Fusion of high resolution remote sensing images and terrestrial laser scanning for improved biomass estimation of maize

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Volumn 40, Issue 7, 2014, Pages 101-108

  Hutt C ^a   Schiedung, H ^b   Tilly, N ^a   Bareth, G ^a  

^a UNIVERSITY OF COLOGNE   (Germany)

^b UNIVERSITY OF BONN   (Germany)

Author keywords

Agriculture; Change Detection; Crop; Fusion; High resolution; Monitori; Multispectral; TLS

Indexed keywords

BIOMASS; IMAGE ENHANCEMENT; IMAGE FUSION; LASER APPLICATIONS; REMOTE SENSING; SCANNING; STEEL BEAMS AND GIRDERS; SURFACE ANALYSIS; SURVEYING INSTRUMENTS;

BIOMASS ESTIMATION; CHANGE DETECTION; CROP HEIGHT; HIGH RESOLUTION; HIGH-RESOLUTION REMOTE SENSING IMAGES; MONITORUS; MULTI-SPECTRAL; SURFACE MODELING; TERRESTRIAL LASER SCANNING; WORLDVIEW-2;

CROPS;

EID: 84924256454     PISSN: 16821750     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5194/isprsarchives-XL-7-101-2014     Document Type: Conference Paper

References (21)

1. Claverie , M., Demarez, V., Duchemin, B., Hagolle, O., Ducrot, D., Marais-Sicre, C., Dejoux, J.-F., Huc, M., Keravec, P., Béziat, P., Fieuzal, R., Ceschia, E. and Dedieu, G., 2012. Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data. Remote Sensing of Environment, 124, pp. 844-857. doi: 10.1016/j.rse.2012.04.005
2. Colombo R., Bellingeri, D., Fasolini, D., Marino, C.M. 2003. Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sensing of Environment, 86, pp.120-131, doi: 10.1016/S0034-4257(03)00094-4
3. Davidson, E. A., Keller, M.; Erickson, H. E., Verchot, L. V. and Veldkamp, E. 2000. Testing a conceptual model of soil emissions of nitrous and nitric oxides. BioScience, 50, pp. 667-680. doi: 10.1641/0006-3568(2000)050[0667:TACMOS]2.0.CO;2
4. Gates, D. M., Keegan, H. J., Schleter, J. C., Weidner, V. R. 1965. Spectral properties of plants. Applied optics, 4(1), pp. 11-20.
5. Gebbers, Robin, and Viacheslav I. Adamchuk., 2010. Precision agriculture and food security. Science, 327, pp 828-831.
6. Geobasis NRW 2014. Digitale Orthophotos. http://www.bezregkoeln.nrw.de/brk-internet/geobasis/luftbilderzeugnisse/digitale-orthophotos/index.html
7. 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.
8. Hoffmeister, D., Waldhoff, G., Curdt, C., Tilly, N., Bendig, J. and Bareth, G., 2013. Spatial variability detection of crop height in a single field by terrestrial laser scanning. - In: Stafford, J.V. (Ed.), Precision agriculture '13, Proc. of the 9th European Conference on Precision Agriculture (JUL 07-11, 2013), Lleida, Spain. Wageningen Academic Publishers, pp. 267-274.
9. Höfle, B., 2014. 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.
10. Moulin, S., Bondeau, A., Delecolle, R. 1998. Combining agricultural crop models and satellite observations: From field to regional scales. International Journal of Remote Sensing 19, pp. 1021-1036, doi: 10.1080/014311698215586
11. 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.
12. Mutanga, O., Adama, E., Cho, M.A., 2012. High density biomass estimation for wetland vegetation using WorldView-2. imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation, 18, pp. 399-406
13. Lemaire, G., Jeuffroy, M.H., Gastal F., 2008. Diagnosis tool for plant and crop N status in vegetative stage theory and practices for crop N management. European Journal of Agronomy, 28, pp. 614-624
14. Oliver, M., 2013. An overview of precision agriculture. In M. Oliver, T. Bishop, & B. Marchant (Eds.), Precision Agriculture for Sustainability and Environmental Protection. USA.
15. Riegl LMS GmbH, 2010. Datasheet Riegl LMS-Z420i. http://www.riegl.com/uploads/tx-pxpriegldownloads/10-DataS heet-Z420i-03-05-2010.pdf (30. Jul. 2014).
16. R Development Core Team, 2014. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
17. Tilly, N., Hoffmeister, D., Aasen, H., Brands, J. and Bareth, G., 2014a. Multi-temporal Crop Surface Models derived from terrestrial laser scanning for accurate plant height measurement and biomass estimation of barley. Kölner Geographische Arbeiten, 94, 83-91. doi:10.5880/TR32DB.KGA94.12
18. Tilly, N., Hoffmeister, D., Schiedung, H., Hütt, C., Brands, J., Bareth, G. 2014. Terrestrial laser scanning for plant height measurement and biomass estimation of maize. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7, 29 September - 2 October 2014, Istanbul, Turkey - in review.
19. Tilman, D., Balzer, C., Hill, J., Belfort, B. L. 2011. Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences of the United States of America, 108. doi: 10.1073/pnas.1116437108
20. Topcon Positioning Systems, Inc. 2006. HiPer Pro Operator's Manual. http://www.top-survey.com/topsurvey/downloads/HiPerPro-om.pdf (30. Jul. 2014)
21. Walton, G., 1951. Survey of literature relating to infant methemoglobinemia due to nitrate-contaminated water. American Journal of Public Health and the Nations Health 41, pp. 986-996.