Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-04-17T23:16:01Z No. of bitstreams: 1 BrunoCesarPereiraDaCosta_TESE.pdf: 9035673 bytes, checksum: b47a178b546c68bed34b29cd6050cae3 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-04-20T22:33:39Z (GMT) No. of bitstreams: 1 BrunoCesarPereiraDaCosta_TESE.pdf: 9035673 bytes, checksum: b47a178b546c68bed34b29cd6050cae3 (MD5) / Made available in DSpace on 2017-04-20T22:33:39Z (GMT). No. of bitstreams: 1 BrunoCesarPereiraDaCosta_TESE.pdf: 9035673 bytes, checksum: b47a178b546c68bed34b29cd6050cae3 (MD5) Previous issue date: 2016-11-18 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES) / As imagens de sat?lites t?m sido utilizadas para mapear, monitorar e quantificar a qualidade dos recursos naturais. O mapeamento detalhado da vegeta??o de mangue ? uma demanda crescente por se tratar de um valoroso instrumento de conhecimento, manuten??o e gest?o do ecossistema manguezal em rela??o ?s modifica??es provocadas pelas atua??es antr?picas e/ou naturais, frente ?s mudan?as globais. Este trabalho combinou dados multiespectrais da regi?o do vis?vel e infravermelho pr?ximo do sistema LANDSAT-8 com dados monoespectrais do RADARSAT-2, aliado a dados hiperespectrais de espectrorradiometria e ?ndice de Vegeta??o na segmenta??o e classifica??o de alguns manguezais no Nordeste do Brasil, levando em considera??o a diversidade de ambientes presentes na ?rea total do estudo. Como resultado final do processo de segmenta??o e classifica??o, calculamos que os manguezais da ?rea estudada ocupam ?rea total de aproximadamente 5.392 ha. A esp?cie R. mangle ? a esp?cie dominante, totalizando uma ?rea de 3.350 ha, representando 62,13% da ?rea total, deste total cerca de 2.861 ha s?o ocupados pela R. mangle I (porte e adensamento convencional), representando 53,06% da ?rea total; 489 ha pela condi??o fitoestrutural R. mangle II (porte baixo e bastante adensado), cerca de 9,07% da ?rea total; regi?es mistas de esp?cies ou de transi??o entre elas ocupam ?rea de 1.092 ha, cerca de 20,25% da ?rea total, seguida da esp?cie A. schaueriana ocupando uma ?rea de 950 ha, cerca de 17,62% da ?rea total. Este estudo atendeu ?s expectativas em obter uma maior efici?ncia no levantamento espacial com alta acur?cia para o monitoramento da qualidade desse ecossistema altamente sens?vel ?s altera??es ambientais: como subs?dio ? sua preserva??o e transforma??o em um Projeto de Mecanismo de Desenvolvimento Limpo. Para tal, foi realizada a estimativa de CO2 aprisionado nas florestas de mangue pertencentes a Reserva de Desenvolvimento Sustent?vel Estadual Ponta do Tubar?o (RDSEPT) por meio de M?todo Indireto n?o destrutivo. As estimativas totais das m?dias de CO2 aprisionado em cada hectare ocupado pela esp?cie R. mangle ? de 39,93 t, L. schaueriana ? de 28,47 t e as regi?es de esp?cies mistas ? de 34,20 t. Estima-se que a quantifica??o total de CO2 aprisionado na RDSEPT seja 17.156,51 t. Mediante os valores obtidos, percebemos que o manguezal da RDSEPT de maneira geral possui um grande potencial de gerar biomassa e consequentemente aprisionar CO2. Podendo gerar uma valiosa oportunidade financeira, justificando a preserva??o deste ecossistema. / The satellite images have been used to map, monitor and quantify the quality of natural resource. The detailed mapping of mangrove vegetation is an increasing demand because it is a valorous Valorous instrument of knowledge, maintenance and management of the mangrove ecosystem in relation to changes caused by anthropic actions and/or natural activities to global changes. This work combined multispectral data from the visible and near infrared of the LANDSAT-8 system with Monospectral data from RADARSAT-2, combined with hyperspectral data of the spectroradiometry and Vegetation Index in the segmentation and classification of some mangroves in the Northeast of Brazil, taking into account the diversity of environments present in the total area of the study. As the final result of the segmentation and classification process, we calculate that the mangroves in the study area occupy