Breast cancer is the most crucial cancer type among all other cancer types. There are many imaging techniques used to screen breast carcinoma. These are mammography, ultrasound, computed tomography, magnetic resonance imaging, infrared imaging, positron emission tomography and electrical impedance tomography. However, there is no gold standard in breast carcinoma diagnosis. The object of this study is to create a hybrid system that uses thermal and electrical imaging methods together for breast cancer diagnosis. Body tissues have different electrical conductivity values depending on their state of health and types. Consequently, one can get information about the anatomy of the human body and tissue&rsquo / s health by imaging tissue conductivity distribution. Due to metabolic heat generation values and thermal characteristics that differ from tissue to tissue, thermal imaging has started to play an important role in medical diagnosis. To increase the temperature contrast in thermal images, the characteristics of the two imaging modalities can be combined. This is achieved by implementing thermal imaging applying electrical currents from the body surface within safety limits (i.e., thermal imaging in active mode). Electrical conductivity of tissues changes with frequency, so it is possible to obtain more than one thermal image for the same body. Combining these images, more detailed information about the tumor tissue can be acquired. This may increase the accuracy in diagnosis while tumor can be detected at deeper locations. Feasibility of the proposed technique is investigated with analytical and numerical simulations and experimental studies. 2-D and 3-D numerical models of the female breast are developed and feasibility work is implemented in the frequency range of 10 kHz and 800 MHz. Temporal and spatial temperature distributions are obtained at desired depths. Thermal body-phantoms are developed to simulate the healthy breast and tumor tissues in experimental studies. Thermograms of these phantoms are obtained using two different infrared cameras (microbolometer uncooled and cooled Quantum Well Infrared Photodetectors). Single and dual tumor tissues are determined using the ratio of uniform (healthy) and inhomogeneous (tumor) images. Single tumor (1 cm away from boundary) causes 55 &deg / mC temperature increase and dual tumor (2 cm away from boundary) leads to 50 &deg / mC temperature contrast. With multi-frequency current application (in the range of 10 kHz-800 MHz), the temperature contrast generated by 3.4 mm3 tumor at 9 mm depth can be detected with the state-of-the-art thermal imagers.

Anthropogenic influences and climate change are warming rivers in New Brunswick and threatening the cold water habitats of native salmonids. When ambient river temperatures in summer exceed the tolerance level of Atlantic salmon and brook trout, individuals behaviourally thermoregulate by seeking out cold water refugia. These critical thermal habitats are often created by tributaries and concentrated groundwater discharge. Thermal infrared imagery was used to map cold water anomalies along a 53 km reach of the Cains River on 23 July 2008. Although efficient and useful for mapping surface temperature of a continuous stream reach, the fish did not use all identified thermal anomalies as refugia. Overall, 100 % of observed large brook trout >35 cm in length were found in 30 % of the TIR-mapped cold water anomalies. Ninety eight percent of observed small brook trout 8 – 30 cm in length were found in 80 % of the mapped cold water anomalies and their densities within anomalies were significantly higher than densities outside of anomalies. Fifty nine percent of observed salmon parr were found in 65 % of the mapped anomalies; however, they were dispersed within study sites and their densities were not significantly different within anomalies compared to outside of the anomalies. No brook trout were observed at the seven noncold water study sites that were investigated. Preference curves for various habitat variables including velocity, temperature, depth, substrate, and deep water availability near cold water anomalies were developed based on field investigations during high temperature events (ambient river temperature >21 oC). Combined with thermal imagery, managers can use the physical descriptions of thermal refugia developed here as a tool to help conserve and restore critical thermal refugia for Atlantic salmon and brook trout on the Cains River, and potentially similar river systems.

