Crop height variability detection in a single field by multi-temporal terrestrial laser scanning
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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).
KeywordsTerrestrial laser scanning RTK–GPS Crop surface models Spatial variability Crop height
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.
Compliance with Ethical Standards
Conflict of interest
We declare no conflict of interest.
- 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.
- 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.Google Scholar
- 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
- 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
- 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.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.CrossRefGoogle 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