Vancouver Island, British Columbia, is at the northern extent of natural climate zones conducive for grape growing, making vineyards susceptible to any changing weather patterns and temperature extremes. Grapevine monitoring is an important aspect of the viticulture industry, and remote sensing technologies are a powerful aid in reporting vegetation information for better vineyard management practices. However, the understanding of vine spectral responses as viewed by optical sensors has to be developed further, and was undertaken in this study. Chlorophyll pigments drive photosynthesis, a biochemical process in plants, which contributes to physiological performance and productivity, making it an appropriate leaf characteristic for detailed examination. This study aimed to develop a thorough understanding of the relationship between (i) leaf-level spectral reflectance and transmittance properties and (ii) pigment concentrations, via ground-based sampling. This was achieved through the examination of two ground campaign tools, as well as current spectral data processing techniques and workflow methods. A spectrometer and SPAD chlorophyll meter collected nondestructive measurements during leaf senescence and grape harvest, and wet chemical extraction methods determined chlorophyll content (expressed in terms of unit leaf area and leaf fresh weight). Reflectance indices,first order derivative indices, and a continuum removal approach were used to generate eighteen reflectance-based attributes. This study performed a series of chlorophyll estimation models through iterative ordinary least square regression, followed by two methods of model validation. Performance metrics indicated strong models with high explanatory power; the continuum removed depth normalized total area metric was presented as the optimal nondestructive attribute for accurate chlorophyll estimation for leaf level field campaigns (R2 = 0.93). Chlorophyll expressed in units of fresh weight yielded more consistent models than in units of leaf area. The chlorophyll meter also presented compelling results (R2 ≥ 0.78), and both sensors were determined to be appropriate for field validation campaigns for this vineyard study. / Graduate

Hyperspectral (HS) imaging combines spectral measurement of a pixel with 2D imaging technology. It is capable to provide a series of images containing both spectral and spatial information, and has been widely used in medical domain. However, most researches on medical HS imaging are regarding ex-vivo biopsy or skin and oral mucosa. The study on HS imaging regarding in-vivo disease lags far behind. In this thesis, we developed a novel flexible HS endoscope system. It is capable to obtain a series of HS images in vivo in a non-contact way among the wavelength range of 405 – 665 nm. After a lot of time-consuming modifying and debugging work, this new system has high stability and convenience to be applied in clinic now. We evaluated this system in clinic. First, we got ethics approval for clinical trials. Then, we obtained HS images regarding gastrointestinal (GI) diseases inside patients using this system. As far as we know, this type of in-vivo image data has not been reported in previous literatures. Thus using these HS images, we built a database for GI mucosa. Next, we analyzed some typical HS images tentatively. The method of Recursive Divergence is implemented to extract valuable and diagnostic information from these HS images. The results prove the effect and applicability of this new HS endoscope system, which has shown the great potential to be used as a platform and guidance for further medical studies. To further apply the analysis results in clinic, we propose a novel Adaptive Narrow-Band Imaging (ANBI) method based on band selection of HS images of a specific type of disease. It is expected that the new technique has higher accuracy, sensitivity, and specificity compared to conventional Narrow-Band Imaging (NBI) technique. In this thesis, we also discuss the future direction of the system improvement. Especially, to improve light intensity and signal-noise-ratio of HS images in wide-field view, we propose a new imaging method using broad- and overlapped-band filters. Although this method only performs greatly on the foundation of accurate image registration, we hope to apply it in our system in the future.

