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

Overview

Little Harbour, NS

• Eelgrass Mapping

Isle Madame, NS

• Tanker Safety Program Shag Harbour, NS

• Acadian Seaplants Limited

Little Harbour

Eelgrass MappingDepartment of Fisheries and Oceans

Little HarbourEelgrass 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

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

Multispectral Imagery

Visible Issues: Swirls/blocky sections

throughout the mosaic. Color balancing Issue

at the sensor level

Image Classification

Resample20cm -> 2m

Maximum Likelihood

Classification Sieve Filter

Threshold: 20px

Workflow

Isle Madame

World Class Tanker Safety Program Department of Fisheries and Oceans & Transport Canada

Isle Madame World Class Tanker Safety Program

Purpose1. 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

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.

Imagery

Overview of the study area (5cm resolution).

Note: Mosaic produced by Nathan Crowell

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

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

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

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

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

Tidal Ranges

Classification

Shag Harbour

Rockweed MappingAcadian Seaplants Limited

Shag Harbour Rockweed Mapping

Purpose To determine the rockweed’s spatial extent.

Data: Multispectral Imagery (RGB + NIR) Bathymetric LiDAR

Source: Acadian Seaplants Limited

Rockweed Significance

Plays a very important role in the Bay of Fundy ecosystem Fish & Waterfowl

Used in fertilizer production Stimplex

Source: Acadian Seaplants LimitedSource: AGRG

Multispectral Imagery

Overview of the study area (20cm resolution).

Image Classification

Maximum Likelihood Classification

Sieve FilterThreshold: 20px

Workflow

Image Classification

Image Texture

LiDAR

Classified Data (with Noise)

LiDAR

Low Tide

High Tide

LiDAR

Low Tide + High Tide

LIDAR

Rockweed and Intertidal Surface

LiDAR - TIN

High Tide Low Tide

LiDAR – TIN to Raster

5m LiDAR section in Shag Harbour

High Tide Dataset: Ground Class

High Tide Dataset: Ground + Rockweed

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.

Vegetation (Rockweed) Filter

Ground Filter

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.

Acknowledgement

Special thanks to: AGRG for the opportunity, data, and guidance Canadian Hydrographic Service