Climate Change Impacts and Ecosystem Resilience (CCIER)

Earth observation solutions addressing coastal erosion issues, their impact on ecosystems and the risks to infrastructure assets

This project develops and tests innovative data processing tools to monitor the impact of climate change on marine ecosystems, particularly in coastal areas. It provides new Earth Observation (EO) products, including high-resolution shore ice mapping, which will be integrated into the vulnerability assessment of Canadians living in coastal areas and used for policy planning and implementation.

The project “Earth observation solutions addressing coastal erosion issues, their impact on ecosystems and the risks to infrastructure assets” has been included within the Climate Change Impacts and Ecosystem Resilience (CCIER) portfolio of projects, supported by the Canadian Space Agency (CSA).

Project overview

Coastal areas are home to some of the most productive ecosystems on the planet. For example, seagrass beds and salt marshes are among the most important carbon sinks in the world and provide many ecosystem services. However, these ecosystems are very sensitive to environmental changes, whether due to human activity (infrastructure, resource exploitation, coastal activities, etc.) or to phenomena associated with climate change. Indeed, the decreasing ice cover, the global sea-level rise and the increasing frequency of storms aggravate the impacts of coastal erosion, which is one of the main causes of the disappearance of highly valuable coastal ecosystems. In the context of climate change, Canada therefore urgently needs more operational coastal monitoring methods to better protect coastal ecosystems and preserve the ecological and economic benefits they provide to humans.

Monitoring of coastal habitats for prevention and public safety, ecosystem conservation and other socio-economic objectives requires highly responsive tools that can monitor the territory over large spatial scales, detect changes that occur at different time scales, and yet remain affordable. Although in situ data collection can provide detailed portraits of coastal areas with high-quality data, it is expensive and difficult to carry out on a large scale, so it is mostly ad hoc, rather than recurrent, and confined to specific areas.

In this context, remote sensing is a tool of choice. Earth observation satellites have close overflight frequencies and data acquisition capabilities that open up a world of possibilities for environmental monitoring of coastal areas over large territories.  Remote sensing can thus provide continuous large-scale physical and biological information which, combined with point measurements on the ground in critical or vulnerable areas, can result in continuous, near-real-time monitoring of coastal ecosystems.


This project aims to develop and test innovative tools to monitor the impact of climate change on coastal areas and assess their vulnerability to erosion. It provides new EO solutions to coastal end-users, including high-resolution maps of shore ice, coastline, ecosystems and suspended sediments. This project represents the first step in implementing an effective monitoring system capable of providing up-to-date information on coastal zone dynamics. The new EO products and tools, which are highly sought after for management and decision-making purposes, benefit stakeholders to help mitigate vulnerabilities in the context of climate change.

The main objective is to map the impacts of climate change on coastal ecosystems and assess their vulnerability to coastal erosion. The specific objectives are to:

  • Investigate and develop new EO data analysis routines for the detection, qualification and quantification of shore ice (e.g. ice-foot, sea-ice, frazil) on the shoreline that protects the coastline, in conjunction with the application of ESA’s shoreline mapping algorithm for Canadian waters to assess erosion from cross-shore bathymetric/topographic profiles and cross-shore displacement;
  • Develop a multi-sensor approach (i) to map coastal marine ecosystems based on machine learning and (ii) to assess suspended sediments concentrations. Suspended matter influences water turbidity and light penetration, which impacts on the health of marine habitats.
  • Demonstrate the efficiency of the EO solutions for end-users by producing time series of Level 2 products.


This project is led by ARCTUS Inc. (Rimouski, QC) as prime contractor, in close collaboration with Hatfield Consultants (Vancouver, BC), the Research Chair in Coastal Geoscience of the Université du Québec à Rimouski (Rimouski, QC) and ARGANS Ltd (Plymouth, UK).


Shorelines and waterlines. The waterline is the instantaneous transition between land (or ice) and water detected by a segmentation method, which identifies differences in the physical parameters of features in an EO product. Its spatial resolution is limited to the pixel size of the initial image. The position of this boundary varies in time with the tidal level. As part of the ESA funded Coastal change from space project, our partners at Argans Ltd apply corrections to adjust the lines to the beach profile and to a tidal datum. The adjusted shorelines correspond to a mean (or extreme value) of tidal elevation.

Ice classification
The automated generation of sea ice classification products is based on a machine learning algorithm using spaceborne C-band SAR data. Environmental variables (temperature, salinity, snow cover, etc.) and ice characteristics such as stage of development (ex. first-year ice), shape (ex. floes, ridges) and concentration, affect the appearance of sea ice in SAR images. The different ice characteristics produce a variety of tones and textures in the images, allowing the classification of land, unconsolidated ice, deformed ice, and smooth or rough open water.

Total suspended sediment
Total suspended sediment (TSS) refers to organic (e.g. algae or decaying matter) or inorganic (e.g. mud) particles floating or drifting in the water column. Major hydrodynamic events (ex. waves, storms) or river discharges (ex. flood, spring freshet) contribute to increased TSS in coastal areas. Excessive suspended sediment can impair water quality for aquatic life, for example by increasing nutrient pollution and preventing light from entering the water column. To monitor TSS in coastal water, a retrieval algorithm connects satellite measurements (i.e. surface reflectance) to TSS concentration.

Before Image After Image

Ecosystem Classification
Multispectral satellite imagery contains enough information to discriminate between several vegetation types and to map different habitats in optically shallow waters (i.e. shallow and clear enough water to allow the satellite to detect the bottom). High and very high spatial resolutions (i.e. <30 m pixel resolution) provide sufficient coverage and revisit time to monitor annual to decadal variations in vegetation distribution and ecosystem changes. A pixel-by-pixel supervised classification method is used to classify cloud-free (< 10%) Sentinel-2 and Landsat-8 images acquired at low tide (<1m charts zero) during summer and early fall (July-October). The classification maps identify salt marshes, submerged vegetation, submerged and emerged sand and optically deep water.

Find out more about our innovative EO solutions to tackle coastal erosion issues

Web mapping platform

In order to promote operational EO data for integrated coastal zone management, our products are published on interactive web maps specially designed for end-users.
Click here to access the web platform (an account is required).
Click here to fill in the registration form to create an account (please allow up to 48 business hours).

Watch the platform presentation made by Arctus at the CartoVista webinar [in French]

Coastal evolution from spaceOnline seminar and workshop

Watch the presentations made by Arctus, Argans and the Research Chair in Coastal Geoscience (UQAR) in November 2020 [in French]

ARCTUSA video presentation by the Canadian Space Agency