A lack of surveillance systems is an impediment to public health intervention for perennial vector-borne disease transmission in northern tropical savanna region of Kenya. The population in this area are mostly poor nomadic pastoralists with little acquired functional immunity to Plasmodium falciparum, due to infrequent challenges with the parasite. A common characteristic in tropical savanna climatic zone is the availability of riverbeds that have anthropogenic boreholes that provide malaria vector mosquitoes, such as Anopheles gambiae s.l and Anopheles funestus, with aquatic refuge habitats for proliferation and endemic transmission to proximity human households during the dry-season. Unfortunately, currently there have been no entomological investigations employing field or remotely sensed data that can characterize and model anthropogenic borehole habitats focusing on the dry-land ecology of immature Anopheles mosquitoes in sub-Sahara Africa. The goal of this investigation was three-fold: (I) to employ WorldView-3 (0.31 meter spatial resolution) visible and near infra-red waveband sensor data to image sub-Saharan land cover associated with vector-borne disease transmission; (II) to remotely identify anthropogenic boreholes in three riverbeds that were surveyed to determine whether they provide malaria vectors with refuge habitat and maintain their population during the dry season in Chemolingot, Kenya, and (III) to obtain a radiometric/spectral signature model representing boreholes from the remotely-sensed data. The signature model was then interpolated to predict unknown locations of boreholes with the same spectral signature in Nginyang Riverbed, Kenya. Ground validation studies were subsequently conducted to assess model’s precision based on sensitivity and specificity tests.

In this report, the principles of man-made object detection in satellite images is investigated. An overview of terminology and of how the detection problem is usually solved today is given. A three level system to solve the detection problem is proposed. The main branches of this system handle road, and city detection respectively. To achieve data source flexibility, the Logical Sensor notion is used to model the low level system components. Three Logical Sensors have been implemented and tested on Landsat TM and SPOT XS scenes. These are: BDT (Background Discriminant Transformation) to construct a man-made object property field; Local-orientation for texture estimation and road tracking; Texture estimation using local variance and variance of local orientation. A gradient magnitude measure for road seed generation has also been tested.

<p>In this report, the principles of man-made object detection in satellite images is investigated. An overview of terminology and of how the detection problem is usually solved today is given. A three level system to solve the detection problem is proposed. The main branches of this system handle road, and city detection respectively. To achieve data source flexibility, the Logical Sensor notion is used to model the low level system components. Three Logical Sensors have been implemented and tested on Landsat TM and SPOT XS scenes. These are: BDT (Background Discriminant Transformation) to construct a man-made object property field; Local-orientation for texture estimation and road tracking; Texture estimation using local variance and variance of local orientation. A gradient magnitude measure for road seed generation has also been tested.</p>

A discriminação de espécies de macrófitas através de Sensoriamento Remoto vem de encontro à necessidades econômicas e sanitárias geradas pelo aumento dos conglomerados urbanos e seus danos aos ambientes aquáticos continentais. A ausência de pesquisa básica relacionada à obtenção de respostas espectrais específicas tem dificultado a discriminação das espécies de macrófitas infestantes, em imagens multiespectrais. Objetivos: Tendo o conhecimento das necessidades de pesquisa básica nesta área, este estudo tem como objetivos analisar a resposta espectral de espécies de macrófitas, buscando sua distinção, e comparar a reposta espectral do Infravermelho Próximo (IVP) às características anatômicas espessura da folha, espessura do parênquima esponjoso, proporção do parênquima esponjoso na folha, e proporção de espaços aéreos no parênquima esponjoso. Metodologia: Para atingir os objetivos, foram coletados dados espectroradiométricos de espécies de macrófitas. Num primeiro momento foi testada a distinção entre duas espécies em imagem orbital. Em seguida, foram realizados diversos experimentos em laboratório, os quais foram posteriormente analisados matematicamente buscando suas relações. Foi também realizado o estudo anatômico de folhas de cinco espécies de macrófitas, cujos valores foram comparados com a resposta espectral no IVP. Resultados: O banco de dados de respostas espectrais gerado foi comparado inter e intraespecificamente, e foram usados descritores matemáticos para verificar as possibilidades de diferenciação. As folhas com máxima, média e mínima reflectância de cinco espécies escolhidas por serem mais importantes em infestações foram analisadas anatomicamente e foram calculados os valores da espessura da folha, espessura do parênquima esponjoso, proporção do parênquima esponjoso na folha, e proporção de espaços aéreos no parênquima esponjoso. Conclusões: Os descritores utilizados para analisar as respostas espectrais se mostraram eficientes na separação