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8/13/2019 176-2375-2-PBDelineation of Landcover Boundaries in Areas Used or Avoided by Female Woodland Caribou (Rangi
1/23
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.
Deeat f Ladver Bdare Area Ued r Avded by Femae
Wdad Carb (Rg ) Ug Pby Avaabe
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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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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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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