A adoção de medidas afinadas à conservação do solo não é apenas uma necessidade, há muito passou a ser estratégico desenvolver técnicas que preencham as lacunas de informação e maximizem o uso adequado do solo. A diferenciação textural entre horizontes de solos é um dos principais aspectos a ser considerado quando do planejamento do uso do solo, pois se relaciona estreitamente com fatores como o enraizamento, a percolação e disponibilidade de água e a susceptibilidade dos solos à erosão. Diante disso, objetivou-se desenvolver Índices Espectroscópicos de Relação Textural (IERT) que estabeleçam quantitativamente o grau de diferenciação textural em perfis de solos via espectroscopia VisNIR-SWIR (350-2.500 nm) e MidIR (4000 - 400 cm-1). Foram utilizados dados espectrais de 150 perfis de solos descritos morfologicamente pertencentes a nove municípios das regiões Sudeste e Centro-Oeste do Brasil. Três modelos espectrais, VisNIR-SWIR, MidIR e VisNIR-SWIR-MidIR foram associados aos teores analíticos de matéria orgânica dos horizontes A e B, totalizando nove modelos, afim de se verificar o potencial daqueles puramente espectrais. Todos os modelos foram estudados por análises de funções discriminantes (CP-AFD) e regressão por mínimos quadrados (PLSR). A construção dos IERT\'s, num total de 15, cinco para cada modelo espectral, considerou a diferença de reflectância em bandas específicas ou associações entre elas, as quais foram selecionadas com base no estudo qualitativo e quantitativo das curvas espectrais. De modo geral, a diferenciação entre horizontes é ditada pelas regiões espectrais associadas principalmente aos óxidos de Fe, minerais de argila e quartzo. É possível determinar o grau de diferenciação textural entre horizontes de perfis de solos com excelente acurácia através de dados espectrais com taxa de acerto global de até 100 %, R2 de 0,76 a 0,82 e RPD de 2,02 a 2,35 nos modelos puramente espectrais, com destaque à região MidIR. O uso de IERT\'s produz bons a excelentes resultados, com R2 de 0,71 a 0,80 e RDP de 1,84 a 2,21 para os melhores índices de cada região espectral. O uso de índices espectrais reduz a dependência de métodos estatísticos avançados e dá suporte ao desenvolvimento de equipamentos óticos que trabalhem em regiões espectrais específicas, reduzindo custos e maximizando a aplicabilidade da técnica. Estudos abrangentes e exaustivos são indispensáveis antes que novos métodos como este se estabeleçam, particularmente em ciência do solo, na qual o objeto de estudo é, por natureza, complexo e intrigante. / The adoption of measures related to soil conservation is not only a necessity, long it has become strategic to develop techniques that meet the information deficits and maximize the appropriate use of land. The textural differentiation between soil horizons is one of the main aspects to be considered dealing with land use planning as it closely relates to factors such as rooting, percolation and water availability and the susceptibility of soils to erosion. Therefore, it was aimed to develop Spectral Textural Ratio Indices (STRI) that quantitatively establish the degree of textural differentiation in soil profiles through VisNIR-SWIR (350-2500 nm) and MidIR (4000- 400 cm-1) spectroscopy. Spectral data of 150 soil profiles morphologically described belonging to nine municipalities in the Southeast and Midwest of Brazil were used. Three spectral models, VisNIR-SWIR, MidIR and VisNIR-SWIR-MidIR were associated with analytical organic matter content of the A and B horizons, comprising nine models in order to verify the potential of those purely spectral. All models were studied by discriminant functions analysis (CP-DFA) and Partial Least Square Regression (PLSR). The construction of the STRIs, a total of 15, five for each spectral model, considered the reflectance difference in specific bands or associations between them, which were selected based on the qualitative and quantitative study of spectral curves. In general, the differentiation between horizons is dictated primarily by spectral regions associated with iron oxides, clay minerals and quartz. It is possible to determine the degree of textural differentiation between soil profile horizons with great accuracy through spectral data with overall accuracy rate of up to 100%, R2 from 0.76 to 0.82 and RPD from 2.02 to 2.35 in the purely spectral models, with emphasis on MidIR region. The use of STRIs produces good to excellent results, with R2 ranging from 0.71 to 0.80 and RPD ranging from 1.84 to 2.21 for the best rates of each spectral region. The use of spectral indices reduces reliance on advanced statistical methods and supports the development of optical devices that work in specific spectral regions, reducing costs and maximizing the applicability of the technique. Large and exhaustive studies are imperatives before new methods like this are established, particularly in soil science, in which the object of study is by nature complex and intriguing.

