In order to harness seaweed in a sustainable manner it is essential to understand the distribution and the status of the seaweed. Hafrannsóknastofnun (The Icelandic Marine Research Institute) conducted a project in which the total distribution of seaweed at Breiðafjörður (West Iceland) had to be assessed. Svarmi’s task was to design a remote sensing approach in order to assess the final extension. The approach was to use UAV imagery to ground-truth coarser satellite image classification (Landsat). We flew over predefined areas along Breiðafjörður, with both NIR and RGB cameras. The satellite classification was then ground-truthed with the UAV imagery, giving a final error estimation that was used to estimate the classification accuracy of the Landsat images.
Seaweed mapping and classification
Fixed wing drone, multispectral and a high resolution RGB camera. Reflectance target for calibration. Satellite images (Landsat 8 OLI-TIRS).
Satellite image atmospheric correction with Python. UAV and satellite image classification and post-classification conducted with ENVI.
Thousands of high resolution RGB imagery
A high resolution RGB camera was used to deliver high resolution calibrated images during the worst conditions of the Icelandic winter
Multisprectral calibrated imagery
Both a high resolution true color camera along with a multispectral camera were used to be able to identify all the characteristics of the seaweed by using radiometric calibrated imagery
Satellite classification ground-truthing
Satellite imagery (Landsat 8 OLI-TIRS) was classified and ground truthed with UAV imagery. The red pixels at the image are the final classified seaweed extent.