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Habitat Suitability Index for Five-Lined Skink: A species at risk Lizard in Thousand Islands National Park
Project 1918 Final Report
Client: Parks Canada Client Contacts: Josh Van Wieren & Brent Lewis Faculty Advisor: Kendra Chalmers Reader: Paige Wearing Evaluator: Shawn Morgan Authors: Ryan Kekes, Tyrone Kalloo, Kailin Opaleychuk Sir Sandford Fleming College GIS Certificate Program Submission Date: June 13, 2019
This final report is presented in partial fulfilment of the academic requirements for APST 62, GIS Collaborative Project course, Fleming College
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List of Abbreviations
Units: DN: Digital Number HA: Hectare M2: Meters Squared PX: Pixel
File Types:
DEM: Digital Elevation Model CSV: Comma-separated values DRAPE: Digital Raster Acquisition Project for the East ECD: ESRI Classifier Definition TIF: Tagged Image File
Organizations:
SAR: Ontario Species at Risk MNRK: Ministry of Natural Resources and Forestry SOLRIS: Southern Ontario Land Resources Inventory System TINP: Thousand Islands National Park
General:
HSI: Habitat Suitability Index MCDA: Multi-Criteria Decision Analysis OBIA: Object-based Image Analysis
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Abstract Thousand Islands National Park is one only places in Ontario native to the five lined
skink (Plestiodon fasciatus): a small, ground-dwelling reptile that resides in rock barren, lichen
and moss. Due to habitat fragmentation, poaching, road hazards, and predation, the five lined
skink population has drastically decreased since the early 1990’s (Ministry of Natural Resources,
2010). The decline became so significant that the common five lined skink can now be found
under the Species at Risk Act from 2007 (Ontario Nature, 2019). To model these changes a
habitat suitability index has been created to monitor the loss of potential five lined skink habitat
throughout Landon’s Bay, a newly acquired property to Thousand Islands National Park.
Currently there is no solution for rehabilitation because little is known about the scarce
population. The model, developed from 2014 DRAPE data, will allow Thousand Islands
National Park to calibrate the amount of suitable habitat within park boundaries, and monitor the
steady decrease in habitat over incremental periods. This allows them to detect environmental
and anthropologic pressures, and implement recovery strategies when appropriate. The model
will act as a preliminary source of monitoring potential habitat using an automated approach, and
be a potential host for developing more intuitive models that will share statistics about the five
lined skink habitat.
Keywords: Classification, Barren, Habitat, Suitability, Five Lined Skink, GIS, Rock
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Table of Contents
Contents 1 List of Tables and Figures ...................................................................................................................... 4
2 Introduction .......................................................................................................................................... 5
2.1 Objectives...................................................................................................................................... 5
2.2 Getting Comfortable with Pixels ................................................................................................... 6
2.3 Defining Suitability ........................................................................................................................ 7
3 Methodology ......................................................................................................................................... 8
3.1 Project Development Overview .................................................................................................... 8
3.2 Project Toolbox ............................................................................................................................. 8
3.3 Workflow Development ................................................................................................................ 8
3.4 Automating Data Preparation ....................................................................................................... 9
3.5 Object-Based Image Analysis ...................................................................................................... 10
3.6 Spatial Analysis ............................................................................................................................ 11
3.6.1 Aspect .................................................................................................................................. 11
3.6.2 Size of Rock Barren ............................................................................................................. 11
3.6.3 Percent of Rock Barren Present .......................................................................................... 12
3.6.4 Habitat Suitability Index ...................................................................................................... 13
3.7 Visualization ................................................................................................................................ 13
4 Results ................................................................................................................................................. 14
4.1 Choosing the Appropriate Model ............................................................................................... 14
4.2 Segment Mean Shift Tool ............................................................................................................ 16
4.3 Too Much Noise .......................................................................................................................... 17
4.4 Training Site Accuracy ................................................................................................................. 17
4.5 Risks and Limitations ................................................................................................................... 17
4.5.1 Processing Power ................................................................................................................ 18
4.5.2 Software Updates ............................................................................................................... 18
4.6 Ground Truthing .......................................................................................................................... 18
4.7 Benefits ....................................................................................................................................... 19
5 Recommendations and Conclusions ................................................................................................... 19
6 References .......................................................................................................................................... 21
7 Appendices .......................................................................................................................................... 23
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1 List of Tables and Figures
Figures
Figure 1: Model 1 Parameters ...................................................................................................................... 7 Figure 2: Drape, PCA, and OBIA Outputs .................................................................................................... 10 Figure 3: Model Comparison ....................................................................................................................... 15 Figure 4: Model Pixel Count Comparison ................................................................................................... 16
Tables
Table 1: Aspect Classification ...................................................................................................................... 11 Table 2: Size of Rock Barren Classification .................................................................................................. 12 Table 3: Percent of Rock Barren Classification ........................................................................................... 13
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2 Introduction Thousand Islands National Park was established in 1904 (Parks Canada, 2018). The park
is made up of over 20 islands - nine of which are federally owned - and 90 islets ranging from
Main Duck Island to Brockville, Ontario. (Parks Canada, 2018). The park is found within the
Canadian Shield, made up of Precambrian granite formed by the glacial retreat. Today this
known as Thousand Islands National Park, home to the common five lined skink (Plestiodon
fasciatus).