entre as espécies estudadas em laboratório. Dentre eles, o que mostrou melhores resultados para a distinção entre espécies foi o índice da Posição do Limite Vermelho. As características anatômicas obtidas com o uso de microscopia confocal e de luz possibilitaram a discriminação das espécies. Além disso, foi possível verificar que as características anatômicas analisadas foram altamente correlacionadas com a Reflectância de algumas das espécies na região do IVP. / The differentiation of macrophytes species using Remote Sensing is recommended in a world where the economy and healthy are being highly injured by the increased number of urban population and their damages to the aquatic environments. The lack of the basic research related to the acquisition of spectral signatures of species has been raising difficulties on the discrimination of them in multispectral images. Aims: Knowing this lack of basic research, this work has the goals of analyze the spectral signatures of macrophytes species, looking for its differentiation, and compare spectral the reflectance in the Near Infrared (NIR) region of the Electromagnetic Spectrum with the anatomic variables: thickness of the leaf, thickness of the spongy mesophyll, percentage of the spongy mesophyll in the leaf, and percentage of the aerial spaces inside this mesophyll. Methodology: To reach these goals, it was collected spectroradiometric data of macrophytes species. Primarily it was tested the differentiation between two species in an orbital image. Afterward, it was carried out some laboratory experiments, which were mathematically analyzed looking for their relationships. Then, it was accomplished the leaves anatomical studies of five macrophytes species, whose values where compared with the spectral signature in the NIR region. Results: The initial spectral signatures database was compared inter and intra-specifically, and it were used mathematical descriptors to verify the possibilities of species differentiation. Leaves with maximum, medium and minimum reflectance of five species chosen because of their importance on infestations, was anatomically analyzed and it were calculated the values of the thickness of the leaf, thickness of the spongy mesophyll, percentage of the spongy mesophyll in the leaf, and percentage of the aerial spaces inside this mesophyll. Conclusions: The descriptors used to analyze the spectral signatures denote efficiency in the differentiation of macrophytes species in laboratory. Among them, the one that has been showed the best results for the species differentiation was the Red Edge Position. The anatomical characteristics achieved with confocal and light microscopy made feasible to differentiate the species. Besides that, it was possible to verify that the analyzed anatomical characteristics were highly correlated with reflectance in some species in the NIR region.

A discriminação de espécies de macrófitas através de Sensoriamento Remoto vem de encontro à necessidades econômicas e sanitárias geradas pelo aumento dos conglomerados urbanos e seus danos aos ambientes aquáticos continentais. A ausência de pesquisa básica relacionada à obtenção de respostas espectrais específicas tem dificultado a discriminação das espécies de macrófitas infestantes, em imagens multiespectrais. Objetivos: Tendo o conhecimento das necessidades de pesquisa básica nesta área, este estudo tem como objetivos analisar a resposta espectral de espécies de macrófitas, buscando sua distinção, e comparar a reposta espectral do Infravermelho Próximo (IVP) às características anatômicas espessura da folha, espessura do parênquima esponjoso, proporção do parênquima esponjoso na folha, e proporção de espaços aéreos no parênquima esponjoso. Metodologia: Para atingir os objetivos, foram coletados dados espectroradiométricos de espécies de macrófitas. Num primeiro momento foi testada a distinção entre duas espécies em imagem orbital. Em seguida, foram realizados diversos experimentos em laboratório, os quais foram posteriormente analisados matematicamente buscando suas relações. Foi também realizado o estudo anatômico de folhas de cinco espécies de macrófitas, cujos valores foram comparados com a resposta espectral no IVP. Resultados: O banco de dados de respostas espectrais gerado foi comparado inter e intraespecificamente, e foram usados descritores matemáticos para verificar as possibilidades de diferenciação. As folhas com máxima, média e mínima reflectância de cinco espécies escolhidas por serem mais importantes em infestações foram analisadas anatomicamente e foram calculados os valores da espessura da folha, espessura do parênquima esponjoso, proporção do parênquima esponjoso na folha, e proporção de espaços aéreos no parênquima esponjoso. Conclusões: Os descritores utilizados para analisar as respostas espectrais se mostraram eficientes na separação entre as espécies estudadas em laboratório. Dentre eles, o que mostrou melhores resultados para a distinção entre espécies foi o índice da Posição do Limite Vermelho. As características anatômicas obtidas com o uso de microscopia confocal e de luz possibilitaram a discriminação das espécies. Além disso, foi possível verificar que as características anatômicas analisadas foram altamente correlacionadas com a Reflectância de algumas das espécies na região do IVP. / The differentiation of macrophytes species using Remote Sensing is recommended in a world where the economy and healthy are being highly injured by the