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Blanding's Turtle (Emydoidea blandingii) Potential Habitat Mapping Using Aerial Orthophotographic Imagery and Object Based Classification


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Blanding's turtle (Emydoidea blandingii) is a threatened species under Canada's Species at Risk Act. In southern Québec, field based inventories are ongoing to determine its abundance and potential habitat. The goal of this research was to develop means for mapping of potential habitat based on primary habitat attributes that can be detected with high-resolution remotely sensed imagery. Using existing spring leaf-off 20 cm resolution aerial orthophotos of a portion of Gatineau Park where some Blanding's turtle observations had been made, habitat attributes were mapped at two scales: (1) whole wetlands; (2) within wetland habitat features of open water, vegetation (used for camouflage and thermoregulation), and logs (used for spring sun-basking). The processing steps involved initial pixel-based classification to eliminate most areas of non-wetland, followed by object-based segmentations and classifications using a customized rule sequence to refine the wetland map and to map the within wetland habitat features. Variables used as inputs to the classifications were derived from the orthophotos and included image brightness, texture, and segmented object shape and area. Independent validation using field data and visual interpretation showed classification accuracy for all habitat attributes to be generally over 90% with a minimum of 81.5% for the producer's accuracy of logs. The maps for each attribute were combined to produce a habitat suitability map for Blanding's turtle. Of the 115 existing turtle observations, 92.3% were closest to a wetland of the two highest suitability classes. High-resolution imagery combined with object-based classification and habitat suitability mapping methods such as those presented provide a much more spatially OPEN ACCESS Remote Sens. 2012, 4 195 explicit representation of detailed habitat attributes than can be obtained through field work alone. They can complement field efforts to document and track turtle activities and can contribute to species inventory planning, conservation, and management.
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Remote Sens. 2012, 4, 194-219; doi:10.3390/rs4010194
Remote Sensing
ISSN 2072-4292
Blanding’s Turtle (Emydoidea blandingii) Potential Habitat Mapping Using Aerial Orthophotographic Imagery
and Object Based Classification
Rebecca Barker and Douglas J. King *
Geomatics and Landscape Ecology Laboratory, Department of Geography and Environmental Studies,
Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada;
* Author to whom correspondence should be addressed; E-Mail:;
Tel.: +1-613-520-2600 ext. 8439; Fax: +1-613-520-4301.
Received: 14 November 2011; in revised form: 30 December 2011 / Accepted: 31 December 2011 /
Published: 11 January 2012
Abstract: Blanding’s turtle (Emydoidea blandingii) is a threatened species under Canada’s
Species at Risk Act. In southern Québec, field based inventories are ongoing to determine
its abundance and potential habitat. The goal of this research was to develop means for
mapping of potential habitat based on primary habitat attributes that can be detected with
high-resolution remotely sensed imagery. Using existing spring leaf-off 20 cm resolution
aerial orthophotos of a portion of Gatineau Park where some Blanding’s turtle observations
had been made, habitat attributes were mapped at two scales: (1) whole wetlands;
(2) within wetland habitat features of open water, vegetation (used for camouflage and
thermoregulation), and logs (used for spring sun-basking). The processing steps involved
initial pixel-based classification to eliminate most areas of non-wetland, followed by
object-based segmentations and classifications using a customized rule sequence to refine
the wetland map and to map the within wetland habitat features. Variables used as inputs to
the classifications were derived from the orthophotos and included image brightness,
texture, and segmented object shape and area. Independent validation using field data and
visual interpretation showed classification accuracy for all habitat attributes to be generally
over 90% with a minimum of 81.5% for the producer’s accuracy of logs. The maps for
each attribute were combined to produce a habitat suitability map for Blanding’s turtle. Of
the 115 existing turtle observations, 92.3% were closest to a wetland of the two highest
suitability classes. High-resolution imagery combined with object-based classification and
habitat suitability mapping methods such as those presented provide a much more spatially
explicit representation of detailed habitat attributes than can be obtained through field work
Remote Sens. 2012, 4
alone. They can complement field efforts to document and track turtle activities and can
contribute to species inventory planning, conservation, and management.
Keywords: Blanding’s turtle; habitat; wetlands; remote sensing; orthophotos; object based
1. Introduction
Habitat loss and degradation are primary threats to amphibian and reptile populations [1]. Remote
sensing of habitat attributes often uses abiotic and biotic spatial variables such as topography and
vegetation type to characterize and map suitable habitat for a given species [2]. Land cover, which is a
common attribute derived from remotely sensed data, can be used to infer habitat through implied
relationships or through the explicit integration of other spatially referenced information (e.g., data
points representing species presence) or environmental factors [3]. Alternatively, specific habitat
attributes that directly or indirectly manifest in remotely sensed imagery can be extracted and
combined in mapping of potential habitat. For example, using skyward hemispherical photography,
Pasher et al. [4] found that Hooded Warbler (Wilsonia citrina) nest sites were spatially coincident with
greater overhead canopy gap fraction. Based on this, canopy spatial heterogeneity, as measured in
high-resolution satellite imagery, was shown to be a strong predictor of nest occurrence and potential
habitat. This paper presents a study that took a similar species-centered approach for the threatened
Blanding’s turtle (Emydoidea blandingii) to classify and map potential habitat.
Blanding’s turtle is a medium-sized freshwater reptile (up to about 27 cm length) with a rounded
black carapace (shell) that is speckled with yellow dots (Figure 1; [5]). The plastron (belly of the shell)
is yellow with square black blotches and its long neck, throat and chin are bright yellow [6].
Blanding’s turtles are mainly aquatic predators that feed on insect larvae and nymphs, snails, leaches,
crayfish, small fish, frogs and frog eggs, plant material and seeds from aquatic plants [7]. On land,
Blanding’s turtles eat vegetation, grasses, earthworms and slugs [8].
Preferred habitat includes permanent and temporary ponds, streams and wetlands [9–11] that are
often small (e.g., less than 0.40 ha [12]). Upon spring emergence (late March through mid April),
Blanding’s turtles often bask in the sun through May to thermoregulate and increase their metabolism
by sitting on wetland vegetation clumps, logs, or the wetland bank [9,13,14]. It is during this period
that most observations are made because the turtles are more exposed. Marshes and small water
habitats characterized by abundant vegetation are important for juveniles for refuge from predators as
they are often found or captured in emergent sedges or the sedge-water interface [9,10,15–17]. From
early June through July, pregnant females move to suitable sand or gravel nesting locations, often near
ephemeral wetlands, roads, trails, or power line right-of-ways [9,12,18–20].
In summer, they often use marshes and highly productive ponds with abundant aquatic vegetation
[10,11,15,16,19]. Emergent vegetation provides access to food resources and thermoregulation
(warming in spring; cooling in summer) [9,17]. Sedges often grow in tussocks and can provide protective
cover that remains cool in summer (e.g., Figure 2). Various studies have identified the importance of
these types of vegetation forms [9,10,21] as opposed to ponds without aquatic vegetation or wetlands
Remote Sens. 2012, 4
covered by cattail mats [11]. From late August to late October they move to a deeper wetland or pond
to mate and hibernate [22], burying themselves in soft substrate [15]. Deep ponds provide warmer
water temperatures and stable water levels that are critical for overwintering [11,23].
Figure 1. A Blanding’s turtle. Observation made during 2008 population inventory in
Québec [5]. Scale is approximate.
Blanding’s turtle home range size varies from 1.5 ha to 63 ha [22,24] and their mean travel distance
can range from 489 m to 7 km, but more commonly they travel up to 2 km [9–11,16,25]. They have
been found to spend up to 38% of the time in upland areas and travel up to 2 km between wetlands
(90 m–2,050 m) [12,22]. However, upland forest is generally considered as a land cover class of
transit, usually of females to a nesting site. In these travels, they will often stop at vernal pools or at
flooded areas in deciduous forest [8,12,17]. The upland landscape surrounding wetlands is therefore
important to preserve for connectivity purposes [12,26] but was not the focus of this research on
wetland habitat and within wetland habitat features.
The global range of the Blanding’s turtle spans parts of North America, with 20% of the population
residing in Canada. Reasons for its threatened status include: habitat loss and degradation [1,27];
susceptibility to road and predatory kill [11,28] and other biological traits related to recruitment and
environmental tolerance [6,17,20,29].
Based on the above, there is potential to map Blanding’s turtle habitat using high-resolution remote
sensing. Wetlands, its primary broad habitat type, have been mapped for other purposes using methods
ranging from visual interpretation of aerial photographs [30–32] to semi-automated classification of
optical or radar imagery [33–37]. However, mapping of specific within wetland habitat features critical
to the Blanding’s turtle has only been done using manual interpretation of air photos [9,12,26,38]. The
goal of this research was to map the spatial distribution of potential Blanding’s turtle habitat based on
attributes that are most often associated with reported observations and that have potential to be
Remote Sens. 2012, 4
detected by high-resolution remote sensing imagery. From the literature analysis presented above, the
selected habitat attributes were wetlands and within wetland water, vegetation and logs.