The common five lined skink is Canada’s only native lizard. These small, cream colored
reptiles are coined by their 5 black stripes and blue tail. After being threatened by habitat
degradation, poaching and natural predators, the common five lined skink was added to the
Species at Risk Act in 2007 (Ontario Nature, 2019).
Due to its geology, Thousand Islands National Park is full of suitable habitats for the
common five lined skink. An ideal habitat includes large rocky outcrops in forest openings with
little canopy cover (Ontario Nature, 2019). The lack of canopy cover allows the sun to warm the
rock surfaces as basking areas. Other forms of cover, like wood debris (tree stumps, rotting logs)
are used for protection.
2.1 Objectives
Two park members: Josh Van Wieren and Brent Lewis, contacted Fleming College about
developing a Habitat Suitability Index (HSI) for the common five lined skink. Through open
communication and collaborative efforts between students, professionals and organizers a
predictive – educational – model has been created. The objective was to create a model that
could self-identify rock barrens using object-based image analysis. The detected rock barrens
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would undergo a secondary analysis ordering them based on suitability. We limited the software
to ArcMap 10.6, due to client program restraints and funding. This way, a workflow can be
developed and shared with the members of Thousand Islands National Park, and extend beyond
the scope of the initial project.
2.2 Getting Comfortable with Pixels Object-based image analysis (OBIA) is a classification method that groups feature types
based on geometric, spatial and spectral properties. OBIA is able to accurately delineate common
five lined skink habitats. The workflow has been developed using a digital elevation model
(DEM) made from DRAPE data with a 20cm resolution.
Spectral reflectance measures the amount of electromagnetic energy absorbed by an
object. Through the application of remote sensing spectral values have associating features. This
aids in developing the OBIA workflow because it is possible to predict other features of the same
class. This can be difficult to simulate because rock barrens share similar spectral properties with
other land-type features such as roads, and dry fields. Spatial values are difficult to incorporate
due to rock barrens irregular shapes, though distribution and frequency can aid in the process
(Tapas et al., 2009).
Once rock barrens have been identified through OBIA, a habitat suitability index (HSI)
can be developed, ranking habitats from most to least suitable. After expert consultation with
herpetology professionals: Josh Feltham and Shaun Thompson, habitat parameters were
finalized. Two models evolved from the decision-making process, results were compared
directly and the most appropriate model was chosen.
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2.3 Defining Suitability There was a total of 3 parameters that would be fed into the habitat suitability index,
ranking each individual rock barren based on its habitability (Please see Figure 1). Habitat was
ranked on aspect, size of rock barren and the percent of rock barren per window.
Aspect was used to measure the cardinal direction of each rock barren slope. A rock
barrens sun exposure directly affects its daily range in temperature. When rocky outcrops receive
more sunlight, its temperature is increased and maintained throughout the day. South East
aspects are preferred.
The size of rocky outcrops shares information about the total amount of habitable area.
One can assume that larger rock barrens are entitled to more sun due to less central root
development, meaning there is no permeable ground. This will result in less canopy cover and
higher sun exposure.
Calculating the percentage of rock barren at 1ha intervals is expected to identify a causal
relationship between percent of rock barren in an area, and population presence. The higher the
percentage, the higher the number of observations is expected.
Figure 1: Model 1 Parameters
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3 Methodology
3.1 Project Development Overview In order to come up with a suitable development process, it was decided to undertake
several training courses that ESRI provides to better understand the entire process of image
classification. Three web courses where chosen: Introduction to Image Classification,
Performing Supervised Pixel-Based Image Classification, and Performing Supervised Object-
Based Image Classification. With these courses under our belt and materials learned from
multiple courses in the GIS program at Fleming College, we developed a practical workflow that
would provide the results needed to satisfy our clients.
3.2 Project Toolbox The TINP_HSI_FiveLinedSkink.tbx is comprised of all tools, scripts and model
requirements needed to execute the analysis (Appendix A). The items have been added one
location to ease the analysis process, reducing search time. A corresponding workflow has been
developed to guide users through the analysis process. Instructions and reference materials have
also been included to the workflow in order to minimize misconceptions.
3.3 Workflow Development The workflow can be broken down into six easy to follow steps. For further in depth view of
each step (Please see Appendix B). The first being the classification of rock barrens. Next is
vector data, to make sure that it is projected and clipped to the area of interest. Following that, it
is time to get the size of each rock barren and rank them accordingly. After getting the size of
each rock barren, we calculate the acceptable aspect ranges from an existing DEM. Once the
aspect layer is created, percent of rock barren per window is calculated, the window can be of
any size, but best results have come from having one hectare window size. Lastly, weighted
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overlay analysis is now performed on applicable layers with weightings given from five lined
skink specialists.
