Object-based Wetland classification in Newfoundland and Labrador using Random Forest Algorithm Meisam Amani, Bahram Salehi*, Sahel Mahdavi, Jean Granger, Brian Brisco A Presentation for 6th Spatial Knowledge and Information Canada Conference Banff, Alberta, Canada , February 23-25, 2017
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Outlines Introduction
Study areas Data sets Methodology Results Conclusion Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
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Introduction Wetland “land that is saturated with water long enough to promote wetland or aquatic processes as indicated by poorly drained soils, hydrophytic vegetation and various kinds of biological activity which are adapted to a wet environment” (National Wetlands Working Group, 1997)
Wetland service
Filter and purify water Prevent flooding Protect coastline Control erosion Important habitat for plants and animals Store carbon produced by human activities Support recreational and tourist activities Agriculture
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
Wetlands in Newfoundland
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Introduction Canadian Wetland Classification System
Bog
Fen
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
Swamp
Marsh
Shallow Water
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Introduction Wetland classification in NL ~18% of NL is covered by wetlands Over the last decades, industrialization, urbanization, and agricultural activities have posed a serious threats to wetlands in the province Newfoundland has no comprehensive wetland inventory Only Atlantic Canadian province without Only a small portion has been classified Canadian Wetland Inventory Progress Map By Ducks Unlimited Canada
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
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Introduction Wetland classification methods
Field work - Labor intensive - Expensive - Time consuming - Issues with accessibility - Practical for relatively small areas
Remote sensing - Cost effective - Repitivity - Accessibility - Large coverage - Time Conservation
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
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Introduction Remote sensing sensors
Passive (e.g. Optical) Measures reflected sunlight that is emitted from the sun
Active (e.g. RADAR) has its own source of illumination - All weather - Anytime (day and night) - Penetration (cloud, soil, water) - Physical characteristics of targets
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
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Study Areas
- 5 pilot sites: a) Avalon b) Grand Falls-Windsor c) Deer Lake d) Gros Morne e) Goose Bay - ~700 square kilometers each
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
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Data Sets Aerial imagery
Ortho-photo Canadian Digital Surface Model (DSM)
Optical satellite data
RapidEye Landsat 8 Sentinel 2A ASTER
Synthetic Aperture RADAR (SAR) data
RADARSAT 2 ALOS 1, and 2 Sentinel 1 TerraSAR-X
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
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Methodology
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
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Results Classified image for Avalon
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
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Results Classification accuracy for Avalon
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
Class
Producer Accuracy (%)
User Accuracy (%)
Swamp
64
62
Shallow Water
73
53
F en
73
75
Marsh
77
85
Bog
78
85
Deep Water
90
100
Upland
99
91
Urban
96
100
Mean Wetland
73
72
Overall Accuracy
92
Kappa Coefficient
0.89
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Results Classified image for Grand Falls
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
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Results Classification accuracy for Grand Falls
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
Class
Producer Accuracy (%)
User Accuracy (%)
Marsh
61
67
Fen
56
75
Shallow water
78
85
Bog
90
93
Urban
95
91
Upland
100
98
Deep water
100
80
Mean Wetland
71
80
Overall Accuracy
87
Kappa Coefficient
0.84
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Results Classified image for Deer Lake
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
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Results Classification accuracy for Deer Lake
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
Class
Producer Accuracy (%)
User Accuracy (%)
Bog
40
72
Fen
77
56
Marsh
90
45
Swamp
98
58
Shallow water
78
96
Upland
93
100
Deep water
100
98
Mean Wetland
77
65
Overall Accuracy
81
Kappa Coefficient
0.76
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Results Classified image for Gros Morne
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
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Results Classification accuracy for Gros Morne
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
Class
Producer Accuracy (%)
User Accuracy (%)
Swamp
24
45
Marsh
44
41
F en
30
98
Shallow Water
87
82
Bog
99
88
Urban
69
97
Upland
95
98
Deep water
100
100
Mean Wetland
57
71
Overall Accuracy
92
Kappa Coefficient
0.88
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Results Classified image for Goose Bay
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
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Results Classification accuracy for Goose Bay
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
Class
Producer Accuracy (%)
User Accuracy (%)
Shallow water
77
61
Fen
86
53
Bog
52
96
Marsh
93
79
Upland
84
100
Urban
100
100
Sand
100
100
Deep water
100
100
Mean Wetland
77
72
Overall Accuracy
85
Kappa Coefficient
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Conclusion
There are many similarities in terms of spectral and backscattering information of wetlands.
A combination of optical and SAR data is useful for wetland classification and monitoring.
Object-based image analysis is superior to traditional pixel-based method.
The accuracy of classification depends on the accuracy and amount of filed data.
In terms of visual interpretation, the obtained maps are reliable.
Non-wetland classes are classified with higher accuracies compared to the wetland classes.
The highest confusion is between the Bog and Fen classes.
The obtained classification accuracies in NL’s wetland areas is considerably higher than the results obtained by most of the Canadian provinces.
Object-based Wetland Classification in Newfoundland and Labrador using Random Forest Algorithm
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Acknowledgements
Thanks Emails:
[email protected],
[email protected]
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