abstract: Chemical and mineralogical data from Mars shows that the surface has been chemically weathered on local to regional scales. Chemical trends and the types of chemical weathering products present on the surface and their abundances can elucidate information about past aqueous processes. Thermal-infrared (TIR) data and their respective models are essential for interpreting Martian mineralogy and geologic history. However, previous studies have shown that chemical weathering and the precipitation of fine-grained secondary silicates can adversely affect the accuracy of TIR spectral models. Furthermore, spectral libraries used to identify minerals on the Martian surface lack some important weathering products, including poorly-crystalline aluminosilicates like allophane, thus eliminating their identification in TIR spectral models. It is essential to accurately interpret TIR spectral data from chemically weathered surfaces to understand the evolution of aqueous processes on Mars. Laboratory experiments were performed to improve interpretations of TIR data from weathered surfaces. To test the accuracy of deriving chemistry of weathered rocks from TIR spectroscopy, chemistry was derived from TIR models of weathered basalts from Baynton, Australia and compared to actual weathering rind chemistry. To determine how specific secondary silicates affect the TIR spectroscopy of weathered basalts, mixtures of basaltic minerals and small amounts of secondary silicates were modeled. Poorly-crystalline aluminosilicates were synthesized and their TIR spectra were added to spectral libraries. Regional Thermal Emission Spectrometer (TES) data were modeled using libraries containing these poorly-crystalline aluminosilicates to test for their presence on the Mars. Chemistry derived from models of weathered Baynton basalts is not accurate, but broad chemical weathering trends can be interpreted from the data. TIR models of mineral mixtures show that small amounts of crystalline and amorphous silicate weathering products (2.5-5 wt.%) can be detected in TIR models and can adversely affect modeled plagioclase abundances. Poorly-crystalline aluminosilicates are identified in Northern Acidalia, Solis Planum, and Meridiani. Previous studies have suggested that acid sulfate weathering was the dominant surface alteration process for the past 3.5 billion years; however, the identification of allophane indicates that alteration at near-neutral pH occurred on regional scales and that acid sulfate weathering is not the only weathering process on Mars. / Dissertation/Thesis / Ph.D. Geological Sciences 2011

O infravermelho termal (TIR - Thermal InfraRed) é uma porção do espectro eletromagnético com várias aplicações no Sensoriamento Remoto (SR), tais como: geologia, climatologia, análises de processos biológicos, análises geofísicas, avaliação de desastres e detecção de mudanças, entre outras. No TIR a emissão de radiação dos alvos é uma função não linear de duas variáveis, a emissividade e a temperatura do alvo, e a principal dificuldade é calcular/estimar tais variáveis separadamente e de forma confiável. Vários métodos foram desenvolvidos nas últimas décadas para mitigar esta indeterminação, mas independente do método todos tem a mesma deficiência, são desenvolvidos para aplicações específicas como o tipo de sensor, tipo de estudo, o alvo em análise, o número de alvos, tipo de clima, entre outros. Desta forma, o método a ser aplicado depende do estudo em questão, e para obter melhores resultados deve-se escolher o método que melhor se aplica ao problema estudado pelo analista. Neste trabalho, se propõe uma abordagem alternativa para estimar a temperatura e, portanto, a emissividade de um alvo em particular. A abordagem consiste em gerar regressões, em determinados comprimentos de onda, a partir da função linearizada da radiância para dados de laboratório de uma amostra de quartzo em diferentes temperaturas, medida sob condições controladas de humidade e temperatura do ambiente. As regressões visam modelar a variação na temperatura devido as variações na radiância do alvo, de modo a estimar a temperatura a partir da radiância em determinado comprimento de onda e sem o conhecimento prévio da emissividade do alvo. Os dados de laboratório foram divididos em dois grupos, treinamento e controle, no grupo de treinamento várias regressões polinomiais foram aplicados enquanto os dados de controle serviram para validar e avaliar as regressões. Foram realizados 5 experimentos: 1) dados de laboratório em comprimentos de onda específicos, 2) nos comprimentos de ondas centrais das bandas TIR-ASTER, 3) nas simulações das bandas TIR-ASTER, 4) com a simulação da atmosfera (seca e úmida) para as bandas simuladas TIRASTER e 5) numa imagem L1B TIR-ASTER da região de estudo e validado com o produto AST08. Como resultado, foi possível estimar a temperatura com erros menores que 0.2K para os dados de laboratório e com erro médio menor que 1.5K para imagens LIB TIR-ASTER. Além disso, o método requer apenas uma banda espectral na imagem, viabilizando sua aplicação em sensores termais monoespectrais. Resultados satisfatórios foram obtidos com uma regressão linear simples, e melhoram ao aumentar o comprimento de onda. No entanto, aumentando o comprimento de onda e, simultaneamente, o grau do polinômio da regressão os resultados também melhoram com relação a regressão linear, porém não são significativos, e desta forma o ajuste linear é a melhor opção. Desta forma, o método proposto se mostrou promissor, sinalizando que futuras pesquisas são necessárias. / The thermal infrared (TIR) is a portion of the electromagnetic spectrum with multiple remote Sensing applications in the field of geology, climatology, biological processes analysis, geophysical analysis, disaster assessment, change detection and many others. In TIR, radiation emission of the target is a nonlinear function of two unknowns – the emissivity and the temperature, and the main difficulty is to calculate/estimate these two variables separately and reliably. Several methods have been developed in the recent decades to mitigate this problem. However, regardless of the method, all have developed similar incapacities for specific applications such as the type of sensor, study type, the target in question, the number of targets, type of weather, among others. Thus, the method to be applied depends on the study in question and the best results can be reached choosing the best fit method for that problem. In this work, we propose an alternative approach for estimating the temperature, and therefore the emissivity, of a particular target. The approach consists of generating statistical regressions in some wavelengths from linearized radiance function of laboratory data from a quartz sample at different temperatures, measured under controlled conditions of humidity and room temperature. The aim of regressions is to model the variation in temperature due to the variations in the radiance of the target in order to estimate the temperature from radiance data on a certain wavelength and without prior knowledge of the target emissivity. Laboratory datasets were divided into two groups - training and control. In the training group, several polynomial regressions were applied while the control group served to validate and evaluate the regressions. Five experiments were performed: (1) laboratory data at specific wavelengths (2) the central wave lengths of ASTER-TIR bands (3) simulations of ASTER-TIR bands (4) simulation of the atmosphere (dry and wet) for simulated bands of ASTER-TIR and (5) an image L1B ASTER-TIR of the study area validated with the AST08 product. As a result, it was possible to estimate the temperature with errors less than 0.2K from laboratory data and with mean error less than 1.5K from L1B ASTER-TIR images. Furthermore, the method requires only a spectral band in the image, enabling their application in monospectral thermal sensors. Satisfactory results were obtained with a simple linear regression and improved by increasing the wavelength. However, increasing the wavelength and, simultaneously, the degree of polynomial regression the results also improve with respect to linear regression results, but this improvement is insignificant, and thus the linear fit is the best option. Thus, the proposed method has shown promise, signaling that further research is needed.

Master of Science / Department of Biological & Agricultural Engineering / Ajay Sharda / Precise water application conserves resources, reduces costs, and optimizes plant performance and quality. Existing irrigation scheduling utilizes single, localized measurements that do not account for spatial crop water need; but, quick, single-point sensors are impractical for measuring discrete variations across large coverage areas. Thermography is an alternate approach for measuring spatial temperatures to quantify crop health. However, agricultural studies using thermography are limited due to previous camera expense, unfamiliar use and calibration, software for image acquisition and high-throughput processing specifically designed for thermal imagery mapping and monitoring spatial crop water need. Recent advancements in thermal detectors and sensing platforms have allowed uncooled thermal infrared (TIR) cameras to become suited for crop sensing. Therefore, a small, lightweight thermal infrared imaging system (TIRIS) was developed capable of radiometric temperature measurements. One-time (OT) and real-time (RT) radiometric calibrations methods were developed and validated for repeatable, temperature measurements while compensating for strict environmental conditions within a climate chamber. The Tamarisk® 320 and 640 analog output yielded a measurement accuracy of ±0.82°C or 0.62ºC with OT and RT radiometric calibration, respectively. The Tamarisk® 320 digital output yielded a measurement accuracy of ±0.43 or 0.29ºC with OT and RT radiometric calibration, respectively. Similarly, the FLIR® Tau 2 analog output yielded a measurement accuracy of ±0.87 or 0.63ºC with OT and RT radiometric calibration, respectively. A TIRIS was then built for high-throughput image capture, correction, and processing and RT environmental compensation for monitoring crop water stress within a greenhouse and temperature mapping aboard a small unmanned aerial systems (sUAS). The greenhouse TIRIS was evaluated by extracting plant temperatures for monitoring full-season crop water stress index (CWSI) measurements. Canopy temperatures demonstrated that CWSI explained 82% of the soil moisture variation. Similarly, validation aboard a sUAS provided radiometric thermal maps with a ±1.38°C (α=0.05) measurement accuracy. Due to the TIR cameras’ performance aboard sUAS and greenhouse platforms, a TIRIS provides unparalleled spatial coverage and measurement accuracy capable of monitoring subtle crop stress indicators. Further studies need to be conducted to produce spatial crop water stress maps at scales necessary for variable rate irrigation systems.

