Aasen, Helge (2016). The acquisition of Hyperspectral Digital Surface Models of crops from UAV snapshot cameras. PhD thesis, Universität zu Köln.

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Abstract

This thesis develops a new approach to capture information about agricultural crops by utilizing advances in the field of robotics, sensor technology, computer vision and photogrammetry: Hyperspectral digital surface models (HS DSMs) generated with UAV snapshot cameras are a representation of a surface in 3D space linked with hyperspectral information emitted and reflected by the objects covered by that surface. The overall research aim of this thesis is to evaluate if HS DSMs are suited for supporting a site-specific crop management. Based on six research studies, three research objectives are discussed for this evaluation. Firstly the influences of environmental effects, the sensing system and data processing of the spectral data within HS DSMs are discussed. Secondly, the comparability of HS DSMs to data from other remote sensing methods is investigated and thirdly their potential to support site-specific crop management is evaluated. Most data within this thesis was acquired at a plant experimental-plot experiment in Klein-Altendorf, Germany, with six different barley varieties and two different fertilizer treatments in the growing seasons of 2013 and 2014. In total, 22 measurement campaigns were carried out in the context of this thesis. HS DSMs acquired with the hyperspectral snapshot cameras Cubert UHD 185-Firefly show great potential for practical applications. The combination of UAVs and the UHD allowed data to be captured at a high spatial, spectral and temporal resolution. The spatial resolution allowed detection of small-scale heterogeneities within the plant population. Additionally, with the spectral and 3D information contained in HS DSMs, plant parameters such as chlorophyll, biomass and plant height could be estimated within individual, and across different growing stages. The techniques developed in this thesis therefore offer a significant contribution towards increasing cropping efficiency through the support of site-specific management.

Item Type: Thesis (PhD thesis)
Creators:
CreatorsEmailORCID
Aasen, Helgehelge.aasen@uni-koeln.deUNSPECIFIED
URN: urn:nbn:de:hbz:38-69444
Identification Number: 10.18716/kups.6944
Subjects: Data processing Computer science
Natural sciences and mathematics
Earth sciences
Life sciences
Technology (Applied sciences)
Agriculture
Geography and travel
Uncontrolled Keywords:
KeywordsLanguage
hyperspectral digital surface model, radiometric calibration, quality assurance information, precision agriculture, barley, specific field of view, unmanned aerial vehicle (UAV), bidirectional reflectance distribution function (BRDF), angular properties, field-spectroscopy, imaging spectroscopy, low-altitude remote sensing, plant height, chlorophyll, vegetation indices, terrestrial laser scanning, structure from motion, 3D, vegetationEnglish
Hyperspektrale Digitale Oberflächenmodelle, radiometrische Kalibierung, Qualitätssicherung, Präzisionslandwirtschaft, Gerste, Spezifisches Blickfeld, Drohnen, bidirektionale Reflektanzverteilungsfunktion, angulare Eigenschaften, Feldspektroskopie, bildgebende Spektroskopie, nah-erdliche Fernerkundung, Pflanzenhöhe, Chlorophyll, Vegetationsindices, Terrestrisches Laserscanning, VegetationGerman
Faculty: Faculty of Mathematics and Natural Sciences
Divisions: Faculty of Mathematics and Natural Sciences > Institute for Geography
Language: English
Date: 10 September 2016
Date of oral exam: 28 June 2016
Referee:
NameAcademic Title
Bareth, GeorgProf. Dr.
Full Text Status: Public
Related URLs:
Date Deposited: 04 Oct 2016 12:33
URI: http://kups.ub.uni-koeln.de/id/eprint/6944

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