Figure 2. Sedge tussock in wetland. Such within wetland vegetation is an important
Blanding’s turtle habitat indicator. Scale is approximate.
2. Study Area and Previous Blanding’s Turtle Observations
This research was conducted in Gatineau Park, Québec, Canada, a 361 km
protected area [39]
centered at 45°30′N, 75°52′W that is comprised mostly of forest and wetlands (Figure 3; [40–42]). One
goal of park management is to protect species at risk and implement recovery programs. When the
Blanding’s turtle was designated as threatened in 2005 there was little known about the size and
distribution of the Québec population [6,25] except from a 19961999 inventory conducted outside the
park near the Ottawa River (Figure 4, yellow outline, [18,43–46]). In 2007, additional field inventory
was conducted in a specific area within Gatineau Park (Figure 4, purple outline) that augmented
previous opportunistic observations [18]. Based on this, the study area for this research was selected as
the area outlined in red in Figures 3 and 4. Figure 4 shows the accumulated observations to 2010 and
identifies (red arrow) a hypothesized corridor between the northwest portion of the park and the
Ottawa River wetlands [43].
Wetland vector data available for the study area were compiled by Ducks Unlimited Canada [47],
and included the following datasets: (1) Québec Topographic Database (BDTQ) [48] with 82 wetlands;
(2) the Conservation Atlas of Wetlands [49] with 14 wetlands mapped; (3) National Topographic
Database (NTDB) with eight wetlands (three that don’t overlap with data sources 1 and 2 above) [45],
and (4) 282 water polygons (lakes) from NTDB (several overlapping other mapped wetlands from data
sources above) [45]. However, field observation found small wetlands between about 100 m
Remote Sens. 2012, 4
1,000 m
that had not been mapped in these sources. It was therefore expected that the high-resolution
air photos would facilitate mapping of these and provide an improved representation of the spatial
distribution of potential Blanding’s turtle habitat.
Figure 3. The approximate extents of the 16 × 10 km study area (red outline) shown with
respect to Canada, the province of Québec and Gatineau Park (central green area). Adapted
from [40–42].
3. Materials and Methods
3.1. Field Site Selection and Data Acquisition
The map sources described above and spring 2007 leaf-off digital aerial orthophotos (described in
Section 3.2) were used to locate wetlands to survey and plan field routes. For the purposes of this
study, wetlands were defined as areas with standing water in spring, including small ponds, some
being ephemeral, as well as areas with typical wetland vegetation of grasses, sedges, and cattails
(Typha spp.). Inundated forest areas were excluded because they were not expected to be easily
mapped due to the often significant presence of submerged or floating leaf litter, which had similar
spectral and spatial image properties to adjacent non-inundated forest areas. The large lake in the
center of the study area was also excluded as it was not expected to be suitable habitat.
Wetlands were visually identified, delineated in a GIS and categorized into priority levels for field
survey based on the: (1) presence of more than approximately 100 m² of open water as, below this
threshold, interpretation of wetlands from the orthophotos was not believed to be reliable due to
confusion with tree shadows, and literature has shown Blanding’s wetland habitat to be typically larger
than this (e.g., [12]); (2) presence of within wetland vegetation with moderate cover (e.g., 25–75%)
Remote Sens. 2012, 4
being higher priority than no vegetation or large per cent cover [11,14]; (3) presence of logs (at least
1–2 in small wetlands, more in larger wetlands) ([6,50,51]); and (4) need to represent the variety of
wetland conditions and types in the study area for collection of sample training data for classification.
One hundred and seventy wetland field sites were visited between June and August 2008.
Non-differential GPS with an expected horizontal accuracy of under 10 m [52] was used to navigate
to selected wetlands and to geo-reference observation locations. Descriptions of each wetland,
including vegetation types and approximate vegetation cover in 10 % intervals were recorded and field
photos were taken with a view angle perpendicular to the shore for later reference in classification
training and validation.
Figure 4. Study area (red outline) in relation to the other primary nearby population
(yellow outline) and the hypothesized ecological corridor between them (red arrow).
Adapted from [18,43–46]. The purple rectangle shows an area of clustered observations
near a road and system of trails.
Remote Sens. 2012, 4
3.2. Acquisition, Processing and Classification of High Resolution Digital Orthophotos
It was determined that high-resolution satellite imagery, while providing good spectral information,
would not provide the spatial resolution needed to detect and identify very small wetlands and within
wetland vegetation and logs. Also, leaf-off imagery was expected to be more suitable than summer
imagery because, in examination of 2007 summer airborne digital camera imagery with 0.25 m pixels
for another area in Gatineau Park [53], it was evident that deciduous forest foliage can overtop and
hide portions of wetlands from view by an overhead sensor. Consequently, the existing leaf-off air
photos mentioned above were adopted for this study.
They were 23 cm format colour photos that had been acquired 6 May 2007 between 12:00 and
16:30 local time using Wild RC-20 and Zeiss Top 15 cameras flown simultaneously in two aircraft.
The lens focal length was 152.4 mm and the average scale was 1:16,000 resulting in 2 km × 2 km
coverage per photo. The photos had been digitized into 8-bit 20 cm nominal pixel size red, green and
blue spectral bands. Although the exact band centers and bandwidths were not known, standard color
film was used, which typically has significant overlap in spectral sensitivity between the color
layers [54]. The photos had been orthorectified and combined into a mosaic with positional accuracy
of better than 1 m [29]. In total, for the 16 km × 10 km study area, 38 photos were used in this
research. A 4 km × 2 km area (2 photos) in the northeast corner was excluded (see Figure 4) because it
was outside the park boundary.
3.3. Object-Based Segmentation and Classification of Wetlands and Within Wetland Habitat Features
The goal of segmentation was to produce spatial entities representing wetlands, and within wetland
features of water, vegetation, and logs. There has been an abundance of literature identifying the
benefits of object classification (e.g., [55–58]), including additional image object attributes that can be
used as input variables (e.g., texture, size and shape; inter-object topological information). eCognition
software ([59]; now distributed by Trimble Inc.), was used as it offers several segmentation algorithms
and over 100 different feature expressions that can be implemented in sequential classification
rules. The multi-resolution segmentation algorithm was applied in the processing steps described in
Sections 3.3.1 and 3.3.2. It is a region growing technique that locally minimizes the average
heterogeneity of an image object for a given resolution. Objects are grown simultaneously across the
scene based on spectral similarity of pixels and the contrast of an object with neighbouring objects. It
was implemented using a top down approach where the higher scale (larger) objects are typically broad
land cover types that are divided into lower level (smaller) objects until all targeted classes are
identified [60,61]. A key unitless scale parameter is related to the image pixel size; larger scale values
result in larger delineated objects. It is also related to the parameters of ‘colour(spectral information
such as object mean brightness) and ‘shape’ [58,59]. For brevity, their detailed definitions are not
presented here. Each is weighted from 0 to 1 with their respective weights summing to 1.0. The shape
parameter has two sub-parameters, smoothness (of object borders) and compactness (in relation to a
circle) [59]. In implementation, iterative testing of the parameter weights is commonly required until
the resulting segments best match the desired landscape objects [55,57,62].
Remote Sens. 2012, 4
The main landcover classes were first segmented at a coarse object scale to classify wetlands and
non-wetlands and later a finer object scale was used to identify thematic classes. In the second
segmentation phase the potential wetland entities were segmented and classified into water and non
water. The non water class objects were allocated into two classes, wetland vegetation and logs (based
on parameters described in Section 3.3.2). The interactive software interface allowed for quick testing
to be conducted on image subsets. The results could be visually assessed with confidence due to the
very high spatial resolution of the imagery. Taking this approach, a series of steps, outlined below and
in Figure 5, was implemented to produce and refine a wetland map followed by segmentation and
classification of within wetland habitat features.
3.3.1. Wetland Segmentation and Classification
As the mosaic file was large (38 photos × 286 Mb/photo = 10.9 Gb, or 10.9 billion pixels), object
based segmentation of the entire mosaic was not possible with the available computing power. Thus,
an initial maximum likelihood pixel-based classification (MLC) was conducted to remove as much of
the non wetland pixels as possible. In terms of image brightness, the distributions for moderately bright
senescent spring wetland vegetation, deciduous forest leaf litter, and tree trunks overlapped
significantly in each spectral band as did the histograms for darker features such as water, shadow and
coniferous forest. Consequently, image texture was used to discriminate these classes. In the leaf-off
imagery, trunks, branches and shadows created a texture distinct from that of wetland vegetation as
shown in Figure 6. Tests were conducted of several Grey Level Co-occurrence Matrix (GLCM) and
Grey Level Difference Vector (GLDV) [63] texture metrics extracted from the red band (it visually
showed the greatest texture differences between land cover types) using omni-directional pixel pair
sampling in window sizes of 3 × 3, 5 × 5, 11 × 11, and 25 × 25. From these, 5 × 5 GLDV Angular
Second Moment (ASM) was found visually to best discriminate the features noted above. It was input
to a supervised MLC using multiple training polygons for classes of water, wetland vegetation,
deciduous forest, coniferous forest/shadow, power line corridor and agriculture (a small area in the
corner of the mosaic). Training polygons were selected to be dispersed throughout the mosaic and
representative of the range of conditions that could be visually discerned within the high-resolution
imagery. The resulting classification was aggregated into two classes, Potential Wetland (i.e., pixels
classified as water or wetland vegetation) and other. This eliminated most of the upland areas and the
output wetland map was checked to ensure that no visually interpretable wetlands were eliminated.