3.4 Automating Data Preparation In order to facilitate timely data preparation, when hundreds – if not thousands – of tiled
imagery was needed to perform spatial analysis, multiple python scripts were created. The first
script called copyfiles.py takes a CSV file created using a selection from the tile index of the
2014 DRAPE Imagery and 2014 DRAPE DEM files (Please see Appendix C). With this
selection, the script would then copy these files to a new folder.
The next script for data preparation was the drop4thband.py (Please see Appendix C).
This script does exactly what it name implies, if a raster has more than four bands, it will drop all
exceeding the first three. This was needed as certain imagery functions would not work if a raster
had more than three bands. It can also be used if hard drive space is at a premium, as less bands
equates to less space needed to host them.
The last script for data preparation is the rastermerge.py script (Please see Appendix C).
This script merges all applicable rasters into one mosaic. As this was built upon a standard
feature in ArcMap, this script has several advantages, as it is able to go through and entire folder
based on file type, and it also has a filter that can be used if the user is looking for specific strings
in the file names such as names or dates.
All these scripts are based on existing tools in ArcMap, but build upon them with features
that are found within the Arcpy environment, such as filters and setting entire folders as
workspaces. These scripts will save users many hours of time if they had to manually run the
original tools conventionally.
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3.5 Object-Based Image Analysis The 2014 DRAPE data received from Land Information Ontario (LIO) was used as the
foundation for the OBIA classification (20cm resolution) (Please see Figure 2).
Principal Component Analysis (PCA) was used to transform the original DRAPE
imagery into a set of smaller, uncorrelated images (ESRI, 2016). PCA reduces the number of
bands required for analysis (Please see Figure 2).
Next, training sites were sampled across the imagery to ensure enough spectral range was
gathered for each land-feature type. Categories were based off frequency of observations for
each land-type. OBIA is categorized as a type of supervised
classification, meaning is requires user input. The difference
between supervised and OBIA classification is defined as OBIA
is not pixel-based. OBIA uses groups of pixels, referred to as
objects (Gronemeyer, 2012). These objects are categorized based
on their color, shape and size.
An ESRI Classifier Definition (ECD) file was created
using the grouped training sites. Using the training sites, a
support vector machine (SVM) classification was performed on
the DRAPE raster dataset. SVM is a machine-learning algorithm
(Ray, 2017). The tool uses support vectors – also referred to as
observations – in a shapefile or feature class format as training sites (Ray, 2017). With this
information, the SVM is able to distinguish between the various feature types in the raster. This
also evaluates the distinct number of adequate support vectors (Patel, 2017).
Figure 2: Drape, PCA, and OBIA Outputs
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Training samples and ECD file were then inspected to evaluate their accuracy. The
inspection revealed what areas within training sites that were being misclassified. This was
implemented as a quality control protocol before continuing the OBIA.
Once adjusted, the ECD file is used to classify the DRAPE imagery. Results were
grouped identically to those of the training sites. The classified raster was then reclassified to
show a binary output regarding the presence of rock barren.
3.6 Spatial Analysis
3.6.1 Aspect Using a DEM, the aspect was used to identify the maximum change of values at a
downslope angle. Slopes are measured clockwise in degrees and flat areas area assigned a value
of -1 (ESRI, 2016). This is useful for finding solar hotspots, which attract the common five lined
skink. The aspect raster was reclassified to favour areas facing South, East, and South East, with
South East being most influential (Please see Table 1).
Value Rank Flat High Suitability SE High Suitability E Moderately Suitable S Moderately Suitable SW Suitable NE Suitable W Low Suitability NW Low Suitability N Low Suitability
Table 1: Aspect Classification
3.6.2 Size of Rock Barren Using the binary output, the rock barrens were isolated. The remaining features were
vectorized. Rock barrens smaller than 0.2m2 were deleted from the feature class, removing any
unnecessary or misclassified polygons. A 2m buffer was applied to the polygons, accounting for
excessive canopy cover. The buffered layer was then dissolved based on ObjectID, made into
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single-part features and connecting polygons were individualized or split. A new field was added
into the attribute area denoted: Rank. A python script was loaded into the field calculator,
assigning integer values to the Rank field based on Area (Please see Table 2). The geometries
were then rasterized based on the Rank field, removing the need to reclassify.
