Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Estimating tree height and biomass of a poplar plantation with image-based UAV technology

1 Institute of Agricultural Sciences-CSIC, Madrid, Spain
2 Institute for Sustainable Agriculture-CSIC, Cordoba, Spain
3 Center for Automation and Robotics-CSIC, Arganda del Rey, Madrid, Spain

Special Editions: The next generation of Precision Horticulture Technologies

Poplar is considered one of the forest crops with greatest potential for lignocellulose production, so rapid and non-destructive measurements of tree growth (in terms of height and biomass) is essential to estimate productivity of poplar plantations. As an alternative to tedious and costly manual sampling of poplar trees, this study evaluated the ability of UAV technology to monitor a one-year-old poplar plantation (with trees 4.3 meters high, on average), and specifically, to assess tree height and estimate dry biomass from spectral information (based on the Normalized Difference Vegetation Index, NDVI) and Digital Surface Models (DSM). We used an UAV flying at 100 m altitude over an experimental poplar plantation of 95 × 60 m2 (3350 trees approx.), and collected remote sensing images with a conventional visible-light camera for the generation of the DSM and a multi-spectral camera for the calculation of NDVI. Prior to the DSM generation, several adjustments of image enhancement were tested, which improved DSM accuracy by 19–21%. Next, UAV-based data (i.e., tree height, NDVI, and the result of fusing these variables) were evaluated with a validation set of 48 tree-rows by applying correlation and linear regression analysis. Correlation between actual and DSM-based tree heights was acceptable (R2 = 0.599 and RMSE = 0.21 cm), although DSM did not detect the narrow apexes in the top of the poplar trees (1 m length, on average), which led to notable underestimates. Linear regression equations for tree dry biomass showed the highest correlation with NDVI × Tree-height (R2 = 0.540 and RMSE = 0.23 kg/m2) and the lowest correlation with NDVI (R2 = 0.247 and RMSE = 0.29 kg/m2). The best results were used to determine the distribution of the trees according to their dry biomass, providing information about potential productivity of the entire poplar plantation by applying a fast and non-destructive procedure.
  Figure/Table
  Supplementary
  Article Metrics

Keywords remote sensing; unmanned aerial vehicle; digital surface model (DSM); image enhancement adjustments; NDVI; tree dimensions; woody crops

Citation: José M Peña, Ana I de Castro, Jorge Torres-Sánchez, Dionisio Andújar, Carolina San Martín, José Dorado, César Fernández-Quintanilla, Francisca López-Granados. Estimating tree height and biomass of a poplar plantation with image-based UAV technology. AIMS Agriculture and Food, 2018, 3(3): 313-326. doi: 10.3934/agrfood.2018.3.313