The MLC wetland map was then refined through object segmentation and classification. As small
coniferous/shadow pixels were often misclassified as small wetlands, the two class MLC wetland map
was segmented using a scale parameter value of 10, to delineate small objects about the size of these
features. The colour value was set at 0.8 to give priority over shape, as the image was binary.
However, within the shape parameter, the compactness value was set high, at 0.8, (with smoothness
low) given the compact nature of shadows in relation to many of the small water bodies. The segments
produced were then classified as coniferous/shadow or wetland based on their distance from the mean
brightness value of training samples collected for each class. Classified shadow segments were then
assigned to the other class while the pixels in the classified wetland segments were merged with the
larger set of potential wetlands pixels.
Remote Sens. 2012, 4
Figure 5. Image classification steps to produce segmented and classified wetlands and
within wetland habitat features of water, vegetation and logs. Rectangles are image products;
ellipses are processes. MLC = maximum likelihood classification. OBSC = object-based
segmentation and classification. For each segmentation, the scale value (S) is given. For
each classification the discriminating variable type is given. T = data threshold as
described in the text. DN = image brightness digital number. L = length. W = width.
The potential wetland map at this stage was still comprised of pixels from the MLC and not objects,
so segmentation was conducted to delineate the wetlands. The scale value was set to be 300 to delineate
whole wetlands. Values above 300 produced objects comprised of more than one wetland while values
of 100 and 200 segmented smaller objects within wetlands. Image brightness (colour = 0.8) was
weighted more than object shape (0.2) in defining segment boundaries, as the wetlands were bright
while their masked out surroundings were black. For shape, objects were defined more by the degree
of boundary smoothness (value = 0.9) than their compactness (value = 0.1) as was expected for these
large land cover entities.
Following this, some residual erroneous forest segments were removed (assigned to Other) in two
steps. First, after iterative testing, segments with GLCM Contrast texture of greater than 200 DN (i.e.,
Remote Sens. 2012, 4
high texture) were found to be forest and were removed. This texture metric was derived from sample
pairs at 45 degrees to each other to approximately align with the tree shadow angle and resulting
texture of deciduous forest (Figure 6) and it was calculated from all pixels in each segment (i.e., not
using a moving window). Second, the few remaining residual forest segments were removed using a
brightness threshold (DN > 120) as their mean brightness was higher than wetland segments which
included water.
Figure 6. Leaf-off orthophoto examples of deciduous forest (left) and examples of two
extensive sections of senescent wetland vegetation that border open water sections of
wetlands or streams.
In the final wetland mapping step, wetland segments enclosed by a common boundary were merged
into whole wetlands. The wetland map was then used to classify wetland habitat attributes as described
3.3.2. Segmentation and Classification of Within Wetland Habitat Features: Water, Vegetation, Logs
Image segmentation using the red spectral band was conducted within the wetland objects created
above to delineate within wetland water, vegetation and logs. Through iterative testing of the scale,
color/shape and smoothness/compactness parameters, optimal values were determined for each. Given
the very high resolution of the imagery and interactive software interface, visual assessment of
multiple segmentation results was straightforward and efficient to implement. Figure 7 shows the
impact of the scale parameter in a portion of a wetland in the red band. Segmented objects representing
logs and vegetation patches (brighter objects) are shown at three scale values: 100, 50, and 20. A scale
value of 20 was selected as it was visually evident that smaller values often produced multiple
segments per log or vegetation patch, while larger values did not accurately delineate individual logs
or delineate the edge between wetland vegetation and water. Color and shape weights of 0.6 and 0.4,
respectively, were found to be optimal. Brightness was weighted stronger than shape due to the
significant differences between the brighter vegetation or logs and the darker water. However, shape
Remote Sens. 2012, 4
was deemed to be important given the shape differences between the three habitat features, so a
significant though smaller weight was selected. For the shape sub parameters, object compactness was
found to be more important than boundary smoothness in delineating vegetation patches and logs and
weights of 0.7 and 0.3 for compactness and smoothness, respectively, were found to be optimal. These
color and shape weights were used for all three segmentations in Figure 7.
Figure 7. Example segmentation of a portion of a wetland in the red band to determine the
optimal scale parameter value for the within wetland classes of log and wetland vegetation
(both bright) versus water (dark). (SP = Scale Parameter.)
In classification of the segments, it was found that brightness generally varied between water
(darkest), logs (moderate), and vegetation (brightest) but that their histograms overlapped significantly.
Remote Sens. 2012, 4
Consequently, texture, shape and size were used in addition to brightness to classify these features.
Through interactive histogram and image analysis segments with mean brightness greater than a
selected threshold were classified as wetland vegetation and logs while segments darker than this
threshold were classified as water. Three thresholds were required for different portions of the mosaic
due to minor differences in photo exposure between flight lines. The thresholds were 90 DN for a
block of 24 photos, 85 DN for a block of 8 photos and 80 DN for the remaining block of 6 photos.
Such iterative testing was simple and fast to implement using the Feature View tool as all objects
meeting a given criterion could be temporarily highlighted, allowing iterative manipulation of these
thresholds to determine which most effectively distinguished water from the other two classes.
The above process effectively separated water segments from segments that were either wetland
vegetation or logs. To separate logs from vegetation, because segmented log length (average = 6.2 m;
standard deviation = 3.4 m) was much greater than log width, segments with a length to width ratio
greater than 2.75 were classified as log. In addition, log segments satisfying this criterion that were
longer than 50 m were removed (assigned to the Other class) as, geometrically, they were unlikely to
be logs or wetland vegetation but were residual parts of linear features outside wetlands (e.g., road
edges, the power-line corridor). Of the remaining log segments, those that were larger than 500 m
area were re-classified as wetland vegetation, as the extensive fieldwork and air photo interpretation,
did not identify any log clumps larger than this.
3.4. Classification Accuracy Assessment
The map of wetlands and within wetland habitat features was assessed for classification accuracy
using random sampling. Field data and objects visually interpreted from the orthophotos served as the
reference data. For wetland accuracy assessment, a sample of 50 wetlands was desired to be
representative of the approximately 700 wetlands in the study area (see Section 4.1). To achieve this,
random points were generated until they occurred in 50 wetlands. The total sample was 521 points,
with 66 in wetlands (some wetlands had more than one point) and 455 in the Other class. A point was
deemed to be in wetland if it fell within 20 m of a reference wetland, as the shore and near shore area
of each wetland was generally comprised of wetland vegetation and considered to be suitable habitat.
These reference data were tabulated against the Wetland/Other map in a 2 × 2 error matrix.
To determine the classification accuracy of within wetland habitat features, five random points were
placed within each of the 50 wetlands selected above and each point was visually interpreted on the
orthophoto to determine if it was water, wetland vegetation or log. However, since water was the
dominant class by area within the wetlands, it was selected much more often than wetland vegetation
or logs. Therefore, in each wetland where they existed, two visually interpreted logs and two wetland
vegetation points were manually selected in the orthophotos without reference to the classification.
Through this process, all wetlands had some wetland vegetation that was sampled for validation, while
nine of the 50 sample wetlands did not have logs present. In assessing the class of the thematic map,
sample points were deemed to be classified as wetland vegetation or water if the point and the area
immediately surrounding the point (in about a 1 m or 23 pixel radius) was classified as these classes,
respectively. Sample reference logs were deemed to be correctly classified if at least half their area was
classified as log. These reference data were tabulated against the classified map in an error matrix.
Remote Sens. 2012, 4
3.5. Potential Habitat Mapping
The classification results for the four primary habitat attributes (wetlands; within wetland water,
vegetation and logs) were combined to produce a potential habitat map. There are many ways to
combine habitat attributes in such mapping (e.g., [3,64,65]) so the map presented is just one example
of the type of product that could be derived from data types and methods similar to those of this study.