Value Rank <500m2 Low Suitability 500m2-5000m2 Suitable >5000m2 High Suitability
Table 2: Size of Rock Barren Classification
3.6.3 Percent of Rock Barren Present Percent of rock barren was the most involved parameter. A 1ha grid was generated for the
park extent, using the boundary layer internally released by Thousand Islands National Park. The
grid was than queried by Select by Location, this selected all features intersecting the boundary
layer. The selection was inverted. All polygons not intersecting with the boundary was then
deleted. A table was made using the tabulate area tool, summarizing the pixel count in each 1ha
grid using a unique identifying field called GRID_ID. Within the table, two new fields were
added: Percent and Rank. The Percent field was populated using the field calculator and
following equation:
Percent = (ROCK_BARREN / 10000) * 100
10000 is the total size of each square polygon in metres squared. Calculations were
verified manually using three randomly selected records. Then, a secondary python script was
loaded into the field calculator to assign a ranking to each record based on Percent (Please see
Table 3). A join connected the tabulated area table with the generated grid attribute table so the
new fields can be used when rasterizing the rock barren polygon layer. This removes the need to
reclassify.
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Value Rank <25% Low Suitability 25-75% Suitable >75% High Suitability
Table 3: Percent of Rock Barren Classification
3.6.4 Habitat Suitability Index The weighted overlay analysis used the final raster outputs for each index parameter
(Please see Appendix D). Due to tool limitations, all values are required to be in integer – raster
– format. An index range of 5 was chosen to account for variant suitability. By not exceeding 5,
it reduces the possibility of redundant categories. The output was a habitat suitability index
labelling rock barrens as high suitability to low suitability.
3.7 Visualization The visualization will have two cartographic outputs to highlight habitat suitability. The
geographical location was 20 minutes East, of Kingston Ontario located at Thousand Island
National Park, Landon’s Bay. The projection used is NAD 1983 UTM Zone 18N. The base-layer
was derived from a 2m resolution 2014 aerial DRAPE imagery from the Ministry of Natural
Resources. The cartographic output will contain a feature layer of the HSI output, but due to the
sensitivity of the data this layer will be generalized in order to protect the five lined skink. It will
be overlaid on top of the drape imagery as well as a wooded area downloaded from LIO to help
bring out the vegetation and help see the canopy of the trees. Underneath the drape imagery is a
hillshade layer created using the Terrain Tool v1.1 to help exaggerate elevation to bring out the
features in the map. The supplementary text will be found throughout online resources, reports,
and a professor at Fleming College: Joshua Feltham, a five lined skink expert. The infographic
will have various stats data displayed for example percentage of rock barren loss from 2008 to
2014 DRAPE imagery. Further additional information within the resources will be used. The
HSI output will be tailored to our clients at TINP. It will be used on ground truthing explorations
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to gain insight on how accurate the HSI model is in predicting results of rock barrens around the
park. For simplicity, the gradient created will use colours that Parks Canada staff are familiar
with to ensure the results are user friends and easy to understand. A table will also be provided to
help solidify the data to quickly gain insight on the habitat suitability. Finally the parameters
used for the HSI output was model one that contained aspect, percentage of rock barren per
window, and size of rock barren.
4 Results
4.1 Choosing the Appropriate Model In the beginning of this project, there was 8 parameters that were expected to influence
the model. This was reduced to 3 main variables, after agreeing the other 5 would not have a
significant impact on the results.
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A total of 4 habitat suitability indexes were created using different parameters (Please see
Appendix E). The second model, that was not continued, classified rock barrens based on their
proximity to water as well as the other parameters previously described. It was given a
significantly low weighting of 5%, yet made drastic changes to the output. The model was biased
to any areas close to water, even if the rock barrens were not the most suitable for five lined
skink habitat (Please See Figure 3).
Both models were then ran with two different window sizes regarding rock barren
percent. The change in window size was not significant and did not add to the model results. The
most variation can be seen between Model 1 and Model 2 with 1Ha window sizes – and even this
is minimal (Please see Figure 4).
Figure 3: Model Comparison
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Figure 4: Model Pixel Count Comparison
4.2 Segment Mean Shift Tool Before PCA was introduced to the workflow, the classification process was done using of
an alternative OBIA tool. Segment Mean shift is a tool that spectrally categorizes neighboring
pixels in a raster image. The Segment Mean Shift tool will generate a new raster file based on
these groupings, as well as a band index showing the variance of the 3-raster bands. Spectral
and spatial values were adjusted based on the significance of features in the raster (Butler,
2015) (Please see Appendix F). For example, a higher spatial value should be given to images
with densely populated features (ESRI, 2016). After comparing the segment mean shift outputs
to the PCA outputs, we found that PCA was able to more efficiently differentiate between
various objects more effectively (Please see Appendix G). Whereas Segment mean shift would
group objects with similar spectral properties which lead to multiple misclassifications.
1 2 3 4 5Model 1 1090 7859 18258 33540 29318Model 2 1090 8812 19241 34528 26358
05000
10000150002000025000300003500040000
Pixe
l Cou
nt
Comparing Models (1Ha Window Size)
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4.3 Too Much Noise When investigating the classification process using secondary research, there was a lot of
challenges documented. We would soon learn that the problems indicated in the literature would
be similar to the ones we’d face. Spectral properties are catalogued in the image numerically.
