High-Throughput 3-D Monitoring of Agricultural-Tree Plantations with Unmanned Aerial Vehicle (UAV) Technology

The geometric features of agricultural trees such as canopy area, tree height and crown volume provide useful information about plantation status and crop production. However, these variables are mostly estimated after a time-consuming and hard field work and applying equations that treat the trees as geometric solids, which produce inconsistent results. As an alternative, this work presents an innovative procedure for computing the 3-dimensional geometric features of individual trees and tree-rows by applying two consecutive phases: 1) generation of Digital Surface Models with Unmanned Aerial Vehicle (UAV) technology and 2) use of object-based image analysis techniques. Our UAV-based procedure produced successful results both in single-tree and in tree-row plantations, reporting up to 97% accuracy on area quantification and minimal deviations compared to in-field estimations of tree heights and crown volumes. The maps generated could be used to understand the linkages between tree grown and field-related factors or to optimize crop management operations in the context of precision agriculture with relevant agro-environmental implications.

[1]  Qionghai Dai,et al.  Biology's drones: new and improved. , 2014, Science.

[2]  Thomas Blaschke,et al.  Geographic Object-Based Image Analysis – Towards a new paradigm , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[3]  R. Reulke,et al.  Remote Sensing and Spatial Information Sciences , 2005 .

[4]  Barbara Koch,et al.  Automatic Single Tree Detection in Plantations using UAV-based Photogrammetric Point clouds , 2014 .

[5]  F. Nex,et al.  UAV for 3D mapping applications: a review , 2014 .

[6]  Simon Bennertz,et al.  Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging , 2014, Remote. Sens..

[7]  Pablo J. Zarco-Tejada,et al.  Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods , 2014 .

[8]  F. López-Granados,et al.  Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images , 2013, PloS one.

[9]  Jorge Torres-Sánchez,et al.  An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops , 2015, Comput. Electron. Agric..

[10]  Borja Velázquez-Martí,et al.  Different methodologies for calculating crown volumes of Platanus hispanica trees using terrestrial laser scanner and a comparison with classical dendrometric measurements , 2013 .

[11]  Pablo J. Zarco-Tejada,et al.  High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials , 2015, Remote. Sens..

[12]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[13]  P. West.  Tree and Forest Measurement , 2003, Springer International Publishing.

[14]  R. Sanz,et al.  A review of methods and applications of the geometric characterization of tree crops in agricultural activities , 2012 .

[15]  F. López-Granados,et al.  Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management , 2013, PloS one.

[16]  Erle C. Ellis,et al.  High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision , 2013 .

[17]  Miguel Pastor Muñoz-Cobo.  Cultivo del olivo con riego localizado : diseño y manejo del cultivo y las instalaciones, programación de riegos y fertirrigación , 2005 .

[18]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[19]  Chunhua Zhang,et al.  The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.

[20]  P. Contreras,et al.  Modalidades de contratación y empleo joven: novedades del Real Decreto-ley 8/2014, de 4 de julio, de aprobación de medidas urgentes para el crecimiento, la competitividad y la eficiencia) , 2014 .

[21]  R A Diaz-Varela,et al.  Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle. , 2014, Journal of environmental management.

[22]  Pablo J. Zarco-Tejada,et al.  Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data , 2011 .

[23]  Emma Marris,et al.  Drones in science: Fly, and bring me data , 2013, Nature.

[24]  G. Meyer,et al.  Color indices for weed identification under various soil, residue, and lighting conditions , 1994 .

[25]  John F. Reid,et al.  Stereo vision three-dimensional terrain maps for precision agriculture , 2008 .

[26]  Tomislav Hengl,et al.  Finding the right pixel size , 2006, Comput. Geosci..

[27]  F. López-Granados,et al.  Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV , 2014 .

[28]  Rongjun Qin,et al.  An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images , 2014, Remote. Sens..

[29]  A. Rango,et al.  Image Processing and Classification Procedures for Analysis of Sub-decimeter Imagery Acquired with an Unmanned Aircraft over Arid Rangelands , 2011 .