References

  • 1. European Union (2009) Directive No. 2009/28/EC of the European Parliament and of the Council of April 23, 2009, on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives No. 2001/77/EC and No. 2003/30/EC.
  • 2. Sixto H, Cañellas I, van Arendonk J, et al. (2015) Growth potential of different species and genotypes for biomass production in short rotation in Mediterranean environments. For Ecol Manage 354: 291–299.    
  • 3. Ríos-Saucedo JC, Acuña-Carmona E, Cancino-Cancino J, et al. (2016) Allometric equations commonly used for estimating shoot biomass in short-rotation wood energy species: A review. Rev Chapingo Ser Cienc For Ambiente 22: 193–202.
  • 4. Yu N, Li L, Schmitz N, et al. (2016) Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform. Remote Sens Environ 187: 91–101.    
  • 5. Du M, Noguchi N (2017) Monitoring of wheat growth status and mapping of wheat yield's within-field spatial variations using color images acquired from UAV-camera system. Remote Sens 9: 289.    
  • 6. de Castro AI, Torres-Sánchez J, Peña JM, et al (2018) An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sens 10: 285.    
  • 7. Nex F, Remondino F (2014) UAV for 3D mapping applications: A review. Appl Geomat 6: 1–15.    
  • 8. Iqbal K, Abdul Salam R, Osman A, et al. (2007) Underwater image enhancement using an integrated colour model. IAENG Int J Comput Sci 34: 2.
  • 9. Jiménez-Brenes FM, López-Granados F, de Castro AI, et al (2017) Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling. Plant Methods 13: 55.    
  • 10. Malambo L, Popescu SC, Murray SC, et al. (2018) Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery. Int J Appl Earth Obs Geoinformation 64: 31–42.    
  • 11. Bendig J, Bolten A, Bennertz S, et al. (2014) Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sens 6: 10395–10412.    
  • 12. Tilly N, Aasen H, Bareth G (2015) Fusion of plant height and vegetation indices for the estimation of barley biomass. Remote Sens 7: 11449–11480.
  • 13. Stanton C, Starek MJ, Elliott N, et al. (2017) Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment. J Appl Remote Sens 11: 026035.    
  • 14. Comba L, Gay P, Primicerio J, et al. (2015) Vineyard detection from unmanned aerial systems images. Comput Electron Agric 114: 78–87.    
  • 15. Torres-Sánchez J, López-Granados F, Serrano N, et al. (2015) High-throughput 3-D monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology. PLoS one 10: e0130479.    
  • 16. de Castro AI, Maja JM, Owen J, et al. (2018) Experimental approach to detect water stress in ornamental plants using sUAS-imagery. In: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III. International Society for Optics and Photonics, 106640N.
  • 17. Gatziolis D, Lienard JF, Vogs A, et al. (2015) 3D tree dimensionality assessment using photogrammetry and small unmanned aerial vehicles. PLoS one 10: e0137765.    
  • 18. Wallace L, Lucieer A, Malenovský Z, et al. (2016) Assessment of forest structure using two uav techniques: A comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests 7: 62.    
  • 19. San Martín C, Andújar D, Fernández-Quintanilla C, et al. (2016) Spatio-temporal dynamics of Sorghum halepense in poplar short-rotation coppice under several vegetation management systems. For Ecol Manage 379: 37–49.
  • 20. Torres-Sánchez J, López-Granados F, De Castro AI, et al. (2013) Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PLoS one 8: e58210.    
  • 21. Mesas-Carrascosa FJ, Torres-Sánchez J, Clavero-Rumbao I, et al. (2015) Assessing optimal flight parameters for generating accurate multispectral orthomosaicks by UAV to support site-specific crop management. Remote Sens 7: 12793–12814.    
  • 22. Mesas-Carrascosa FJ, Rumbao IC, Torres-Sánchez J, et al. (2017) Accurate ortho-mosaicked six-band multispectral UAV images as affected by mission planning for precision agriculture proposes. Int J Remote Sens 38: 2161–2176.    
  • 23. de Castro AI, Jiménez-Brenes FM, Torres-Sánchez J, et al. (2018) 3-D characterization of vineyards using a novel UAV imagery-based OBIA procedure for precision viticulture applications. Remote Sens 10: 584.
  • 24. Herve C, Ceulemans R (1996) Short-rotation coppiced vs non-coppiced poplar: A comparative study at two different field sites. Biomass Bioenergy 11: 139–150.    
  • 25. Schaefer MT, Lamb DW (2016) A combination of plant NDVI and LiDAR measurements improve the estimation of pasture biomass in tall fescue (Festuca arundinacea var. Fletcher). Remote Sens 8: 109.    
  • 26. Pádua L, Vanko J, Hruška J, et al. (2017) UAS, sensors, and data processing in agroforestry: A review towards practical applications. Int J Remote Sens 38: 2349–2391.    
  • 27. Weiss M, Baret F (2017) Using 3D point clouds derived from UAV RGB imagery to describe vineyard 3D macro-structure. Remote Sens 9: 111.    
  • 28. Zarco-Tejada PJ, Diaz-Varela R, Angileri V, et al. (2014) Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur J Agron 55: 89–99.    
  • 29. Kattenborn T, Sperlich M, Bataua K, et al. (2014) Automatic single palm tree detection in plantations using UAV-based photogrammetric point clouds. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Zurich, Switzerland, 139–144.
  • 30. Moeckel T, Dayananda S, Nidamanuri RR, et al. (2018) Estimation of vegetable crop parameter by multi-temporal UAV-borne images. Remote Sens 10: 805.    
  • 31. Madec S, Baret F, de Solan B, et al. (2017) High-throughput phenotyping of plant height: Comparing unmanned aerial vehicles and ground LiDAR estimates. Front Plant Sci 8: 2002.    
  • 32. Torres-Sánchez J, López-Granados F, Borra-Serrano I, et al. (2018) Assessing UAV-collected image overlap influence on computation time and digital surface model accuracy in olive orchards. Precis Agric 19: 115–133.    

 

Reader Comments

your name: *   your email: *  

© 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved