자료유형  

 학위논문

Control Number  

0016932747

International Standard Book Number  

9798379843199

Dewey Decimal Classification Number  

612

Main Entry-Personal Name  

Zhu, Yan.

Publication, Distribution, etc. (Imprint  

[S.l.] : Purdue University., 2022

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2022

Physical Description  

1 online resource(132 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-01, Section: A.

General Note  

Advisor: Cherkauer, Keith A.

Dissertation Note  

Thesis (Ph.D.)--Purdue University, 2022.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Thermal and multispectral imagery can provide users with insights into the water stress status and evapotranspiration demand of crops. However, traditional platforms, such as satellites, for these thermal and multispectral sensors are limited in their usefulness due to low spatial and temporal resolution. Small unmanned aircraft system (UAS) have the potential to have similar sensors installed and provide canopy temperature and reflectance information at spatial and temporal resolutions more useful for crop management; however, most of the existing research on the calibration or the estimation of water status were established based on the satellite platforms either for the sensors calibration or water status quantification. There is, therefore, a need to develop methods specifically for UAS-mounted sensors. In this research, a pixel-based calibration and an atmospheric correction method based on in-field approximate blackbody sources were developed for an uncooled thermal camera, and the higher accurate vegetative temperature acquired after calibration was used as inputs to an algorithm developed for high-resolution thermal imagery for calculating crop latent heat flux. At last, a thermal index based on the Bowen ratio is proposed to quantify the water deficit stress in a crop field, along with this, a method for plot-level analysis of various vegetation and thermal indices have been demonstrated to illustrate its broad application to genetic selection. The objective was to develop a workflow to use high-resolution thermal and multispectral imagery to derive indices that can quantify crops water status on a plot level which will facilitate the research related to breeding selection.The camera calibration method can effectively reduce the root mean square error (RMSE) and variability of measurements. The pixel-based thermal calibration method presented here was able to reduce the measurement uncertainty across all the pixels in the images, thus improving the accuracy and reducing the between-pixel variability of the measurements. During field calibration, the RMSE values relative to ground reference targets for two flights in 2017 were reduced from 6.36°C to 1.24°C and from 4.56°C to 1.32°C, respectively. The latent heat flux estimation algorithm yields an RMSE of 65.23 W/m2compared with the ground reference data acquired from porometer. The Bowen ratio has a high correlation with drought conditions quantified using the soil moisture index, stomatal conductance, and crop water stress index (CWSI), which indicates the potential of this index to be used as a water deficit stress indicator. The thermal and multispectral indices on a plot level displayed will facilitate the breeding selection.

Subject Added Entry-Topical Term  

Physiology.

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Humidity.

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Weather.

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Crops.

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Unmanned aerial vehicles.

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Energy.

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Registration.

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Performance evaluation.

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Radiation.

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Aircraft.

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Agriculture.

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Vegetation.

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Cameras.

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Sensors.

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Satellites.

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Chlorophyll.

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Aerospace engineering.

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Atmospheric sciences.

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Meteorology.

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Robotics.

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Transportation.

Added Entry-Corporate Name  

Purdue University.

Host Item Entry  

Dissertations Abstracts International. 85-01A.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:643554