These are often referred to as Digital Numbers or DN values. When feature-types share a similar
ranges in DN values, the analysis groups them as the same entity. Until switching our strategy to
use PCA, rock barrens and roads were being classified as the same object. However, image
shadows continued to be problematic. The classifying results would amalgamate shadowed areas
into the wetland class. Without using a more advanced remote sensing software, there was no
possible way to remove these shadows. The decision to leave the shadows was made due to
client software limitations.
4.4 Training Site Accuracy To create training sites, we used the Image Classification toolbar. As training sites were
delineated, they were grouped, labelled and managed in the Training Sample Manager. When
delineating samples, we looked for feature-representative areas. When choosing training sites we
tried to only capture content of that feature-type. Even though we used the Inspect Training
Samples tool, we realized that the not all our training sites are 100% accurate, and marginal error
is expected in this process (Please see Appendix H).
4.5 Risks and Limitations
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4.5.1 Processing Power There are several limitations that will have a severe impact on the running of this model.
The main limitation is processing power. Imagery continues to get better with every sensor
iteration. When coupled with the amount of images needed to asses the area of interest, the
processing is increased. Landon’s Bay consisted of 314 (1km by 1km) tiles. Using a computer at
school, running PCA on this area took approximately28 hours. As the workflow and process is
ever changing, re-running PCA or classify raster, is a very time consuming endeavour. Having
use of a multi-processor computer would have the potential to save an indeterminate amount of
time.
4.5.2 Software Updates Another limitation that could cause issues in the future is the use of Generate Tessellation
tool in ArcMap. As this is a script based on the old argisscripting module, it may be phased out
without an adequate replacement and it does not work outside of the ArcMap environment. This
problem could be encountered with other tools as ESRI continues to enhance their software.
4.6 Ground Truthing On June 10, 2019, Thousand Islands National Park invited us out to view the project
results and ground truth. The 4 model outputs were printed off for comparison. This is when
Model 1 with 1Ha windows determined most accurate, and the other models were removed from
the workflow.
After arriving at Landon’s Bay, one of the paper maps was used to guide us to highly
suitable areas. On the way, rock barrens were encountered that were not depicted on the map.
After talking with Josh Feltham and park Employees: Josh Van Weiren and Brent Lewis, we
agreed this was an acceptable range of error due to the dense canopy cover.
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When exploring the barrens, we realized depressions in the rock surface collect a high
amount of moisture and promote the growth of natural moss and lichen. There was evidence of
this in the DRAPE imagery were portions of rock surface had been overgrown, making it
difficult for the computer to categorize (Please see Appendix I). Some areas were harder to
recognize because the analysis was conducted on imagery from 5 years ago. The natural
landscape had evolved since then, and will continue to evolve. This has been raised as a project
limitation.
It was also noted that the model was unable to detect table rocks, which area large flat
rocks with space for species to burrow underneath (Georgian Bay Biosphere Reserve, 2019). We
agreed was beyond the scope of the project and should be flagged as a limitation considering the
resolution of the data.
4.7 Benefits The five lined skink is one of many species that uses rock barren as their primary habitat.
This model can be customized for other rock dwelling species. Other species at risk, use rocky
outcrops throughout their life cycle (e.g. gestation site) (Georgian Bay Biosphere Reserve, 2019).
This makes the workflow versatile, allowing different renditions of the model to be
individualized for different species and areas. The ability to easily monitor and track habitat
degradation will allows Thousand Islands National Park and other potential users to develop
their existing strategies. It is our hope that the model will impact areas outside of Thousand
Islands National Park.
5 Recommendations and Conclusions The project will demonstrate Thousand Islands National Park’s on-going conservation
efforts to preserve five lined skink habitat. The five lined skink has been native to Thousand
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Islands National Park since park establishment in 1904, and we hope that its presence is
maintained in future decades. The model will aid as a decision-making tool, helping to monitor
habitat loss across Landon’s Bay. Through the collection of knowledge between public and
internal sources, Fleming College was able to assist in improving monitoring techniques and
creating a solution for a timely problem. The model approach is based on current – available –
information, and as new data is collected or the model is applied to new areas, properties of the
workflow will change (Ministry of Natural Resources, 2010). We plan to continue to explore
different methodologies in order to improve our workflow and create a provincially used tool.
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6 References
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ESRI. “ArcGIS Pro.” Understanding Reclassification-Help | ArcGIS Desktop, pro.arcgis.com/en/- pro-app/tool-reference/spatial-analyst/understanding-reclassification.htm.
ESRI. “ArcMap.” Add Field-Help | ArcGIS Desktop, desktop.arcgis.com/en/arcmap/10.6/tools/data-management-toolbox/add-field.htm.
ESRI. “ArcMap.” Calculate Field Examples-Help | ArcGIS Desktop, desktop.arcgis.com/en/arcmap/10.6/manage-data/tables/calculate-field-examples.htm.
