The species richness and biodiversity of vegetation in Hungary are increasingly threatened by invasive plant species brought in from other continents and foreign ecosystems. These invasive plant species have spread aggressively in the natural and semi-natural habitats of Europe. Common milkweed (Asclepias syriaca) is one of the species that pose the greatest ecological menace. Therefore, the primary purpose of the present study is to map and monitor the spread of common milkweed, the most common invasive plant species in Europe. Furthermore, the possibilities to detect and validate this special invasive plant by analyzing hyperspectral remote sensing data were investigated. In combination with field reference data, high-resolution hyperspectral aerial images acquired by an unmanned aerial vehicle (UAV) platform in 138 spectral bands in areas infected by common milkweed were examined. Then, support vector machine (SVM) and artificial neural network (ANN) classification algorithms were applied to the highly accurate field reference data. As a result, common milkweed individuals were distinguished in hyperspectral images, achieving an overall accuracy of 92.95% in the case of supervised SVM classification. Using the ANN model, an overall accuracy of 99.61% was achieved. To evaluate the proposed approach, two experimental tests were conducted, and in both cases, we managed to distinguish the individual specimens within the large variety of spreading invasive species in a study area of 2 ha, based on centimeter spatial resolution hyperspectral UAV imagery.

For developing global strategies against the dramatic spread of invasive species, we need to identify the geographical, environmental, and socioeconomic factors determining the spatial distribution of invasive species. In our study, we investigated these factors influencing the occurrences of common milkweed (Asclepias syriaca L.), an invasive plant species that is of great concern to the European Union (EU). In a Hungarian study area, we used country-scale soil and climate databases, as well as an EU-scale land cover databases (CORINE) for the analyses. For the abundance data of A. syriaca, we applied the field survey photos from the Land Use and Coverage Area Frame Survey (LUCAS) Land Cover database for the European Union. With machine learning algorithm methods, we quantified the relative weight of the environmental variables on the abundance of common milkweed. According to our findings, soil texture and soil type (sandy soils) were the most important variables determining the occurrence of this species. We could exactly identify the actual land cover types and the recent land cover changes that have a significant role in the occurrence the common milkweed in Europe. We could also show the role of climatic conditions of the study area in the occurrence of this species, and we could prepare the potential distribution map of common milkweed for the study area.

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Radiative transfer models (RTM) provide universally applicable, highly accurate prospects for plant parameter retrieval. Due to the ill-posed nature of radiative transfer theory, however, the retrieval of plant parameters requires sophisticated strategies for model inversion. We argue that object-based image analysis (OBIA) works as an effective regularization measure to cope with this ill-posedness. Despite similar findings reported in the literature, OBIA and RTM are rarely used in a combined manner. Additionally, there is a clear lack of software solutions ready for operational usage. Therefore, we propose OBIA4RTM as an approach to combine OBIA and RTM using Python and PostgreSQL/PostGIS spatial databases in a fully Open Geospatial Consortium (OGC) compliant way. First results obtained in agricultural regions in southern Germany and Austria using Sentinel-2 data during the 2017 and 2018 growing season show root mean squared errors (RMSE) in the leaf area index (LAI) of 1.47 m²/m² in the case of silage maize and 1.31 m²/m² in the case of winter cereals. Issues of integrating space and time as well as defining appropriate validation strategies, however, require further research.

<p>Mountain environments are particularly vulnerable to ongoing climatic and environmental changes. Specifically, alpine grasslands are seriously threatened by shallow erosion which has been increasingly detected during the last decades on alpine meadows and pastures. It has been suggested that a high plant species diversity of alpine grassland communities may increase the erosion resistance of soils, mainly through positive effects on root length, number of root tips and foliage abundance. Moreover, high plant biodiversity has shown to stabilize water channels by giving slope instability. Against this background, we used Earth Observation to map grassland communities and to understand the link between species diversity and the presence of shallow erosion spots in an alpine region.</p><p>Our study site is within the valley of Funes in South Tyrol, Italy where shallow erosion spots have multiplied in the last years and decades. The study site is over 2300 m above sea level and covers an area of approximately 5 ha. We mapped the grassland vegetation in this area with using different technologies: The main data source was a hyperspectral image with overall 28 spectral bands (506 nm to 896 nm) and a 5 cm spatial accuracy acquired from a UAV flight campaign in 04.09.2019. Our reference data set comprised detailed ground measurements within 50x50 centimeter plots. Overall, we acquired field spectroradiometer measurements covering the spectral range from 339 nm to 2500 nm (1024 spectral bands), ground-based hyperspectral measurements and sampled the different grassland communities within the plots. Based on the data integration of two different scaled field measurements and the UAV mapping we were able to detect the main grassland community occurrences and hotspots in species-level with high accuracy.</p>