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Article

Detection of Longhorned Borer Attack and Assessment in Eucalyptus Plantations Using UAV Imagery

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RAIZ—Forest and Paper Research Institute, Quinta de S. Francisco, Rua José Estevão (EN 230-1), 3800-783 Aveiro, Portugal
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Terradrone, Av. E.U.A. Nº 97, 12 Dto. Sala 03, 1700-167 Lisboa, Portugal
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(19), 3153; https://doi.org/10.3390/rs12193153
Received: 31 August 2020 / Revised: 21 September 2020 / Accepted: 23 September 2020 / Published: 25 September 2020
Eucalyptus Longhorned Borers (ELB) are some of the most destructive pests in regions with Mediterranean climate. Low rainfall and extended dry summers cause stress in eucalyptus trees and facilitate ELB infestation. Due to the difficulty of monitoring the stands by traditional methods, remote sensing arises as an invaluable tool. The main goal of this study was to demonstrate the accuracy of unmanned aerial vehicle (UAV) multispectral imagery for detection and quantification of ELB damages in eucalyptus stands. To detect spatial damage, Otsu thresholding analysis was conducted with five imagery-derived vegetation indices (VIs) and classification accuracy was assessed. Treetops were calculated using the local maxima filter of a sliding window algorithm. Subsequently, large-scale mean-shift segmentation was performed to extract the crowns, and these were classified with random forest (RF). Forest density maps were produced with data obtained from RF classification. The normalized difference vegetation index (NDVI) presented the highest overall accuracy at 98.2% and 0.96 Kappa value. Random forest classification resulted in 98.5% accuracy and 0.94 Kappa value. The Otsu thresholding and random forest classification can be used by forest managers to assess the infestation. The aggregation of data offered by forest density maps can be a simple tool for supporting pest management. View Full-Text
Keywords: Phoracantha spp.; unmanned aerial vehicle (UAV); multispectral imagery; vegetation index; thresholding analysis; Large Scale Mean-Shift Segmentation (LSMS); Random Forest (RF) Phoracantha spp.; unmanned aerial vehicle (UAV); multispectral imagery; vegetation index; thresholding analysis; Large Scale Mean-Shift Segmentation (LSMS); Random Forest (RF)
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MDPI and ACS Style

Duarte, A.; Acevedo-Muñoz, L.; Gonçalves, C.I.; Mota, L.; Sarmento, A.; Silva, M.; Fabres, S.; Borralho, N.; Valente, C. Detection of Longhorned Borer Attack and Assessment in Eucalyptus Plantations Using UAV Imagery. Remote Sens. 2020, 12, 3153. https://doi.org/10.3390/rs12193153

AMA Style

Duarte A, Acevedo-Muñoz L, Gonçalves CI, Mota L, Sarmento A, Silva M, Fabres S, Borralho N, Valente C. Detection of Longhorned Borer Attack and Assessment in Eucalyptus Plantations Using UAV Imagery. Remote Sensing. 2020; 12(19):3153. https://doi.org/10.3390/rs12193153

Chicago/Turabian Style

Duarte, André, Luis Acevedo-Muñoz, Catarina I. Gonçalves, Luís Mota, Alexandre Sarmento, Margarida Silva, Sérgio Fabres, Nuno Borralho, and Carlos Valente. 2020. "Detection of Longhorned Borer Attack and Assessment in Eucalyptus Plantations Using UAV Imagery" Remote Sensing 12, no. 19: 3153. https://doi.org/10.3390/rs12193153

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