Robinia pseudoacacia L. in short rotation coppice: Seed and stump shoot reproduction as well as UAS-based spreading analysis

Carl, C; Lehmann, JRK; Landgraf, D; Pretzsch, H.

Forschungsartikel (Zeitschrift) | Peer reviewed

Zusammenfassung

Varying reproduction strategies are an important trait that tree species need in order both to survive and to spread. Black locust is able to reproduce via seeds, stump shoots, and root suckers. However, little research has been conducted on the reproduction and spreading of black locust in short rotation coppices. This research study focused on seed germination, stump shoot resprout, and spreading by root suckering of black locust in ten short rotation coppices in Germany. Seed experiments and sample plots were analyzed for the study. Spreading was detected and measured with unmanned aerial system (UAS)-based images and classification technology—object-based image analysis (OBIA). Additionally, the classification of single UAS images was tested by applying a convolutional neural network (CNN), a deep learning model. The analyses showed that seed germination increases with increasing warm-cold variety and scarification. Moreover, it was found that the number of shoots per stump decreases as shoot age increases. Furthermore, spreading increases with greater light availability and decreasing tillage. The OBIA and CNN image analysis technologies achieved 97\% and 99.5\% accuracy for black locust classification in UAS images. All in all, the three reproduction strategies of black locust in short rotation coppices differ with regards to initialization, intensity, and growth performance, but all play a role in the survival and spreading of black locust.

Details zur Publikation

FachzeitschriftForests
Jahrgang / Bandnr. / Volume10
Ausgabe / Heftnr. / Issue3
Seitenbereich235-235
StatusVeröffentlicht
Veröffentlichungsjahr2019
Sprache, in der die Publikation verfasst istEnglisch
DOI10.3390/f10030235
Link zum Volltexthttps://www.mdpi.com/1999-4907/10/3/235
StichwörterConvolutional neural network (CNN); Object-based image analysis (OBIA); Reproduction; Robinia pseudoacacia L.; Short rotation coppice; Spreading; Unmanned aerial system (UAS)

Autor*innen der Universität Münster

Lehmann, Jan
Professur für Remote Sensing und Spatial Modelling (Prof. Meyer)