Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning
We are very grateful to the reviewers for their valuable comments that helped to improve the paper. We appreciate the support of a vice-director of the “Stolby” State Nature Reserve, Anastasia Knorre. We also thank two Ph.D. students Egor Trukhanov and Anton Perunov from Siberian Federal University...
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Online Access: | http://hdl.handle.net/10481/61827 https://doi.org/10.3390/rs11060643 |
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ftunivgranada:oai:digibug.ugr.es:10481/61827 2023-05-15T18:19:40+02:00 Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning Safonova, Anastasiia Tabik, Siham Alcaraz Segura, Domingo Rubtsov, Alexey Maglinets, Yuriy Herrera Triguero, Francisco 2019-03-16 http://hdl.handle.net/10481/61827 https://doi.org/10.3390/rs11060643 eng eng MDPI EC/H2020/641762 Safonova, A.; Tabik, S.; Alcaraz-Segura, D.; Rubtsov, A.; Maglinets, Y.; Herrera, F. Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning. Remote Sens. 2019, 11, 643. [doi:10.3390/rs11060643] http://hdl.handle.net/10481/61827 doi:10.3390/rs11060643 Atribución 3.0 España http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess CC-BY Multi-class classification Drone Aerial photography Siberian fir Siberia Deep learning Convolutional neural networks Forest health info:eu-repo/semantics/article 2019 ftunivgranada https://doi.org/10.3390/rs11060643 2021-04-27T23:20:45Z We are very grateful to the reviewers for their valuable comments that helped to improve the paper. We appreciate the support of a vice-director of the “Stolby” State Nature Reserve, Anastasia Knorre. We also thank two Ph.D. students Egor Trukhanov and Anton Perunov from Siberian Federal University for their help in data acquisition (aerial photography from UAV) on two research plots in 2016 and raw imagery processing. Invasion of the Polygraphus proximus Blandford bark beetle causes catastrophic damage to forests with firs (Abies sibirica Ledeb) in Russia, especially in Central Siberia. Determining tree damage stage based on the shape, texture and colour of tree crown in unmanned aerial vehicle (UAV) images could help to assess forest health in a faster and cheaper way. However, this task is challenging since (i) fir trees at different damage stages coexist and overlap in the canopy, (ii) the distribution of fir trees in nature is irregular and hence distinguishing between different crowns is hard, even for the human eye. Motivated by the latest advances in computer vision and machine learning, this work proposes a two-stage solution: In a first stage, we built a detection strategy that finds the regions of the input UAV image that are more likely to contain a crown, in the second stage, we developed a new convolutional neural network (CNN) architecture that predicts the fir tree damage stage in each candidate region. Our experiments show that the proposed approach shows satisfactory results on UAV Red, Green, Blue (RGB) images of forest areas in the state nature reserve “Stolby” (Krasnoyarsk, Russia). A.S. was supported by the grant of the Russian Science Foundation No. 16-11-00007. S.T. was supported by the Ramón y Cajal Programme (No. RYC-2015-18136). S.T. and F.H. received funding from the Spanish Ministry of Science and Technology under the project TIN2017-89517-P. D.A.-S. received support from project ECOPOTENTIAL, which received funding from the European Union Horizon 2020 Research and Innovation Programme under grant agreement No. 641762, from the European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612 and from project 80NSSC18K0446 of the NASA’s Group on Earth Observations Work Programme 2016. A.R. was supported by the grant of the Russian Science Foundation No. 18-74-10048. Y. M. was supported by the grant of Russian Foundation for Basic Research No. 18-47-242002, Government of Krasnoyarsk Territory and Krasnoyarsk Regional Fund of Science. Article in Journal/Newspaper Sibirica Siberia DIGIBUG: Repositorio Institucional de la Universidad de Granada Stolby ENVELOPE(129.531,129.531,62.999,62.999) Remote Sensing 11 6 643 |
institution |
Open Polar |
collection |
DIGIBUG: Repositorio Institucional de la Universidad de Granada |
op_collection_id |
ftunivgranada |
language |
English |
topic |
Multi-class classification Drone Aerial photography Siberian fir Siberia Deep learning Convolutional neural networks Forest health |
spellingShingle |
