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The RE band of electromagnetic radiation has recently become the subject of interest in remote sensing due to its greater penetration into the plant structure than the commonly used NIR band. It is particularly important in cultivating corn, which is characterised by considerable thick foliage during the growth period. While sensors equipped with this channel are used in satellite remote sensing and onboard drones, they are not implemented in airborne imaging systems. An airborne remote sensing station was constructed, including, in addition to the traditional R, G, B and NIR image components, also the RE channel and a laser scanner (ALS). Data processing involves geometric calibration and the creation of a multi-channel orthophoto map. The data processed in this way was tested by analysing several series of aerial recordings of a corn field, which involved developing interpretation keys based on selected vegetation indices and assigning individual groups of pixels with five plant health classes. This study focused on the comparative assessment of the effects of using the NDVI, GNDVI, NDRE and SAVI indices, comparing their results to yield measurements (CHM) and the results of field measurements of plants at the end of the growing season. Promising results with a high degree of correlation were obtained.
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1--17
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Bibliogr. 55 poz., rys., tab.
Twórcy
autor
- GISPRO SA, Szczecin, Poland
autor
- GISPRO SA, Szczecin, Poland
autor
- GISPRO SA, Szczecin, Poland
- Faculty of Civil and Environmental Engineering and Architecture Bydgoszcz University of Science and Technology, Poland
- GISPRO SA, Szczecin, Poland
Bibliografia
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- Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., Villa, P., Stroppiana, D., Boschetti, M., Goulart, L.R. (2015). Advanced methods of plant disease detection. A Review Agronomy for Sustainable Development, 35(1), 1-25. https://doi.org/10.1007/s13593-014-0246-1
- Misra, G., Cawkwell, F., Wingler, A. (2020). Status of Phenological Research Using Sentinel-2 Data: A Review. Remote Sensing, 12(17), 2760. https://doi.org/10.3390/rs12172760
- Mogili, UM R., Deepak, B.B.V.L. (2018). Review on Application of Drone Systems in Precision Agriculture. Procedia Computer Science, 133, 502-509. https://doi.org/10.1016/j.procs.2018.07.063
- Mulla, D.J. (2013). Twenty-five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng., 114, 358-371. https://doi.org/10.1016/j.biosystemseng.2012.08.009
- Padwick, C., Deskevich, M., Pacifici, F., Smallwood, S. (2010). WorldView-2 pan-sharpening. In Proceedings of the ASPRS 2010 Annual Conference, San Diego, CA, USA, April 27, 2010, 1-14.
- Prashar, A., Jones, H.G. (2016). Assessing drought responses using thermal infrared imaging. In: Environmental Responses in Plants, Humana Press: New York, NY, USA. 209-219. https://doi.org/10.1007/978-1-4939-3356-3_17
- Radočaj, D., Jurišić, M., Gašparović, M. (2022). The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilisation in Precision Agriculture. Remote Sensing, 14, 778. https://doi.org/10.3390/rs14030778
- Rajawat, M., Gautam, S. (2021). Weather conditions and its effects on UAS. Int. Research Journal of Modernization in Engineering Technology and Science, 12, 255-261. Retrieved from: https://www.irjmets.com/uploadedfiles/paper/volume_3/issue_12 (1.11.2023)
- Rose, D.C., and Chilvers, J. (2018). Agriculture 4.0: Broadening Responsible Innovation in an Era of Smart Farming. Front. Sustain. Food Syst., 2, 87. https://doi.org/10.3389/fsufs.2018.00087
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- Rowlands, A., Sarris A. (2007). Detection of exposed and subsurface archaeological remains using multi-sensor remote sensing. Journal of Archaeological Science, 34(5), 795-803. https://doi.org/10.1016/j.jas.2006.06.018
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- Sieczkiewicz, M., Jedynak, Ł, Wyczałek, I., Strzeliński, P., Wyczałek-Jagiełło, M. Wielosensorowy lotniczy system teledetekcyjny MultiSen-1PL na potrzeby precyzyjnego rolnictwa i leśnictwa. (2024). Multi-sensor airborne remote sensing system MultiSen-1PL for precision agriculture and forestry. Przegląd Geodezyjny. (in Polish) <in the review>
- Spadoni, G.L., Cavalli, A., Congedo, L., Munafò, M. (2020). Analysis of Normalised Difference Vegetation Index (NDVI) multi-temporal series for the production of forest cartography. Remote Sensing Applications: Society and Environment, 20, 100419. https://doi.org/10.1016/j.rsase.2020.100419
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Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.baztech-75e29fdd-316a-488b-9369-48a4f80fe1d0