increased number of urban population and their damages to the aquatic environments. The lack of the basic research related to the acquisition of spectral signatures of species has been raising difficulties on the discrimination of them in multispectral images. Aims: Knowing this lack of basic research, this work has the goals of analyze the spectral signatures of macrophytes species, looking for its differentiation, and compare spectral the reflectance in the Near Infrared (NIR) region of the Electromagnetic Spectrum with the anatomic variables: thickness of the leaf, thickness of the spongy mesophyll, percentage of the spongy mesophyll in the leaf, and percentage of the aerial spaces inside this mesophyll. Methodology: To reach these goals, it was collected spectroradiometric data of macrophytes species. Primarily it was tested the differentiation between two species in an orbital image. Afterward, it was carried out some laboratory experiments, which were mathematically analyzed looking for their relationships. Then, it was accomplished the leaves anatomical studies of five macrophytes species, whose values where compared with the spectral signature in the NIR region. Results: The initial spectral signatures database was compared inter and intra-specifically, and it were used mathematical descriptors to verify the possibilities of species differentiation. Leaves with maximum, medium and minimum reflectance of five species chosen because of their importance on infestations, was anatomically analyzed and it were calculated the values of the thickness of the leaf, thickness of the spongy mesophyll, percentage of the spongy mesophyll in the leaf, and percentage of the aerial spaces inside this mesophyll. Conclusions: The descriptors used to analyze the spectral signatures denote efficiency in the differentiation of macrophytes species in laboratory. Among them, the one that has been showed the best results for the species differentiation was the Red Edge Position. The anatomical characteristics achieved with confocal and light microscopy made feasible to differentiate the species. Besides that, it was possible to verify that the analyzed anatomical characteristics were highly correlated with reflectance in some species in the NIR region.

Somente no ano de 2011 foram adquiridos mais de 1.000TB de novos registros digitais de imagem advindos de Sensoriamento Remoto orbital. Tal gama de registros, que possui uma progressão geométrica crescente, é adicionada, anualmente, a incrível e extraordinária massa de dados de imagens orbitais já existentes da superfície da Terra (adquiridos desde a década de 70 do século passado). Esta quantidade maciça de registros, onde a grande maioria sequer foi processada, requer ferramentas computacionais que permitam o reconhecimento automático de padrões de imagem desejados, de modo a permitir a extração dos objetos geográficos e de alvos de interesse, de forma mais rápida e concisa. A proposta de tal reconhecimento ser realizado automaticamente por meio da integração de técnicas de Análise Espectral e de Inteligência Computacional com base no Conhecimento adquirido por especialista em imagem foi implementada na forma de um integrador com base nas técnicas de Redes Neurais Computacionais (ou Artificiais) (através do Mapa de Características Auto- Organizáveis de Kohonen SOFM) e de Lógica Difusa ou Fuzzy (através de Mamdani). Estas foram aplicadas às assinaturas espectrais de cada padrão de interesse, formadas pelos níveis de quantização ou níveis de cinza do respectivo padrão em cada uma das bandas espectrais, de forma que a classificação dos padrões irá depender, de forma indissociável, da correlação das assinaturas espectrais nas seis bandas do sensor, tal qual o trabalho dos especialistas em imagens. Foram utilizadas as bandas 1 a 5 e 7 do satélite LANDSAT-5 para a determinação de cinco classes/alvos de interesse da cobertura e ocupação terrestre em três recortes da área-teste, situados no Estado do Rio de Janeiro (Guaratiba, Mangaratiba e Magé) nesta integração, com confrontação dos resultados obtidos com aqueles derivados da interpretação da especialista em imagens, a qual foi corroborada através de verificação da verdade terrestre. Houve também a comparação dos resultados obtidos no integrador com dois sistemas computacionais comerciais (IDRISI Taiga e ENVI 4.8), no que tange a qualidade da classificação (índice Kappa) e tempo de resposta. O integrador, com classificações híbridas (supervisionadas e não supervisionadas) em sua implementação, provou ser eficaz no reconhecimento automático (não supervisionado) de padrões multiespectrais e no aprendizado destes padrões, pois para cada uma das entradas dos recortes da área-teste, menor foi o aprendizado necessário para sua classificação alcançar um acerto médio final de 87%, frente às classificações da especialista em imagem. A sua eficácia também foi comprovada frente aos sistemas computacionais testados, com índice Kappa médio de 0,86. / Only in 2011 were acquired over 1.000TB of new digital image registers arising from orbital remote sensing. This range of data, which has a geometric progression increasing, is added annually to an extraordinary and incredible mass of data from existing satellite images of Earth's surface (acquired since the 70s of last century). This massive amount of raw data requires computational tools which allow the automatic recognition of image patterns desired to allow the extraction of geographical objects