Drone-Based Identification and Monitoring of Two Invasive Alien Plant Species in Open Sand Grasslands by Six RGB Vegetation Indices

Abstract

Today, invasive alien species cause serious trouble for biodiversity and ecosystem services, which are essential for human survival. In order to effectively manage invasive species, it is important to know their current distribution and the dynamics of their spread. Unmanned aerial vehicle (UAV) monitoring is one of the best tools for gathering this information from large areas. Vegetation indices for multispectral camera images are often used for this, but RGB colour-based vegetation indices can provide a simpler and less expensive solution. The goal was to examine whether six RGB indices are suitable for identifying invasive plant species in the QGIS environment on UAV images. To examine this, we determined the shoot area and number of common milkweed (Asclepias syriaca) and the inflorescence area and number of blanket flowers (Gaillardia pulchella) as two typical invasive species in open sandy grasslands. According to the results, the cover area of common milkweed was best identified with the TGI and SSI indices. The producers’ accuracy was 76.38% (TGI) and 67.02% (SSI), while the user’s accuracy was 75.42% (TGI) and 75.12% (SSI), respectively. For the cover area of blanket flower, the IF index proved to be the most suitable index. In spite of this, it gave a low producer’s accuracy of 43.74% and user’s accuracy of 51.4%. The used methods were not suitable for the determination of milkweed shoot and the blanket flower inflorescence number, due to significant overestimation. With the methods presented here, the data of large populations of invasive species can be processed in a simple, fast, and cost-effective manner, which can ensure the precise planning of treatments for nature conservation practitioners.

The allelochemical potential of an invasive ornamental plant, the Indian blanket flower (Gaillardia pulchella Foug.)

“…In Europe, especially in Central Europe, it exhibits invasive behavior (Hansen, 1976;Wiersema & Le on, 2013). In Hungary, in sandy, dry habitats and on open surfaces, particularly in the protected Kolon Lake area, it has appeared in increasing numbers and abundance in recent years (Bakacsy et al, 2023;Csecserits et al, 2022;Moln ar et al, 2019;Simon, 2000).…”

Multi-Year Monitoring of Asclepias syriaca L. Spread in the Deliblato Sands Protected Reserve in Serbia

“…As this long-distance dispersal can be influenced by other factors and can accelerate dispersal rates [47], it must be taken into account and included in the models that describe the spread of invasive and potentially invasive species. The application of advanced methods in research could provide additional information on the mode of spread of A. syriaca [84]. For example, remote sensing using drones with a multispectral camera could facilitate the observation of parts of the Deliblato Sands protected area that are very difficult to access due to the density of autochthonous vegetation.…”

Airborne Hyperspectral Images and Machine Learning Algorithms for the Identification of Lupine Invasive Species in Natura 2000 Meadows

“…Satellite data have been successfully used to identify invasive trees, shrubs, and tall perennials [11,12]. However, to detect herbaceous plants in the initial phases of invasion, it is helpful to use data with higher spatial resolution from airplanes or unmanned aerial vehicles (UAVs, [13,14]), but such analyses are oriented toward local case studies. Hyperspectral data are useful for morphologically similar plants [15][16][17] due to the identification of unique spectral signatures of plant species [18].…”