Titel:	Convolutional Neural Network Maps Plant Communities in Semi-Natural Grasslands Using Multispectral Unmanned Aerial Vehicle Imagery
Autor(en):	Pöttker, Maren Kiehl, Kathrin Jarmer, Thomas Trautz, Dieter
ORCID des Autors:	https://orcid.org/0000-0002-4652-1640 https://orcid.org/0000-0002-0711-732X
Zusammenfassung:	Semi-natural grasslands (SNGs) are an essential part of European cultural landscapes. They are an important habitat for many animal and plant species and offer a variety of ecological functions. Diverse plant communities have evolved over time depending on environmental and management factors in grasslands. These different plant communities offer multiple ecosystem services and also have an effect on the forage value of fodder for domestic livestock. However, with increasing intensification in agriculture and the loss of SNGs, the biodiversity of grasslands continues to decline. In this paper, we present a method to spatially classify plant communities in grasslands in order to identify and map plant communities and weed species that occur in a semi-natural meadow. For this, high-resolution multispectral remote sensing data were captured by an unmanned aerial vehicle (UAV) in regular intervals and classified by a convolutional neural network (CNN). As the study area, a heterogeneous semi-natural hay meadow with first- and second-growth vegetation was chosen. Botanical relevés of fixed plots were used as ground truth and independent test data. Accuracies up to 88% on these independent test data were achieved, showing the great potential of the usage of CNNs for plant community mapping in high-resolution UAV data for ecological and agricultural applications.
Bibliografische Angaben:	Pöttker, M.; Kiehl, K.; Jarmer, T.; Trautz, D.: Convolutional Neural Network Maps Plant Communities in Semi-Natural Grasslands Using Multispectral Unmanned Aerial Vehicle Imagery. Remote Sens. 2023, 15, 1945.
URL:	https://doi.org/10.48693/487 https://osnadocs.ub.uni-osnabrueck.de/handle/ds-2024021210629
Schlagworte:	convolutional neural networks (CNNs); remote sensing; unmanned aerial vehicles (UAVs); semi-natural grasslands; plant communities
Erscheinungsdatum:	6-Apr-2023
Lizenzbezeichnung:	Attribution 4.0 International
URL der Lizenz:	http://creativecommons.org/licenses/by/4.0/
Publikationstyp:	Einzelbeitrag in einer wissenschaftlichen Zeitschrift [Article]
Enthalten in den Sammlungen:	FB06 - Hochschulschriften Open-Access-Publikationsfonds

Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons