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Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: Description of image acquisition and processing

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Abstract

There are many aspects of crop management that might benefit from aerial observation. Unmanned aerial vehicle (UAV) platforms are evolving rapidly both technically and with regard to regulations. The purpose of this study was to acquire images with conventional RGB cameras using UAVs and process them to obtain geo-referenced ortho-images with the aim of characterizing the main plant growth parameters required in the management of irrigated crops under semi-arid conditions. The paper is in two parts, the first describes the image acquisition and processing procedures, and the second applies the proposed methodology to a case study. In the first part of the paper, the type of UAV utilized is described. It was a vertical take-off and landing quadracopter aircraft with a conventional RGB compact digital camera. Other types of on-board sensors are also described, such as near-infrared sensors and thermal sensors, and the problems of using these types of expensive sensor is discussed. In addition, software developed by the authors for photogrammetry processing, and information extraction from the geomatic products are described and analysed for agronomic applications. This software can also be used in other applications. To obtain agronomic parameters, different strategies were analysed, such as the use of computer vision for canopy cover extraction, as well as the use of vegetation indices derived from the visible spectrum, as a proper solution when very-high resolution imagery is available. The use of high-resolution images obtained with UAVs together with proper treatment might be considered a useful tool for precision in monitoring crop growth and development, advising farmers on water requirements, yield production, weed and insect infestations, among others. More studies, focusing on the calibration and validation of these relationships in other crops are required.

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References

  • Adams, J. B., Sabol, D. E. K. V., Filho, R. A., Roberts, D. A., Smith, M., & Gillespie, R. A. (1995). Classification of multispectral images based on fractions of endmembers: Application to Land-cover change in the Brazilian Amazon. Remote Sensing Environment, 52, 137–154.

    Article  Google Scholar 

  • Arozarena, A., García, L., Villa, G., Hermosilla, J., Papí, F., Valcárcel, N., et al. (2008). Spanish national plan for territory observation (PNOT). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B4), 1729–1733.

    Google Scholar 

  • Baret, F., & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing Environment, 35, 161–173.

    Article  Google Scholar 

  • BOE. (2007). Boletín Oficial del Estado de España. Real Decreto 1071/2007, de 27 de julio, por el que se regula el sistema geodésico de referencia oficial en España (Spanish Official Bulletin. 1071/2007, 27th July Royal Decree, which regulates the official geodesic reference system in Spain). Resource document. http://www.boe.es/buscar/doc.php?id=BOE-A-2007-15822. Accessed 08 Nov 2013.

  • Calera, A., Odi, M., Martínez-Beltrán, C., Campos, I., & González-Piquera, J. (2010). Satellite constellation for crop monitoring: Formosat-2, Deimos-DMC, Landsat 5TM and 7ETM+. In J. A. Sobrino (Ed.), Proceedings of the 3rd International Symposium Recent Advances in Quantitative Remote Sensing (pp. 237–242). Valencia: Publicacions de la Universitat de València.

    Google Scholar 

  • Chen, X., Vierling, L., Rowell, E., & DeFelice, T. (2004). Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM + vegetation cover estimates in a ponderosa pine forest. Remote Sensing of Environment, 91, 14–26.

    Article  Google Scholar 

  • Córcoles, J. I., Ortega, J. F., Hernández, D., & Moreno, M. A. (2013). Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle. Biosystems Engineering, 115, 31–42.

    Article  Google Scholar 

  • ERGNSS (2008). Spanish national GNSS reference stations network. Resource document. http://www.euref.eu/symposia/2008Brussels/06-24-SPAIN_euref2008.pdf. Accessed 1st February 2013.

  • Fraser, C. S. (1997). Digital camera self-calibration ISPRS. Journal of Photogrammetry and Remote Sensing, 52(4), 149–159.

    Article  Google Scholar 

  • Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80(1), 76–87.

