UAV-based mapping of Acacia saligna invasions in the Mediterranean coastal dune

Remote Sensing (RS) is a useful tool for detecting and mapping Invasive Alien Plants (IAPs). IAPs mapping on
dynamic and heterogeneous landscapes, using satellite RS data is not always feasible. Unmanned aerial
vehicles (UAV) with ultra-high spatial resolution data represent a promising tool for IAPs detection and
mapping. This work develops an operational workflow for detecting and mapping Acacia saligna invasion
along Mediterranean coastal dunes. In particular, it explores and tests the potential of RGB (Red, Green,
Blue) and multispectral (Green, Red, Red Edge, Near Infra-Red) UAV images collected in pre-flowering and
flowering phenological stages for detecting and mapping A. saligna. After ortho-mosaics generation, we
derived from RGB images the DSM (Digital Surface Model) and HIS (Hue, Intensity, Saturation) variables,
and we calculated the NDVI (Normalized Difference Vegetation Index). For classifying images of the two
phenological stages we built a set of raster stacks which include different combination of variables. For
image classification, we used the Geographic Object-Based Image Analysis techniques (GEOBIA) in
combination with Random Forest (RF) classifier. All classifications derived from RS information (collected on
pre-flowering and flowering stages and using different combinations of variables) produced A. saligna maps
with acceptable accuracy values, with higher performances on classification derived from flowering period
images, especially using DSM+HIS combination. The adopted approach resulted an efficient method for
mapping and early detection of IAPs, also in complex environments offering a sound support to the
prioritization of conservation and management actions claimed by the EU IAS Regulation 1143/2014.