京都大学 / 新制・課程博士 / 博士(農学) / 甲第24245号 / 農博第2524号 / 新制||農||1094(附属図書館) / 学位論文||R4||N5416(農学部図書室) / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 近藤 直, 教授 飯田 訓久, 准教授 小川 雄一 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Near infrared spectroscopy (NIR) has become increasingly widespread throughout various fields as an alternative method for efficiently phenotyping crops and plants at rates unparalleled by conventional means. With growing reliability, the convergence of NIR spectroscopy and modern machine learning represent a promising methodology offering unprecedented access to rapid, high throughput phenotyping at negligible costs, representing prospects that excite agronomists and plant physiologists alike. However, as is true of all emergent methodologies, progressive refinement towards optimization exposes potential flaws and raises questions, one of which is the cornerstone of this study. Spectroscopic determination of plant functional traits utilizes plants' morphological and biochemical properties to make predictions, and has been validated at the community (inter-family) and individual crop (intraspecific) levels alike, yielding equally reliable predictions at both scales, yet what lies amid these poles on the spectrum of taxonomic scale remains unexplored territory. In this study, we replicated the protocol used in studies of the aforementioned taxonomic scale extremes and applied it to an intermediate scale. Interestingly, we found that predictive models built upon hyperspectral reflectance data collected across three genera of woody plants: Cornus, Rhododendron, and Salix, yielded inconsistent predictions of varying accuracy within and across taxa. Identifying the potential cause(s) underlying variability in predictive power at this intermediate taxonomic scale may reveal novel properties of the methodology, potentially permitting further optimization through careful consideration.

No description available.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24041号 / 情博第797号 / 新制||情||135(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 加納 学, 教授 下平 英寿, 教授 石井 信 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Una característica muy importante del agua es su calidad, tanto para el medio ambiente, como para sus usos. Actualmente, la legislación referente a los recursos hídricos, en distintos países, pone de manifiesto la importancia de conservar su calidad natural, e impone normas y criterios que debe cumplir el vital líquido. Uno de los objetivos de este trabajo es analizar la calidad del agua almacenada en los embalses, en perfil vertical, y a escala temporal diaria, por medio de la modelación multivariante y mecanicista de cuatro parámetros: temperatura del agua (esta variable mediante ambas técnicas), oxígeno disuelto, pH y conductividad, estas tres últimas por medio de métodos multivariantes. Como caso de aplicación se consideró el Embalse Amadorio. Para efectuar la modelación se utilizó información meteorológica de la estación Villajoyosa, así como datos de las variables de estado del almacenamiento, e información de una sonda multiparamétrica instalada en el paramento del embalse. El propósito final de la tesis es la comparación de los métodos multivariantes y mecanicista, en su efectividad y resultados, y presentar sus ventajas e inconvenientes. Se usaron, en la primera parte, varios métodos multivariantes para procesar y analizar los datos: análisis de componentes principales, análisis factorial, análisis cluster, análisis discriminante, el modelo lineal de regresión múltiple, análisis de correlación canónica, y regresión parcial en mínimos cuadrados (PLSR). Con estas técnicas, se obtuvieron modelos, con el propósito de hacer predicciones. Asimismo, se propone un modelo mecanicista multicapa (vertical) para temperatura, con el fin de comparar las características y los resultados de la modelación con métodos multivariantes, con las de un modelo con fundamento físico. Primero se enumeran los datos para el desarrollo de este modelo mecanicista, luego se describe el orden de los procesos a efectuar en las capas del embalse, y se analiza la estabilidad física de las c / Bluhm Gutiérrez, J. (2008). Modelación unidimensional de la calidad del agua en embalses. Análisis comparativo de modelos y multivariantes [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/3345

This study has been performed at Powertrain Engineering Sweden AB (PES), a fully owned subsidiary of Volvo Cars Group, which is constantly working to develop and improve internal combustion engines. As part of this work, durability tests are performed to analyse the impact of wear on the engines. At present, there is a strong focus on visual inspections after the engines have undergone durability tests. PES wants to develop a method where collected data from these tests can be used to explain how the phenomenon of knocking and the control of lambda changes over time. The study analyses one specific durability test and investigates the methodology of data analysis by using the open-source software platform Sympathy for Data, with an add-on developed by Volvo Cars Group, for data management, visualisation and analysis. To execute the analysis, engine parameters that affect these systems as well as parameters suitable to use as response variables are identified through literature studies of internal combustion engine fundamentalsas well as internal material, and knowledge acquired at the company. The result is presented in the form of an analysis generated by the node for partial least squares regression (PLSR) which is pre-programmed in Sympathy for Data as well as the images and graphs obtained as output. For knock, the signal for the final ignition angle was found to be suitable to use as the response variable in the PLSR. A suitable response variable for lambda was more difficult to identify, this is why both signals for the measured lambda and lambda adaptation are analysed. Studies of the internal material and knowledge highlighted the fact that several engine subsystems are highly dependent on each other and that even deeper research would be necessary to fully understand the process and identify the primary cause for the variations observed in the generated models. However, partial least squares regression was performed using parameters derived from literature reviews as input (predictors) in order produce regression models to explain the variance in sought response. Well-fitting models could be created with a varying number of latent variables needed for the different responses. The output obtained from the PLSR enables further studies of the specific cases as well as the methodology itself, hence, increase the use of data analysis with the help of the software used in the department for durability testing at PES. / Denna studie är utförd hos Powertrain Engineering Sweden AB (PES), vilka är ett helägt dotterbolag till Volvo Cars Group, som arbetar med att ta fram och förbättra förbränningsmotorer. En del i detta arbete är att genomföra långtidstest för att analysera hur motorernas egenskaper ändras vid förslitning över tid. I nuläget ligger stort fokus på visuella inspektioner efter att motorerna genomgått långtidstester. PES önskar utveckla en metod där redan insamlad data som registrerats i dessa tester kan förklara hur fenomenet knack och regleringen för lambda förändras över tid. Studien är genomförd i form av en fallstudie av ett specifikt långtidstest där den öppna programvaran Sympathy for Data, tillsammans med det av Volvo Cars Group utvecklade tillägget, används för datahantering, visualisering och analys. Studien undersöker också metodiken för dataanalys med nämnd programvara. För att genomföra detta identifieras motorparametrar som påverkar de undersökta systemen samt parametrar som lämpar sig att användas som responsvariabler i en regressionsmodell. Dessa parametrar togs fram genom litteraturstudier om de fundamentala delarna i en förbränningsmotor samt från företaget förvärvad intern kunskap kring systemen. Resultatet presenteras i form av en analys genomförd med den, i Sympathy for Data, förprogrammerade noden för partial least squares regression(PLSR) samt de bilder och grafer som erhålls. För knack visade det sig att den slutliga tändningsvinkeln var lämplig att använda som respons i PLSR-modellen. En lämplig responsvariabel för lambda var mer svåridentifierad, detta förklarar varför signalerna för uppmätt lambda och lambda adaption analyseras. Inläsning av internt material och grundläggande information om förbränningsmotorer visade att delsystem i ottomotorn är beroende och påverkas av varandra vilket innebär att mer ingående studier i dessa delsystem är nödvändigt för att förstå hela processen och hitta grundorsakerna till variationerna som påvisas för responssignalerna. Vidare utfördes PLSR med de parametrar som härletts från litteraturstudier som indatasignaler (prediktorer) för att skapa en regressionsmodell som förklarar variansen i sökta responssignaler. Beroende av responssignal krävdes varierande antal latenta variabler för att uppnå en tillräckligt precis modell. Resultatet från PLSR möjliggör vidare forskning inom området och metoden som använts och har på så sätt möjliggjort för fortsatt utveckling. Detta i sin tur kan öka användandet av dataanalys med hjälp av den programvara som används vid avdelningen för långtidstest hos PES.

No description available.

<p dir="ltr">Reflectance spectroscopy has been increasingly used in forestry due to its ability to rapidly, efficiently, and non-destructively detect tree stress, enabling timely and cost-effective forest management decisions. This dissertation synthesizes three studies and five experiments to understand and improve our ability to use spectral data to estimate a variety of foliar physiochemical traits and identify spectral responses in multi-stress environments, thus, advancing our understanding and application of hyperspectral data in forest management.</p><p dir="ltr">The first study seeks to refine the hyperspectral approach to monitoring tree stress by selecting optimal wavelength ranges to enhance the estimation of foliar traits, such as CO₂ assimilation rate, specific leaf area, leaf water content, and concentrations of foliar nitrogen, sugars, and gallic acid. The study revealed that model performance varied significantly across the different wavelength ranges tested and consistently, including longer wavelength regions improved trait estimation for all traits modeled. This research also established a framework for discovering novel or previously unknown absorption features associated with functional traits, thereby laying the groundwork for expanded spectral applications. This advancement enables the estimation of diverse foliar traits and facilitates detailed stress detection in trees.</p><p dir="ltr">The second study focuses on assessing the effectiveness of hyperspectral data in estimating foliar functional trait responses to various biotic and abiotic stressors and to differentiate those stressors in black walnut (<i>Juglans nigra </i>L.) and red oak (<i>Quercus rubra</i> L.) seedlings. We demonstrated that spectral data can reliably estimate a wide range of foliar traits, highlighting its potential as a surrogate for reference data in understanding plant responses to stress. This research revealed that spectral leaf predictions can effectively provide stress-specific insights into tree physiochemical responses to biotic and abiotic stressors.