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Open AccessArticle

Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning

1
Faculty of Agriculture, Yamagata University, Tsuruoka 997-8555, Japan
2
Faculty of Science, Yamagata University, Yamagata 990-8560, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 260; https://doi.org/10.3390/rs13020260
Received: 24 December 2020 / Accepted: 10 January 2021 / Published: 13 January 2021
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
Insect outbreaks are a recurrent natural phenomenon in forest ecosystems expected to increase due to climate change. Recent advances in Unmanned Aerial Vehicles (UAV) and Deep Learning (DL) Networks provide us with tools to monitor them. In this study we used nine orthomosaics and normalized Digital Surface Models (nDSM) to detect and classify healthy and sick Maries fir trees as well as deciduous trees. This study aims at automatically classifying treetops by means of a novel computer vision treetops detection algorithm and the adaptation of existing DL architectures. Considering detection alone, the accuracy results showed 85.70% success. In terms of detection and classification, we were able to detect/classify correctly 78.59% of all tree classes (39.64% for sick fir). However, with data augmentation, detection/classification percentage of the sick fir class rose to 73.01% at the cost of the result accuracy of all tree classes that dropped 63.57%. The implementation of UAV, computer vision and DL techniques contribute to the development of a new approach to evaluate the impact of insect outbreaks in forest. View Full-Text
Keywords: deep learning; computer vision; UAV; individual tree detection; tree classification; sick tree detection deep learning; computer vision; UAV; individual tree detection; tree classification; sick tree detection
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MDPI and ACS Style

Nguyen, H.T.; Lopez Caceres, M.L.; Moritake, K.; Kentsch, S.; Shu, H.; Diez, Y. Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning. Remote Sens. 2021, 13, 260. https://doi.org/10.3390/rs13020260

AMA Style

Nguyen HT, Lopez Caceres ML, Moritake K, Kentsch S, Shu H, Diez Y. Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning. Remote Sensing. 2021; 13(2):260. https://doi.org/10.3390/rs13020260

Chicago/Turabian Style

Nguyen, Ha T.; Lopez Caceres, Maximo L.; Moritake, Koma; Kentsch, Sarah; Shu, Hase; Diez, Yago. 2021. "Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning" Remote Sens. 13, no. 2: 260. https://doi.org/10.3390/rs13020260

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