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Article

UAV-Based Automatic Detection and Monitoring of Chestnut Trees

1
School of Science and Technology, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
2
Centre for Robotics in Industry and Intelligent Systems (CRIIS), INESC Technology and Science (INESC-TEC), 4200-465 Porto, Portugal
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 855; https://doi.org/10.3390/rs11070855
Received: 8 February 2019 / Revised: 2 April 2019 / Accepted: 5 April 2019 / Published: 9 April 2019
(This article belongs to the Section Remote Sensing Image Processing)
Unmanned aerial vehicles have become a popular remote sensing platform for agricultural applications, with an emphasis on crop monitoring. Although there are several methods to detect vegetation through aerial imagery, these remain dependent of manual extraction of vegetation parameters. This article presents an automatic method that allows for individual tree detection and multi-temporal analysis, which is crucial in the detection of missing and new trees and monitoring their health conditions over time. The proposed method is based on the computation of vegetation indices (VIs), while using visible (RGB) and near-infrared (NIR) domain combination bands combined with the canopy height model. An overall segmentation accuracy above 95% was reached, even when RGB-based VIs were used. The proposed method is divided in three major steps: (1) segmentation and first clustering; (2) cluster isolation; and (3) feature extraction. This approach was applied to several chestnut plantations and some parameters—such as the number of trees present in a plantation (accuracy above 97%), the canopy coverage (93% to 99% accuracy), the tree height (RMSE of 0.33 m and R2 = 0.86), and the crown diameter (RMSE of 0.44 m and R2 = 0.96)—were automatically extracted. Therefore, by enabling the substitution of time-consuming and costly field campaigns, the proposed method represents a good contribution in managing chestnut plantations in a quicker and more sustainable way. View Full-Text
Keywords: unmanned aerial vehicles; remote sensing; automatic plantation monitoring; chestnut trees; image processing; photogrammetric processing; multi-temporal analysis unmanned aerial vehicles; remote sensing; automatic plantation monitoring; chestnut trees; image processing; photogrammetric processing; multi-temporal analysis
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MDPI and ACS Style

Marques, P.; Pádua, L.; Adão, T.; Hruška, J.; Peres, E.; Sousa, A.; Sousa, J.J. UAV-Based Automatic Detection and Monitoring of Chestnut Trees. Remote Sens. 2019, 11, 855. https://doi.org/10.3390/rs11070855

AMA Style

Marques P, Pádua L, Adão T, Hruška J, Peres E, Sousa A, Sousa JJ. UAV-Based Automatic Detection and Monitoring of Chestnut Trees. Remote Sensing. 2019; 11(7):855. https://doi.org/10.3390/rs11070855

Chicago/Turabian Style

Marques, Pedro, Luís Pádua, Telmo Adão, Jonáš Hruška, Emanuel Peres, António Sousa, and Joaquim J. Sousa. 2019. "UAV-Based Automatic Detection and Monitoring of Chestnut Trees" Remote Sensing 11, no. 7: 855. https://doi.org/10.3390/rs11070855

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