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You searched for subject:(spectrale gegevens). Showing records 1 – 2 of 2 total matches.

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1. Polder, G. Spectral imaging for measuring biochemicals in plant material.

Degree: 2004, NARCIS

Spectral imaging for measuring biochemicals in plant material De kwaliteitseisen die consumenten stellen aan fruit en ander plantaardig voedsel, nemen steeds toe. Belangrijke kwaliteitskenmerken zijn; smaak, rijpheid en gezondheidsbevorderende eigenschappen. Deze aspecten worden vooral bepaald door de aan- en afwezigheid van biochemische inhoudsstoffen in het voedsel. Dit proefschrift gaat over de rol van spectrale beeldverwerking voor het visualiseren en kwantificeren van deze inhoudsstoffen in plantaardig materiaal. De promovendus hoopt dat het een bijdrage levert aan de toepassing van spectrale beeldverwerking voor het kwaliteit-sorteren van plantaardige producten. Vergeleken met traditionele machine vision applicaties, op basis van kleuren camera's, of spectroscopische punt metingen zonder ruimtelijke informatie, is spectrale beeldverwerking een nieuwe uitdaging voor real-time sorteer toepassingen. Omdat het opname systeem, veel complexer is, is kalibratie van het systeem ook veel lastiger. Ook de grote hoeveelheid data die het systeem oplevert, vraagt om data reductie methoden en geavanceerde analyse methoden. In dit proefschrift worden verschillende technieken voor kalibratie, data-analyse, datareductie en het meten van de ruimtelijke distributie van inhoudsstoffen beschreven. Advisors/Committee Members: Delft University of Technology, I.T. Young.

Subjects/Keywords: spectraalanalyse; spectrale gegevens; beeldverwerking; afbeelden; plantensamenstelling; gewasanalyse; vruchten; voedselkwaliteit; gegevensanalyse; ruimtelijke verdeling; ruimtelijke variatie; Kwaliteit van levensmiddelen, voedselveiligheid; spectral analysis; spectral data; image processing; imagery; plant composition; plant analysis; fruits; food quality; data analysis; spatial distribution; spatial variation; Food Quality and Safety

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Polder, G. (2004). Spectral imaging for measuring biochemicals in plant material. (Doctoral Dissertation). NARCIS. Retrieved from http://library.wur.nl/WebQuery/wurpubs/333087 ; urn:nbn:nl:ui:32-333087 ; urn:nbn:nl:ui:32-333087 ; http://library.wur.nl/WebQuery/wurpubs/333087

Chicago Manual of Style (16th Edition):

Polder, G. “Spectral imaging for measuring biochemicals in plant material.” 2004. Doctoral Dissertation, NARCIS. Accessed October 20, 2020. http://library.wur.nl/WebQuery/wurpubs/333087 ; urn:nbn:nl:ui:32-333087 ; urn:nbn:nl:ui:32-333087 ; http://library.wur.nl/WebQuery/wurpubs/333087.

MLA Handbook (7th Edition):

Polder, G. “Spectral imaging for measuring biochemicals in plant material.” 2004. Web. 20 Oct 2020.

Vancouver:

Polder G. Spectral imaging for measuring biochemicals in plant material. [Internet] [Doctoral dissertation]. NARCIS; 2004. [cited 2020 Oct 20]. Available from: http://library.wur.nl/WebQuery/wurpubs/333087 ; urn:nbn:nl:ui:32-333087 ; urn:nbn:nl:ui:32-333087 ; http://library.wur.nl/WebQuery/wurpubs/333087.

Council of Science Editors:

Polder G. Spectral imaging for measuring biochemicals in plant material. [Doctoral Dissertation]. NARCIS; 2004. Available from: http://library.wur.nl/WebQuery/wurpubs/333087 ; urn:nbn:nl:ui:32-333087 ; urn:nbn:nl:ui:32-333087 ; http://library.wur.nl/WebQuery/wurpubs/333087

2. Zurita Milla, R. Mapping and monitoring heterogeneous landscapes: spatial, spectral and temporal unmixing of MERIS data.

