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Crop height variability detection in a single field by multi-temporal terrestrial laser scanning

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Abstract

Information on crop height, crop growth and biomass distribution is important for crop management and environmental modelling. For the determination of these parameters, terrestrial laser scanning in combination with real-time kinematic GPS (RTK–GPS) measurements was conducted in a multi-temporal approach in two consecutive years within a single field. Therefore, a time-of-flight laser scanner was mounted on a tripod. For georeferencing of the point clouds, all eight to nine positions of the laser scanner and several reflective targets were measured by RTK–GPS. The surveys were carried out three to four times during the growing periods of 2008 (sugar-beet) and 2009 (mainly winter barley). Crop surface models were established for every survey date with a horizontal resolution of 1 m, which can be used to derive maps of plant height and plant growth. The detected crop heights were consistent with observations from panoramic images and manual measurements (R2 = 0.53, RMSE = 0.1 m). Topographic and soil parameters were used for statistical analysis of the detected variability of crop height and significant correlations were found. Regression analysis (R2 < 0.31) emphasized the uncertainty of basic relations between the selected parameters and crop height variability within one field. Likewise, these patterns compared with the normalized difference vegetation index (NDVI) derived from satellite imagery show only minor significant correlations (r < 0.44).

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References

  • Bendig, J., Bolten, A., & Bareth, G. (2013). UAV-based imaging for multi-temporal, very high resolution crop surface models to monitor crop growth variability. Photogrammetrie-Fernerkundung-Geoinformation, 6, 551–562.

    Article  Google Scholar 

  • Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., & Bareth, G. (2014). Estimating biomass of Barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing, 6(11), 10395–10412.

    Article  Google Scholar 

  • Besl, P. J., & McKay, N. D. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239–256.

    Article  Google Scholar 

  • CRC/TR32 (2015). transregional collaborative research centre 32: Patterns in soil-vegetation-atmosphere-systems. Retrieved July 24, 2015, from http://www.tr32.uni-koeln.de.

  • Ehlert, D., Adamek, R., & Horn, H.-J. (2009). Laser rangefinder-based measuring of crop biomass under field conditions. Precision Agriculture, 10(5), 395–408.

    Article  Google Scholar 

  • Ehlert, D., Horn, H. J., & Adamek, R. (2008). Measuring crop biomass density by laser triangulation. Computers and Electronics in Agriculture, 61(2), 117–125.

    Article  Google Scholar 

  • Eitel, J. U. H., Vierling, L. A., & Long, D. S. (2010). Simultaneous measurements of plant structure and chlorophyll content in broadleaf saplings with a terrestrial laser scanner. Remote Sensing of Environment, 114(10), 2229–2237.

    Article  Google Scholar 

  • Eitel, J. U. H., Vierling, L. A., Long, D. S., & Hunt, E. R. (2011). Early season remote sensing of wheat nitrogen status using a green scanning laser. Agricultural and Forest Meteorology, 151(10), 1338–1345.

    Article  Google Scholar 

  • Gnyp, M. L., Yu, K., Aasen, H., Yao, Y., Huang, S., Miao, Y., et al. (2013). Analysis of Crop Reflectance for Estimating Biomass in Rice Canopies at Different Phenological Stages. Photogrammetrie-Fernerkundung-Geoinformation, 2013(4), 351–365.

    Article  Google Scholar 

  • Gómez-Candón, D., De Castro, A. I., & López-Granados, F. (2013). Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, 15(1), 44–56.

    Article  Google Scholar 

  • Heuer, A., Casper, M. C., & Herbst, M. (2011). Correlation of topography, soil properties and spatial variability of biomass: application of the self-organizing map method. Zeitschrift für Geomorphologie, Supplementary Issues, 55(3), 169–178.

    Article  Google Scholar 

  • Hoffmeister, D., Bolten, A., Curdt, C., Waldhoff, G., & Bareth, G. (2010). High resolution crop surface models (CSM) and crop volume models (CVM) on field level by terrestrial laser scanning. In H. Guo, & C. Wang (Eds.), Proceedings of SPIE-7840, 6th International Symposium on Digital Earth: Models, Algorithms, and Virtual Reality, 2010 (p. 78400E). Göttingen, Germany: Copernicus Publications.