and targets of interest more quickly and concisely. The proposal for such recognition to be performed automatically through Spectral Analysis and Computational Intelligence integration, based on knowledge acquired by image experts, was implemented as an integrator based on Computational Neural Networks (via Kohonens Self-Organizing Feature Maps - SOM) and Fuzzy Logic (through Mamdani) techniques. These techniques were applied to the spectral signatures pattern formed by the quantization levels or gray levels of the corresponding pattern in each spectral band of each pattern of interest, so that the pattern classification will depend, in an inseparable manner, of the spectral signatures correlation of the six bands of the sensor, like the work of image experts. Bands 1 to 5 and 7 of the Landsat-5 satellite were used for the determination of five classes / targets of interest in cover and land occupation, in three test areas located in the State of Rio de Janeiro (Guaratiba, Mangaratiba and Magé) in this integration with comparison of results with those derived from the interpretation of the imaging expert, which was corroborated by checking the ground truth. There was also a results comparison obtained with two commercial computer systems (IDRISI Taiga and ENVI 4.8) with the integrator, regarding the quality of classification (Kappa) and response time. The integrator, with hybrid classifications (supervised and unsupervised) in its implementation, proved to be effective in multispectral automatic (unsupervised) pattern recognition and in learning of these patterns, because as the input of a new test area occurs, the lower became the process of learning, which achieve a final average accuracy o f 87%, compared to the experts classifications. Its efficacy was also demonstrated compared to systems tested, with average Kappa of 0.86.

Somente no ano de 2011 foram adquiridos mais de 1.000TB de novos registros digitais de imagem advindos de Sensoriamento Remoto orbital. Tal gama de registros, que possui uma progressão geométrica crescente, é adicionada, anualmente, a incrível e extraordinária massa de dados de imagens orbitais já existentes da superfície da Terra (adquiridos desde a década de 70 do século passado). Esta quantidade maciça de registros, onde a grande maioria sequer foi processada, requer ferramentas computacionais que permitam o reconhecimento automático de padrões de imagem desejados, de modo a permitir a extração dos objetos geográficos e de alvos de interesse, de forma mais rápida e concisa. A proposta de tal reconhecimento ser realizado automaticamente por meio da integração de técnicas de Análise Espectral e de Inteligência Computacional com base no Conhecimento adquirido por especialista em imagem foi implementada na forma de um integrador com base nas técnicas de Redes Neurais Computacionais (ou Artificiais) (através do Mapa de Características Auto- Organizáveis de Kohonen SOFM) e de Lógica Difusa ou Fuzzy (através de Mamdani). Estas foram aplicadas às assinaturas espectrais de cada padrão de interesse, formadas pelos níveis de quantização ou níveis de cinza do respectivo padrão em cada uma das bandas espectrais, de forma que a classificação dos padrões irá depender, de forma indissociável, da correlação das assinaturas espectrais nas seis bandas do sensor, tal qual o trabalho dos especialistas em imagens. Foram utilizadas as bandas 1 a 5 e 7 do satélite LANDSAT-5 para a determinação de cinco classes/alvos de interesse da cobertura e ocupação terrestre em três recortes da área-teste, situados no Estado do Rio de Janeiro (Guaratiba, Mangaratiba e Magé) nesta integração, com confrontação dos resultados obtidos com aqueles derivados da interpretação da especialista em imagens, a qual foi corroborada através de verificação da verdade terrestre. Houve também a comparação dos resultados obtidos no integrador com dois sistemas computacionais comerciais (IDRISI Taiga e ENVI 4.8), no que tange a qualidade da classificação (índice Kappa) e tempo de resposta. O integrador, com classificações híbridas (supervisionadas e não supervisionadas) em sua implementação, provou ser eficaz no reconhecimento automático (não supervisionado) de padrões multiespectrais e no aprendizado destes padrões, pois para cada uma das entradas dos recortes da área-teste, menor foi o aprendizado necessário para sua classificação alcançar um acerto médio final de 87%, frente às classificações da especialista em imagem. A sua eficácia também foi comprovada frente aos sistemas computacionais testados, com índice Kappa médio de 0,86. / Only in 2011 were acquired over 1.000TB of new digital image registers arising from orbital remote sensing. This range of data, which has a geometric progression increasing, is added annually to an extraordinary and incredible mass of data from existing satellite images of Earth's surface (acquired since the 70s of last century). This massive amount of raw data requires computational tools which allow the automatic recognition of image patterns desired to allow the extraction of geographical objects and targets of interest more quickly and concisely. The proposal for such recognition to be performed automatically through Spectral Analysis and Computational Intelligence integration, based on knowledge acquired by image experts, was implemented as an integrator based on Computational Neural Networks (via Kohonens Self-Organizing Feature Maps - SOM) and Fuzzy Logic (through Mamdani) techniques. These techniques