Using high-resolution drone data to assess apparent agricultural field heterogeneity at different spatial resolutions
| dc.contributor.author | Merz, Quirina Noëmi | |
| dc.contributor.author | Walter, Achim | |
| dc.contributor.author | Aasen, Helge | |
| dc.contributor.editor | Gandorfer, Markus | |
| dc.contributor.editor | Hoffmann, Christa | |
| dc.contributor.editor | El Benni, Nadja | |
| dc.contributor.editor | Cockburn, Marianne | |
| dc.contributor.editor | Anken, Thomas | |
| dc.contributor.editor | Floto, Helga | |
| dc.date.accessioned | 2022-02-24T13:34:45Z | |
| dc.date.available | 2022-02-24T13:34:45Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Fertilizer distribution can be improved by the use of variable rate technology with which plants only receive the amount of fertilizer they actually need. This amount is often calculated with vegetations indexes, such as the NDVI. The NDVI can be derived from drones or satellites. Drones offer more high-resolution imagery than satellites, but satellite data is more readily available. This study focuses on the spatio-temporal difference of apparent field heterogeneity at different spatial resolutions, resampled to 0.5 m and 20 m from high-resolution drone data, throughout the vegetation period and the error induced by low-resolution image data. | en |
| dc.identifier.isbn | 978-3-88579-711-1 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/38396 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | 42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-317 | |
| dc.subject | spectral imaging | |
| dc.subject | drone-based systems | |
| dc.subject | low-altitude remote sensing | |
| dc.subject | field heterogeneity | |
| dc.subject | variable rate technology | |
| dc.subject | return of investment | |
| dc.title | Using high-resolution drone data to assess apparent agricultural field heterogeneity at different spatial resolutions | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 200 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 195 | |
| gi.conference.date | 21.-22. Februar 2022 | |
| gi.conference.location | Tänikon, Online |
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