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Article

Monitoring Invasive Plant Species Using Hyperspectral Remote Sensing Data

1
Department of Physical Geography and Geoinformatics, University of Szeged, Egyetem utca 2-6, H-6722 Szeged, Hungary
2
Department of Soil Mapping and Environmental Informatics, Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Herman Ottó út 15, H-1022 Budapest, Hungary
*
Author to whom correspondence should be addressed.
Land 2021, 10(1), 29; https://doi.org/10.3390/land10010029
Received: 14 December 2020 / Revised: 27 December 2020 / Accepted: 29 December 2020 / Published: 1 January 2021
(This article belongs to the Special Issue Identifying Endangered Terrestrial Ecosystems)
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. View Full-Text
Keywords: invasive species; common milkweed; hyperspectral imaging; UAV; artificial neural networks; SVM classification invasive species; common milkweed; hyperspectral imaging; UAV; artificial neural networks; SVM classification
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MDPI and ACS Style

Papp, L.; van Leeuwen, B.; Szilassi, P.; Tobak, Z.; Szatmári, J.; Árvai, M.; Mészáros, J.; Pásztor, L. Monitoring Invasive Plant Species Using Hyperspectral Remote Sensing Data. Land 2021, 10, 29. https://doi.org/10.3390/land10010029

AMA Style

Papp L, van Leeuwen B, Szilassi P, Tobak Z, Szatmári J, Árvai M, Mészáros J, Pásztor L. Monitoring Invasive Plant Species Using Hyperspectral Remote Sensing Data. Land. 2021; 10(1):29. https://doi.org/10.3390/land10010029

Chicago/Turabian Style

Papp, Levente, Boudewijn van Leeuwen, Péter Szilassi, Zalán Tobak, József Szatmári, Mátyás Árvai, János Mészáros, and László Pásztor. 2021. "Monitoring Invasive Plant Species Using Hyperspectral Remote Sensing Data" Land 10, no. 1: 29. https://doi.org/10.3390/land10010029

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