ESRI. “ArcMap.” Calculating Area, Length, and Other Geometric Properties-Help | ArcGIS Desktop, desktop.arcgis.com/en/arcmap/10.6/manage-data/tables/calculating-area-length-and-other-geometric-properties.htm
ESRI. “ArcMap.” Euclidean Distance-Help | ArcGIS Desktop, desktop.arcgis.com/en/arcmap/10.6/tools/spatial-analyst-toolbox/euclidean-distance.htm.
ESRI. “ArcMap.” How Principal Components Works-Help | ArcGIS Desktop, desktop.arcgis.com/en/arcmap/10.6/tools/spatial-analyst-toolbox/how-principal-components-works.htm.
ESRI. “ArcMap.” Principal Components-Help | ArcGIS Desktop, desktop.arcgis.com/en/arcmap/10.6/tools/spatial-analyst-toolbox/principal-components.htm.
ESRI. “ArcMap.” Understanding Euclidean Distance Analysis-Help | ArcGIS Desktop, desktop.arcgis.com/en/arcmap/10.6/tools/spatial-analyst-toolbox/understanding-euclidean-distance-analysis.htm.
“Five-Lined Skink | Reptiles & Amphibians in Ontario.” Ontario Nature, ontarionature.org/programs/citizen-science/reptile-amphibian-atlas/five-lined-skink/.
ESRI. “Help.” Classify Raster-Help | ArcGIS for Desktop, desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/classify-raster.htm.
ESRI. “Help.” How Aspect Works-Help | ArcGIS for Desktop, desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/how-aspect-works.htm.
ESRI. “Help.” Polygon to Raster-Help | ArcGIS for Desktop, desktop.arcgis.com/en/arcmap/10.3/tools/conversion-toolbox/polygon-to-raster.htm.
ESRI. “Help.” Using the Pixel Inspector-Help | ArcGIS for Desktop, desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/using-the-pixel-inspector.htm.
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ESRI. “Help.” Zonal Histogram-Help | ArcGIS for Desktop, desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/zonal-histogram.htm.
Georgian Bay Biosphere Reserve, 2019. Building in the Biosphere Habitat Screening Tool. http://www.gbbr.ca/building-in-the-biosphere-habitat-screening-tool/
Land Information Ontario, 2012. Discovering Ontario. Ontario.ca, www.ontario.ca/page/land-information-ontario.
GIS Geography, 2018. “OBIA - Object-Based Image Analysis (GEOBIA) - Think Objects, Not Pixels.” gisgeography.com/obia-object-based-image-analysis-geobia/.
Ministry of Natural Resources, 2010. Common Five-Lined Skink, Ontario Recovery Strategy Series. https://files.ontario.ca/environment-and-energy/species-at-risk/stdprod_066853.pdf.
Parks Canada Agency, and Government of Canada, 2018. “History.” History - Thousand Islands National Park, 7 Mar, www.pc.gc.ca/en/pn-np/on/1000/culture/histoire-history
“Pass the Classification but Hold the Salt and Pepper!” ArcGIS Blog, www.ESRI- .com/arcgis-blog/products/product/imagery/- pass-the-classification-but-hold-the-salt-and-pepper/.
Patel, Savan. “Chapter 2 : SVM (Support Vector Machine) - Theory.” Medium, Machine Learning 101, 3 May 2017, medium.com/machine-learning-101/chapter-2-svm-support-vector-machine-theory-f0812effc72.
The Landscape Toolbox, 2012. Object-Based Classification []. wiki.landscapetoolbox.org/doku.php/remote_sensing_methods:object-based_classification.
Ray, Sunil, and Business Analytics and Intelligence. “Understanding Support Vector Machine Algorithm from Examples (along with Code).” Analytics Vidhya, 11 Mar. 2019, www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/.
Shippert, Peg. “Digital Number, Radiance, and Reflectance.” Harris Geospatial Solutions, Harris Geospatial Solutions, 6 Mar. 2017, www.harrisgeospatial.com/Learn/Blogs/Blog-Details/ArtMID/10198/ArticleID/16278/Digital-Number-Radiance-and-Reflectance.
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7 Appendices
Appendix A
Project Toolbox:
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Appendix B
Classification Workflow:
Get raster-based data
Are there more than 3 bands?
NO
Use RasterMerge tool in the toolbox to create a mosiac
What is your area of interest?
Use Drop4thBand.py to
remove the last band
YES
Open the new mosaic into the
Table of Contents
Windows > Image Analysis
Search for the Principal
Components tool
Check to see if the Tool output is in
the Table of Contents
Customize > Toolbar > enable
Image Classification Toolbar
Customize > Toolbar > enable
Image Classification Toolbar
Enable Training Sample Manager
Make and group training sites into
the following categories
Save training sites as shapefile or feature class
Search and run Train Support
Vector Machine Classifier
Search and run Classify Raster
Turn on classified raster and original
imagery
Use the Swipe tool to compare the
results
Repeat until acceptable
classification layer produced
Search and Run Inspect Training
Sites
Are the results accurate?