A subjective linear additive scoring system was defined as follows (Table 1). First, ponds with only
open water and no logs or vegetation were given a score of 2 as they represent better habitat than the
non wetland (Other) class. Next, wetland size was divided into four categories, wetland vegetation
cover into five categories, and the number of logs per wetland into three categories. Scores for
vegetation presence and abundance were varied based on wetland size. Lower scores were assigned to
medium and large wetlands with low per cent vegetation cover as they are not as good habitat (provide
less protection) as smaller wetlands with low vegetation cover. Also, small wetlands with very high
vegetation cover and little open water were not deemed to be better habitat than similar sized wetlands
with moderate vegetation, i.e., beyond a certain per cent cover suitability does not increase.
Table 1. Scoring system used as an illustrative example of potential Blanding’s turtle
habitat mapping from the habitat attributes wetlands (including open water), and within
wetland vegetation and logs. The highest score of 10 would be obtained for a wetland
(score = 2 not shown in the table) with medium vegetation cover (score = 5) and 15 or > 5
logs if the wetland is small, or > 5 logs if the wetland is medium or large in size (score = 3).
Wetland Vegetation
Wetland Size
Cover Range
Very Small
(<500 m²)
(500–2,499 m²)
(2,50022,500 m²)
(>22,500 m²)
None or Very low
0 to 4.99
5 to 24
25 to 49
50 to 74
Very High
75 to 100
Some scores were consistent across wetland sizes (e.g., medium-high vegetation cover), because for
any wetland size, this proportion of vegetation cover should provide adequate habitat resources.
Finally, scores for log presence and abundance were varied based on wetland size. The number of logs
also reaches a threshold of maximum suitability (i.e., the relationship is asymptotic). For example, in a
small wetland, a couple of logs are sufficient as optimal habitat [50]. In a larger wetland, the threshold
is slightly higher so the maximum was set at five. The potential habitat score for each wetland was
calculated by summing the scores for wetland vegetation and logs together and adding the two base
points for being a wetland. Using this scoring system, non wetlands have a score of zero and a wetland
Remote Sens. 2012, 4
could potentially have a score of two to ten. The scores were then grouped into classes from unsuitable
to very high suitability for map display and analysis (Table 2).
Validation of the habitat suitability map was conducted through frequency analysis of the recorded
observation locations of Blanding’s turtles (1996–2011) [18,43,44,46] against the suitability class of
the most proximal wetland. It was not possible to conduct validation independently and rigorously
using random sampling because of the spatial clustering of the observations, mostly near an easily
accessed road crossing the park within the purple rectangle in Figure 4. Also, there were many
wetlands within the study area without observations that could be suitable habitat, i.e., no observations
at a wetland rated as good potential habitat does not mean the habitat score is wrong. For the 74
existing single point observations within the study area, only ten were farther than 250 m from a
wetland. Of these, seven were on or beside roads and two were in bays of the large lake within the
study area. The remaining 65 observations were combined with the 20092011 telemetry data. In the
telemetry data, turtles were associated with the closest wetland given there was one within 250 m, and
given multiple telemetry location points for a single turtle, only one wetland was used as an
Table 2. Potential habitat levels assigned to the resulting habitat scores.
Habitat Suitability Score
Habitat Suitability Level
Very High
4. Results
4.1. Wetland and Within Wetland Habitat Attribute Classification
Wetland mapping identified 693 wetlands in the study area. From all known sources (as described
earlier), only 383 wetlands and open water bodies had been previously mapped. The high-resolution
orthophotos and the processing methods used in this research were able to identify wetlands/water
bodies that were probably smaller than the minimum mapping unit or resolution of previous mapping
efforts. Of the 693 classified wetlands, 133 were smaller than 100 m². These were removed from
further analysis because they were difficult to reliably interpret in the photos so validation was not
possible. They were also below the commonly reported minimum wetland sizes for Blanding’s turtles
(e.g., average 0.18 ha [12]; 11.5 ha [26]). Of the classified wetlands, 82% had wetland vegetation
(5%–50% cover) and 90% had log(s) present, with 59% having more than five logs.
Figure 8 [66] shows an example wetland in the imagery and as its classification for habitat features.
The accuracy assessment for wetlands (Table 3) showed very low error. Seven of the 66 wetland
reference points were misclassified as the Other class and seven of the 455 Other sample points were
misclassified as wetland. The error matrix for the within wetland habitat attributes (Table 4) also
shows very high accuracies for all classes.
Remote Sens. 2012, 4
Figure 8. Orthophoto of a wetland (from the wetland mask) and the resulting classification
of three Blanding’s turtle habitat attributes: water, wetland vegetation, and logs [66].
Table 3. Error matrix for wetland classification.
Reference Interpretation
User’s Accuracy (%)
Wetland 59 7 66 89.4
Producer’s Accuracy (%) 89.4 98.5 Overall Accuracy 97.5%
Table 4. Error matrix for within wetland habitat feature classification.
Reference Interpretation
Users Accuracy (%)
Wetland Vegetation
Wetland Vegetation
Producer’s Accuracy (%)
Overall accuracy 93.5%
Interpretation of the orthophotos at the sample locations and at other locations revealed that
classification error, although small, could be mainly attributed to a few sources. In creation of the
wetland mask, continuous wetland vegetation along borders of wetlands, at ends of wetlands, or in
Remote Sens. 2012, 4
narrow areas between two wetland sections, was not retained as much in the wetland class as water
bodies with vegetation patches dispersed throughout. Continuous wetland vegetation with no
surrounding water was often confused with forest floor leaf litter and therefore removed with
deciduous forest during the processing. Removal of this continuous vegetation made some connected
wetland sections appear spatially separated. These areas were very small in relation to the wetlands
with open water that were mapped, but they could represent good habitat.
Within wetlands, shadows created by isolated trees and tree groups were rare but they were
generally misclassified as water. Classified within wetland vegetation was in general agreement with
the orthophotos and field survey data. Some logs that were visually evident as linear structures were
misclassified as vegetation or water, particularly if they were submerged. This is evident in Figure 8,
where all the logs visible in the photo are not classified as logs in the map. While this type of error
affected the absolute number of logs detected, relative abundance in the habitat scoring system was not
adversely impacted.
4.2. Potential Habitat Mapping
Figure 9 shows a portion of the potential habitat map using the generalized classes of Table 2. An
unsuitable score (23) generally indicated a wetland with none or very low vegetation cover and no
logs, or a wetland with very high vegetation cover, little open water, and no logs. Conversely, very
highly suitable wetlands (910) had medium to high (25–75%) vegetation cover and more than one log
present. Intermediate scores represented various non-optimal combinations of these attributes. Based
on these, the study area was found to have an abundance of potential habitat.
Table 4. Frequency distribution of existing Blanding’s Turtle observations in the study
area with respect to wetland habitat suitability score. Each observation was matched to the
closest wetland within a 250 m radius.
Habitat Suitability Score
Number of Observations
Of the combined set of 115 single point and telemetry Blanding’s turtle observations within 250 m
of a wetland, six wetlands were mapped for the first time in this study. Table 4 presents the habitat
suitability classes associated with these observations. The vast majority (92.3%) of the observations
were closest to a wetland of high or very high suitability. Six per cent of the observations were closest
to wetlands of medium suitability while these wetlands represented 16.7% of the wetlands mapped and
assessed in the study area. Only 1.7% were closest to wetlands with scores of 4 or less while these
wetlands represented 4.9% of those mapped and assessed. Thus, despite the clustered nature of the
existing observations, these results indicate a strong preference of Blanding’s turtles for wetlands with
a mix of open water, within wetland vegetation and logs that were classified as high and very high
suitability. Of note is that twice as many observations were closest to wetlands with scores of 7 or 8
Remote Sens. 2012, 4
versus 9 or 10, indicating (with reference to Table 1) that Blanding’s turtles may prefer higher amounts
of vegetation cover, in the 5075% range, rather than the medium amounts that were scored highest in
Table 1, or that only one or a few logs are needed in larger wetlands for use in spring basking rather
than ‘more than 5’, which was scored higher in Table 1.
Figure 9. Example of a portion of the Blanding’s turtle habitat suitability map for the study
area that was derived from an additive scoring system for wetlands, and the presence and
abundance of wetland vegetation, and logs (Road source: [45]).
4. Discussion
Object-based segmentation and rule-based classification of high-resolution orthophotos using a
combination of spectral, texture, shape and size information was very successful at mapping
Blanding’s turtle habitat. Two scales of mapping were conducted: a broader scale to discriminate
potential wetlands from other land cover types, and a finer scale to map water, vegetation patches, and
Remote Sens. 2012, 4
logs within wetlands. These are among the most important single indicators of habitat and life cycle
activities of the Blanding’s turtle [10,11,13,14,21]
At the broader wetland scale, many previously unmapped wetlands were detected and delineated.