The ability to resolve multiple fluorescent emissions from different biological targets in video rate applications, such as endoscopy and intraoperative imaging, has traditionally been limited by the use of filter-based imaging systems. Hyper and multispectral imaging facilitate the detection of both spatial and spectral information in a single data acquisition, however, instrumentation for spatiospectral data acquisition is typically complex, bulky and expensive. This thesis seeks to overcome these limitations by using recently commercialised compact and robust hyper/multispectral cameras based on spectrally resolved detector arrays. Following sensor calibrations, which devoted particular attention to the angular sensitivity of the sensors, we integrated spectrally resolved detector arrays into a wide-field and an endoscopic imaging platform. This allowed multiplexed reflectance and fluorescence imaging with spectrally resolved detector array technology in vitro, in tissue mimicking phantoms, in an ex vivo oesophageal model and in vivo in a mouse model. A hyperspectral linescan sensor was first integrated in a wide-field near-infrared reflectance based imaging set-up to assess the suitability of spectrally resolved detector arrays for in vivo imaging of exogenous fluorescent contrast agents. Using this fluorescence hyperspectral imaging system, we could accurately resolve the presence and concentration of seven fluorescent dyes in solution. We also demonstrated high spectral unmixing precision, signal linearity with dye concentration, at depth in tissue mimicking phantoms, and delineation of four fluorescent dyes in vivo. After the successful demonstration of multiplexed fluorescence imaging in a wide-field set-up, we proceeded to combine near-infrared multiplexed fluorescence imaging with visible light spectral reflectance imaging in an endoscopic set-up. A multispectral endoscopic imaging system, capable of simultaneous reflectance and fluorescence imaging, was developed around two snapshot spectrally resolved detector arrays. In the process of system integration and characterisation, methods to characterise and predict the imaging performance of spectral endoscopes were developed. With the endoscope we demonstrated simultaneous imaging and spectral unmixing of chemically oxy/deoxygenated blood and three fluorescent dyes in a tissue mimicking phantom, and of two fluorescent dyes in an ex vivo oesophageal porcine model. With further developments, this technology has the potential to become applicable in medical imaging for detection of diseases such as gastrointestinal cancers.

Detection of targets in hyperspectral images is a specific case of one-class classification. It is particularly relevant in the area of remote sensing and has received considerable interest in the past few years. The thesis proposes the use of wavelet functions as kernels with Support Vector Data Description for target detection in hyperspectral images. Specifically, it proposes the Adaptive Wavelet Kernel Support Vector Data Description (AWK-SVDD) that learns the optimal wavelet function to be used given the target signature. The performance and computational requirements of AWK-SVDD is compared with that of existing methods and other wavelet functions. An introduction to target detection and target detection in the context of hyperspectral images is given. This thesis also includes an overview of the thesis and lists the contributions of the thesis. A brief mathematical background into one-class classification in reference to target detection is included. Also described are the existing methods and introduces essential concepts relevant to the proposed approach. The use of wavelet functions as kernels with Support Vector Data Description, the conditions for use of wavelet functions and the use of two functions in order to form the kernel are checked and analyzed. The proposed approach, AWKSVDD, is mathematically described. The details of the implementation and the results when applied to the Urban dataset of hyperspectral images with a random target signature are given. The results confirm the better performance of AWK-SVDD compared to conventional kernels, wavelet kernels and the two-function Morlet-Radial Basis Function kernel. The problems faced with convergence during the Support Vector Data Description optimization are discussed. The thesis concludes with the suggestions for future work.

Removing the effects of the atmosphere from remote sensing data requires accurate knowledge of the physical properties of the atmosphere during the time of measurement. There is a nonlinear relationship that maps atmospheric composition to emitted spectra, but it cannot be easily inverted. The time evolution of atmospheric composition is approximately Markovian, and can be estimated using hyperspectral measurements of the atmosphere with particle filters. The difficulties associated with particle filtering high-dimension data can be mitigated by incorporating future measurement data with the proposal density.

Global modeling of gross primary production (GPP) is a critical component of climate change research. On local scales, GPP can be assessed from measuring CO₂ exchange above the plant canopy using tower-based eddy covariance (EC) systems. The limited footprint inherent to this method however, restricts observations to relatively few discrete areas making continuous predictions of global CO₂ fluxes difficult. Recently, the advent of high resolution optical remote sensing devices has offered new possibilities to address some of the scaling issues related to GPP using remote sensing. One key component for inferring GPP spectrally is the efficiency (ε) with which plants can use absorbed photosynthetically active radiation to produce biomass. While recent years have seen progress in measuring ε using the photochemical reflectance index (PRI), little is known about the temporal and spatial requirements for up-scaling these findings continuously throughout the landscape. Satellite observations of canopy reflectance are subject to view and illumination effects induced by the bi-directional reflectance distribution function(BRDF) which can confound the desired PRI signal. Further uncertainties include dependencies of PRI on canopy structure, understorey, species composition and leaf pigment concentration. The objective of this research was to investigate the effects of these factors on PRI to facilitate the modeling of GPP in a continuous fashion. Canopy spectra were sampled over a one-year period using an automated tower-based, multi-angular spectroradiometer platform (AMSPEC), designed to sample high spectral resolution data. The wide range of illumination and viewing geometries seen by the instrument permitted comprehensive modeling of the BRDF. Isolation of physiologically induced changes in PRI yielded a high correlation (r²=0.82, p<0.05) to EC-measured ε, thereby demonstrating the capability of PRI to model ε throughout the year. The results were extrapolated to the landscape scale using airborne laser-scanning (light detection and ranging, LiDAR) and high correlations were found between remotely-sensed and EC-measured GPP (r²>0.79, p<0.05). Permanently established tower-based canopy reflectance measurements are helpful for ongoing research aimed at up-scaling ε to landscape and global scales and facilitate a better understanding of physiological cycles of vegetation and serve as a calibration tool for broader band satellite observations.