were applied to the spectral signatures pattern formed by the quantization levels or gray levels of the corresponding pattern in each spectral band of each pattern of interest, so that the pattern classification will depend, in an inseparable manner, of the spectral signatures correlation of the six bands of the sensor, like the work of image experts. Bands 1 to 5 and 7 of the Landsat-5 satellite were used for the determination of five classes / targets of interest in cover and land occupation, in three test areas located in the State of Rio de Janeiro (Guaratiba, Mangaratiba and Magé) in this integration with comparison of results with those derived from the interpretation of the imaging expert, which was corroborated by checking the ground truth. There was also a results comparison obtained with two commercial computer systems (IDRISI Taiga and ENVI 4.8) with the integrator, regarding the quality of classification (Kappa) and response time. The integrator, with hybrid classifications (supervised and unsupervised) in its implementation, proved to be effective in multispectral automatic (unsupervised) pattern recognition and in learning of these patterns, because as the input of a new test area occurs, the lower became the process of learning, which achieve a final average accuracy o f 87%, compared to the experts classifications. Its efficacy was also demonstrated compared to systems tested, with average Kappa of 0.86.

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.

Dans la forêt boréale, l’augmentation de la fréquence et de la superficie d’îlots de pessières à lichens sur le territoire québécois a déjà été observée et pourrait résulter en une migration vers le sud de la limite nordique des pessières à mousses. Ce phénomène survient après des échecs de régénération, qui ont lieu lorsque le milieu est préalablement fragilisé lorsqu’une nouvelle perturbation affecte le peuplement. Avec la possibilité de détecter ce stress en analysant les propriétés spectrales de la végétation, les zones perturbées pourraient alors être identifiées. L’objectif principal de la présente étude est d’établir des liens entre d’une part, les informations extraites des signatures spectrales et d’autre part, les indices de végétation et les différents types de stress affectant les écosystèmes boréaux. Cela permettra de savoir s’il est possible d’identifier les pessières à mousses à risque de subir un accident de régénération en étudiant les propriétés spectrales de la végétation comme indicateur de stress. Pour répondre à cet objectif, des sites d’échantillonnage ont été positionnées aux 51e, 52e et 53e parallèles le long de la route de la baie James. Les placettes ont été regroupées en paires afin de faire des tests appariés et ainsi comparer les deux types de peuplements. Sur le terrain, les signatures spectrales ont été prises sur les feuilles aléatoirement prélevées sur cinq épinettes noires. Ces mesures ont été prises tout au long de la saison de croissance (3 campagnes d’échantillonnage). Quatre indices de végétation (NDVI, NDWI, PRI et SIPI) ont été extraits des signatures spectrales, et la pente moyenne du red-edge a été calculée. Les résultats obtenus ont permis de déterminer que certaines des pessières à mousses ont des valeurs très proches de celles des pessières à lichens, qui sont considérées comme des écosystèmes stressés. À partir de ces résultats, il est possible de supposer que le stress peut également être identifié à l’échelle du paysage (sur les images satellitaires) et ainsi permettre un suivi et une gestion après les feux et les épidémies afin de limiter les pertes de ce précieux écosystème. / In the boreal forest of northern Québec, the size and quantity of lichen woodlands patches is increasing, and taking over the spruce-moss forest territory. The phenomenon has been observed, and scientists now believe that the northern limit of the spruce-moss forest will slowly move south. This shift of ecosystem happens when the forest stand is already fragilized, and a perturbation occurs. Vegetation’s spectral properties can be used as a tool to assess and identify disturbed forest areas The main objective of this study is to establish relations between data extracted from spectral signatures, vegetation indexes and different types of stress that could affect boreal ecosystems. The identification spruce-moss woodlands prone to regeneration failure could be achieved with the study of spectral properties as stress indicators. In order to achieve this objective, sites from 3 latitudes (51, 52 and 53) have been sampled on James Bay Road. Plots have been regrouped in pairs for subsequent pairwise statistical tests to compare results from both forest stand types. Spectral signatures have been measured on 5 randomly chosen black spruces. These measurements were taken throughout the growing season (3 sampling campaigns). Four vegetation indexes have been extracted from spectral signatures (NDVI, NDWI, PRI and SIPI), and the mean slope of the red-edge area have been calculated. Results have shown that some of the spruce-moss stands have had very similar values to those from the lichen woodlands, that are considered as stressed ecosystems. From these results, it is possible to assume that stressed ecosystems can be detected at landscape level (on satellite images). Monitoring vegetation stress can help improve forest management after forest fires and insect’s epidemics to prevent the loss of this beautiful ecosystem.

Tesis por compendio / [ES] El conocimiento del estado nutricional de los cultivos permite corregir o ajustar cualquier exceso o deficiencia nutricional en los mismos, a lo largo de su ciclo vegetativo, asegurando un alto rendimiento en la producción y una óptima calidad del fruto. Tradicionalmente, para la realización del diagnóstico nutricional se ha utilizado el análisis de la ionómica de diferentes órganos de la planta, especialmente las hojas, por su facilidad de muestreo y por ser el órgano fotosintético de excelencia en las plantas. Por ello es necesario implementar estrategias sostenibles que nos permitan ajustar la dosis de fertilización según las necesidades del cultivo con el mínimo riesgo de contaminación. El objetivo de esta tesis doctoral es desarrollar métodos y modelos que permitan el diagnóstico nutricional en cultivos mediante métodos ópticos no destructivos, como la espectroscopia y la imagen hiperespectral en el rango Vis-NIR, en combinación con técnicas de quimiometría. De este modo, el primer bloque centrado en el cultivo de caqui cv. 'Rojo Brillante', comprende los estudios publicados en dos artículos científicos. En el primero de estos artículos se estudió el potencial de la espectroscopia Vis-NIR (430-1040 nm), con el objetivo de predecir macros y micronutrientes utilizando modelos de regresión PLS. Los resultados mostraron que es posible predecir de forma precisa macronutrientes como, fósforo (P), calcio (Ca) y magnesio (Mg), con un coeficiente de determinación en la predicción (R2P) de 0,78 a 0,63. En los micronutrientes, el boro (B) y el manganeso (Mn) fueron los que obtuvieron mejores coeficientes de predicción, con R2P de 0,79 y 0,69, respectivamente. En el segundo artículo se ha evaluado, para la estimación de la concentración de nutrientes, el uso de imágenes hiperespectrales en el rango entre 500 y 980 nm. Los resultados mostraron la predicción de los macronutrientes como N, P, potasio (K), Ca y Mg con R2P de 0,80 a 0,62 y, para los micronutrientes, solo en el B se obtuvo un valor aceptable para la estimación (R2p = 0,69). Además, utilizando el método de reducción de variables de influencia en la proyección (VIP) se obtuvo una predicción fiable para los nutrientes de N (R2P = 0,76) y B (R2P = 0,61). En el segundo bloque, se ha estudiado otro cultivo emblemático en la Comunidad Valenciana por su importancia social y económica, como son los cítricos. En este caso, se desarrollaron herramientas de estimación del cv. 'clementina de Nules', descritas en otros dos artículos científicos. En el tercer artículo se ha estudiado la capacidad de la espectroscopía para determinar la concentración de nutrientes en las hojas de los cítricos en un ciclo vegetativo completo. Los resultados mostraron una predicción con un R2P de 0,70 a 0,65 para el P, K Ca y B. Utilizando el coeficiente de regresión de ponderado (BW) se determinó un subconjunto de bandas importantes para determinar la concentración de P, K y B. Los resultados mostraron que las bandas de mayor relevancia para estos nutrientes se situaron en la región del visible (430-750 nm), asociada a la absorción de pigmentos fotosintéticos. Finalmente, en el cuarto artículo se ha estudiado el potencial de la imagen hiperespectral para discriminar entre hojas jóvenes y hojas de ciclos vegetativos anteriores, lo que mejoraría el diagnóstico dado que las tablas de referencia en este cultivo están realizadas en hojas de la brotación de primavera. Partiendo de esa hipótesis, se obtuvo que es posible realizar la discriminación entre ambos tipos de hojas. Posteriormente se realizó la predicción de concentración de nutrientes de hojas jóvenes, utilizando 49 bandas espectrales, obteniendo mejores resultados para los nutrientes P, K, Ca, hierro (Fe) y Mn con R2P de 0,69 a 0,60. Además, se realizó la predicción de estos nutrientes minimizando el número de bandas a diez, con el BW y se obtuvo un R2P de 0,67 a 0,57. / [CA] El coneixement de l'estat nutricional dels cultius permet corregir o ajustar