Bendig, Juliane Viktoria (2015). Unmanned aerial vehicles (UAVs) for multi-temporal crop surface modelling. A new method for plant height and biomass estimation based on RGB-imaging. PhD thesis, Universität zu Köln.
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Bendig_PhD_2014_Ort&Datum_final_noCV.pdf - Accepted Version Bereitstellung unter der CC-Lizenz: Creative Commons Attribution No Derivatives. Download (8MB) |
Abstract
Data collection with unmanned aerial vehicles (UAVs) fills a gap on the observational scale in re-mote sensing by delivering high spatial and temporal resolution data that is required in crop growth monitoring. The latter is part of precision agriculture that facilitates detection and quan-tification of within-field variability to support agricultural management decisions such as effective fertilizer application. Biophysical parameters such as plant height and biomass are monitored to describe crop growth and serve as an indicator for the final crop yield. Multi-temporal crop surface models (CSMs) provide spatial information on plant height and plant growth. This study aims to examine whether (1) UAV-based CSMs are suitable for plant height modelling, (2) the derived plant height can be used for biomass estimation, and (3) the combination of plant height and vegetation indices has an added value for biomass estimation. To achieve these objectives, UAV-flight campaigns were carried out with a red-green-blue (RGB) camera over controlled field experiments on three study sites, two for summer barley in Western Germany and one for rice in Northeast China. High-resolution, multi-temporal CSMs were derived from the images by using computer vision software following the structure from motion (SfM) approach. The results show that plant height and plant growth can be accurately modelled with UAV-based CSMs from RGB imaging. To maximise the CSMs’ quality, accurate flight planning and well-considered data collection is necessary. Furthermore, biomass is successfully estimated from the derived plant height, with the restriction that results are based on a single-year dataset and thus require further validation. Nevertheless, plant height shows robust estimates in comparison with various vegetation indices. As for biomass estimation in early growth stages additional po-tential is found in exploiting visible band vegetation indices from UAV-based red-green-blue (RGB) imaging. However, the results are limited due to the use of uncalibrated images. Combining visible band vegetation indices and plant height does not significantly improve the performance of the biomass models. This study demonstrates that UAV-based RGB imaging delivers valuable data for productive crop monitoring. The demonstrated results for plant height and biomass estimation open new possi-bilities in precision agriculture by capturing in-field variability.
Item Type: | Thesis (PhD thesis) | ||||||||
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URN: | urn:nbn:de:hbz:38-60185 | ||||||||
Date: | 2015 | ||||||||
Language: | English | ||||||||
Faculty: | Faculty of Mathematics and Natural Sciences | ||||||||
Divisions: | Faculty of Mathematics and Natural Sciences > Department of Geosciences > Geographisches Institut | ||||||||
Subjects: | Geography and travel | ||||||||
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Date of oral exam: | 12 January 2015 | ||||||||
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Refereed: | Yes | ||||||||
URI: | http://kups.ub.uni-koeln.de/id/eprint/6018 |
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