Submitted by CRISTHY WILLY DA SILVA ROMERO null (cr_willy@hotmail.com) on 2016-10-27T14:34:18Z No. of bitstreams: 1 DISSERTAÇÃO_ROMERO_CWS.pdf: 6833770 bytes, checksum: a5741a19b6228cf9dc2defe0760b12ef (MD5) / Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2016-11-01T16:08:56Z (GMT) No. of bitstreams: 1 romero_cws_me_ilha.pdf: 6833770 bytes, checksum: a5741a19b6228cf9dc2defe0760b12ef (MD5) / Made available in DSpace on 2016-11-01T16:08:56Z (GMT). No. of bitstreams: 1 romero_cws_me_ilha.pdf: 6833770 bytes, checksum: a5741a19b6228cf9dc2defe0760b12ef (MD5) Previous issue date: 2016-09-05 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Diariamente a temperatura vem aumentando a cada dia, esta mudança climática ocorre em sua maior parte em áreas urbanas, ocorrendo o fenômeno denominado Ilhas de Calor Urbana (ICU), onde a temperatura é mais alta no centro urbano do que suas áreas adjacentes, colocando risco à saúde da população local e gerando o desconforto térmico. O fenômeno ilha de calor urbana (ICU) em Ilha Solteira – SP, cidade de pequeno porte, foi estudado por meio da utilização de dados de sensoriamento remoto, com o objetivo de associar a temperatura de superfície a diferentes classes de uso e ocupação do solo, associados a dados climatológicos e a orientação de vertente, com imagens dos anos de 2013, 2014 e 2015. Para a realização deste trabalho foram utilizadas imagens de satélite captadas na região do infravermelho termal (TIRS/ Landsat-8) e Sistemas de Informações Geográficas (SIG’s) para processamento destas imagens para determinar a temperatura de superfície. As classes de uso e ocupação do solo nas áreas de maiores temperaturas, foram classificadas com base em imagem de alta resolução espacial, do satélite Pleiades. As diferentes classes de uso e ocupação do solo influenciaram diretamente na temperatura de superfície, constatando a pavimentação asfáltica e telhado cerâmico como principais influenciadores nas áreas identificadas com ilha de calor urbana. / The urban heat island phenomenon (ICU) in Ilha Solteira - SP, Small Porte City, was studied through the use of remote sensing data, with the goal of linking a surface temperature one different land use classes, and associate climatological data and strand orientation, with images of 2013, 2014 and 2015. For this work were used satellite images captured in the region of thermal infrared (TIRS / Landsat-8) and Geographic Information Systems (GIS 's) paragraph of these image processing for determining the surface temperature. The land use classes and land use in areas largest temperatures were classified based on high spatial resolution image, Pleiades satellite. How Different use classes and occupation do directly influenced the soil on the surface temperature, noting an asphalt paving and ceramic roof as key influencers in areas identified with urban heat island.

Orientador: Marlene Cristina Alves / Resumo: Diariamente a temperatura vem aumentando a cada dia, esta mudança climática ocorre em sua maior parte em áreas urbanas, ocorrendo o fenômeno denominado Ilhas de Calor Urbana (ICU), onde a temperatura é mais alta no centro urbano do que suas áreas adjacentes, colocando risco à saúde da população local e gerando o desconforto térmico. O fenômeno ilha de calor urbana (ICU) em Ilha Solteira – SP, cidade de pequeno porte, foi estudado por meio da utilização de dados de sensoriamento remoto, com o objetivo de associar a temperatura de superfície a diferentes classes de uso e ocupação do solo, associados a dados climatológicos e a orientação de vertente, com imagens dos anos de 2013, 2014 e 2015. Para a realização deste trabalho foram utilizadas imagens de satélite captadas na região do infravermelho termal (TIRS/ Landsat-8) e Sistemas de Informações Geográficas (SIG’s) para processamento destas imagens para determinar a temperatura de superfície. As classes de uso e ocupação do solo nas áreas de maiores temperaturas, foram classificadas com base em imagem de alta resolução espacial, do satélite Pleiades. As diferentes classes de uso e ocupação do solo influenciaram diretamente na temperatura de superfície, constatando a pavimentação asfáltica e telhado cerâmico como principais influenciadores nas áreas identificadas com ilha de calor urbana. / Mestre

