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Detecting and mapping tree seedlings in UAV imagery using convolutional neural networks and field-verified data

  • Mapping of tree seedlings is useful for tasks ranging from monitoring natural succession and regeneration to effective silvicultural management. Development of methods that are both accurate and cost-effective is especially important considering the dramatic increase in tree planting that is required globally to mitigate the impacts of climate change. The combination of high-resolution imagery from unmanned aerial vehicles and object detection by convolutional neural networks (CNNs) is one promising approach. However, unbiased assessments of these models and methods to integrate them into geospatial workflows are lacking. In this study, we present a method for rapid, large-scale mapping of young conifer seedlings using CNNs applied to RGB orthomosaic imagery. Importantly, we provide an unbiased assessment of model performance by using two well-characterised trial sites together containing over 30,000 seedlings to assemble datasets with a high level of completeness. Our results showed CNN-based models trained on two sites detected seedlings with sensitivities of 99.5% and 98.8%. False positives due to tall weeds at one site and naturally regenerating seedlings of the same species led to slightly lower precision of 98.5% and 96.7%. A model trained on examples from both sites had 99.4% sensitivity and precision of 97%, showing applicability across sites. Additional testing showed that the CNN model was able to detect 68.7% of obscured seedlings missed during the initial annotation of the imagery but present in the field data. Finally, we demonstrate the potential to use a form of weakly supervised training and a tile-based processing chain to enhance the accuracy and efficiency of CNNs applied to large, high-resolution orthomosaics.

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Author:Grant D. Pearse, Alan Y.S. Tan, Michael S. Watt, Matthias O. FranzORCiDGND, Jonathan P. Dash
Parent Title (English):ISPRS Journal of Photogrammetry and Remote Sensing
Document Type:Article
Year of Publication:2020
Release Date:2021/01/18
Tag:Deep learning; Convolutional networks; Tree seedlings; Unmanned aerial vehicles; Forest establishment; Object detection
Issue:Vol. 168
First Page:156
Last Page:169
Institutes:Institut für Optische Systeme - IOS
DDC functional group:500 Naturwissenschaften und Mathematik
Relevance:Peer reviewed Publikation in Master Journal List
Licence (English):License LogoLizenzbedingungen Elsevier