Precise plant height monitoring and biomass estimation with Terrestrial Laser Scanning in paddy rice

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Volumn 2, Issue 5W2, 2013, Pages 295-300

  Tilly, N ^a,c   Hoffmeister, D ^a,c   Cao, Q ^b,c   Lenz Wiedemann, V ^a,c   Miao, Y ^b,c   Bareth, G ^a,c  

^a UNIVERSITY OF COLOGNE   (Germany)

^b CHINA AGRICULTURAL UNIVERSITY   (China)

^c International Center for Agro Informatics and Sustainable Development (ICASD)   (Germany)

Author keywords

Agriculture; Change Detection; Crop; Monitoring; Multitemporal; Spatial; TLS

Indexed keywords

EID: 85019388980     PISSN: 21949042     EISSN: 21949050     Source Type: Conference Proceeding    
DOI: 10.5194/isprsannals-II-5-W2-295-2013     Document Type: Conference Paper

References (34)

1. Bareth, G., Bolten, A. And Bendig, J., 2011. Potentials of lowcost mini-UAVs. In: Lenz-Wiedemann, V. And Bareth, G. (eds). Proc. of the Workshop on Remote Sensing Methods for Change Detection and Process Modelling, Cologne, Germany. Kölner Geographische Arbeiten, 192, pp. 1-8.
2. Bendig, J., Bolten, A. And Bareth, G., 2013 (accepted). UAVbased imaging for multi-temporal, very high resolution crop surface models (CSMs) to monitor crop growth variability. Photogrammetrie, Fernerkundung, Geoinformation, 12p.
3. Casanova, D., Epema, G.F. And Goudriaan, J., 1998. Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crops Research, 55, pp. 83-92.
4. Confalonieri, R., Bregaglio, S., Rosenmund, A.S., Acutis, M. And Savin, I., 2011. A model for simulating the height of rice plants. European Journal of Agronomy, 34, pp. 20-25.
5. Domrös, M. And Gongbing, P., 1988. The Climate of China. Springer Verlag, Berlin.
6. Ehlert, D., Adamek, R. And Horn, H-J., 2009. Laser rangefinder-based measuring of crop biomass under field conditions. Precision Agriculture, 10 (5), pp. 395-408. doi: 10.1007/s11119-009-9114-4.
7. Ehlert, D., Horn, H-J. And Adamek, R., 2008. Measuring crop biomass density by laser triangulation. Computers and Electronics in Agriculture, 61, pp. 117-125. doi: 10.1016/j.compag.2007.09.013.
8. FAO (Food and Agriculture Organization) 2013. http://faostat.fao.org/(Accessed 27 March 2013)
9. Gao, J. And Liu, Y., 2011. Climate warming and land use change in Heilongjiang Province, Northeast China. Applied Geography, 31, pp. 476-482.
10. Gebbers, R., Ehlert, D. And Adamek, R., 2011. Rapid Mapping of the Leaf Area Index in Agricultural Crops. Agronomy Journal, 103 (5), pp. 1532-1541.
11. Gnyp, M.L., Yu, K., Aasen, H., Yao, Y., Huang, S., Miao, Y. And Bareth, G., 2013. Analysis of crop reflectance for estimating biomass in rice canopy at different phenological stages. Photogrammetrie, Fernerkundung, Geoinformation, 4/2013, pp. 267-281.
12. Hoffmeister, D., Bolten, A., Curdt, C., Waldhoff, G. And Bareth, G., 2010. High resolution Crop Surface Models (CSM) and Crop Volume Models (CVM) on field level by terrestrial laserscanning. Proc. SPIE, 7840, 78400E, 6 p. doi: 10.1117/12.872315.
13. Hoffmeister, D., Tilly, N., Bendig, J. Curdt, C. And Bareth, G., 2012. Detektion von Wachstumsvariabilität in vier Zuckerrübensorten durch multi-temporales terrestrisches Laserscanning. In: Clasen, M., Fröhlich, G., Bernhardt, H., Hildebrand, K., Theuvsen, B. (Eds.). Informationstechnologie für eine nachhaltige Landbewirtschaftung, Proc. 32. GILJahrestagung, Freising, 29 Februar-1 März, pp. 135-138.
14. Höfle, B., 2013 (in press). Radiometric Correction of Terrestrial LiDAR Point Cloud Data for Individual Maize Plant Detection. IEEE Geoscience and Remote Sensing Letters, 5 p. doi: 10.1109/LGRS.2013.2247022.
15. Hosoi, F. And 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, pp. 151-158.
16. Hosoi, F. And 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, pp. 11-19.
17. Huang, J., He, F., Cui, K., Buresh, R.J., Xu, B., Gong, W. And Peng, S., 2008. Determination of optimal nitrogen rate for rice varieties using a chlorophyll meter. Field Crops Research, 105 (1-2), pp. 70-80.
18. Keightley, K.E. And Bawden, G.W., 2010. 3D volumetric modeling of grapevine biomass using Tripod LiDAR. Computers and Electronics in Agriculture, 74, pp. 305-312.
19. Koppe, W., Gnyp, M., Hütt, C., Yao, Y., Miao, Y., Chen, X. And Bareth, G., 2013. Rice monitoring with multi-temporal and dual-polarimetric TerraSAR-X data. International Journal of Applied Earth Observation and Geoinformation, 21, pp. 568-576.
20. Kukko, A. Kaartinen, H., Hyyppä, J. And Chen, Y., 2012. Multiplatform Mobile Laser Scanning: Usability and Performance. Sensors, 12, pp. 11712-11733.
21. Lopez-Sanchez, J.M., Ballester-Berman, J.D. And Hainsek, I., 2010. First results of rice monitoring practices in Spain by means of time series of TerraSAR-X dualpol images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4 (2), pp. 412-422.
22. Lumme, J., Karjalainen, M., Kaartinen, H., Kukko, A., Hyyppä, J., Hyyppä, H., Jaakkola, A. And Kleemola, J., 2008. Terrestrial Laser Scanning of agricultural crops. In: The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing, China, Vol. XXXVII, Part B5, pp. 563-566.
23. Mulla, D.J., 2013. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114, pp. 358-371.
24. Ntanos, D.A. And Koutroubas, S.D., 2002. Dry matter and N accumulation and translocation for Indica and Japonica rice under Mediterranean conditions. Field Crops Research, 74, pp. 93-101.
25. Ribbes, F. And Le-Toan, T., 1999. Rice field mapping and monitoring with RADARSAT data. International Journal of Remote Sensing, 20 (4), pp. 745-765.
26. Riegl LMS GmbH, 2010. Datasheet Riegl LMS-Z420i. http://www.riegl.com/uploads/tx-pxpriegldownloads/10-DataS heet-Z420i-03-05-2010.pdf (7. Jun 3013)
27. Ryu, C., Suguri, M. And Umeda, M., 2011. Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing. Field Crops Research, 122, pp. 214-224.
28. Saeys, W., Lenaerts, B., Craessaerts, G. And De Baerdemaeker, J., 2009. Estimation of the crop density of small grains using LiDAR sensors. Biosystems Engineering, 102, pp. 22-30.
29. Stroppiana, D., Boschetti, M., Brivio, P.A. And Bocchi, S., 2009. Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Research, 111, pp. 119-129.
30. Tilly, N., Hoffmeister, D., Liang, H., Cao, Q., Lenz-Wiedemann, V., Miao, Y. And Bareth, G., 2012. Evaluation of terrestrial laser scanning for rice growth monitoring. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia, Vol. XXXIX, Part B7, pp. 351-356.
31. Wang, K., Zhou, H., Wang, B., Jian, Z., Wang, F., Huang, J., Nie, L., Cui, K. And Peng, S., 2013. Quantification of border effect on grain yield measurement of hybrid rice. Field Crops Research, 141, pp. 47-54.
32. Yihui, D. And Chan, J.C.L., 2005. The East Asian summer monsoon: an overview. Meteorology and Atmospheric Physics, 89, pp. 117-142.
33. Yu, K., Li, F., Gnyp, M.L., Miao, Y., Bareth, G. And 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, pp. 102-115.
34. Zhang, L. And Grift, T.E., 2012. A LIDAR-based crop height measurement system for Miscanthus giganteus. Computers and Electronics in Agriculture, 85, pp. 70-76.