O infravermelho termal (TIR - Thermal InfraRed) é uma porção do espectro eletromagnético com várias aplicações no Sensoriamento Remoto (SR), tais como: geologia, climatologia, análises de processos biológicos, análises geofísicas, avaliação de desastres e detecção de mudanças, entre outras. No TIR a emissão de radiação dos alvos é uma função não linear de duas variáveis, a emissividade e a temperatura do alvo, e a principal dificuldade é calcular/estimar tais variáveis separadamente e de forma confiável. Vários métodos foram desenvolvidos nas últimas décadas para mitigar esta indeterminação, mas independente do método todos tem a mesma deficiência, são desenvolvidos para aplicações específicas como o tipo de sensor, tipo de estudo, o alvo em análise, o número de alvos, tipo de clima, entre outros. Desta forma, o método a ser aplicado depende do estudo em questão, e para obter melhores resultados deve-se escolher o método que melhor se aplica ao problema estudado pelo analista. Neste trabalho, se propõe uma abordagem alternativa para estimar a temperatura e, portanto, a emissividade de um alvo em particular. A abordagem consiste em gerar regressões, em determinados comprimentos de onda, a partir da função linearizada da radiância para dados de laboratório de uma amostra de quartzo em diferentes temperaturas, medida sob condições controladas de humidade e temperatura do ambiente. As regressões visam modelar a variação na temperatura devido as variações na radiância do alvo, de modo a estimar a temperatura a partir da radiância em determinado comprimento de onda e sem o conhecimento prévio da emissividade do alvo. Os dados de laboratório foram divididos em dois grupos, treinamento e controle, no grupo de treinamento várias regressões polinomiais foram aplicados enquanto os dados de controle serviram para validar e avaliar as regressões. Foram realizados 5 experimentos: 1) dados de laboratório em comprimentos de onda específicos, 2) nos comprimentos de ondas centrais das bandas TIR-ASTER, 3) nas simulações das bandas TIR-ASTER, 4) com a simulação da atmosfera (seca e úmida) para as bandas simuladas TIRASTER e 5) numa imagem L1B TIR-ASTER da região de estudo e validado com o produto AST08. Como resultado, foi possível estimar a temperatura com erros menores que 0.2K para os dados de laboratório e com erro médio menor que 1.5K para imagens LIB TIR-ASTER. Além disso, o método requer apenas uma banda espectral na imagem, viabilizando sua aplicação em sensores termais monoespectrais. Resultados satisfatórios foram obtidos com uma regressão linear simples, e melhoram ao aumentar o comprimento de onda. No entanto, aumentando o comprimento de onda e, simultaneamente, o grau do polinômio da regressão os resultados também melhoram com relação a regressão linear, porém não são significativos, e desta forma o ajuste linear é a melhor opção. Desta forma, o método proposto se mostrou promissor, sinalizando que futuras pesquisas são necessárias. / The thermal infrared (TIR) is a portion of the electromagnetic spectrum with multiple remote Sensing applications in the field of geology, climatology, biological processes analysis, geophysical analysis, disaster assessment, change detection and many others. In TIR, radiation emission of the target is a nonlinear function of two unknowns – the emissivity and the temperature, and the main difficulty is to calculate/estimate these two variables separately and reliably. Several methods have been developed in the recent decades to mitigate this problem. However, regardless of the method, all have developed similar incapacities for specific applications such as the type of sensor, study type, the target in question, the number of targets, type of weather, among others. Thus, the method to be applied depends on the study in question and the best results can be reached choosing the best fit method for that problem. In this work, we propose an alternative approach for estimating the temperature, and therefore the emissivity, of a particular target. The approach consists of generating statistical regressions in some wavelengths from linearized radiance function of laboratory data from a quartz sample at different temperatures, measured under controlled conditions of humidity and room temperature. The aim of regressions is to model the variation in temperature due to the variations in the radiance of the target in order to estimate the temperature from radiance data on a certain wavelength and without prior knowledge of the target emissivity. Laboratory datasets were divided into two groups - training and control. In the training group, several polynomial regressions were applied while the control group served to validate and evaluate the regressions. Five experiments were performed: (1) laboratory data at specific wavelengths (2) the central wave lengths of ASTER-TIR bands (3) simulations of ASTER-TIR bands (4) simulation of the atmosphere (dry and wet) for simulated bands of ASTER-TIR and (5) an image L1B ASTER-TIR of the study area validated with the AST08 product. As a result, it was possible to estimate the temperature with errors less than 0.2K from laboratory data and with mean error less than 1.5K from L1B ASTER-TIR images. Furthermore, the method requires only a spectral band in the image, enabling their application in monospectral thermal sensors. Satisfactory results were obtained with a simple linear regression and improved by increasing the wavelength. However, increasing the wavelength and, simultaneously, the degree of polynomial regression the results also improve with respect to linear regression results, but this improvement is insignificant, and thus the linear fit is the best option. Thus, the proposed method has shown promise, signaling that further research is needed.

