Habitat Suitability Index for Five-Lined Skink: A species ... · Authors: Ryan Kekes, Tyrone...

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. “ArcMap.” Calculate Field Examples-Help | ArcGIS Desktop, desktop.arcgis.com/en/arcmap/10.6/manage-data/tables/calculate-field-examples.htm.

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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.

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“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

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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

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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

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Aspect Workflow:

Search and use the Aspect tool

Reclassify

Find a DEM layer

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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!)

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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:

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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, "", "")

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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

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__esri_field_calculator_splitter__ TextValue( !Shape_Area!)

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Appendix D

Reclassified Parameters:

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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

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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













8 1km184160491202014DRAPE.jp2, 1km184150491202014DRAPE.jp2, 1km184150491302014DRAPE.jp2

20 18 10 meanshift1


20 19 5 meanshift2


20 20 5 meanshift3


20 18 40 meanshift4


20 20 80 meanshift5


20 20 10 meanshift9


20 20 20 meanshift10

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Appendix G

Comparing SMS to PCA:

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Appendix H

Training Sample Inspection:

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Appendix I

Overgrown rock barren from 2014 DRAPE data:

Habitat Suitability Index for Five-Lined Skink: A species ... · Authors: Ryan Kekes, Tyrone...

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