Combining unmanned aerial and satellite data for detecting Non-Native Tree Species: an insight on Acacia saligna invasion in the Mediterranean coast

Invasive Alien Species (IAS) threaten biodiversity worldwide, thus early detection and timely monitoring tools are still needed. We explored the potential of Unmanned Aerial Vehicle (UAV) images in providing intermediate reference data able to link IAS field occurrence and satellite information. Specifically, we used ultra-high spatial resolution UAV data depicting A. saligna occurrence as calibration data for satellite imagery to predict its spread on Mediterranean coastal dunes. Starting from two free satellite platforms (PlanetScope and Sentinel-2), we developed a procedure to map A. saligna cover following four steps: aggregation of UAV-based ultra-high resolution maps for A. saligna to satellite spatial resolution (3 m and 10 m) by calculating the IAS fractional cover (FCover); selection of monthly multispectral (blue, green, red and near infra-red bands) cloud-free images; calculation of monthly spectral variables depicting leaf and plant characteristics, canopy biomass, soil features and surface water and of Hue, Intensity and Saturation values; prediction of A. saligna FCover and identification of the most important spectral variables using Random Forest model. RF models calibrated for both satellite platforms showed high predictive performances (R2 > 0.6; RMSE < 0.008), with accurate spatially-explicit predictions of the invaded areas. While Sentinel-2 performed slightly better, PlanetScope-based model effectively delineated invaded areas edges and small patches. The summer leaf chlorophyll content followed by soil spectral variables resulted the most important variables discriminating A. saligna patches from native vegetation. Such information is consistent with the field-observed phenology of A. saligna as well as the well-documented alterations in leaf litter content and soil organic matter usually occurring in invaded patches. We presented new evidence of the importance of ultra-high spatial resolution UAV data to fill the gap between field observation of A. saligna and satellite data, offering new tools for detecting and monitoring IAS spread in a cost-effective and timely manner.