Coastal Erosion

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Product Category

• Land use
• Land cover
• Natural Disaster
• Climate Change
• Coast Management 

• Marine
• Earth's Surface Motion

Financial Domains

• Investment management 
• Risk Analysis 
• Insurance management 
• Green finance

User requirements 

UN12: Analysis of potential risks in specific regions

UN14: Need to screen the feasibility of projects against different hazard criteria.

UN37: Projection of risk to portfolio assets into future

UN40: Need to monitor the risk of sea level rise threatening coastal property, infrastructure, and supply chains

Description

Coastal erosion poses significant risks to properties, developments, and investments in coastal areas. Monitoring erosion enables financial institutions to identify high-risk properties, estimate potential losses, and make informed decisions about lending, insurance, and investment activities' paraphrase. Earth observation data allows for the comparison of optical images acquired at different times to detect changes along the coastline. By analysing these images, it is possible to identify erosional hotspots, quantify shoreline retreat, and assess the magnitude of coastal erosion.

Spatial Coverage Target

Coasts

Data Throughput

Rapid tasking 

Data availability

• High
• High

• Low
• Low

PRODUCT SPECIFICATIONS

Main processing steps

Coastal erosion monitoring involves tracking changes in shoreline dynamics over time. This approach can be developed using medium-resolution optical satellite imagery like Sentinel-2 and Landsat (for long-term coastal erosion monitoring). The fundamental concept is to establish a reference dataset representing the normal shoreline and then compare it with images from various time periods. This process identifies regions experiencing erosion or accretion and quantifies the rate of change over the years. To accurately differentiate between water and land areas and identify the shoreline, spectral indices like the Normalized Difference Water Index (NDWI) or Modified NDWI can be utilized, applying a threshold to distinguish these regions. To estimate the rate of coastline change, a transect-based method can be employed. This involves calculating the distance between a user-defined reference baseline and multitemporal coastlines using transects generated along the baseline at specified intervals.

Input data sources

Optical:  Sentine-2, Landsat

Radar:   N.A

Supporting data:   N.A

Accessibility

Sentinel-2: freely and publicly available from ESA.

Landsat: freely and publicly available from NASA.

Spatial resolution

Sentinel-2: 10 m

Landsat: 30 m

Frequency (Temporal resolution)

Sentinel-2: 6 days

Landsat: 16 days

Latency

≤ 1 day

Geographical scale coverage

Globally

Delivery/ output format

Data type: Raster

File format: GeoTIFF

Accuracies

Thematic accuracy: 80-90%

Spatial accuracy: 1.5-2 pixels of input data

Constraints and limitations

• Cloud presence
• Variability in sea level due to tides, storm surges, and other factors can introduce noise and uncertainty in detecting shoreline shifts.
• Subpixel changes in shoreline positions might be challenging to detect and measure accurately, impacting erosion rate calculations.
• Availability of high spatial and temporal resolution historical satellite imagery might be limited.

User's level of knowledge and skills to extract information and perform further analysis on the EO products.

Skills: Essential

Knowledge: Essential