing the island of Newfoundland.

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