Satellite-Based Sargassum Detection & Stranding Prediction

Sat, 01 Jun 2024 00:00:00 +0000

Context

Sargassum strandings disrupt Caribbean coastlines. The goal: detect algae rafts at sea early and predict where and how much will strand, so municipalities and environmental authorities can plan cleanup — and interventions at sea — in advance.

The problem: raw reflectance is hard to read

In raw multispectral satellite imagery, Sargassum is a faint signal on a large, variable ocean background. Rafts are thin, fragmented and often low-contrast: their edges blur into the surrounding water, and the overall structure of a band — where it starts, where it thins out, where it breaks up — is difficult to make out by eye or with generic indices.

Detecting that there is Sargassum is not enough. To act, one needs to see what it looks like.

Core contribution: an original contrast indicator

I designed a dedicated contrast indicator for Sargassum. Rather than simply flagging pixels, it is built to give a very clear reading of the data and of the shapes: rafts and bands stand out sharply from the water, with well-defined contours and their internal structure preserved. (Formulation details withheld for now.)

What it brings:

Clear delineation of rafts and of elongated Sargassum bands.
A readable picture of how the mass is organised, not just where it is.
A cleaner input for segmentation, quantification and drift modelling downstream.

Why shape matters

The geometry of the bands is the key information for operations:

Dispersion — is the Sargassum concentrated in a dense raft or scattered in many small patches?
Trailing (“traîne”) — how the band stretches and elongates behind its head, revealing how it is being pulled by currents and wind.
Orientation and drift direction — which way the band is heading, and therefore which stretches of coast are exposed.

Reading these shapes is what makes it possible to plan interventions on site (where to deploy collection at sea or barriers) and to anticipate strandings rather than react to them.

Pipeline

flowchart TB
 A["Satellite imagery — multispectral"] --> B["Cloud / water masking"]
 B --> C["Original Sargassum contrast indicator"]
 C --> D["Raft & band delineation — shape, dispersion, trailing"]
 D --> E["Quantification at sea"]
 E --> F["Drift / stranding model"]
 F --> G["Alerts & intervention planning"]

Methods

Multispectral satellite imagery analysis.
Original contrast indicator for a sharp reading of Sargassum rafts and bands.
Shape analysis of the bands: dispersion, elongation/trailing, orientation.
Raft detection and surface-area quantification at sea.
Predictive drift/stranding modelling to anticipate landing zones and volumes.

Results

Clear, readable maps of Sargassum rafts and bands at sea.
Early-warning capability for coastal municipalities.
Quantitative stranding forecasts supporting proactive cleanup and intervention planning.

Visuals and further details will be added.

Stack

Python · satellite imagery · spectral analysis · predictive modelling

Remote Sensing on Boris Dupoux