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Assimilating high temporal frequency SPOT data to describe canopy functioning: the ADAM project
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Prise en compte de structures spatiales pour l’assimilation variationnelle de données de télédétection. Exemple sur un modèle simple de croissance de végétation
No full text[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAUInternational audienceEn assimilation de données, une série temporelle de données-image devrait être traitée explicitement pour en extraire toute l'information. Cette étude propose d'adapter une méthode d'assimilation variationnelle d'images de LAI (Leaf Area Index) dans un modèle de végétation, afin d'intégrer l'information liée à l'aspect spatial des données. Pour cela, on considère que les paramètres sont contrôlés spatialement à certains niveaux: variété, parcelle, pixel, ou stables temporellement sur l'ensemble de l'image. Ces contraintes réduisent la taille du problème inverse, en transformant le schéma d'assimilation habituel à des ensembles de pixels simultanés. La méthode est appliquée sur le modèle semi-mécaniste BONSAÏ et évaluée sur la qualité de prédiction du LAI et d'estimation des paramètres d'entrée par expériences jumelles, ainsi que sur sa sensibilité à la fréquence des observations. Les contraintes spatio-temporelles améliorent la robustesse et les estimations lorsque la quantité d'observations disponibles diminue, par rapport à la méthode classique, où chaque pixel.date est considéré indépendamment des autres. / Information contained in time series of image data should be explicitly exploited in data assimilation methods instead of operating over single pixels. This study proposes to adapt a variational data assimilation method of LAI (Leaf Area Index) images in a crop model. The method assumes that the parameters are governed spatially at some levels (cultivar, field, and pixel), while some of them are assumed to be stable temporally over the whole image. Such constraints help at reducing the size of the inverse problem, transforming the usual assimilation scheme into simultaneous pixel patterns. DA with constraints is applied to the semi-mechanistic model BONSAÏ and evaluated by twin experiments both on the quality of LAI prediction and on parameter estimates. Sensitivity to the observations frequency is also evaluated. The constraints improve the method's robustness and estimates when the number of observations available decreases, compared to the conventional method
Prise en compte de structures spatiales pour l’assimilation variationnelle de données de télédétection. Exemple sur un modèle simple de croissance de végétation
No full text[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAUInternational audienceEn assimilation de données, une série temporelle de données-image devrait être traitée explicitement pour en extraire toute l'information. Cette étude propose d'adapter une méthode d'assimilation variationnelle d'images de LAI (Leaf Area Index) dans un modèle de végétation, afin d'intégrer l'information liée à l'aspect spatial des données. Pour cela, on considère que les paramètres sont contrôlés spatialement à certains niveaux: variété, parcelle, pixel, ou stables temporellement sur l'ensemble de l'image. Ces contraintes réduisent la taille du problème inverse, en transformant le schéma d'assimilation habituel à des ensembles de pixels simultanés. La méthode est appliquée sur le modèle semi-mécaniste BONSAÏ et évaluée sur la qualité de prédiction du LAI et d'estimation des paramètres d'entrée par expériences jumelles, ainsi que sur sa sensibilité à la fréquence des observations. Les contraintes spatio-temporelles améliorent la robustesse et les estimations lorsque la quantité d'observations disponibles diminue, par rapport à la méthode classique, où chaque pixel.date est considéré indépendamment des autres. / Information contained in time series of image data should be explicitly exploited in data assimilation methods instead of operating over single pixels. This study proposes to adapt a variational data assimilation method of LAI (Leaf Area Index) images in a crop model. The method assumes that the parameters are governed spatially at some levels (cultivar, field, and pixel), while some of them are assumed to be stable temporally over the whole image. Such constraints help at reducing the size of the inverse problem, transforming the usual assimilation scheme into simultaneous pixel patterns. DA with constraints is applied to the semi-mechanistic model BONSAÏ and evaluated by twin experiments both on the quality of LAI prediction and on parameter estimates. Sensitivity to the observations frequency is also evaluated. The constraints improve the method's robustness and estimates when the number of observations available decreases, compared to the conventional method
Utilisation de contraintes spatiales dans un schéma d'assimilation d'images de télédétection. Exemple sur un modèle de culture simple, Bonsaï
No full text[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAUInternational audienceAssimilation of remote sensing data into crop models is generally applied pixel by pixel, while the whole image data is available. Estimating the crop model parameters of a pixel independently from the neighboring ones generates at least four issues: (1) the method does not take into account the possible spatial structures although there are not necessarily existing or easy to quantify (2) the spatial properties of images, and even more of time series of images constitute an additional source of information to use, which is neglected in this case (3) the inverse problem is generally ill-posed when applied at the pixel level and (4) repeating the same action a great number of times is not computer efficient and provides sub-optimal solutions. When inverting models of significant size, computational cost becomes a clear limitation. Some constraints are sometimes added to ensure better spatial consistency through the regularization of the behavior of a pixel by that the neighboring ones. However, this ignores the spatial dependencies of the parameters to be estimated. We propose here to exploit some spatial structures of the parameters to reduce the size of the problem and make its inversion manageable. It is applied to process concurrently a set of pixels using variational assimilation using the adjoint model. This method was applied on a simple model of plant growth, the BONSAI model, which simulates LAI (Leaf Area Index) as a function of 6 parameters. The method proposed here assumes that the parameters are governed by spatial structures depending on several levels: the cultivar, the field, and the pixel, while some of them are assumed to be stable over the whole image. For example, cultivar parameters govern phenological stages. At a lower level, some parameters depend on the field, such as agricultural practices. Other parameters depend on the pixel level, such as soil parameters. Finally, to improve the robustness of the method and reduce the space of realization, the parameters to which the model is not sensitive were considered fixed to a default value on all plots and all cultivars. The constrained variational method has been tested on twin experiments (with virtual data) and on actual observations from the ADAM experiement and evaluated on (1) the quality of the estimation of the model input parameters, (2) the quality of LAI simulation and (3) its sensitivity on the frequency of observations, i.e. satellite revisit time. If it is not always relevant to assert that space constraints allow a better reproduction of the trajectory of LAI as compared to a conventional method when a lot of observations are available, the method allows obtaining an equally satisfactory result for a much lower computational cost. However, the spatial constraints allow more stable and robust results when the frequency of observations is relaxed as compared to the assimilation by pixel. In addition, this new method requires the minimization of a single cost function easier to control when there are divergence or local minimum. Finally, estimate of the model input parameters, which is generally the main objective of data assimilation, is much less dependent on the number of observations assimilated by using these spatial constraints
Prise en compte de structures spatiales pour l’assimilation variationnelle de données de télédétection. Exemple sur un modèle simple de croissance de végétation
Get PDF[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAUInternational audienceEn assimilation de données, une série temporelle de données-image devrait être traitée explicitement pour en extraire toute l'information. Cette étude propose d'adapter une méthode d'assimilation variationnelle d'images de LAI (Leaf Area Index) dans un modèle de végétation, afin d'intégrer l'information liée à l'aspect spatial des données. Pour cela, on considère que les paramètres sont contrôlés spatialement à certains niveaux: variété, parcelle, pixel, ou stables temporellement sur l'ensemble de l'image. Ces contraintes réduisent la taille du problème inverse, en transformant le schéma d'assimilation habituel à des ensembles de pixels simultanés. La méthode est appliquée sur le modèle semi-mécaniste BONSAÏ et évaluée sur la qualité de prédiction du LAI et d'estimation des paramètres d'entrée par expériences jumelles, ainsi que sur sa sensibilité à la fréquence des observations. Les contraintes spatio-temporelles améliorent la robustesse et les estimations lorsque la quantité d'observations disponibles diminue, par rapport à la méthode classique, où chaque pixel.date est considéré indépendamment des autres. / Information contained in time series of image data should be explicitly exploited in data assimilation methods instead of operating over single pixels. This study proposes to adapt a variational data assimilation method of LAI (Leaf Area Index) images in a crop model. The method assumes that the parameters are governed spatially at some levels (cultivar, field, and pixel), while some of them are assumed to be stable temporally over the whole image. Such constraints help at reducing the size of the inverse problem, transforming the usual assimilation scheme into simultaneous pixel patterns. DA with constraints is applied to the semi-mechanistic model BONSAÏ and evaluated by twin experiments both on the quality of LAI prediction and on parameter estimates. Sensitivity to the observations frequency is also evaluated. The constraints improve the method's robustness and estimates when the number of observations available decreases, compared to the conventional method
Utilisation de contraintes spatiales dans un schéma d'assimilation d'images de télédétection. Exemple sur un modèle de culture simple, Bonsaï
No full text[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAUInternational audienceAssimilation of remote sensing data into crop models is generally applied pixel by pixel, while the whole image data is available. Estimating the crop model parameters of a pixel independently from the neighboring ones generates at least four issues: (1) the method does not take into account the possible spatial structures although there are not necessarily existing or easy to quantify (2) the spatial properties of images, and even more of time series of images constitute an additional source of information to use, which is neglected in this case (3) the inverse problem is generally ill-posed when applied at the pixel level and (4) repeating the same action a great number of times is not computer efficient and provides sub-optimal solutions. When inverting models of significant size, computational cost becomes a clear limitation. Some constraints are sometimes added to ensure better spatial consistency through the regularization of the behavior of a pixel by that the neighboring ones. However, this ignores the spatial dependencies of the parameters to be estimated. We propose here to exploit some spatial structures of the parameters to reduce the size of the problem and make its inversion manageable. It is applied to process concurrently a set of pixels using variational assimilation using the adjoint model. This method was applied on a simple model of plant growth, the BONSAI model, which simulates LAI (Leaf Area Index) as a function of 6 parameters. The method proposed here assumes that the parameters are governed by spatial structures depending on several levels: the cultivar, the field, and the pixel, while some of them are assumed to be stable over the whole image. For example, cultivar parameters govern phenological stages. At a lower level, some parameters depend on the field, such as agricultural practices. Other parameters depend on the pixel level, such as soil parameters. Finally, to improve the robustness of the method and reduce the space of realization, the parameters to which the model is not sensitive were considered fixed to a default value on all plots and all cultivars. The constrained variational method has been tested on twin experiments (with virtual data) and on actual observations from the ADAM experiement and evaluated on (1) the quality of the estimation of the model input parameters, (2) the quality of LAI simulation and (3) its sensitivity on the frequency of observations, i.e. satellite revisit time. If it is not always relevant to assert that space constraints allow a better reproduction of the trajectory of LAI as compared to a conventional method when a lot of observations are available, the method allows obtaining an equally satisfactory result for a much lower computational cost. However, the spatial constraints allow more stable and robust results when the frequency of observations is relaxed as compared to the assimilation by pixel. In addition, this new method requires the minimization of a single cost function easier to control when there are divergence or local minimum. Finally, estimate of the model input parameters, which is generally the main objective of data assimilation, is much less dependent on the number of observations assimilated by using these spatial constraints
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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