qualsevol excés o deficiència nutricional en estos, al llarg del seu cicle vegetatiu, assegurant un alt rendiment en la producció i una òptima qualitat del fruit. Tradicionalment, per a la realització del diagnòstic nutricional s'han utilitzat l'anàlisi de la ionómica de diferents òrgans de la planta, especialment les fulles, per la seua facilitat de mostreig i per ser l'òrgan fotosintètic d'excellència en les plantes. Per això és necessari implementar estratègies sostenibles que ens permeten ajustar la dosi de fertilització segons les necessitats del cultiu amb el mínim risc de contaminació. L'objectiu d'esta tesi doctoral és desenvolupar mètodes i models que permeten la predicció del diagnòstic nutricional en cultius mitjançant mètodes òptics no destructius, com l'espectroscòpia Vis-NIR, en combinació amb tècniques quimio mètriques. D'esta manera, el primer capítol de publicacions es centra en el cultiu del caqui cv. Rojo Brillante, comprés per dos articles (I i II). En el primer d'estos articles es va estudiar el potencial de l'espectroscòpia Vis-NIR (430-1040 nm), amb l'objectiu de predir macros i micronutrients utilitzant models de regressió PLS. En este estudi es van aplicar tractaments diferencials per als nutrients de N (0 %, 33 %, 50 % i 100 %) i per a K2O (0 %, 50 % i 100 %) de la demanda del cultiu. Els resultats van mostrar que, sí que és possible predir de manera precisa macronutrients com, fòsfor (P), calci (Ca)i magnesi (Mg), amb un coeficient de determinació en la predicció (R2P) de 0,78 a 0,63. En els micronutrients, com el bor (B) i el manganés (Mn) van ser els que van obtindre millors coeficients de predicció, amb R2P de 0,79 i 0,69, respectivament. En el segon article s'ha avaluat, per a l'estimació de la concentració de nutrients, l'ús d'imatges hiperespectrales en un rang (500-980 nm). Els resultats van mostrar la predicció dels macronutrients com a nitrogen (N), P, potassi (K), Ca i Mg amb R2P de 0,80 a 0,62 i, per als micronutrients, només en el B es va obtindre un valor acceptable per a l'estimació (R2p 0,69). A més, utilitzant el mètode de reducció de variables d'influència en la projecció (VIP) es va obtindre una predicció fiable per als nutrients de N (R2P 0,76) i B (R2P 0,61). En el segon capítol, s'ha estudiat un altre cultiu emblemàtic a la Comunitat Valenciana d'importància econòmica, com són els cítrics. En este cas, es van desenvolupar ferramentes d'estimació del cv. 'clementina de Nules' compreses en dos articles (III i IV). De tal manera, que en el tercer article s'ha estudiat la capacitat de les tècniques espectromètriques per a determinar la concentració de nutrients en un cicle vegetatiu complet. Els resultats van mostrar una predicció amb un R2P de 0,70 a 0,65 per al P, K, Ca i B. Utilitzant el coeficient de regressió de pes (BW) es va determinar un subconjunt de bandes més influents per als nutrients P, K i B. Els resultats van mostrar que les bandes de major importància, per a estos nutrients, es situen a la regió del Vis (430-750 nm), el qual està associada a l'absorció de pigments fotosintètics. Finalment, en el quart article s'ha estudiat el potencial de les HSI per a discriminar fulles joves de fulles de cicles vegetatius anteriors, la qual cosa milloraria el diagnòstic atés que les taules de referència en este cultiu estan realitzades en fulles de la brotada de primavera. Posteriorment es va realitzar la predicció de concentració de nutrients de fulles joves, utilitzant 49 bandes espectrals, obtenint millors resultats per als nutrients P, K, Ca, ferro (Fe) i Mn amb R2P de 0,69 a 0,60. A més, es va realitzar la predicció d'estos nutrients minimitzant el nombre de bandes a deu, amb el BW i es va obtindre un R2P de 0,67 a 0,57. / [EN] Knowledge of the nutritional status of crops allows for correcting or adjusting any nutritional excess or deficiency throughout their vegetative cycle, ensuring high yields in production and optimal fruit quality. Traditionally, the analysis of the ionomics of different plant organs