Degree: 2008, NARCIS

Our environment is continuously undergoing change. This change takes place at several spatial and temporal scales and it is largely driven by anthropogenic activities. In order to protect our environment and to ensure a sustainable use of natural resources, a wide variety of national and international initiatives have been established. In this context, Earth observation sensors can provide a substantial amount of information about the biotic and abiotic conditions of our planet. For instance, high spatial resolution sensors, like Landsat TM, deliver data that can be used to produce maps of canopy properties and of land cover types. However, the use of this kind of sensors is not feasible for obtaining full coverage of large areas. Furthermore, high spatial resolution sensors generally do not provide sufficient temporal resolution for monitoring vegetation development during the year. This is especially true for areas having severe cloud coverage throughout the year. In this respect, coarse spatial resolution sensors, which deliver nearly daily data, have a higher chance of encountering cloud free areas. This facilitates large scale monitoring studies but at the expense of a lower spatial resolution providing images with potentially many mixed pixels. Recent developments in imaging devices resulted into a new kind of sensor that works at a medium spatial resolution while providing high temporal and spectral resolutions. The MEdium Resolution Imaging Spectrometer (MERIS) aboard the European Space Agency’s ENVISAT platform belongs to this category. MERIS measures the solar radiation reflected from the Earth’s surface in 15 narrow spectral bands and it has a revisit time of 2-3 days. This unprecedented spectral and temporal resolution has resulted in several land, water and atmospheric products. In addition, two vegetation indices have been specifically designed to monitor vegetated canopies using this sensor: the MERIS Terrestrial Chlorophyll index (MTCI) and the MERIS Global Vegetation Index (MGVI). However, the spatial resolution provided by this sensor – 300 m in full resolution (FR mode) – is not sufficient to accurately map and monitor heterogeneous and fragmented landscapes. This is why the synergic use of high spatial resolution and MERIS data is investigated in this thesis. More precisely, the objective of this thesis is to develop a multi-sensor and multi-resolution data fusion approach that allows mapping and monitoring of heterogeneous and highly fragmented landscapes using MERIS data. The Netherlands is selected as study area because of its mixed landscapes where patches of arable land, natural vegetation, forests, and water bodies can be found next to each other. Besides this, The Netherlands also suffers from frequent cloud coverage, which severely hampers operational mapping and monitoring with both high spatial and high temporal resolution. Chapter 1 outlines the challenges of mapping and monitoring heterogeneous and fragmented landscapes using data from the current optical Earth observing… Advisors/Committee Members: Wageningen University, Michael Schaepman, Jan Clevers.

Subjects/Keywords: remote sensing; cartografie; monitoring; landschap; gegevensverwerking; spectrale gegevens; variatie in de tijd; satellietbeelden; satellietkarteringen; geodata; Remote sensing en geografische informatiesystemen (algemeen); remote sensing; mapping; monitoring; landscape; data processing; spectral data; temporal variation; satellite imagery; satellite surveys; geodata; Remote Sensing and Geographical Information Systems (General)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zurita Milla, R. (2008). Mapping and monitoring heterogeneous landscapes: spatial, spectral and temporal unmixing of MERIS data. (Doctoral Dissertation). NARCIS. Retrieved from http://library.wur.nl/WebQuery/wurpubs/368567 ; urn:nbn:nl:ui:32-368567 ; urn:nbn:nl:ui:32-368567 ; http://library.wur.nl/WebQuery/wurpubs/368567

Chicago Manual of Style (16th Edition):

Zurita Milla, R. “Mapping and monitoring heterogeneous landscapes: spatial, spectral and temporal unmixing of MERIS data.” 2008. Doctoral Dissertation, NARCIS. Accessed October 20, 2020. http://library.wur.nl/WebQuery/wurpubs/368567 ; urn:nbn:nl:ui:32-368567 ; urn:nbn:nl:ui:32-368567 ; http://library.wur.nl/WebQuery/wurpubs/368567.

MLA Handbook (7th Edition):

Zurita Milla, R. “Mapping and monitoring heterogeneous landscapes: spatial, spectral and temporal unmixing of MERIS data.” 2008. Web. 20 Oct 2020.

Vancouver:

Zurita Milla R. Mapping and monitoring heterogeneous landscapes: spatial, spectral and temporal unmixing of MERIS data. [Internet] [Doctoral dissertation]. NARCIS; 2008. [cited 2020 Oct 20]. Available from: http://library.wur.nl/WebQuery/wurpubs/368567 ; urn:nbn:nl:ui:32-368567 ; urn:nbn:nl:ui:32-368567 ; http://library.wur.nl/WebQuery/wurpubs/368567.

Council of Science Editors:

Zurita Milla R. Mapping and monitoring heterogeneous landscapes: spatial, spectral and temporal unmixing of MERIS data. [Doctoral Dissertation]. NARCIS; 2008. Available from: http://library.wur.nl/WebQuery/wurpubs/368567 ; urn:nbn:nl:ui:32-368567 ; urn:nbn:nl:ui:32-368567 ; http://library.wur.nl/WebQuery/wurpubs/368567

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