A coupled multitemporal UAV-based LiDAR and multispectral data approach to model dry biomass of maize
Autor(en): | Rettig, Robert Storch, Marcel Wittstruck, Lucas Ansah, Christabel Edena Bald, Richard Janis Richard, David Trautz, Dieter Jarmer, Thomas |
Herausgeber: | Stein, A. Hoffmann, C. Ruckelshausen, A. Steckel, T. Helga, F. Muller, H. |
Stichwörter: | 'Dry' [; Aboveground biomass; biomass; Data fusion; Dry biomass; LiDAR; maize; MLR; Multi-spectral; Multi-spectral data; Multi-temporal; Multiple linear regression; multisensorial; multispectral; Optical radar; Unmanned aerial vehicles (UAV) | Erscheinungsdatum: | 2023 | Herausgeber: | Gesellschaft fur Informatik (GI) | Journal: | Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI) | Volumen: | P-330 | Startseite: | 483 – 488 | Zusammenfassung: | The presented approach attempts to highlight the capabilities of a data fusion approach that combines UAV LiDAR (RIEGL – miniVUX-1UAV) and multispectral data (Micasense – Altum) to assess the dry above ground biomass (AGB) for maize. The combined acquisition of both LiDAR and multispectral data not only supports estimates of AGB when fusing them, but also helps to evaluate phenological stage-specific modelling differences on the individual sensor data. A multiple linear regression was applied on the multisensorial UAV data from two appointments in 2021. The resulting R2 of 0.87 and RMSE of 14.35 g/plant for AGB was then transferred to AGB in dt/ha. © 2023 Gesellschaft fur Informatik (GI). All rights reserved. |
Beschreibung: | Cited by: 0; Conference name: 43. Jahrestagung der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft - Resiliente Agri-Food-Systeme: Herausforderungen und Losungsansatze, GIL 2023 - 43rd Annual Conference of the Society for Informatics in Agriculture, Forestry, and Food Industry - Resilient Agri-Food Systems: Challenges and Solutions, GIL 2023; Conference date: 13 February 2023 through 14 February 2023; Conference code: 193633 |
ISBN: | 9783885797241 | ISSN: | 1617-5468 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176298606&partnerID=40&md5=91ba4ff010cca471e4694f5246a02fe5 |
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geprüft am 15.05.2024