    Article  Google Scholar 

  • Gonzalez-Dugo, M. P., Neale, C. M. U., Mateos, L., Kustas, W. P., Prueger, J. H., Anderson, M. C., et al. (2009). A comparison of operational remote sesnsing-based models for estimating crop evapotranspiration. Agricultural and Forest Meteorology, 149(11), 1843–1853.

    Article  Google Scholar 

  • Herwitz, S. R., Johnson, L. F., Dunagan, S. E., Higgins, R. G., Sullivan, D. V., Zheng, J., et al. (2004). Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support. Computers and Electronics in Agriculture, 44, 49–61.

    Article  Google Scholar 

  • Hunt, E. R, Jr, Cavigelli, M., Daughtry, C. S. T., McMurtrey, J. E, I. I. I., & Walthall, C. L. (2005). Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agriculture, 6, 359–378.

    Article  Google Scholar 

  • Kise, M., & Zhang, Q. (2008). Creating a panoramic field image using multi-spectral stereovision system. Computers and Electronics in Agriculture, 60, 67–75.

    Article  Google Scholar 

  • Kraus, K. (2007). Photogrammetry (2nd ed., Vol. 1). Bonn: Dümmler.

    Book  Google Scholar 

  • Lamb, D. W., Trotter, M. G., & Schneider, D. A. (2009). Ultra low-level airborne (ULLA) sensing of crop canopy reflectance: A case study using a CropCircleTM sensor. Computers and Electronics in Agriculture, 69, 86–91.

    Article  Google Scholar 

  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60, 91–110.

    Article  Google Scholar 

  • Pierrot-Deseilligny, M., & Cléry, I. (2011). APERO, an open source bundle adjustment software for automatic calibration and orientation of a set of images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(5/W16), 269–276.

    Google Scholar 

  • Pierrot-Deseilligny, M., De Luca, L., & Remondino, F. (2011). Automated image-based procedures for accurate artefacts 3D modelling and orthoimage generation. Geoinformatics FCE CTU Journal, 6, 291–299.

    Google Scholar 

  • Remondino, F., Barazzetti, L., Nex, F., Scaioni, M., & Sarazzi, D. (2011). UAV photogrammetry for mapping and 3D modeling—current status and future perspectives. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(1/C22), 25–31.

    Google Scholar 

  • Stafford, J. V. (2000). Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research, 76, 267–275.

    Article  Google Scholar 

  • Triggs, B., McLauchlan, P., Hartley, R., & Fitzgibbon, A. (2000). Bundle adjustment—a modern synthesis. Lecture Notes in Computer Science, 1883, 298–372.

    Article  Google Scholar 

  • Warren, G., & Metternicht, G. (2005). Agricultural applications of high-resolution digital multispectral imagery: Evaluating within-field spatial variability of canola (Brassica napus) in Western Australia. Photogrammetric Engineering and Remote Sensing, 71, 595–602.

    Article  Google Scholar 

  • White, M. A., Asner, G. P., Nemani, R. R., Privette, J. L., & Running, S. W. (2000). Measuring fractional cover and leaf area index in arid ecosystems. Digital camera, radiation transmittance, and laser altimetry methods. Remote Sensing of Environment, 74(1), 45–57.

    Article  Google Scholar 

  • Zhang, Ch., & Kovacs, J. M. (2012). The small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13, 693–712.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Education Ministry of Spain for its financing with a University Teaching Scholarship (Formación de Profesorado Universitario, FPU) from Researching Human Resources Education National Program, included in Scientific Researching, Development and Technological Innovation National Plan 2008–2011 (EDU/3083/2009). We also wish to thank to the Water User Association SORETA located in Tarazona de La Mancha, Albacete, Spain and the Irrigation Users’ Association of “Eastern Mancha” for their support of this work.

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Correspondence to R. Ballesteros.

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Ballesteros, R., Ortega, J.F., Hernández, D. et al. Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: Description of image acquisition and processing. Precision Agric 15, 579–592 (2014). https://doi.org/10.1007/s11119-014-9355-8

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