Mapping invasive Lupinus polyphyllus Lindl. in  semi-natural grasslands using object-based analysis of UAV-borne images

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Authors

Jayan Sri Jeewantha Wijesingha , Thomas Astor, Damian Schulze-Brüninghoff, Michael Wachendorf

Abstract

Knowledge on the spatio-temporal distribution of invasive plant species is vital to maintain biodiversity in grasslands which are threatened by the invasion of such plants and to evaluate the effect of control activities conducted. Manual digitising of aerial images with field verification is the standard method to create maps of the invasive Lupinus polyphyllus Lindl. (Lupine) in semi-natural grasslands of the UNESCO biosphere reserve “Rhön”. As the standard method is labour- and time-intensive, a workflow was developed to map lupine coverage using an unmanned aerial vehicle (UAV)-borne remote sensing (RS) along with object-based image analysis (OBIA). UAV-borne red, green, blue (R, G, B) and thermal imaging, as well as photogrammetric canopy height modelling (CHM), were applied. Images were segmented by unsupervised parameter optimisation into image objects representing lupine plants and grass vegetation. Image objects obtained were classified using random forest classification modelling based on objects’ attributes. The developed classification model was employed to create lupine distribution maps of test areas and predicted data were compared with manually digitised lupine coverage maps. The classification models yielded a mean prediction accuracy of 89 %, and 0.78 mean kappa statistics. The maximum difference in lupine area between classified and digitised lupine maps was 5 %. Moreover, the pixel-wise map comparison showed that 88 % of all pixels matched between classified and digitised maps. Our results indicate that lupine coverage mapping using UAV-borne RS data and OBIA provides similar results as the standard manual digitising method and, thus, offers a valuable tool to map invasive lupine on grasslands.

DOI

https://doi.org/10.31223/osf.io/cs3tp

Subjects

Biodiversity, Earth Sciences, Environmental Sciences, Life Sciences, Physical Sciences and Mathematics, Plant Sciences

Keywords

UAV, OBIA, grassland, invasive plant species, object-based image analysis, unmanned aerial vehicle

Dates

Published: 2020-02-28 19:33

Last Updated: 2020-06-15 13:35

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License

Academic Free License (AFL) 3.0

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