Detection of the invasive tree species Hovenia dulcis Thumb. (Rhamnaceae) in an urban remnant of Atlantic Forest through Remote Sensing techniques combined with RGB images obtained by an unmanned aerial vehicle (UAV)

Abstract

The invasive Hovenia dulcis is considered the main invasive species in the Atlantic Forest, capable of altering environmental conditions on a local scale and producing profound changes in the composition of the plant community. Combining images obtained by drones and satellite imagery can make forest monitoring more efficient. It enables a more efficient and targeted response to contain the spread of invasive species. Additionally, phytosociological studies can contribute to understanding the structure of the native tree community and the impacts generated by biological invasion. This research aimed to use CBERS4-A satellite images with a high-resolution suitable for applying land use and land cover classification methods. Automatic supervised object-directed classification was performed through the Dzetsaka Classification Tool, using the Gaussian Mixture Model method. 150 hectares of georeferenced orthomosaics obtained through drones was using to confirm the identification of the invader. The entire area was traversed to determine the tree community, and 72 random sample plots were established, each with a fixed area of 100 m². Calculated indices of Shannon Index (H’) = 3.65 and Uniformity (J’) = 78% demonstrate that the plant community has a high diversity of species. H. dulcis had the highest number of sampled individuals (146), being the species with the highest Relative Density (9.14) within the community, and the second-highest in Relative Frequency (5.10%), Coverage Importance Value (8.85%), and Importance Value Index (7.60%). The methodology employed for invader identification through satellite and drone images allowed for rapid and precise data collection, covering an area of 86.44 hectares.