Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning
| Autor: | Francisco Herrera, Domingo Alcaraz-Segura, Anastasiia Safonova, Alexey V. Rubtsov, Yuriy Maglinets, Siham Tabik |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Canopy
Bark beetle multi-class classification drone aerial photography Siberian fir Siberia deep-learning convolutional neural networks forest health 010504 meteorology & atmospheric sciences Computer science 0211 other engineering and technologies Aerial photography 02 engineering and technology 01 natural sciences Convolutional neural network Multi-class classification Forest health lcsh:Science 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing biology Crown (botany) Deep learning 15. Life on land Abies sibirica biology.organism_classification Drone Tree (data structure) General Earth and Planetary Sciences Convolutional neural networks lcsh:Q Stage (hydrology) |
| Zdroj: | Remote Sensing, Vol 11, Iss 6, p 643 (2019) Digibug. Repositorio Institucional de la Universidad de Granada instname Remote Sensing; Volume 11; Issue 6; Pages: 643 |
| ISSN: | 2072-4292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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