O infravermelho termal (TIR - Thermal InfraRed) é uma porção do espectro eletromagnético com várias aplicações no Sensoriamento Remoto (SR), tais como: geologia, climatologia, análises de processos biológicos, análises geofísicas, avaliação de desastres e detecção de mudanças, entre outras. No TIR a emissão de radiação dos alvos é uma função não linear de duas variáveis, a emissividade e a temperatura do alvo, e a principal dificuldade é calcular/estimar tais variáveis separadamente e de forma confiável. Vários métodos foram desenvolvidos nas últimas décadas para mitigar esta indeterminação, mas independente do método todos tem a mesma deficiência, são desenvolvidos para aplicações específicas como o tipo de sensor, tipo de estudo, o alvo em análise, o número de alvos, tipo de clima, entre outros. Desta forma, o método a ser aplicado depende do estudo em questão, e para obter melhores resultados deve-se escolher o método que melhor se aplica ao problema estudado pelo analista. Neste trabalho, se propõe uma abordagem alternativa para estimar a temperatura e, portanto, a emissividade de um alvo em particular. A abordagem consiste em gerar regressões, em determinados comprimentos de onda, a partir da função linearizada da radiância para dados de laboratório de uma amostra de quartzo em diferentes temperaturas, medida sob condições controladas de humidade e temperatura do ambiente. As regressões visam modelar a variação na temperatura devido as variações na radiância do alvo, de modo a estimar a temperatura a partir da radiância em determinado comprimento de onda e sem o conhecimento prévio da emissividade do alvo. Os dados de laboratório foram divididos em dois grupos, treinamento e controle, no grupo de treinamento várias regressões polinomiais foram aplicados enquanto os dados de controle serviram para validar e avaliar as regressões. Foram realizados 5 experimentos: 1) dados de laboratório em comprimentos de onda específicos, 2) nos comprimentos de ondas centrais das bandas TIR-ASTER, 3) nas simulações das bandas TIR-ASTER, 4) com a simulação da atmosfera (seca e úmida) para as bandas simuladas TIRASTER e 5) numa imagem L1B TIR-ASTER da região de estudo e validado com o produto AST08. Como resultado, foi possível estimar a temperatura com erros menores que 0.2K para os dados de laboratório e com erro médio menor que 1.5K para imagens LIB TIR-ASTER. Além disso, o método requer apenas uma banda espectral na imagem, viabilizando sua aplicação em sensores termais monoespectrais. Resultados satisfatórios foram obtidos com uma regressão linear simples, e melhoram ao aumentar o comprimento de onda. No entanto, aumentando o comprimento de onda e, simultaneamente, o grau do polinômio da regressão os resultados também melhoram com relação a regressão linear, porém não são significativos, e desta forma o ajuste linear é a melhor opção. Desta forma, o método proposto se mostrou promissor, sinalizando que futuras pesquisas são necessárias. / The thermal infrared (TIR) is a portion of the electromagnetic spectrum with multiple remote Sensing applications in the field of geology, climatology, biological processes analysis, geophysical analysis, disaster assessment, change detection and many others. In TIR, radiation emission of the target is a nonlinear function of two unknowns – the emissivity and the temperature, and the main difficulty is to calculate/estimate these two variables separately and reliably. Several methods have been developed in the recent decades to mitigate this problem. However, regardless of the method, all have developed similar incapacities for specific applications such as the type of sensor, study type, the target in question, the number of targets, type of weather, among others. Thus, the method to be applied depends on the study in question and the best results can be reached choosing the best fit method for that problem. In this work, we propose an alternative approach for estimating the temperature, and therefore the emissivity, of a particular target. The approach consists of generating statistical regressions in some wavelengths from linearized radiance function of laboratory data from a quartz sample at different temperatures, measured under controlled conditions of humidity and room temperature. The aim of regressions is to model the variation in temperature due to the variations in the radiance of the target in order to estimate the temperature from radiance data on a certain wavelength and without prior knowledge of the target emissivity. Laboratory datasets were divided into two groups - training and control. In the training group, several polynomial regressions were applied while the control group served to validate and evaluate the regressions. Five experiments were performed: (1) laboratory data at specific wavelengths (2) the central wave lengths of ASTER-TIR bands (3) simulations of ASTER-TIR bands (4) simulation of the atmosphere (dry and wet) for simulated bands of ASTER-TIR and (5) an image L1B ASTER-TIR of the study area validated with the AST08 product. As a result, it was possible to estimate the temperature with errors less than 0.2K from laboratory data and with mean error less than 1.5K from L1B ASTER-TIR images. Furthermore, the method requires only a spectral band in the image, enabling their application in monospectral thermal sensors. Satisfactory results were obtained with a simple linear regression and improved by increasing the wavelength. However, increasing the wavelength and, simultaneously, the degree of polynomial regression the results also improve with respect to linear regression results, but this improvement is insignificant, and thus the linear fit is the best option. Thus, the proposed method has shown promise, signaling that further research is needed.