  • Hoffmeister, D., Tilly, N., Bendig, J., Curdt, C., & Bareth, G. (2012). Detection of crop growth variability of four sugar beet cultivars by multi-temporal terrestrial laser scanning. In M. Clasen, G. Fröhlich, H. Bernhardt, K. Hildebrand, & B. Theuvsen (Eds.), Informationstechnologie für eine nachhaltige Landbewirtschaftung, 32. GIL Jahrestagung (pp. 135–138). Bonn, Germany: Köllen Verlag.

    Google Scholar 

  • Höfle, B. (2014). Radiometric correction of terrestrial LiDAR point cloud data for individual maize plant detection. IEEE Geoscience and Remote Sensing Letters, 11(1), 94–98.

    Article  Google Scholar 

  • Hosoi, F., & Omasa, K. (2009). Estimating vertical plant area density profile and growth parameters of a wheat canopy at different growth stages using three-dimensional portable lidar imaging. ISPRS Journal of Photogrammetry and Remote Sensing, 64(2), 151–158.

    Article  Google Scholar 

  • Hosoi, F., & Omasa, K. (2012). Estimation of vertical plant area density profiles in a rice canopy at different growth stages by high-resolution portable scanning lidar with a lightweight mirror. ISPRS Journal of Photogrammetry and Remote Sensing, 74, 11–19.

    Article  Google Scholar 

  • Hütt, C., Schiedung, H., Tilly, N., & Bareth, G. (2014). Fusion of high resolution remote sensing images and terrestrial laser scanning for improved biomass estimation of maize. In F. Sunar, O. Altan, & M. Taberner (Eds.), ISPRS Archives (Vol. XL-7, pp. 101–108). Göttingen, Germany: Copernicus Publications.

    Google Scholar 

  • Kaspar, T. C., Colvin, T. S., Jaynes, D. B., Karlen, D. L., James, D. E., & Meek, D. W. (2003). Relationship between six years of corn yields and terrain attributes. Precision Agriculture, 4, 87–101.

    Article  Google Scholar 

  • Koppe, W., Gnyp, M. L., Hutt, C., Yao, Y. K., Miao, Y. X., Chen, X. P., et al. (2013). Rice monitoring with multi-temporal and dual-polarimetric TerraSAR-X data. International Journal of Applied Earth Observation and Geoinformation, 21, 568–576.

    Article  Google Scholar 

  • Korres, W., Koyama, C. N., Fiener, P., & Schneider, K. (2010). Analysis of surface soil moisture patterns in agricultural landscapes using empirical orthogonal functions. Hydrology and Earth System Sciences, 14(5), 751–764.

    Article  Google Scholar 

  • Korres, W., Reichenau, T. G., & Schneider, K. (2013). Patterns and scaling properties of surface soil moisture in an agricultural landscape: An ecohydrological modeling study. Journal of Hydrology, 498, 89–102.

    Article  Google Scholar 

  • Koyama, C. N., Korres, W., Fiener, P., & Schneider, K. (2010). Variability of surface soil moisture observed from multi-temporal C-band SAR and field data. Vadose Zone Journal, 9(4), 1014–1024.

    Article  Google Scholar 

  • Kravchenko, A. N., Robertson, G. P., Thelen, K. D., & Harwood, R. R. (2005). Management, topographical, and weather effects on spatial variability of crop grain yields. Agronomy Journal, 97(2), 514–523.

    Article  Google Scholar 

  • Lenz-Wiedemann, V. I. S., Klar, C. W., & Schneider, K. (2010). Development and test of a crop growth model for application within a Global Change decision support system. Ecological Modelling, 221(2), 314–329.

    Article  Google Scholar 

  • Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2006). Geographic information systems and science. West Sussex, UK: Wiley.

    Google Scholar 

  • Lumme, J., Karjalainen, M., Kaartinen, H., Kukko, A., Hyyppä, J., Hyyppä, H., et al. (2008). Terrestrial Laser Scanning of agricultural crops. In J. Chen, J. Jiang, & H.-G. Maas (Eds.), ISPRS archives, Proceedings of the XXI. ISPRS Conference (Vol. XXXVII Part B5, pp. 563–566). Göttingen, Germany: Copernicus Publications.

  • McKinion, J. M., Willers, J. L., & Jenkins, J. N. (2010a). Comparing high density LIDAR and medium resolution GPS generated elevation data for predicting yield stability. Computers and Electronics in Agriculture, 74(2), 244–249.

