Geospatial Approaches to Monitoring the Spread of Invasive Species of Solidago spp.

Abstract

Global climate change influences plant invasion which spreads all over the Europe. Invasive plants are predominantly manifest negative impacts, which require increased attention not only from ecologists. The research examines the possibilities offered by geospatial technologies in mapping the spatial spread of invasive plants of the genus Solidago. Invasive plant population was investigated at two localities, Malý Šariš and Chminianska Nová Ves in Slovakia, as well as the mapping of the area by multispectral imaging to determine the spectral reflectance curve of the monitored plant species. Using spatial analyses in the geographic information system, we evaluated changes in the plant density in the two localities. Based on the obtained results, we found that the number of individuals (ramets) in the Malý Šariš is significantly increasing, while in the examined area of Chminianska Nová Ves, there is a decrease in the number of Solidago spp. in the last monitored year. At the same time, we can state that in the areas with the highest increase in the number of ramets, the highest plant density per hectare was also recorded. We can also say that due to the spectral proximity of the surrounding vegetation, the spectral resolution in four spectral bands is insufficient for the classification of multispectral records in the case of Solidago spp. and cannot replace the advantages of high spectral resolution hyperspectral imaging, which significantly refines the feature space for Solidago spp. and the surrounding vegetation.

Survey of the Solidago canadensis L. Morphological Traits and Essential Oil Production: Aboveground Biomass Growth and Abundance of the Invasive Goldenrod Appears to Be Reciprocally Enhanced within the Invaded Stands

“…Our suggestion can provide understanding on the progressing increase of the number of ramets, as well as the clones of Canadian goldenrod, which were observed during the consecutive three years by the mapping of Solidago invaded plots with the help of various geospatial technologies. Moreover, the highest increase of both ramets and clones was observed just within the plots with the highest goldenrod % representation [33].…”

Systematic review and best practices for drone remote sensing of invasive plants

“…(Bolch et al., 2021) used a Trimble Geo7X RTK kit with a Zephyr‐3 antenna to record GNSS locations to conduct a differential correction using GPS Pathfinder Office to improve positional accuracies for monitoring aquatic plant invasions in the Sacramento‐San Joaquin River Delta, and Koco et al. (2021) surveyed Goldenrod‐invaded areas using a GNSS‐RTK GPS unit with an accuracy of <2 cm in Slovakia. If high accuracy GNSS is not available, upscaling the image GSD can help overcome positional errors (Gränzig et al., 2021).…”

“…The use of post-processed kinematic (PPK) or real-time kinematic (RTK) global navigation satellite system (GNSS) can help capture the locational precision of GCPs needed to reference high spatial resolution drone imagery, and about 28% of studies reported using RTK-or PPK-GNSS for this purpose. Of note, Bolch et al (Bolch et al, 2021) used a Trimble Geo7X RTK kit with a Zephyr-3 antenna to record GNSS locations to conduct a differential correction using GPS Pathfinder Office to improve positional accuracies for monitoring aquatic plant invasions in the Sacramento-San Joaquin River Delta, and Koco et al (2021) surveyed Goldenrod-invaded areas using a GNSS-RTK GPS unit with an accuracy of <2 cm in Slovakia. If high accuracy GNSS is not available, upscaling the image GSD can help overcome positional errors (Gränzig et al, 2021).…”

Identification of invasive trees in a Brazilian subtropical forest using remotely piloted aircraft systems and machine learning

“…15,16 In this regard, the use of remotely piloted aircraft systems (RPAS) for detecting invasive species in large and hard-to-reach areas has become more frequent, 7 driven by the development of sensors and positioning systems that enable the collection of high-resolution and accurate images at a low cost. 17 RPAS equipped with RGB, multispectral, and hyperspectral sensors play an essential role in the identification and monitoring of areas invaded by IAS, as reported in the studies by da Silva et al, 18 Koco et al, 19 Liang et al, 20 and Lopatin et al. 21 Furthermore, the processing of highresolution images obtained by RPAS can be handled by machine learning (ML) algorithms such as support vector machine (SVM) 22 and random forest (RF).…”

“…RPAS equipped with RGB, multispectral, and hyperspectral sensors play an essential role in the identification and monitoring of areas invaded by IAS, as reported in the studies by da Silva et al., 18 Koco et al., 19 Liang et al., 20 and Lopatin et al. 21 .…”