Synergetic use of unmanned aerial vehicle and satellite images for detecting non-native tree species: An insight into Acacia saligna invasion in the Mediterranean coast Flavio Marzialetti 1 , Mirko Di Febbraro 1 *, Ludovico Frate 1 , Walter De Simone 2 , Alicia Teresa Rosario Acosta 3 * and Maria Laura Carranza 1 1 Envix-Lab, Department of Biosciences and Territory, Molise University, Isernia, Italy, 2 LaCEMod, Department of Life, Health and Environmental Sciences, Environmental Sciences Sect., University of L’Aquila, L’Aquila, Italy, 3 Vegetation Ecology Laboratory, Department of Sciences, Roma Tre University, Roma, Italy Invasive alien plants (IAPs) are increasingly threatening biodiversity worldwide; thus, early detection and monitoring tools are needed. Here, we explored the potential of unmanned aerial vehicle (UAV) images in providing intermediate reference data which are able to link IAP field occurrence and satellite information. Speci fically, we used very high spatial resolution (VHR) UAV maps of A. saligna as calibration data for satellite-based predictions of its spread in the Mediterranean coastal dunes. Based on two satellite platforms (PlanetScope and Sentinel-2), we developed and tested a dedicated procedure to predict A. saligna spread organized in four steps: 1) setting of calibration data for satellite-based predictions, by aggregating UAV-based VHR IAP maps to satellite spatial resolution (3 and 10 m); 2) selection of monthly multispectral (blue, green, red, and near infra-red bands) cloud-free images for both satellite platforms; 3) calculation of monthly spectral variables depicting leaf and plant characteristics, canopy biomass, soil features, surface water and hue, intensity, and saturation values; 4) prediction of A. saligna distribution and identification of the most important spectral variables discriminating IAP occurrence using a fandom forest (RF) model. RF models calibrated for both satellite platforms showed high predictive performances (R 2 > 0.6; RMSE <0.008), with accurate spatially explicit predictions of the invaded areas. While Sentinel-2 performed slightly better, the PlanetScope-based model effectively delineated invaded area edges and small patches. The summer leaf chlorophyll content followed by soil spectral variables was regarded as the most important variables discriminating A. saligna patches from native vegetation. Such variables depicted the characteristic IAP phenology and typically altered leaf litter and soil organic matter of invaded patches. Overall, we presented new evidence of the importance OPEN ACCESS EDITED BY Thomas Campagnaro, University of Padua, Italy REVIEWED BY M Arasumani, University of Greifswald, Germany Francesco Chianucci, Council for Agricultural and Economics Research (CREA), Italy Joseph J Erinjery, Kannur University, India Vahid Nasiri, University of Tehran, Iran *CORRESPONDENCE Mirko Di Febbraro, mirko.difebbraro@unimol.it Alicia Teresa Rosario Acosta, aliciateresarosario.acosta@uniroma3.it SPECIALTY SECTION This article was submitted to Conservation and Restoration Ecology, a section of the journal Frontiers in Environmental Science RECEIVED 21 February 2022 ACCEPTED 07 July 2022 PUBLISHED 08 August 2022 CITATION Marzialetti F, Di Febbraro M, Frate L, De Simone W, Acosta ATR and Carranza ML (2022), Synergetic use of unmanned aerial vehicle and satellite images for detecting non-native tree species: An insight into Acacia saligna invasion in the Mediterranean coast. Front. Environ. Sci. 10:880626. doi: 10.3389/fenvs.2022.880626 COPYRIGHT © 2022 Marzialetti, Di Febbraro, Frate, De Simone, Acosta and Carranza. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Frontiers in Environmental Science frontiersin.org 01 TYPE Original Research PUBLISHED 08 August 2022 DOI 10.3389/fenvs.2022.880626