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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV / The AFSSI-C is a spectral imager that generates spectral classification directly, in fewer measurements than are required by traditional systems that measure the spectral datacube (which is later interpreted to make material classification). By utilizing adaptive features to constantly update conditional probabilities for the different hypotheses, the AFSSI-C avoids the overhead of directly measuring every element in the spectral datacube. The system architecture, feature design methodology, simulation results, and preliminary experimental results are given.

Hyper-spectral (HS) sensors are the instruments of choice for remote sensing applications involving environmental monitoring, littoral survey, and military assessment. Accurate band-to-band sensor radiometric calibration is critical for successful data mining of such HS spectral sets. Current calibration is often performed with methods not necessarily developed for HS applications. This work describes two advances which facilitate laboratory source calibrations. First, an analytical solution to the attenuation of flux within an integrating sphere, the best laboratory source of non-directional radiance for numerous radiometric applications, is given. Relative component attenuations due to integrating sphere coating, exit port escape, and atmospheric absorption are derived employing a geometrical PDF of summed probabilities. Equations providing the attenuation ratios and mean number of reflections for the three outcomes are obtained, yielding the three partial mean pathlengths and variances of all quantities. This work then describes an approach allowing accurate radiometric calibration of HS sensor bands using well-characterized and stable multi-spectral transfer radiometers. The resulting high-quality calibration enables the best representation of the truth spectral signature of the imaged scene. In order to obtain the best calibration with the least instrument complexity and expense, it is critical that the radiometer samples the source with the fewest samples at those optimal wavelengths which predict that source with the highest accuracy. The optimal source-specific bands are determined efficiently by application of the Direct Search methodology described here. Using the minimal selection of multi-spectral radiometer measurements obtained from the optimized transfer radiometer bands, one can obtain a complete and accurate calibration set for the continuum of calibration coefficients required for a robust HS application. Degradation of the prediction is documented for several typical error sources encountered with calibration, thereby defining limitations on the usefulness of the optimization approach.

Global modeling of gross primary production (GPP) is a critical component of climate change research. On local scales, GPP can be assessed from measuring CO₂ exchange above the plant canopy using tower-based eddy covariance (EC) systems. The limited footprint inherent to this method however, restricts observations to relatively few discrete areas making continuous predictions of global CO₂ fluxes difficult. Recently, the advent of high resolution optical remote sensing devices has offered new possibilities to address some of the scaling issues related to GPP using remote sensing. One key component for inferring GPP spectrally is the efficiency (ε) with which plants can use absorbed photosynthetically active radiation to produce biomass. While recent years have seen progress in measuring ε using the photochemical reflectance index (PRI), little is known about the temporal and spatial requirements for up-scaling these findings continuously throughout the landscape. Satellite observations of canopy reflectance are subject to view and illumination effects induced by the bi-directional reflectance distribution function(BRDF) which can confound the desired PRI signal. Further uncertainties include dependencies of PRI on canopy structure, understorey, species composition and leaf pigment concentration. The objective of this research was to investigate the effects of these factors on PRI to facilitate the modeling of GPP in a continuous fashion. Canopy spectra were sampled over a one-year period using an automated tower-based, multi-angular spectroradiometer platform (AMSPEC), designed to sample high spectral resolution data. The wide range of illumination and viewing geometries seen by the instrument permitted comprehensive modeling of the BRDF. Isolation of physiologically induced changes in PRI yielded a high correlation (r²=0.82, p<0.05) to EC-measured ε, thereby demonstrating the capability of PRI to model ε throughout the year. The results were extrapolated to the landscape scale using airborne laser-scanning (light detection and ranging, LiDAR) and high correlations were found between remotely-sensed and EC-measured GPP (r²>0.79, p<0.05). Permanently established tower-based canopy reflectance measurements are helpful for ongoing research aimed at up-scaling ε to landscape and global scales and facilitate a better understanding of physiological cycles of vegetation and serve as a calibration tool for broader band satellite observations.