Multi-class classification Drone Aerial photography Siberian fir Siberia Deep learning Convolutional neural networks Forest health Safonova, Anastasiia Tabik, Siham Alcaraz Segura, Domingo Rubtsov, Alexey Maglinets, Yuriy Herrera Triguero, Francisco Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning |
topic_facet |
Multi-class classification Drone Aerial photography Siberian fir Siberia Deep learning Convolutional neural networks Forest health |
description |
We are very grateful to the reviewers for their valuable comments that helped to improve the paper. We appreciate the support of a vice-director of the “Stolby” State Nature Reserve, Anastasia Knorre. We also thank two Ph.D. students Egor Trukhanov and Anton Perunov from Siberian Federal University for their help in data acquisition (aerial photography from UAV) on two research plots in 2016 and raw imagery processing. Invasion of the Polygraphus proximus Blandford bark beetle causes catastrophic damage to forests with firs (Abies sibirica Ledeb) in Russia, especially in Central Siberia. Determining tree damage stage based on the shape, texture and colour of tree crown in unmanned aerial vehicle (UAV) images could help to assess forest health in a faster and cheaper way. However, this task is challenging since (i) fir trees at different damage stages coexist and overlap in the canopy, (ii) the distribution of fir trees in nature is irregular and hence distinguishing between different crowns is hard, even for the human eye. Motivated by the latest advances in computer vision and machine learning, this work proposes a two-stage solution: In a first stage, we built a detection strategy that finds the regions of the input UAV image that are more likely to contain a crown, in the second stage, we developed a new convolutional neural network (CNN) architecture that predicts the fir tree damage stage in each candidate region. Our experiments show that the proposed approach shows satisfactory results on UAV Red, Green, Blue (RGB) images of forest areas in the state nature reserve “Stolby” (Krasnoyarsk, Russia). A.S. was supported by the grant of the Russian Science Foundation No. 16-11-00007. S.T. was supported by the Ramón y Cajal Programme (No. RYC-2015-18136). S.T. and F.H. received funding from the Spanish Ministry of Science and Technology under the project TIN2017-89517-P. D.A.-S. received support from project ECOPOTENTIAL, which received funding from the European Union Horizon 2020 Research and Innovation Programme under grant agreement No. 641762, from the European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612 and from project 80NSSC18K0446 of the NASA’s Group on Earth Observations Work Programme 2016. A.R. was supported by the grant of the Russian Science Foundation No. 18-74-10048. Y. M. was supported by the grant of Russian Foundation for Basic Research No. 18-47-242002, Government of Krasnoyarsk Territory and Krasnoyarsk Regional Fund of Science. |
format |
Article in Journal/Newspaper |
author |
Safonova, Anastasiia Tabik, Siham Alcaraz Segura, Domingo Rubtsov, Alexey Maglinets, Yuriy Herrera Triguero, Francisco |
author_facet |
Safonova, Anastasiia Tabik, Siham Alcaraz Segura, Domingo Rubtsov, Alexey Maglinets, Yuriy Herrera Triguero, Francisco |
author_sort |
Safonova, Anastasiia |
title |
Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning |
title_short |
Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning |
title_full |
Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning |
title_fullStr |
Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning |
title_full_unstemmed |
Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning |
title_sort |
detection of fir trees (abies sibirica) damaged by the bark beetle in unmanned aerial vehicle images with deep learning |
publisher |
MDPI |
publishDate |
2019 |
url |
http://hdl.handle.net/10481/61827 https://doi.org/10.3390/rs11060643 |
long_lat |
ENVELOPE(129.531,129.531,62.999,62.999) |
geographic |
Stolby |
geographic_facet |
Stolby |
genre |
Sibirica Siberia |
genre_facet |
Sibirica Siberia |
op_relation |
EC/H2020/641762 Safonova, A.; Tabik, S.; Alcaraz-Segura, D.; Rubtsov, A.; Maglinets, Y.; Herrera, F. Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning. Remote Sens. 2019, 11, 643. [doi:10.3390/rs11060643] http://hdl.handle.net/10481/61827 doi:10.3390/rs11060643 |
op_rights |
Atribución 3.0 España http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3390/rs11060643 |
container_title |
Remote Sensing |
container_volume |
11 |
container_issue |
6 |
container_start_page |
643 |
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1766196856413487104 |