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Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure

EMMAH, INRA, Université d’Avignon et des Pays du Vaucluse, 84000 Avignon, France
Author to whom correspondence should be addressed.
Academic Editors: Farid Melgani, Francesco Nex, Clement Atzberger and Prasad S. Thenkabail
Remote Sens. 2017, 9(2), 111;
Received: 27 November 2016 / Revised: 13 January 2017 / Accepted: 23 January 2017 / Published: 28 January 2017
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
PDF [6582 KB, uploaded 6 February 2017]


In the context of precision viticulture, remote sensing in the optical domain offers a potential way to map crop structure characteristics, such as vegetation cover fraction, row orientation or leaf area index, that are later used in decision support tools. A method based on the RGB color model imagery acquired with an unmanned aerial vehicle (UAV) is proposed to describe the vineyard 3D macro-structure. The dense point cloud is first extracted from the overlapping RGB images acquired over the vineyard using the Structure from Motion algorithm implemented in the Agisoft PhotoScan software. Then, the terrain altitude extracted from the dense point cloud is used to get the 2D distribution of height of the vineyard. By applying a threshold on the height, the rows are separated from the row spacing. Row height, width and spacing are then estimated as well as the vineyard cover fraction and the percentage of missing segments along the rows. Results are compared with ground measurements with root mean square error (RMSE) = 9.8 cm for row height, RMSE = 8.7 cm for row width and RMSE = 7 cm for row spacing. The row width, cover fraction, as well as the percentage of missing row segments, appear to be sensitive to the quality of the dense point cloud. Optimal flight configuration and camera setting are therefore mandatory to access these characteristics with a good accuracy. View Full-Text
Keywords: UAV; 3D; point cloud; vineyards; structure; row characteristics UAV; 3D; point cloud; vineyards; structure; row characteristics

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Weiss, M.; Baret, F. Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure. Remote Sens. 2017, 9, 111.

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