    Article  Google Scholar 

  • McKinion, J. M., Willers, J. L., & Jenkins, J. N. (2010b). Spatial analyses to evaluate multi-crop yield stability for a field. Computers and Electronics in Agriculture, 70(1), 187–198.

    Article  Google Scholar 

  • Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358–371.

    Article  Google Scholar 

  • Murakami, T., Yui, M., & Amaha, K. (2012). Canopy height measurement by photogrammetric analysis of aerial images: Application to buckwheat (Fagopyrum esculentum Moench) lodging evaluation. Computers and Electronics in Agriculture, 89, 70–75.

    Article  Google Scholar 

  • Rudolph, S., van der Kruk, J., von Hebel, C., Ali, M., Herbst, M., Montzka, C., et al. (2015). Linking satellite derived LAI patterns with subsoil heterogeneity using large-scale ground-based electromagnetic induction measurements. Geoderma, 241–242, 262–271.

    Article  Google Scholar 

  • Samseemoung, G., Soni, P., Jayasuriya, H. P. W., & Salokhe, V. M. (2012). Application of low altitude remote sensing (LARS) platform for monitoring crop growth and weed infestation in a soybean plantation. Precision Agriculture, 13(6), 611–627.

    Article  Google Scholar 

  • Schmidt, M., Reichenau, T. G., Fiener, P., & Schneider, K. (2012). The carbon budget of a winter wheat field: An eddy covariance analysis of seasonal and inter-annual variability. Agricultural and Forest Meteorology, 165, 114–126.

    Article  Google Scholar 

  • Seeber, G. (2003). Satellite geodesy. Berlin, Germany: Walter de Gruyter.

    Book  Google Scholar 

  • Stadler, A., Rudolph, S., Kupisch, M., Langensiepen, M., van der Kruk, J., & Ewert, F. (2015). Quantifying the effects of soil variability on crop growth using apparent soil electrical conductivity measurements. European Journal of Agronomy, 64, 8–20.

    Article  Google Scholar 

  • Taylor, J. A., & Bates, T. R. (2013). A discussion on the significance associated with Pearson’s correlation in precision agriculture studies. Precision Agriculture, 14(5), 558–564.

    Article  Google Scholar 

  • Tilly, N., Hoffmeister, D., Cao, Q., Huang, S. Y., Lenz-Wiedemann, V., Miao, Y. X., et al. (2014). Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice. Journal of Applied Remote Sensing, 8(1), 083671.

    Article  Google Scholar 

  • Vosselman, G., & Maas, H.-G. (Eds.). (2010). Airborne and terrestrial laser scanning. Dunbeath, UK: Whittles Publishing.

    Google Scholar 

  • Waldhoff, G., Curdt, C., Hoffmeister, D., & Bareth, G. (2012). Analysis of multitemporal and multisensor remote sensing data for crop rotation mapping. In M. Shortis, W. Wagner, & J. Hyyppä (Eds.), XXII ISPRS Congress, Technical Commission VII (Vol. I-7, pp. 177–182). Göttingen, Germany: Copernicus Publications.

    Google Scholar 

  • Yu, K., Li, F., Gnyp, M. L., Miao, Y., Bareth, G., & Chen, X. (2013). Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain. ISPRS Journal of Photogrammetry and Remote Sensing, 78, 102–115.

    Article  Google Scholar 

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Acknowledgments

We thank the anonymous reviewers, who significantly improved the paper. We gratefully acknowledge financial support from the CRC/TR32, funded by the Deutsche Forschungsgemeinschaft (DFG). We also like to thank Topcon GmbH (Germany) and RIEGL Laser Measurement Systems GmbH (Austria) for continuous support.

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Authors and Affiliations

  1. Institute of Geography, University of Cologne, Albertus-Magnus-Platz, 50923, Cologne, Germany

    Dirk Hoffmeister, Guido Waldhoff, Wolfgang Korres, Constanze Curdt & Georg Bareth

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  1. Dirk Hoffmeister
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  2. Guido Waldhoff
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  3. Wolfgang Korres
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  4. Constanze Curdt
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  5. Georg Bareth
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Correspondence to Dirk Hoffmeister.

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Hoffmeister, D., Waldhoff, G., Korres, W. et al. Crop height variability detection in a single field by multi-temporal terrestrial laser scanning. Precision Agric 17, 296–312 (2016). https://doi.org/10.1007/s11119-015-9420-y

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  • DOI: https://doi.org/10.1007/s11119-015-9420-y

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