</p><p dir="ltr">The third study explores the application of hyperspectral reflectance to identify drought-induced foliar responses in black walnut seedlings during their initial field establishment. Chemometric models developed from greenhouse experiments were applied to spectral data collected in the field to assess their transferability and accuracy in predicting various leaf traits under drought stress. Using only spectral data, we demonstrated that seedlings show distinct spectral responses to past and ongoing drought stress, with varying degrees depending on seed provenances. This research aims to provide practical insights for utilizing spectral analysis in real-world conditions and understanding the challenges of using spectral tools in the field.</p><p dir="ltr">Collectively, this dissertation demonstrates the robust potential of hyperspectral reflectance technology in advancing the monitoring of tree health. By optimizing spectral range selection, reliably estimating tree foliar traits under stress conditions, differentiating various stressors in controlled environments, and effectively detecting current and past drought stress in field conditions, this research offers valuable insights for improving forest health monitoring and management strategies in response to environmental challenges.</p>

Les tourbières ombrotrophes, ou bogs sont particulièrement vulnérables à l’augmentation de la déposition atmosphérique d’azote. Cet apport d’un nutriment normalement limitant altère la capacité des tourbières à accumuler le carbone (C), en plus de mener à des changements de leur composition végétale. L’imagerie spectrale est une approche prometteuse puisqu’elle rend possible la détection des espèces végétales et de certaines caractéristiques chimiques des plantes, à distance. Toutefois, l’ampleur des différences spectrales intra- et interespèces n’est pas encore connue. Nous avons évalué la façon dont la chimie, la structure et la signature spectrale des feuilles changent chez Chamaedaphne calyculata, Kalmia angustifolia, Rhododendron groenlandicum et Eriophorum vaginatum, dans trois tourbières du sud du Québec et de l’Ontario, incluant une tourbière où se déroule une expérience de fertilisation à long terme. Nous avons mesuré des changements dans les traits fonctionnels dus aux différences dans la quantité d’azote disponible dans les sites. Toutefois, la déposition atmosphérique d’azote a eu relativement peu d’effet sur les spectres foliaires ; les variations spectrales les plus importantes étaient entre les espèces. En fait, nous avons trouvé que les quatre espèces ont un spectre caractéristique, une signature spectrale permettant leur identification au moyen d’analyses discriminantes des moindres carrés partiels (PLSDA). De plus, nous avons réussi à prédire plusieurs traits fonctionnels (l’azote, le carbone ; et la proportion d’eau et de matière sèche) avec moins de 10 % d’erreur grâce à des régressions des moindres carrés partiels (PLSR) des données spectrales. Notre étude fournit de nouvelles preuves que les variations intraspécifiques, causées en partie par des variations environnementales considérables, sont perceptibles dans les spectres foliaires. Toutefois, les variations intraspécifiques n’affectent pas l’identification des espèces ou la prédiction des traits. Nous démontrons que les spectres foliaires comprennent des informations sur les espèces et leurs traits fonctionnels, confirmant le potentiel de la spectroscopie pour le suivi des tourbières. / Abstract Bogs, as nutrient-poor ecosystems, are particularly sensitive to atmospheric nitrogen (N) deposition. Nitrogen deposition alters bog plant community composition and can limit their ability to sequester carbon (C). Spectroscopy is a promising approach for studying how N deposition affects bogs because of its ability to remotely determine changes in plant species composition in the long term as well as shorter-term changes in foliar chemistry. However, there is limited knowledge on the extent to which bog plants differ in their foliar spectral properties, how N deposition might affect those properties, and whether subtle inter- or intraspecific changes in foliar traits can be spectrally detected. Using an integrating sphere fitted to a field spectrometer, we measured spectral properties of leaves from the four most common vascular plant species (Chamaedaphne calyculata, Kalmia angustifolia, Rhododendron groenlandicum and Eriophorum vaginatum) in three bogs in southern Québec and Ontario, Canada, exposed to different atmospheric N deposition levels, including one subjected to a 18 years N fertilization experiment. We also measured chemical and morphological properties of those leaves. We found detectable intraspecific changes in leaf structural traits and chemistry (namely chlorophyll b and N concentrations) with increasing N deposition and identified spectral regions that helped distinguish the site-specific populations within each species. Most of the variation in leaf spectral, chemical and morphological properties was among species. As such, species had distinct spectral foliar signatures, allowing us to identify them with high accuracy with partial least squares discriminant analyses (PLSDA). Predictions of foliar traits from spectra using partial least squares regression (PLSR) were generally accurate, particularly for the concentrations of N and C, soluble C, leaf water, and dry matter content (<10% RMSEP). However, these multi-species PLSR models were not accurate within species, where the range of values was narrow. To improve the detection of short-term intraspecific changes in functional traits, models should be trained with more species-specific data. Our field study showing clear differences in foliar spectra and traits among species, and some within-species differences due to N deposition, suggest that spectroscopy is a promising approach for assessing long-term vegetation changes in bogs subject to atmospheric pollution.