Shrub species, cover and biomass from affordable UAV observations

Monitoring habitat condition is becoming increasingly important in light of the biodiversity crisis. Advances in UAV remote sensing and artificial intelligence are creating opportunities to complement field-based habitat monitoring or provide effective alternatives. As part of MAMBO, an EU-funded project, we aim to develop generic workflows that can deliver crucial habitat condition metrics using affordable drone remote sensing. Shrub cover and biomass in grassland, wetland, and shrub habitats are important for monitoring rewilding or habitat restoration efforts and above ground carbon. Here we describe a workflow, involving deep learning and allometry, developed to map the biomass of individual hawthorn shrub clumps. Our use case is a rewilded farm in Bedfordshire, UK. Results show that (i) U-Net variants are suitable for accurately mapping hawthorn within a complex shrub matrix, and (ii) allometry, based on structure-from-motion derived height, is an effective and affordable solution for shrub biomass mapping.