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Convolutional Neural Network Maps Plant Communities in Semi-Natural Grasslands Using Multispectral Unmanned Aerial Vehicle Imagery

  • 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.

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Author:Maren Pöttker, Kathrin KiehlORCiD, Thomas Jarmer, Dieter TrautzORCiD
Title (English):Convolutional Neural Network Maps Plant Communities in Semi-Natural Grasslands Using Multispectral Unmanned Aerial Vehicle Imagery
DOI:https://doi.org/10.3390/rs15071945
Parent Title (English):Remote Sensing
Document Type:Article
Language:English
Date of Publication (online):2023/04/06
Release Date:2024/01/09
Tag:Convolutional neural networks (CNNs); Plant communities; Remote sensing; Semi-natural grasslands; Unmanned aerial vehicles (UAVs)
Volume:15
Issue:7
Article Number:1945
Page Number:16
Faculties:Fakultät AuL
DDC classes:500 Naturwissenschaften und Mathematik / 500 Naturwissenschaften
Review Status:Veröffentlichte Fassung/Verlagsversion
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International