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.