1. Land Classification on the Kaibuskong River Subwatershed
The Reduction of Blue Green Algae in Lake Nosbonsing
Land Classification on the Kaibuskong River Subwatershed
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2. Outline Project Team Study Area Project Statement Overview Methodology
Data
Mosaic
Unsupervised Classification
Supervised Classification
Statistical Analysis
Cost Analysis
Challenges
Recommendations
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3. Client Project team Scott Higgins Janet Finlay Bennett White
GIS Specialist
Department of Drinking Water Source Protection
Project team
Janet Finlay
Project Advisor
B.Sc McMaster University
Instructor at Niagara College Canada
Bennett White
Project Manager
Hons. BA Wilfird Laurier University
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6. Problem Statement Project Objective Recent Blue Green Algae Events
Lack of Information pertaining to land classes
Lake Nosbonsing and Kaibuskong Subwatershed
Project Objective
Identification of Land Classes
Comparable Analysis
Compare NEW LAND data against OLD LAND CLASS
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7. Benefits
Identification of land classes that have increased the abundance of phytoplankton in the watershed
Nutrient rich vegetation
Tangible items;
Updated land classes of unclassified subwatershed
Spatial Overlay Analysis
Hard and soft copy maps
Documentation
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8. Data Mosaic Unsupervised Supervised Spatial Overlay Analysis
Methodology
Data
Mosaic
Unsupervised
Supervised
Spatial Overlay Analysis
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9. Data QuickBird Imagery Forest Resource Inventory Data
Multispectral Imagery
2cm resolution
271 Tiles
1 tile = 130mb
Forest Resource Inventory Data
Panchromatic Imagery
Shape File
Forest Type / Area
Mgmt. Practices
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10. 271 tiles 130mb per file Final Image 70GB
Unsupervised Classification
Mosaic Process
271 tiles
130mb per file
Final Image
70GB
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11. Unsupervised Classification
ISODATA
PCA
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12. Unsupervised Classification
11 Classes were selected
Algorithm splits and merges clusters
ISODATA Algorithum
Iterative
Select
Organizing
Data
Analysis
Technique
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14. Principal Component Analysis
Creates a new set of uncorrelated variables
Principle Components or eigenchannel
Linear Combinations of the original bands
Makes the image smooth and clean, without data lose
Data reduction technique
Compresses a number of bands into a few components
Reduction in dimensionality of the data without a loss of information
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16. Analysis of 11 Classes
Unsupervised VS. PCA
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19. Training Sites (AOIs) Spectral Signatures
Supervised
Training Sites (AOIs)
Spectral Signatures
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20. Supervised Classification
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22. Maximum Likelihood Input bands of data have a normal distribution
-- Parametric Rule
Input bands of data have a normal distribution
Assumes that a pixel belongs to a particular class
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24. Old vs New Land Class Data
Overlay Analysis
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29. Project management
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30. EVM (October 12 – June 14) End First Term End Second Term June 14
Planned Value
Actual Value
End First Term
8,060.00
End Second Term
25,494.74
June 14
48,174.00
End First Term
8,306.01
End Second Term
14,742.35
June 14
33,375.35
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31. Cost Analysis Baseline 1: December 14, 2012 Baseline 1:
$55,393.05
Baseline 2: March 22, 2013
$45,409.14
Final Cost: June 10th, 2013
$ 38,038.84
Cost Savings of $17,354.21
Baseline 1:
Data Acquisition, Proposal
Baseline 2:
Mosaic, Progress Report
Baseline 3:
Unsupervised & Supervised classification, Statistical Analysis
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33. Challenges Data & Storage Space Processing Speed
1tile= 130mb, Mosaic= 70GB
Processing Speed
Mosaic
Raster to Polygon Tool in ArcGIS
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35. = Recommendations Added Z-Values
Obtain appropriate method to transfer data
Niagara College Corporate DropBox Account
Determine the compatibilities between =
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36. Acknowledgments North Bay Mattawa Conservation Authority
Ministry of Natural Resources (MNR)
Niagara College Canada
Instructors
IT Specialist (Colin Bissell)
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38. Questions Thank you Bennett White bennett.white88@gmail.com
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