RS applications in coastal environment

1. Remote Sensing Applications in
the Coastal Environment
By Matthew Brigley and Natasha Fee

2. Overview
• Eelgrass Mapping
Little Harbour, NS
• Tanker Safety Program
Isle Madame, NS
• Acadian Seaplants Limited
Shag Harbour, NS

3. Little Harbour
Eelgrass Mapping
Department of Fisheries and Oceans

4. Little Harbour
Eelgrass Mapping
 Purpose:
1. To classify the spatial extent of coastal eelgrass.
2. To inventory the near-coast environment.
 Data:
 Multispectral Imagery (RGB + NIR)
Source: Cornell Extension Marine Program

5. Eelgrass Significance
 Among the most productive & Biologically diverse ecosystems
 Functions:
 Seafloor & Shoreline Stabilization, Flora & Fauna habitat, supports the Detrital food web foundation.
 Considered a sentinel species for evaluating ecosystem health
 Biggest Disturbances:
 Declining water quality & Physical Disturbance

6. Multispectral Imagery
 Visible Issues:
 Swirls/blocky sections
throughout the mosaic.
 Color balancing Issue at
the sensor level

7. Image Classification
Resample
20cm -> 2m
Maximum Likelihood
Classification
Sieve Filter
Threshold: 20px
Workflow

8. Isle Madame
World Class Tanker Safety Program
Department of Fisheries and Oceans & Transport Canada

9. Isle Madame
World Class Tanker Safety Program
 Purpose
1. To create an inventory of the coastal land cover in areas with heavy tanker
traffic. This will be used to:
1. Determine the area’s vulnerability to marine-sourced oil spills.
2. Plan future tanker routes in order to minimize the impact on vulnerable
coastlines.
3. Develop Area Response Plans to be use in the event of a spill.
 Data:
 Multispectral Imagery (RGB + NIR)
Source: Transport Canada

10. Significance
 Each year, 80 million tonnes of oil are shipped in Canadian
coastal waters (Transport Canada)
 Oil is transported by waves and accumulates in the intertidal
zone
 Knowing the intertidal zone’s composition is paramount
Source: National Park Service (USA)
Source: Jacqui Michel - Research Planning Inc.

11. Imagery
 Overview of the study area (5cm
resolution).
Note: Mosaic produced by Nathan Crowell

12. Classification
Clip
•Change 0 values to NoData
Generate Signature Files
•Select training areas from 1 image per flight line
Maximum Likelihood Classification
•Using 1 signature file per flight line
Workflow

13. Mosaic per Flight Line
Create Mosaic Dataset
• Creates empty mosaic datasets for
each flight line (11 in total)
Add Rasters to Mosaic Dataset
• Using a wildcard to differentiate
flight lines (ie. o_ims001_*)
Build Seamlines
•Method: Radiometric
•No smoothing (categorical data)
Mosaic to New Raster
•Generates a mosaicked image for
each flight line
Workflow

14. Class Aggregation
Add Field
•“Class” -> A number from 1-10 representing universal classes
Lookup Tool
•Generates a raster for each flight line based on the universal “Class”
field
Class ID Classes
1 Sand
2 Pebbles
3 Rocks
4 Eelgrass
5 Rockweed
6 Coastal Grasses
7 Driftwood
8 Shadow
9 Deep Water
10 Cultural Features
Workflow

15. Final Mosaic
Create Mosaic Dataset
• Creates an empty mosaic dataset
Add Rasters to Mosaic Dataset
• Adds each mosaicked flight line to
the final mosaic dataset
Build Seamlines
•Method: Radiometric
•No smoothing (categorical data)
Mosaic to New Raster
•Generates a mosaic based on the
composite of all 11 flight lines

16. Tidal Ranges
 Based on a 2m DEM
 Values required:
Type Value Source
Chart Datum Offset 0.545m Canadian Hydrographic Service
Lowest Astronomical Tide (LAT)
Highest Astronomical Tide (HAT)
LAT: -0.36m (CD), -
0.905m (CGVD28)
HAT: 2.16m (CD),
1.615m (CGVD28)
Closest active tidal gage was used as a proxy (North Sydney)
• Acquired 19 years worth of hourly data (01-01-1999 to 01-01-2015)
• Minimum and maximum tidal height values were found with Excel
• Converted from Chart Datum to CGVD28 ortho-height using the Chart Datum Offset for
North Sydney
Tidal Surge (1m & 2m) 2.615m and 3.615m HAT + surge value

17. Tidal Ranges

18. Classification

19. Shag Harbour
Rockweed Mapping
Acadian Seaplants Limited

20. Shag Harbour
Rockweed Mapping
 Purpose
 To determine the rockweed’s spatial extent.
 Data:
 Multispectral Imagery (RGB + NIR)
 Bathymetric LiDAR
Source: Acadian Seaplants Limited

21. Rockweed Significance
 Plays a very important role in the Bay of Fundy ecosystem
 Fish & Waterfowl
 Used in fertilizer production
 Stimplex
Source: Acadian Seaplants Limited
Source: AGRG

22. Multispectral Imagery
 Overview of the study area (20cm resolution).

23. Image Classification
Maximum Likelihood Classification
Sieve Filter
Threshold: 20px
Workflow

24. Image Classification

25. Image Texture

26. LiDAR
Classified Data (with Noise)

27. LiDAR
Low Tide
High Tide

28. LiDAR
Low Tide + High Tide

29. LIDAR
Rockweed and Intertidal Surface

30. LiDAR - TIN
High Tide Low Tide

31. LiDAR – TIN to Raster

32. 5m LiDAR section in Shag Harbour

33. High Tide Dataset: Ground Class

34. High Tide Dataset: Ground + Rockweed

35. Accuracy Assessment (LiDAR vs. Ground Truth)
• Utilizing ArcMap 10.4’s new LAS tool, (LAS Point Statistics by Area), statistics including Min Z, Max Z, Mean
Z, & Standard Deviation were calculated inside of a 5, 3, and 1 meter buffer.
• The buffers were generated around individual Ground Truth GPS points, eight were used in this assessment.
• The results were statistics derived out of the LiDAR points
that could then be compared to the same measurements
collected through Ground Truth.

36. Vegetation (Rockweed) Filter

37. Ground Filter

38. Conclusions
 High resolution multispectral imagery can be a valuable asset to decision-makers
 Allows for the acquisition of surface data on a large scale
 More efficient than ground-based techniques
 Based off of the results comparing the Ground Truthing to the LiDAR collected:
 It’s plausible that just flying a high tide scan could provide the user with enough information to map the
inventory of coastal vegetation.
 More transects would have to be evaluated on accuracy and the ground class would have to be a bit more
refined.

39. Acknowledgement
 Special thanks to:
 AGRG for the opportunity, data, and guidance
 Canadian Hydrographic Service