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Automated Detection of Conifer Seedlings in Drone Imagery Using Convolutional Neural Networks

1
Institute for Informatic, Ludwig-Maximilians-Universität München, Oettingenstraße 67, D-80333 Munich, Germany
2
Northern Forestry Centre, 5320 122 Street Northwest, Edmonton, AB T6H 3S5, Canada
3
Department of Geography, University of Calgary, Calgary, AB T2N 1N4, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2585; https://doi.org/10.3390/rs11212585
Received: 11 October 2019 / Revised: 31 October 2019 / Accepted: 1 November 2019 / Published: 4 November 2019
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
Monitoring tree regeneration in forest areas disturbed by resource extraction is a requirement for sustainably managing the boreal forest of Alberta, Canada. Small remotely piloted aircraft systems (sRPAS, a.k.a. drones) have the potential to decrease the cost of field surveys drastically, but produce large quantities of data that will require specialized processing techniques. In this study, we explored the possibility of using convolutional neural networks (CNNs) on this data for automatically detecting conifer seedlings along recovering seismic lines: a common legacy footprint from oil and gas exploration. We assessed three different CNN architectures, of which faster region-CNN (R-CNN) performed best (mean average precision 81%). Furthermore, we evaluated the effects of training-set size, season, seedling size, and spatial resolution on the detection performance. Our results indicate that drone imagery analyzed by artificial intelligence can be used to detect conifer seedling in regenerating sites with high accuracy, which increases with the size in pixels of the seedlings. By using a pre-trained network, the size of the training dataset can be reduced to a couple hundred seedlings without any significant loss of accuracy. Furthermore, we show that combining data from different seasons yields the best results. The proposed method is a first step towards automated monitoring of forest restoration/regeneration. View Full-Text
Keywords: convolutional neural networks; forest restoration; regeneration surveys; seedling detection; UAVs; sRPAS convolutional neural networks; forest restoration; regeneration surveys; seedling detection; UAVs; sRPAS
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MDPI and ACS Style

Fromm, M.; Schubert, M.; Castilla, G.; Linke, J.; McDermid, G. Automated Detection of Conifer Seedlings in Drone Imagery Using Convolutional Neural Networks. Remote Sens. 2019, 11, 2585.

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