NO
Re-iterate
YES
25
Percent of Rock Barren Workflow:
Field Calculator Expression
Search and use the Tabulate Area tool
Insert two new fields (Rank,
Percent): Table Options > Add Field
Calculate Percent by right clicking your new field > Field Calculator
Verify the calculations are
correct
Populate Rank field using the following expression (Replace values as needed)
Access your (OBIA) Classified Layer
Join to Generate Tessellation output based on Grid_ID
ROCK_BARREN / 10000 * 100
def TextValue(percent): if percent > 10: return 3 elif percent < 10 and percent > 5: return 2 else: return 1__esri_field_calculator_splitter__TextValue( !PERCENT! )
Search and run Polygon To Raster
Search and run Generate
Tessellation
26
Aspect Workflow:
Search and use the Aspect tool
Reclassify
Find a DEM layer
27
Size of Rock Barren Workflow
Field Calculator Expression
Access your (OBIA) Classified Layer
Search and run Raster to Polyon
Add a new field (Rank) to the
attribute table
Calculate Rank by Calculating Geometry
Search and run Buffer
Search and run Polygon to Raster
Search and run Dissolve
def TextValue(area): if area >= 500: return 5 elif area < 500 and area > 250: return 4 elif area < 250 and area > 100: return 3 elif area < 100 and area > 50: return 2 else: return 1__esri_field_calculator_splitter__TextValue( !Shape_Area!)
28
Appendix C
Python Scripts:
copyfiles.py
#copy from csv to folder import os, shutil, csv csv_file = "Cselection.csv" #csv listing files to be copied, header MUST be filename, filepath existing_path_prefix = "E:/collab/drape/PackageC" #location of data to be cpoied, must match filepath column in csv new_path_prefix = "E:/collab/drape/Cselect" #new location to move data with open(csv_file, 'r') as f: reader = csv.reader(f) cntr = 0 for i, row in enumerate(reader): if i == 0: pass # Skip header row else: filename, filepath = row new_filename = os.path.join(new_path_prefix, filename) old_filename = os.path.join(filepath, filename) shutil.copy(old_filename, new_filename) cntr += 1 print cntr, "files copied" drop4thband.py # drop 4th band # Import system modules import arcpy arcpy.env.workspace = "E:/collab/test" #input folder location tifList = arcpy.ListRasters("*.jp2") #Replace file extension to what you have for inTIFF in tifList:
29
print inTIFF outTIFF = "E:/collab/test/output/" + inTIFF #output folder location # Create a value table to hold the multivalue parameters for the Union_analysis vtab = arcpy.CreateObject("ValueTable") vtab.addRow(inTIFF + "\\Band_1") vtab.addRow(inTIFF + "\\Band_2") vtab.addRow(inTIFF + "\\Band_3") # Process: Composite Bands... print "Composite Bands 1 2 3 " + str(inTIFF) arcpy.CompositeBands_management(vtab, outTIFF) rastermerge.py #raster merge import os, arcpy workspace = "E:/collab/test3/TINP_PCA" list_raster= [] # the list must exist before you can append walk = arcpy.da.Walk(workspace, type="tif") #type will need to be changed if the files are of different file type output_location = "E:/collab/test3" #Mosaic to Raster Variables sr = "E:/collab/test3/TINP_DEM/1km184130491102014DRAPE.img" #spatial reference pixel_type = "32_BIT_SIGNED" #pixel type cell_size = "0.2" #cell size bands = "3" #number of bands out_name = "TINP_DRAPE_PCA_MERGED.tiff" for dirpath, dirnames, filenames in walk: cntr = 0 for file in filenames: if "DRAPE" in file.upper(): #this is used if you are looking for specifics in files, ie dates etc, otherwise comment this line out and remove 1 indent in next 2 lines list_raster.append(os.path.join(dirpath,file)) # FULL path to each raster cntr += 1 print file, "Processed" arcpy.MosaicToNewRaster_management(list_raster, output_location, out_name, sr, pixel_type, cell_size, bands, "", "")
30
print cntr, " Files Merged" Update field for Zonal Histogram list = [] def update(Area): import arcpy global list if len(list) == 0: with arcpy.da.SearchCursor(r"e:\collab\test\zonalhi_squares1", ["Area"]) as cursor: for row in cursor: list.append(row[0]) del cursor, row S = sum(list) return Area / S * 100 rank_rockbarren_percent.cal def TextValue(percent): if percent > 10: return 3 elif percent < 10 and percent > 5: return 2 else: return 1 __esri_field_calculator_splitter__ TextValue( !PERCENT! ) rank_rockbarren_area.cal def TextValue(area): if area >= 500: return 5 elif area < 500 and area > 250: return 4 elif area < 250 and area > 100: return 3 elif area < 100 and area > 50: return 2 else: return 1