Master of Science / Department of Biological & Agricultural Engineering / Ajay Sharda / Discovered in 1971, sudden death syndrome (SDS), caused by the fungus Fusarium virguliforme, has spread from the US to South American and European countries. It has potential to infect soybean crops worldwide, causing yield losses of 10% to 15% and even 70% in extreme cases. There is a need for rapid spatial assessment of SDS. Currently, the extent and severity of SDS are scored using visual symptoms as indicators. This method can take hours to collect and is subject to human bias and changing environmental conditions. Color infrared (CIR) and thermal infrared (TIR) imagery detect changes in light reflectance (visible and near-infrared bands) and emittance (canopy temperature), respectively. Stressed crops may show deviations in light reflectiveness, as well as elevated canopy temperatures. The use of CIR and TIR imagery and flexible aerial remote sensing platforms offer an alternative for SDS detection and diagnosis compared to hand scoring methods. Crop stress and diseases have been detected using manned and unmanned aerial systems previously. Yet, to date, SDS has not been remotely assessed using CIR or TIR imagery collected with aerial platforms. The following research utilizes high throughput CIR and TIR imagery collected using a small unmanned aerial system (sUAS) to detect and assess SDS. A comparative evaluation of ground-based and aerial CIR methods for assessing SDS was conducted to understand the effectiveness of novel aerial SDS detection methods. Furthermore, a TIR case study investigating the use of potential thermal canopy changes for SDS detection was conducted to investigate the possibility of using TIR as an SDS indicator. CIR reflectance measured from a ground-based spectrometer and sUAS was collected data over a two-year period. Ground-based spectrometer data were collected weekly, while a sUAS collected aerial imagery late in the growing season each year before plant maturity. Pigment index (PI) values were derived from ground-based and aerial data. Results showed a strong negative correlation between SDS score and PI values. Aerial and ground-based data both showed strong correlations to SDS score, however, aerial data displayed a stronger relationship possibly due to minimal changes in environmental conditions. High SDS scores correlated strongly to aerial derived PI (R2 = 0.8359). Rapidly assessed high SDS allows for accurate screening of SDS critical for soybean breeding. The second year of the study investigated each component of SDS score, severity, and incidence. PI proved to have the best correlation with severity (R2 = 0.6313 and ρ = -0.8016) rather than incidence or SDS score. PI also correlated to SDS scores with R2 = 0.6159 and ρ = -0.7916. A sUAS mounted TIR camera collected imagery four times during the growing season when SDS foliar symptoms were just starting to appear. At the start of the study period, the correlation between canopy temperature and SDS is low (ρ = -0.2907), but increases over the growing season as SDS prevalence increases ending with a strong correlation (ρ = -0.7158). Early identification of SDS leads to the implementation of mitigation practices and changes in irrigation scheduling before the disease reaches severe symptoms. Early mitigation of SDS reduces yield loses for farmers. The use of both CIR and TIR aerial imagery captured using sUAS can provide rapid spatial assessments of SDS, which is required by both producers and plant breeders. PI derived from CIR imagery showing strong correlations to SDS score reinforce the idea of replacing the time-consuming traditional ground-based systems with the more flexible, faster, sUAS methods. TIR imagery was shown to be reliable in assessing SDS in soybeans further establishing another possible aerial method for early detection of SDS.