Figure 10 shows an example of such a wetland, that had a high potential habitat score, and at which a
Blanding’s turtle was observed by the authors. In comparison to the literature, wetland mapping has
been conducted at diverse scales using many sensor types and classification methods. Many studies
report high accuracies similar to the current study although the minimum wetland size that can be
accurately classified is scale dependent. For example, similar accuracies were achieved using MLC of
Landsat ETM+ imagery to classify a pond and marsh wetland class [67], and using fusion of radar and
Landsat ETM+ data [68] but for wetland sizes much larger than many of those classified in this study
(e.g., minimum wetland size about 3,600 m² [67]). Dingle Robertson and King [60], although reporting
lower accuracies, found object based wetlands classification using eCognition and Landsat 5 TM data
to be significantly better than MLC pixel based classification in both single date and temporal land
cover change analysis. At a finer scale, high wetland mapping accuracy was achieved with 4 m pixel
hyperspectral imagery [69]. By combining a 1 m LiDAR DEM with fused IKONOS Panchromatic
(1 m pixels) and Multispectral (4 m pixels) imagery, Maxa and Bolstad [70], were able to map five
distinct wetland types. Thus, as spatial resolution of the imagery increases, the minimum wetland size
and attribute detail that can be accurately mapped decreases and increases, respectively. In the current
study, the minimum wetland size retained was 100 m
, which represented 500 pixels. While wetlands
of this size might have been detectable with high-resolution satellite imagery, the same accuracy of
within wetland habitat feature classification would be unlikely.
The accurate mapping of within wetland vegetation and logs can aid the field sampling design of
future inventories as many observations of Blanding’s turtle are made in spring when it is often seen
on either one of these two features. The object based approach was capable of producing more
homogeneous class extents than would be expected for pixel-based classification. For example, in the
case of wetland vegetation, brightness variability would result in a speckled pixel-based map, whereas
segmentation and classification produced a single class assignment for each vegetation patch.
It is also surmised that logs were more effectively mapped using the object-based approach. The use
of length: width as a discriminator of logs, which were spectrally similar to forest leaf litter and some
senescent wetland vegetation, required segmented objects. In comparison to the literature, as above,
many studies have been conducted at coarser scales than the current study, and all for within wetland
water and vegetation types; no studies have been found that detect and map deadwood within wetlands
as a significant habitat feature. For example, Dillabaugh et al. [34] and Dechka et al. [71] used
brightness and texture of 4 m pixel IKONOS imagery to classify within wetland vegetation communities
with high accuracy. Gilmore et al. [72] utilized LiDAR and multi-temporal Quickbird imagery with an
object-oriented classification approach to classify three marsh vegetation species. Dogan et al. [73] and
Yuan and Zhang [74] used 2.8 m pixel QuickBird imagery to classify submerged plants within
wetlands. Thus, overall the accuracies achieved in this study for classification of within wetland
vegetation and open water compare to other studies but, as stated above, the minimum mapping unit of
this study is generally much smaller, providing capability to detect and map smaller patches of these
features as well as logs that could not be achieved with high-resolution satellite imagery.
Remote Sens. 2012, 4
The presence and abundance of these habitat features were used to construct and map a potential
habitat scoring system. As noted earlier, habitat suitability mapping is a large and diverse field with
many sophisticated methods proposed in the literature (e.g., [75–77]). The simple method used provided
a very strong representation of potential habitat distribution in the study area and was a significant
improvement over what was previously available. It can be used to aid targeting of future inventories
and other studies related to conservation management. For example, the connectivity between
wetlands, which is critical to the Blanding’s turtle [12], could be analyzed by integrating the map
produced here with topography and the telemetry data collected for some turtles in 2009–2011 to
define areas of multiple wetlands that together provide strong habitat potential.
Figure 10. An example wetland, 0.15 ha in area, that was not classified before this
research that has had Blanding’s turtle observations. Orthophoto (upper left), classified
habitat features (upper right), a Blanding’s turtle observed in May 2009 (bottom).
This research focused on four habitat attributes that had distinct potential for mapping using
existing leaf-off high resolution color orthophotos and that are key providers of food, protection,
thermal regulation, hibernation, etc., for the Blanding’s turtle. Other habitat types such as vernal pools
beneath forest canopies that are often used in transit across upland areas were not included because
they were deemed to have lower potential for detection using remotely sensed imagery. The primary
limitation of the orthophotos was their poor spectral information content as the three spectral bands
had wide and overlapping bandwidths. Also, in spring most vegetation was senescent and spectrally
similar to forest leaf litter and tree trunks. Summer imagery of more narrow bandwidths and including
a near infrared band would aid in vegetation discrimination, provided the wetland vegetation was not
Remote Sens. 2012, 4
overtopped by tree canopies, which was found to be problematic in this study area as stated in
Section 3.2. In image processing, to accurately assign segmented objects to the correct class, iterative
testing was required to develop the optimal sequential rule-base. This rule-base is site specific; for a
different area, image scale, or time of year (sun angleview angle differences), it would have to be
modified. However, the iterative and interactive method in general was found to be fast and easy to
implement, and validation was efficient due to the very high spatial resolution of the photos so this is
not viewed as a significant limitation.
An alternative approach would be to input training data into a classifier such as a classification and
regression tree (CART) (e.g., [78,79]) to attempt to automatically find optimal rules and data
thresholds for classification of wetlands and the within wetland habitat features. A more automated
approach such as this was not conducted in this study because it was felt the multiple decision points,
data types (brightness, texture, shape and size) and thresholds that were needed to first classify the
features and then to refine the classifications would result in very complex and unwieldy sets of rules
in a decision tree.
5. Conclusions
Using leaf-off 20 cm pixel orthophotos and object-based segmentation and classification, habitat
critical to the threatened Blanding’s turtle was mapped. Two scales of analysis were implemented:
(1) at a coarse scale (object segmentation scale parameter of 300) to delineate and classify whole
wetlands, and (2) at a finer scale (segmentation scale parameter of 20) to delineate and classify within
wetland habitat features of water, vegetation and logs. High accuracies were achieved at both scales.
At the coarse scale, wetland classification accuracy was 89.4% and, at the finer scale, accuracy of the
within wetland features varied between 81.5% and 98.5%. The number of mapped wetlands in the
study area almost doubled, as 310 new wetlands were identified, most being smaller than the minimum
mapping unit of previous mapping efforts. Six new wetlands were mapped that also had recorded
Blanding’s turtle observations. A map combining the selected wetland and within wetland attributes
showed the spatial distribution of potential habitat throughout the study area. Of the 115 existing
Blanding’s turtle observations, 92.3% were closest to a wetland of high or very high suitability. These
results are being used in further field inventory planning and in combination with other data such as
turtle movement obtained through telemetry to advance understanding of how the Blanding’s turtle
selects and utilizes habitat. Future research will include validation of Blanding’s turtle habitat
preferences and movement in relation to the mapped wetlands as well as evaluation of spatial
extension of the rule base to the other main habitat area near the Ottawa River.
This research was funded by an NSERC Discovery grant to D. King. Field support was provided by
the Carleton University Geomatics and Landscape Ecology Research Laboratory through the Canada
Foundation for Innovation and other funding. The National Capital Commission provided access to the
Gatineau Park aerial orthophotos and other support that was vital to this research. Blanding’s turtle
observation and telemetry data were provided by the following: (1) Ministère des Ressources
naturelles et de la Faune, Québec, Direction de l’aménagement de la faune de l’Outaouais);
Remote Sens. 2012, 4
(2) Conservation de la Nature Canada (2008 data); (3) Atlas des Amphibiens et Reptiles du Québec,
(4) National Capital Commission, Ottawa, Canada. We are very grateful to Yohann Dubois of the
Ministère des Ressources naturelles et de la Faune, Québec for scientific advice on the Blanding’s
turtle, field inventory methods and existing data. Gabrièle Fortin, MSc candidate in Biology at the
University of Ottawa kindly provided GIS shapefiles for some of the observations and telemetry data.
The field assistance of Valerie Torontow and scientific advice of Scott Mitchell (Geography, Carleton
University) are greatly appreciated.
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... Complementing conservation genetic research on Blanding's turtle are efforts to employ advances in SDMs, geographic information systems (GIS), and remote sensing data to map habitat-based structural connectivity for Blanding's turtle populations (Barker and King 2012;Millar and Blouin-Demers 2012;Markle and Chow-Fraser 2016;Stryszowska et al. 2016). Recent studies have also enlisted habitat models to evaluate functional connectivity associated with Blanding's turtle seasonal movement patterns (Mui et al. 2017) and future climate and landscape changes (Hamilton et al. 2018). ...
... Delineating suitable habitat at the patch scale required a different suite of environmental predictor variables than those used by Stryszowska et al. (2016). Previous fine scale habitat modeling efforts for Blanding's turtle classified habitat features using high resolution aerial photographs (Barker and King 2012) and derived categorical habitat classes from satellite imagery (Markle and Chow-Fraser 2016). We focused on temporally-variable Landsat-derived vegetation indices obtained throughout the Blanding's turtle active season and percentage of preferred land cover classes (forested and scrub-shrub wetlands) for SDM development. ...
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Context Regional conservation efforts should incorporate fine scale landscape genetic and habitat suitability data for management decisions. This information permits conservation measures to be tailored to a specific landscape. Objectives We investigated the landscape determinants of gene flow and habitat suitability for the state-threatened Blanding’s turtle (Emydoidea blandingii) in northeastern New York (NNY). We applied the results from each to examine their complementary contributions to local connectivity and genetic structuring. Methods We conducted population and individual-based genetic analyses with microsatellite data to evaluate genetic structuring and landscape genetics in NNY. We coupled these genetic analyses with species distribution modeling (SDM) to estimate the extent of suitable habitat across this important region for species persistence in the state. Results Gene flow was strongly associated with open water and cultivated land, indicating the role open water channels play in connecting neighboring activity centers, and the propensity of females to select cultivated land to nest. Species distribution models based on Landsat-derived vegetation indices and percentage of scrub-shrub wetlands accurately identified Blanding’s turtle habitat. Connectivity estimates from our NNY focal area using landscape genetic and SDM resistance surfaces showed potential movement constraints between the two genetic clusters. Conclusions Land cover better explained genetic distance data than geographic distance for Blanding’s turtles in our focal area. Accurate SDMs were developed for our focal area with a small number of occurrences (< 50). Using both gene flow and habitat-informed resistance surfaces revealed localized connectivity constraints associated with each, permitting more comprehensive landscape planning.
... These uncertainties can originate from the heterogeneity in local pixel values and similarities among landform types (e.g., dune and barchan), which often lead to the "salt-and-pepper" phenomenon in the mapping outputs. Furthermore, the spatial resolution of commonly used satellite images (e.g., 10 m Sentinel-2 or 30 m Landsat) is inadequate to delineate class boundaries, and accordingly, a spectral combination of various classes at subpixel scales can reduce the accuracy of the classification (Barker and King, 2012;Dronova et al., 2012). The fuzzy rule-based classification, on the other hand, permits the formulation of knowledge giving in a natural language with unclear and inexact expressions (Sebari and He, 2013). ...
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Traditional landform modeling approaches are labor-intensive and time-consuming. We proposed and developed a semi-automated object-based image analysis (OBIA) rule set approach for desert landforms detection and mapping. Sentinel-2 image and digital elevation model (DEM) were acquired for the study area. The multi-resolution segmentation algorithm was employed on the datasets to select relevant features to define appropriate segmentation scales for all landform categories. Object-based rule sets were then employed using spatial (DEM and its derivatives, e.g., slope, aspect, and hillshade) and spectral information for semi-automated classification of the desert landforms. Desert landforms are detected and classified into four classes: saline dome, barchan, playa, and dune. The Fuzzy Synthetic Evaluation (FSE) technique was applied in concert with the error matrix to validate the accuracy of the classification results based on field data, Google Earth, and geological maps. Our findings demonstrated the highest confidence of overall accuracy (OA) 96.21%, 92.58%, 95.99%, and 95.05% respectively, for the saline dome, barchan, playa, and dune. Results showed the strong potential of the rule-based OBIA remote sensing approach for desert landform detection and delineation. Results further demonstrated the efficiency of spatial and spectral features for desert landforms detection and delineation.
... We used orthophotos, satellite imagery, and ground truthing to create land cover polygons within ArcMap, and measured the area of each land cover type to determine availability within the population range. Aerial orthophotographs collected and incorporated into a GIS is an approach for carrying out visual and spatial analysis of Blanding's turtles and their habitat (Maktav et al. 2000, Barker andKing 2012). We relocated turtles in and around muskrat (Ondatra zibethica) dens frequently, which led us to calculate the area of muskrat dens in ArcMap by placing a circular polygon with a diameter of 1 m around each den point location recorded in the field or obtained via orthophotos taken in 2014. ...
Captive breeding or headstarting is a management option to increase population numbers in threatened and endangered animals. The success of these programs depends on increasing population numbers while maintaining fitness within populations that contain some captive‐reared individuals. As part of an ongoing conservation project with Shiawassee National Wildlife Refuge (SNWR) in Saginaw, Michigan, USA, the Detroit Zoological Society (DZS) headstarted Blanding's turtles (Emydoidea blandingii) to 18 months of age. To determine survival, movements, and habitat use of these headstarted Blanding's turtles, we externally attached radio‐transmitters to 24 turtles and released them at 4 sites in release groups of 6 along a transect in a wetland within SNWR during June 2014. We located turtles weekly during the active season (May–Sep) and every 2 weeks during the inactive season (Oct–Apr) for 18 months, starting immediately after release and ending November 2015. We calculated straight‐line distances between release sites and home range centers over the study period. We examined habitat use of the turtles in relation to habitat availability. To predict movement of headstarted Blanding's turtles away from release sites, we used geographically weighted regression (GWR) with measured habitat factors and release sites as independent variables. There were differences in home range sizes across release groups but not between years. Headstarted juvenile Blanding's turtles in this study have home range sizes similar to those measured in other studies for wild‐hatched juveniles but smaller home ranges than those measured for wild‐hatched adults. Our study showed that headstarted Blanding's turtles used habitats with muskrat (Ondatra zibethica) dens and cattails (Typha spp.) more than proportionally available, and used open water, willows (Salix spp.), and lowland forest less than proportionally available. The GWR model was able to predict habitat characteristics (water depth and temperature, duckweed [Lemna minor], cattails, muskrat dens and fields, buttonbush [Cephalanthus occidentalis]) that influenced the movement of turtles away from the release sites. The habitat coefficient's influence on movement varied in relation to the current location of the turtle and as habitat characteristics increased or decreased. Turtle distance from release sites decreased as water depth, water temperature, and duckweed increased, and increased as cattails, muskrat dens and fields, and buttonbush increased. Habitat in the small spatial extent at release sites may affect movement of headstarted Blanding's turtles. Furthermore, this study uses novel methodologies for assessing headstart programs that can aid future conservation and management efforts by providing information on habitat use and movement patterns of headstarted Blanding's turtles after release. © 2018 The Wildlife Society. We aimed to determine survival, movements, habitat use in relation to availability, and impact of release sites on 18‐month‐old headstarted Blanding's turtles by releasing them at 4 different sites in Shiawassee National Wildlife Refuge. We showed high survival of the headstarted Blanding's turtles, reported differences in home range size across release groups, and used geographically weighted regression to explain how turtle distance from release sites varies by measured habitat characteristics; we concluded that habitat characteristics in the small spatial extent at release sites affect movement and space use of headstarted Blanding's turtles.
... Temporary pools were often located around permanent upland wetlands or in forested areas.To create the layer of forest, wetland, rock and open water, we used IKONOS approach at the image object level. Object-based image classification provides benefits over pixel-based classification such as including object shape and size(Blaschke 2010) and has been used to classify habitat for Blanding's turtles in Quebec, Ontario(Barker & King 2012). The NN approach combines multiresolution segmentation and supervised classification to identify object class based on selected training objects(Wang et al. 2004; ...
In Ontario, 7 of the 8 native species of freshwater turtles are listed as at-risk. Protection of species-at-risk requires delineation and identification of their critical habitat to enable the proposal of conservation strategies. Because of pre-existing development and limited resources for conservation, it is difficult to protect land required for species’ survival especially when faced with multiple threats such as habitat loss, degradation, and fragmentation, invasive species, and a changing climate. To improve conservation strategies, we must understand how turtle populations use a variety of different landscapes at a range of spatial scales. The goals of this thesis were to develop and assess landscape-level conservation strategies for imperiled freshwater turtles. I first determined that Blanding's turtles (Emydoidea blandingii) require inland and coastal wetlands, a suitable upland matrix and connecting corridors. Since turtles overwinter for majority of the year, I determined suitable thermal parameters of occupied sites, revealing suitable water temperatures range from 0.44° C to 3.68° C and slowly decline throughout the overwintering period. Using a landscape-level approach, my models support that habitat in Georgian Bay is in good condition; 64% of evaluated islands support habitat for Blanding’s turtles and majority of surveyed coastal wetlands support eastern musk turtles (Sternotherus odoratus). In particular, coastal wetlands with higher proportions of forest cover, lower densities of roads, buildings, and docks, and more variable bathymetric slopes provide more suitable habitat. In addition to anthropogenic stressors, invasive species pose a threat to turtles and their habitat. I provided the first quantitative evidence of the negative consequences of Phragmites australis invasion on availability of habitat for turtles. Since roads are a ubiquitous feature in our landscape, I evaluated strategies aimed at limiting their negative effects on at-risk reptiles. A decade-long dataset of road mortality reveals the ineffectiveness of partial fencing as a mitigation strategy. I recommend emphasis on fence integrity and continuity, limiting impact of edge effects, and conducting a comprehensive monitoring program. Finally, long-term changes in a protected landscape are evaluated and results indicate habitat succession and invasive species can negatively affect habitat suitability, and protection of land alone does not necessarily equate to protection of sensitive herpetofauna. My research integrates geographic information systems and remote sensing to advance our understanding of landscape requirements for turtles and will inform future conservation strategies aimed at mitigating threats to imperiled freshwater turtles.
... In addition to use of the NWI and the TPI approach described here, a variety of other methods have been used to identify potential habitat for a variety of species. For example, Barker and King (2012) used object-based image classification to locate wetlands suitable for the Blanding's turtle. Although their mapping accuracy was similar to what we observed, their image-based methodology excluded wetlands that were under tree canopy, such as vernal pools. ...
Amphibians that primarily breed in ephemeral wetlands are especially vulnerable to climate change because they rely on rainfall or temperature to initiate breeding and create suitable hydroregimes (water duration, timing, frequency, depth) for reproductive success. Hydroregime effects on reproductive success are likely to differ among species because of differences in reproductive strategies: the length and timing of breeding period, rate of larval development, and timing of metamorphosis. We applied an information-theoretic approach to 22 consecutive years of continuous amphibian trapping data at eight ephemeral wetlands to test hypotheses regarding environmental (hydroregime, weather) and biological (adult breeding effort) factors affecting juvenile recruitment (JR) by six focal species representing four reproductive strategies. We hypothesized that (1) JR by species with similar reproductive strategies would be influenced by similar variables; (2) JR would be higher for all species when models encompassed the maximum time span of potential tadpole occurrence and development; and (3) JR rates within individual wetlands and breeding cycles would correlate most closely between species with similar breeding strategies. The best model for all focal species (except Scaphiopus holbrookii) encompassed the maximum time span and indicated that ≥1 hydroregime variable, total precipitation, or both were important drivers of reproductive success; average air temperature was not. Continuous hydroperiod through peak juvenile emigration was an important predictor of JR for species with prolonged breeding periods, slow larval development, and a "fixed" late spring start date for juvenile emigration (regardless of when oviposition occurred, or cohort age; Lithobates capito, Lithobates sphenocephalus), but not for species with rapid larval development and continual emigration as cohorts complete metamorphosis (Anaxyrus terrestris, Anaxyrus quercicus, Gastrophryne carolinensis, S. holbrookii). Total rainfall was positively associated with recruitment for most species; depth characteristics affected species differently. Annual JR was positively correlated among species with similar reproductive strategies. Our results indicate that weather and hydroregime characteristics interact with reproductive strategies that differ among amphibian species and influence reproductive plasticity, opportunity, and success. Effects of altered weather patterns associated with climate change on amphibian reproductive success may correspond more closely among species having similar reproductive strategies, with critical implications for population trends and assemblages.
... In addition to use of the NWI and the TPI approach described here, a variety of other methods have been used to identify potential habitat for a variety of species. For example, Barker and King (2012) used object-based image classification to locate wetlands suitable for the Blanding's turtle. Although their mapping accuracy was similar to what we observed, their image-based methodology excluded wetlands that were under tree canopy, such as vernal pools. ...
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Small, seasonal pools and temporary ponds (<4.0 ha) are the most numerous and biologically diverse wetlands in many natural landscapes. Thus, accurate determination of their numbers and spatial characteristics is beneficial for conservation and management of biodiversity associated with these freshwater systems. We examined the utility of a topographic position index (TPI) landscape classification to identify and classify depressional wetlands. We also assessed relationships between topographic characteristics and ponded duration of known wetlands to allow hydrological characteristics to be extended to non-monitored locations in similar landscapes. Our results indicate that this approach was successful at identifying wetlands, but did have higher errors of commission (10%) than omission (5%). Additionally, the TPI procedure provided a reasonable means to correlate general ponded duration characteristics (long/short) with wetland topography. Although results varied by hydrologic class, permanent/long ponded duration wetlands were more often classified correctly (80%) than were short ponded duration wetlands (67%). However, classification results were improved to 100 and 75% for permanent/long and short ponded duration wetlands, respectively, by removing wetlands occurring on an abrupt marine terrace that erroneously inflated pond topographic characteristics. Our study presents an approach for evaluating wetland suitability for species or guilds that are associated with key habitat characteristics, such as hydroperiod.
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A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importance for various applications. In this regard, Remote Sensing (RS) technology has been widely employed for wetland studies in Canada over the past 45 years. This study evaluates meta-data to investigate the status and trends of wetland studies in Canada using RS technology by reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total). Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms of the wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors, keywords, citations, and publications time), geographic information, and level of classification accuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trend in using multi-source RS datasets along with advanced machine learning algorithms for wetland mapping in Canada. It was also observed that most of the studies were implemented over the province of Ontario. Pixel-based supervised classifiers were the most popular wetland classification algorithms. This review summarizes different RS systems and methodologies for wetland mapping in Canada to outline how RS has been utilized for the generation of wetland inventories. The results of this review paper provide the current state-of-the-art methods and datasets for wetland studies in Canada and will provide direction for future wetland mapping research
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Investigation of Headstarted Blanding's Turtles (Emydoidea blandingii) in Shiawassee National Wildlife Refuge, Saginaw, MI. When implementing management decisions managers should utilize the most cost-effective strategies that also provide the most benefit for the managed species. There are many different management options to consider when the objective is conservation of long lived reptiles (e.g., turtles) with one of them being headstarting. Headstarting is when reptile eggs are collected by managers, hatched in captivity, and hatchlings are raised for a certain time before being released into their native habitat. Blanding's Turtles (Emydoidea blandingii) are a state species of special concern in Michigan, where this study takes place. Headstarted Blanding's Turtles were raised for one and a half years and should benefit from being released in a suitable microhabitat within a wetland. This study's aim was to investigate the success of varying release locations and the headstart program by measuring thermoregulation patterns, survival, and movements of juvenile headstarted Blanding's Turtles. During June 2014, twenty-four Blanding's Turtles were released and tracked for eighteen months using radio telemetry to measure survival and to look at the movements among microhabitats; water and carapace temperature data were used to measure thermoregulation patterns. The headstarted juvenile Blanding's Turtles had an average carapace temperature lower than adults from previous studies and used basking as a thermoregulatory behavior. To evaluate factors that may potentially affect survival of Blanding's Turtles microhabitat factors at turtle relocation points within Shiawassee National Wildlife Refuge, Saginaw, MI, USA were recorded, including water depth, vegetation type and dominance, INVESTIGATION OF HEADSTARTED BLANDING'S TURTLES 3 substrate depth, and air and water temperature. GIS and ground truth data were used to investigate microhabitat factors and map turtle locations. All of the headstarted Blanding's Turtles survived overwintering through spring 2015. Turtles were released in four different locations with different microhabitats. Minimum convex polygon home ranges and movement patterns were analyzed to see if differences occurred across release groups and age classes. Significant differences were found between turtle home range size per release group. Comparisons to studies on wild-hatched Blanding's Turtles were made and showed that these headstarted Blanding's Turtles behaved similar to their wild-hatched counterparts when comparing behaviors at relocations and home range sizes. Use versus availability showed that the juvenile headstarted Blanding's Turtles avoided open water, willows, and lowland forest while preferring muskrat dens and cattails. Geographically weighted regression modeling predicted that these turtles would move towards preferred habitats and away from release sites with avoided non-preferred habitats. This research identified an estimated survival of between 63 and 96% over the eighteen month study period with all Blanding's Turtles surviving their first winter in the refuge. This research suggests that headstarting programs for Blanding's Turtles should release the species in preferred habitats of dense cattails with plenty of muskrat dens for basking and refuge, if available. INVESTIGATION OF HEADSTARTED BLANDING'S TURTLES
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Anthropogenic stress and disturbance of forest ecosystems (FES) has been increasing at all scales from local to global. In rapidly changing environments, in-situ terrestrial FES monitoring approaches have made tremendous progress but they are intensive and often integrate subjective indicators for forest health (FH). Remote sensing (RS) bridges the gaps of these limitations, by monitoring indicators of FH on different spatio-temporal scales, and in a cost-effective, rapid, repetitive and objective manner. In this paper, we provide an overview of the definitions of FH, discussing the drivers, processes, stress and adaptation mechanisms of forest plants, and how we can observe FH with RS. We introduce the concept of spectral traits (ST) and spectral trait variations (STV) in the context of FH monitoring and discuss the prospects, limitations and constraints. Stress, disturbances and resource limitations can cause changes in FES taxonomic, structural and functional diversity; we provide examples how the ST/STV approach can be used for monitoring these FES characteristics. We show that RS based assessments of FH indicators using the ST/STV approach is a competent, affordable, repetitive and objective technique for monitoring. Even though the possibilities for observing the taxonomic diversity of animal species is limited with RS, the taxonomy of forest tree species can be recorded with RS, even though its accuracy is subject to certain constraints. RS has proved successful for monitoring the impacts from stress on structural and functional diversity. In particular, it has proven to be very suitable for recording the short-term dynamics of stress on FH, which cannot be cost-effectively recorded using in-situ methods. This paper gives an overview of the ST/STV approach, whereas the second paper of this series concentrates on discussing in-situ terrestrial monitoring, in-situ RS approaches and RS sensors and techniques for measuring ST/STV for FH.
Multiple human activities can impose significant negative effects on wetland ecosystem. This study aimed to identify and quantify the human threats on the Shuangtai Estuary Ramsar site (SERS) by means of detecting wetland landscape changes using Landsat images from 1988 to 2014. Land-cover changes induced by agricultural reclamation, urban expansion, petroleum industry, aquaculture, and the increases in transportation land were identified using an object-oriented classification method and a decision tree. Human-triggered wetland conversions and landscape metrics-characterized wetland changes were documented in order to assess the protection effectiveness after this wetland was listed as a national nature reserve (1988) and a Ramsar site with international importance (2005). Results indicate that wetlands area at this Ramsar site decreased from 127,526 ha in 1988–117,805 ha in 2005, 113,705 ha in 2014 at an annual averaged loss rate of 531.6 ± 72 ha/yr. The only two vegetated wetland types, Phragmites australis and Suaeda heteroptera, have lost 4788 ha and 12,856 ha, respectively. Aquaculture ponds increased by 9715 ha, and almost all were transformed from natural wetlands. The landscape metrics used in this study show notable fragmentation trend of wetland as a result of the expansion of transportation lands. Multiple human activities occupied large areas of various wetland types. Compared to agricultural reclamation and urbanization, wetland changes at the study site were affected primarily by the development of aquaculture and petroleum industry. Obvious wetland loss and fragmentation determined by the analysis of Landsat images suggests that the protection effect has been relatively low in spite of the fact that the SERS is a national natural reserve and Ramsar site. As an internationally critical rest habitat for migration waterfowl, ecological degradation of the SERS and severe human threats create great challenges for ecosystem managements. This study also indicates that the protection effectiveness of other costal wetland should also be assessed using remote sensing to objectively track the conservation or restoration of these areas.
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Summer occupancy of discrete wetland habitat patches by Blanding’s turtle (Emydoidea blandingii) in Nova Scotia, Canada was modeled using presence/absence assessed from available trapping records (1995-2005) and from habitat variables collected in situ independently by two observers (summer 2005). Observer variation significantly affected results and was incorporated. The best data-based, generalizable and robust model contained three variables: water color, bank overhang vegetation, and aquatic substrate. Occupied wetlands predicted to be unsuitable and unoccupied wetlands predicted to be suitable were identified. Trapping success for Blanding’s turtle was investigated using trapping records from 19 wetlands supporting the species. Blanding’s turtles were caught more often at a mean cloud cover below 72.5% with a minimum of 19.5 trap*days per km of trappable habitat, and in June, July or August rather than May or September. These threshold values should be used to insure that sufficient effort is applied when assessing the species’ presence/absence.
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Eight radio-tagged Blanding's turtles (Emydoidea blandingi), monitored from June 1983 to January 1984, used ponds more often than predicted on the basis of available habitat while marsh habitats were used less than their availability. Most wetlands used by the turtles had water <60 cm deep and summer water temperatures below 28.5 C. Five of eight turtles used marshes, ponds, and creeks, all in close proximity (≤900 m) to each other, between June and September, then moved to two adjacent, deep ponds for overwintering. Five overwintering sites contained organic substrates and had water depths >0.5 m. The activity centers of two males (0.57 ha and 0.94 ha) did not differ significantly in size from those of six females (x̄ = 0.56 ha, SD = 0.293). Male and female activity centers overlapped (x̄ = 12%, SD = 14.7), and female activity centers overlapped with those of other females (x̄ = 26%, SD = 16.5). Two males did not share activity centers, although other males were captured within them. Distances between activity centers of two males (260 m and 635 m) were not significantly different from those among centers of six females (x̄ = 489 m, SD = 338 m). Females moved significantly greater distances per day (N = 56, x̄ = 95.1 m, SD = 79.0) than did males (N = 21, x̄ = 48.4 m, SD = 41.2). Mean nest distances from water, and nonnesting activity centers, were 168 m (SD = 90.8 m) and 620 m (SD = 144.0 m), respectively. At least four nests were destroyed by predators within 24 h of completion, and all nests were eventually destroyed. Fourteen of 16 nests were in grasslands.
In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation functions, optical transforms, digital transforms, textural edgeness, structural element, gray tone co-occurrence, run lengths, and autoregressive models. We discuss and generalize some structural approaches to texture based on more complex primitives than gray tone. We conclude with some structural-statistical generalizations which apply the statistical techniques to the structural primitives. -Author
We sampled a variety of wetlands in the Nebraska sandhills at Valentine National Wildlife Refuge. Significantly more individuals of painted turtles (Chrysemys picta) occurred in lakes and open waters than in marshes or small ponds, and the opposite was true for Blanding's turtles (Emydoidea blandingii). Besides this marked difference in habitat use, 46% of the captured E. blandingii in pond/marsh habitat were juveniles, but only 31.6% in lakes and open water. Current information suggests that marshes and small ponds are important habitat for juvenile turtles, especially Emydoidea blandingii.
Kinosternon flavescens and Emydoidea blandingii are both included on the Missouri list of endangered species, and the Missouri populations of K. flavescens are under consideration for placement upon the federal list. There are four known populations of K. flavescens and two of E. blandingii within the state, all in Clark County in the western floodplain of the Mississippi River. Goose Pond, a small patch of remnant marsh in a former river channel of the Des Moines River, is the last natural habitat in Missouri that contains a population of either species. Sandy soil appears to be a habitat requisite for both species. Kinosternon flavescens appears to tolerate major alterations of the natural habitat, whereas E. blandingii requires clean shallow water with abundant aquatic vegetation and appears to be sensitive to habitat manipulation. Kinosternon flavescens at Goose Pond had an aquatic feeding period of only three months. The species began aquatic feeding during April, but then ceased during July without any later resumption. Emydoidea blandingii fed for approximately 4½ months with two distinct phases: the first phase during April, May, June, and the first half of July; and a second phase during the latter half of August and the first half of September prior to hibernation. Kinosternon flavescens fed predominantly upon hard-shelled snails, and to a lesser extent upon insects, fish, and crayfish. Emydoidea blandingii fed predominantly upon crayfish and insects. The data suggest that E. blandingii in Missouri attain sexual maturity at a carapace length of 170-180 mm.
Wetlands are complex ecological systems that result from a number of environmental factors including hydrologie, geomorphic, and biologic processes. As a result of this complexity, wetlands, as a single feature, are often heterogeneous, making traditional image-based classification techniques for wetland mapping problematic. In this study, we propose a wetland classification approach that uses a combination of terrain-based derivatives to account for variability in ecological characteristics. A site was studied near Bolton, Ontario (43.84°N, 79.68°W). Based on research and experience, the authors propose a two-step multisoftware (i.e., geographic information system. (GIS), statistical, and image processing) technique, with step one involving the delineation of wetland boundaries using topographic data, followed by the separation of wetlands by type (i.e., marsh, swamp, fen, bog). This paper focuses on the exploration of three wetland boundary delineation methods: (i) a visual derivative image threshold, (ii) a logistic regression model, and (iii) a classification and regression tree (CART) model. Each method was applied to a number of spatially distributed digital elevation model (DEM) terrain derivatives originating from a photogrammetrically derived 5 m DEM. Of the three methods investigated, the results showed the CART approach provided the best mapping, with a calibration accuracy of 90% correct and a validation accuracy of 84%. The CART approach classified the wetlands based on the degree of terrain complexity, thus allowing for subcomponents of the wetlands to be evaluated in further detail.
The use of single-polarization (HH) RADARSAT-1 synthetic aperture radar (SAR) data has been shown to be important for wetland water extent characterization. However, the limited capability of the RADARSAT-1 singlepolarization C-band SAR in vegetation type discrimination makes the use of clear-sky-dependent visible near-infrared (VNIR) satellite data necessary for wetland mapping. In this paper, the potential of Polarimetrie RADARSAT-2 data for wetland characterization is investigated. The Touzi incoherent decomposition is applied for the roll-invariant decomposition of wetland scattering. In contrast with the Cloude-Pottier decomposition that characterizes target scattering type with a real entity, α, the Touzi decomposition uses a complex entity, the symmetric scattering type, for unambiguous characterization of wetland target scattering. It is shown that, like the Cloude α scattering type, the magnitude αs of the symmetric scattering is not effective for vegetation type discrimination. The phase Pdblαs of the symmetric scattering type has to be used for better characterization of wetland vegetation species. The unique information provided by Pdblαsfor an improved wetland class discrimination is demonstrated using Convair-580 Polarimetrie C-band SAR data collected over the Mer Bleue wetland in the east of Ottawa, Canada. The use of Pdblαs makes possible the discrimination of shrub bog from sedge fen and even permits the discrimination between conifer-dominated treed bog and upland deciduous forest under leafy conditions.