31
__esri_field_calculator_splitter__ TextValue( !Shape_Area!)
32
Appendix D
Reclassified Parameters:
33
Appendix E
Comparing Model Values:
Model Value Rank Count Area (m2)
Mode 1 (1Ha) 1 Lowest Suitability 1090 43.6 2 Low Suitability 7859 314.36 3 Suitable 18258 730.32 4 High Suitability 33540 1341.6 5 Highest Suitability 29318 1172.72 Mode 1 (0.5Ha) 1 Lowest Suitability 139211 5568.44 2 Low Suitability 1007613 40304.52 3 Suitable 2042650 81706 4 High Suitability 3605407 144216.28 5 Highest Suitability 478138 19125.52 Mode 2 (1Ha) 1 Lowest Suitability 1090 43.6 2 Low Suitability 8812 352.48 3 Suitable 19241 769.64 4 High Suitability 34528 1381.12 5 Highest Suitability 26358 1054.32 Mode 2 (0.5Ha) 1 Lowest Suitability 138206 5528.24 2 Low Suitability 884783 35391.32 3 Suitable 1992913 79716.52 4 High Suitability 3236459 129458.36 5 Highest Suitability 1020658 40826.32
34
Appendix F
Segment Mean Shift Parameter Testing:
Project #1918 - Hab Sutiability Index for Five Lined Skink: Thousand Islands National Park Segment Mean Shift: Parameter Testing - 28/05/2019 Naming convention is <TOOL>_<RUN_ID> RUN_ID Raster Tiles Used
Spectral Detail
Spatial Detail
Min. Segment Output Raster
1 1km184060493802014DRAPE.jp2, 1km184060493702014DRAPE.jp2, 1km184060493602014DRAPE.jp2, 1km184060493502014DRAPE.jp2,1km184050493702014DRAPE.jp2, 1km184050493602014DRAPE.jp2
20 20 10 SegmentMeanShift_1.tif
2 1km184060493802014DRAPE.jp2, 1km184060493702014DRAPE.jp2, 1km184060493602014DRAPE.jp2, 1km184060493502014DRAPE.jp2,1km184050493702014DRAPE.jp2, 1km184050493602014DRAPE.jp2
20 5 10 SegmentMeanShift_2.tif
3 1km184060493802014DRAPE.jp2, 1km184060493702014DRAPE.jp2, 1km184060493602014DRAPE.jp2, 1km184060493502014DRAPE.jp2,1km184050493702014DRAPE.jp2, 1km184050493602014DRAPE.jp2
20 10 5 SegmentMeanShift_3.tif
4 1km184060493802014DRAPE.jp2, 1km184060493702014DRAPE.jp2, 1km184060493602014DRAPE.jp2, 1km184060493502014DRAPE.jp2,1km184050493702014DRAPE.jp2, 1km184050493602014DRAPE.jp2
20 15 10 SegmentMeanShift_4.tif
5 1km184060493802014DRAPE.jp2, 1km184060493702014DRAPE.jp2, 1km184060493602014DRAPE.jp2, 1km184060493502014DRAPE.jp2,1km184050493702014DRAPE.jp2, 1km184050493602014DRAPE.jp2
10 10 10 SegmentMeanShift_5.tif
6 1km184060493802014DRAPE.jp2, 1km184060493702014DRAPE.jp2, 1km184060493602014DRAPE.jp2, 1km184060493502014DRAPE.jp2,1km184050493702014DRAPE.jp2, 1km184050493602014DRAPE.jp2
20 20 3 SegmentMeanShift_6.tif
7 1km184060493802014DRAPE.jp2, 1km184060493702014DRAPE.jp2, 1km184060493602014DRAPE.jp2, 1km184060493502014DRAPE.jp2,1km184050493702014DRAPE.jp2, 1km184050493602014DRAPE.jp2
20 20 30 SegmentMeanShift_7.tif
8 1km184160491202014DRAPE.jp2, 1km184150491202014DRAPE.jp2, 1km184150491302014DRAPE.jp2
20 18 10 meanshift1
9 1km184160491202014DRAPE.jp2, 1km184150491202014DRAPE.jp2, 1km184150491302014DRAPE.jp2
20 19 5 meanshift2
10 1km184160491202014DRAPE.jp2, 1km184150491202014DRAPE.jp2, 1km184150491302014DRAPE.jp2
20 20 5 meanshift3
11 1km184160491202014DRAPE.jp2, 1km184150491202014DRAPE.jp2, 1km184150491302014DRAPE.jp2
20 18 40 meanshift4
12 1km184160491202014DRAPE.jp2, 1km184150491202014DRAPE.jp2, 1km184150491302014DRAPE.jp2
20 20 80 meanshift5
13 1km184160491202014DRAPE.jp2, 1km184150491202014DRAPE.jp2, 1km184150491302014DRAPE.jp2
20 20 10 meanshift9
14 1km184160491202014DRAPE.jp2, 1km184150491202014DRAPE.jp2, 1km184150491302014DRAPE.jp2
20 20 20 meanshift10
35
Appendix G
Comparing SMS to PCA:
36
Appendix H
Training Sample Inspection:
37
Appendix I
Overgrown rock barren from 2014 DRAPE data: