Obtaining the three-dimensional structure of tree orchards from remote 2D terrestrial LIDAR scanning

In recent years, LIDAR (light detection and ranging) sensors have been widely used to measure environmental parameters such as the structural characteristics of trees, crops and forests. Knowledge of the structural characteristics of plants has a high scientific value due to their influence in many biophysical processes including, photosynthesis, growth, CO2-sequestration and evapotranspiration, playing a key role in the exchange of matter and energy between plants and the atmosphere, and affecting terrestrial, above-ground, carbon storage. In this work, we report the use of a 2D LIDAR scanner in agriculture to obtain three-dimensional (3D) structural characteristics of plants. LIDAR allows fast, non-destructive measurement of the 3D structure of vegetation (geometry, size, height, cross-section, etc.). LIDAR provides a 3D cloud of points, which is easily visualized with Computer Aided Design software. Three-dimensional, high density data are uniquely valuable for the qualitative and quantitative study of the geometric parameters of plants. Results are demonstrated in fruit and citrus orchards and vineyards, leading to the conclusion that the LIDAR system is able to measure the geometric characteristics of plants with sufficient precision for most agriculture applications. The developed system made it possible to obtain 3D digitalized images of crops, from which a large amount of plant information – such as height, width, volume, leaf area index and leaf area density – could be obtained. There was a great degree of concordance between the physical dimensions, shape and global appearance of the 3D digital plant structure and the real plants, revealing the coherence of the 3D tree model obtained from the developed system with respect to the real structure. For some selected trees, the correlation coefficient obtained between manually measured volumes and those obtained from the 3D LIDAR models was as high as 0.976.

This research was funded by the CICYT (Comisión Interministerial de Ciencia y Tecnología, Spain), under Agreement No. AGL2002-04260-C04-02. LMS200 and SICK are trademarks of SICK AG, Germany.