has been used for nutritional diagnosis, especially the leaves, due to their ease of sampling and being the photosynthetic organ par excellence in plants. These analyses are carried out by expensive conventional laboratory methods that are destructive, polluting, time-consuming and costly. Therefore, it is necessary to implement sustainable strategies that allow the fertilisation dose to be adjusted according to the crop's needs with the minimum risk of contamination. This doctoral thesis aims to develop methods and models for nutritional diagnosis prediction in crops using non-destructive optical methods, such as Vis-NIR spectroscopy, combined with chemometric techniques. Thus, the first chapter of the publications focuses on cultivating persimmon cv. 'Rojo Brillante', comprising two articles (I and II). In the first of these articles, the potential of Vis-NIR spectroscopy (430-1040 nm) was studied to predict macronutrients and micronutrients using PLS regression models. This study applied differential treatments for N nutrients (0 %, 33 %, 50 % and 100 %) and K2O (0 %, 50 % and 100 %) of crop demand. The results showed that it is possible to accurately predict macronutrients such as phosphorus (P), calcium (Ca) and magnesium (Mg), with a coefficient of determination in the prediction (R2P) of 0.78 to 0.63. Boron (B) and manganese (Mn) obtained the best micronutrient prediction coefficients, with R2P of 0.79 and 0.69, respectively. The second article evaluated hyperspectral imaging (HSI) in the range (500-980 nm) for nutrient concentration estimation. The results showed the prediction of macronutrients such as nitrogen (N), P, potassium (K), Ca and Mg with R2P from 0.80 to 0.62 and, for micronutrients, only in B, an acceptable value for the estimation was obtained (R2p 0.69). In addition, using the projection influence variable reduction (VIP) method, a reliable prediction was obtained for N (R2P 0.76) and B (R2P 0.61) nutrients. In the second chapter, another emblematic crop of economic importance in the Valencian Community, citrus, was studied. Estimation tools were developed for citrus cv. 'Clementina de Nules' and the results were published in two articles (III and IV). Thus, in the third article, the capacity of spectrometric techniques to determine the concentration of nutrients in a complete vegetative cycle was studied. The results showed prediction with an R2P of 0.70 to 0.65 for P, K Ca and B. Using the weight regression coefficient (BW), a subset of more influential bands was determined for P, K and B nutrients. The results showed that the bands of greatest importance for these nutrients are located in the Vis region (430-750 nm), which is associated with photosynthetic pigment uptake. Finally, in the fourth article, the potential of HSI to discriminate young leaves from leaves of previous vegetative cycles has been studied, which would improve the diagnosis given that the reference tables in this crop are made on leaves of spring sprouting. Subsequently, the prediction of nutrient concentration of young leaves was carried out using 49 spectral bands, obtaining better results for the nutrients P, K, Ca, iron (Fe) and Mn with R2P from 0.69 to 0.60. In addition, these nutrients were predicted by minimizing the number of bands to ten, with the BW and an R2P of 0.67 to 0.57. ¿ / Maylin Acosta thanks IFARHU-SENACYT for the Professional Excellence Scholarships, contract No. 270-2021-020. Sandra Munera thanks the Juan de la Cierva-Formación contract (FJC2021-047786-I) co-funded by MCIN/AEI/10.13039/501100011033 and EU NextGenerationEU/PRTR. This work is co-funded by MICIN-AEI through project TED2021-130117B-C31, GVA-IVIA through projects 52203 and 52204, and the European Regional Development Fund (ERDF) of the Generalitat Valenciana 2021–2027. / Acosta Tello, MO. (2024). Predicción de macro y micronutrientes en hojas de cítricos y caqui utilizando métodos ópticos no destructivos [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/207010 / Compendio