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Article

Individual Tree Crown Delineation for the Species Classification and Assessment of Vital Status of Forest Stands from UAV Images

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Deep Learning Laboratory, Siberian Federal University, 660074 Krasnoyarsk, Russia
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Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, 18071 Granada, Spain
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College of Information Technology, Imam Ja’afar Al-Sadiq University, Kirkuk 661001, Iraq
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Marchuk Institute of Numerical Mathematics Russian Academy of Sciences, 119333 Moscow, Russia
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Institute for Scientific Research of Aerospace Monitoring “Aerocosmos”, State Scientific Institution, 105064 Moscow, Russia
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Department of Forest Entomology, Phytopathology and Game Fauna, Forest Research Institute, Bulgarian Academy of Sciences, 1756 Sofia, Bulgaria
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Geografika Ltd., 1504 Sofia, Bulgaria
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Department of Cartography and GIS, Faculty of Geology and Geography, Sofia University St. Kliment Ohridski, 1504 Sofia, Bulgaria
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Author to whom correspondence should be addressed.
Academic Editors: Maggi Kelly and Darren Turner
Drones 2021, 5(3), 77; https://doi.org/10.3390/drones5030077
Received: 28 June 2021 / Revised: 4 August 2021 / Accepted: 5 August 2021 / Published: 7 August 2021
(This article belongs to the Section Drones in Agriculture and Forestry)
Monitoring the structure parameters and damage to trees plays an important role in forest management. Remote-sensing data collected by an unmanned aerial vehicle (UAV) provides valuable resources to improve the efficiency of decision making. In this work, we propose an approach to enhance algorithms for species classification and assessment of the vital status of forest stands by using automated individual tree crowns delineation (ITCD). The approach can be potentially used for inventory and identifying the health status of trees in regional-scale forest areas. The proposed ITCD algorithm goes through three stages: preprocessing (contrast enhancement), crown segmentation based on wavelet transformation and morphological operations, and boundaries detection. The performance of the ITCD algorithm was demonstrated for different test plots containing homogeneous and complex structured forest stands. For typical scenes, the crown contouring accuracy is about 95%. The pixel-by-pixel classification is based on the ensemble supervised classification method error correcting output codes with the Gaussian kernel support vector machine chosen as a binary learner. We demonstrated that pixel-by-pixel species classification of multi-spectral images can be performed with a total error of about 1%, which is significantly less than by processing RGB images. The advantage of the proposed approach lies in the combined processing of multispectral and RGB photo images. View Full-Text
Keywords: remote sensing; pattern recognition; unmanned aerial vehicle; aerial photo and multispectral images; individual tree crowns delineation; species classification; vital status assessment remote sensing; pattern recognition; unmanned aerial vehicle; aerial photo and multispectral images; individual tree crowns delineation; species classification; vital status assessment
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MDPI and ACS Style

Safonova, A.; Hamad, Y.; Dmitriev, E.; Georgiev, G.; Trenkin, V.; Georgieva, M.; Dimitrov, S.; Iliev, M. Individual Tree Crown Delineation for the Species Classification and Assessment of Vital Status of Forest Stands from UAV Images. Drones 2021, 5, 77. https://doi.org/10.3390/drones5030077

AMA Style

Safonova A, Hamad Y, Dmitriev E, Georgiev G, Trenkin V, Georgieva M, Dimitrov S, Iliev M. Individual Tree Crown Delineation for the Species Classification and Assessment of Vital Status of Forest Stands from UAV Images. Drones. 2021; 5(3):77. https://doi.org/10.3390/drones5030077

Chicago/Turabian Style

Safonova, Anastasiia, Yousif Hamad, Egor Dmitriev, Georgi Georgiev, Vladislav Trenkin, Margarita Georgieva, Stelian Dimitrov, and Martin Iliev. 2021. "Individual Tree Crown Delineation for the Species Classification and Assessment of Vital Status of Forest Stands from UAV Images" Drones 5, no. 3: 77. https://doi.org/10.3390/drones5030077

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