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Article

Detecting Individual Tree Attributes and Multispectral Indices Using Unmanned Aerial Vehicles: Applications in a Pine Clonal Orchard

Faculty of Forest Sciences, University of Juarez del Estado de Durango, Río Papaloapan and Blvd. Durango, Valle del Sur s/n, Durango 34120, Mexico
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Remote Sens. 2020, 12(24), 4144; https://doi.org/10.3390/rs12244144
Received: 5 November 2020 / Revised: 10 December 2020 / Accepted: 15 December 2020 / Published: 18 December 2020
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
Modern forestry poses new challenges that space technologies can solve thanks to the advent of unmanned aerial vehicles (UAVs). This study proposes a methodology to extract tree-level characteristics using UAVs in a spatially distributed area of pine trees on a regular basis. Analysis included different vegetation indices estimated with a high-resolution orthomosaic. Statistically reliable results were found through a three-phase workflow consisting of image acquisition, canopy analysis, and validation with field measurements. Of the 117 trees in the field, 112 (95%) were detected by the algorithm, while height, area, and crown diameter were underestimated by 1.78 m, 7.58 m2, and 1.21 m, respectively. Individual tree attributes obtained from the UAV, such as total height (H) and the crown diameter (CD), made it possible to generate good allometric equations to infer the basal diameter (BD) and diameter at breast height (DBH), with R2 of 0.76 and 0.79, respectively. Multispectral indices were useful as tree vigor parameters, although the normalized-difference vegetation index (NDVI) was highlighted as the best proxy to monitor the phytosanitary condition of the orchard. Spatial variation in individual tree productivity suggests the differential management of ramets. The consistency of the results allows for its application in the field, including the complementation of spectral information that can be generated; the increase in accuracy and efficiency poses a path to modern inventories. However, the limitation for its application in forests of more complex structures is identified; therefore, further research is recommended. View Full-Text
Keywords: tree-level characteristics; unmanned aerial vehicles; Pinus clonal orchard tree-level characteristics; unmanned aerial vehicles; Pinus clonal orchard
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MDPI and ACS Style

Gallardo-Salazar, J.L.; Pompa-García, M. Detecting Individual Tree Attributes and Multispectral Indices Using Unmanned Aerial Vehicles: Applications in a Pine Clonal Orchard. Remote Sens. 2020, 12, 4144. https://doi.org/10.3390/rs12244144

AMA Style

Gallardo-Salazar JL, Pompa-García M. Detecting Individual Tree Attributes and Multispectral Indices Using Unmanned Aerial Vehicles: Applications in a Pine Clonal Orchard. Remote Sensing. 2020; 12(24):4144. https://doi.org/10.3390/rs12244144

Chicago/Turabian Style

Gallardo-Salazar, José L., and Marín Pompa-García. 2020. "Detecting Individual Tree Attributes and Multispectral Indices Using Unmanned Aerial Vehicles: Applications in a Pine Clonal Orchard" Remote Sensing 12, no. 24: 4144. https://doi.org/10.3390/rs12244144

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