This is a contract result notice, not an open opportunity. Details from the official award data.

Expertise in remote sensing / earth observation essential.

This project will investigate current best practices and potential impacts of image normalisation in the development of Spring and Autumn satellite mosaic creation used within the Living England workflow for segmentation and habitat classification.

The current mosaicking process includes a "Swiss-cheese" method for creating cloud-free mosaics, which involves removing any cloudy or shadow pixels and stacking the imagery to create the mosaic.

The primary image is taken as the least cloudy image, with any cloud or shadow gaps filled by subsequent images, ordered from least to most cloudy.

However, differences in acquisition timing and ground conditions can result in radiometric differences between the stacked images, the impact of which has not been quantified within the Living England process.

There is potential that normalising the input imagery prior to mosaicking will result in improvements in the segmentation and aid comparisons of the segmentations derived from different mosaics.

This could result in further improvements to how these maps can be used in assessing habitat change in extent over time due to the comparisons of satellite imagery from different seasons and years.

The normalising of imagery values may also impact upon the habitat classification process, as these are used to derive zonal statistics within segment objects and are modelled using a random forest classification model.

As a result, normalising the imagery within the mosaicking process may impact upon the overall accuracy of the habitat classification process.