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Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems

DOI:10.3390/rs11202365 期刊:Remote Sensing 出版年份:2019 更新时间:2025-09-23 15:19:57
摘要: The 3D digital characterization of vegetation is a growing practice in the agronomy sector. Precision agriculture is sustained, among other methods, by variables that remote sensing techniques can digitize. At present, laser scanners make it possible to digitize three-dimensional crop geometry in the form of point clouds. In this work, we developed several methods for calculating the volume of vine wood, with the ?nal intention of using these values as indicators of vegetative vigor on a thematic map. For this, we used a static terrestrial laser scanner (TLS), a mobile scanning system (MMS), and six algorithms that were implemented and adapted to the data captured and to the proposed objective. The results show that, with TLS equipment and the algorithm called convex hull cluster, the volumes of a vine trunk can be obtained with a relative error lower than 7%. Although the accuracy and detail of the cloud obtained with TLS are very high, the cost per unit for the scanned area limits the application of this system for large areas. In contrast to the inoperability of the TLS in large areas of terrain, the MMS and the algorithm based on the L1-medial skeleton and the modelling of cylinders of a certain height and diameter have solved the estimation of volumes with a relative error better than 3%. To conclude, the vigor map elaborated represents the estimated volume of each vine by this method.
作者: Ana del-Campo-Sanchez,David Hernandez-Lopez,Miguel Moreno,Rocio Ballesteros
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The research objective was to develop methods for calculating the volume of vine wood using 3D point clouds obtained with laser scanner systems, with the aim of using these values as indicators of vegetative vigor on a thematic map.

The study concluded that the convex hull cluster algorithm with TLS equipment can estimate vine trunk volumes with a relative error lower than 7%, while the MMS system with the L1-medial skeleton algorithm achieved a relative error better than 3%. The developed methodology and software represent a promising technology for characterizing the geometry of woody crops to aid in crop management decisions.

The limitations include the high cost and limited application of TLS for large areas due to its operational constraints, and the need for manual editing of point clouds in some cases due to anomalies.

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