a total area of approximately 5.392ha. The species R. mangle is the dominant species, totaling an area of 3,350 ha, representing 62.13% of the total area, of this total about 2,861 ha are occupied by R. mangle I (size and conventional densification), representing 53.06% of the total area; 489 ha for structural phyto condition R. mangle II (short stature and very dense), about 9.07% of the total area; mixed regions of species or transition between them occupy area of 1092 ha, about 20.25% of total area, then the specie A. schaueriana occupying an area of 950 ha, about 17.62% of the total area. This study met the expectations to get the greater efficiency in the spatial lifting for monitoring the quality of this highly sensitive ecosystem on the environmental changes: as support their preservation and transformation into a Clean Development Mechanism Project. For such, was made the estimation of trapped CO2 in mangrove forests belonging to Reserva de Desenvolvimento Sustent?vel Estadual Ponta do Tubar?o (RDSEPT) by Indirect Method nondestructive. The total estimates on the mean of trapped CO2 in each hectare occupied by the species R. mangle is 39.93t, L. schaueriana is 28.47t and the regions of mixed species is 34.20t. It is estimated that the total quantification of trapped CO2 in the RDSEPT is 17156.51t. By the obtained values, we realized that the values of mangrove in the RDSEPT in general have a great potential to generate biomass and consequently imprison CO2. It is able to generate a valuable financial opportunity, justifying the preservation of this ecosystem.

This project presents regional velocity measurements for the eastern portion of the St. Elias Mountains, including the entire glaciated area of Kluane National Park, derived from speckle tracking of Radarsat-2 imagery acquired in winter 2011 and 2012. This technique uses a cross-correlation approach to determine the displacement of the ‘speckle’ pattern of radar phase returns between two repeat-pass images. Further reconstruction of past velocities is performed on a selection of key glaciers using feature tracking of Landsat-5 imagery, allowing for the investigation of variability in glacier motion on interannual and decadal time scales. The results of the analysis showed that there is a strong velocity gradient across the region reflecting high accumulation rates on the Pacific-facing slope of the mountain range. These glaciers may have velocities an order of magnitude greater than glaciers of a similar size on the landward slope. Interannual variability was high, both in relation to surge events, of which a number were identified, and variation of other unknown controls on glacier motion. A long-term trend of velocity decrease was observed on the Kaskawulsh Glacier when comparing the results of this analysis to work carried out in the 1960s, the pattern of which is broadly congruent to measurements of surface elevation change over a similar period.

Urban represents one of the most dynamic areas in the global change context. To support rational policies for sustainable urban development, remote sensing technologies such as Synthetic Aperture Radar (SAR) enjoy increasing popularity for collecting up-to-date and reliable information such as urban land cover/land-use. With the launch of advanced spaceborne SAR sensors such as RADARSAT-2, multitemporal fully polarimetric SAR data in high-resolution become increasingly available. Therefore, development of new methodologies to analyze such data for detailed and accurate urban mapping is in demand.   This research investigated multitemporal fine resolution spaceborne polarimetric SAR (PolSAR) data for detailed urban land cover mapping. To this end, the north and northwest parts of the Greater Toronto Area (GTA), Ontario, Canada were selected as the study area. Six-date C-band RADARSAT-2 fine-beam full polarimetric SAR data were acquired during June to September in 2008. Detailed urban land covers and various natural classes were focused in this study.   Both object-based and pixel-based classification schemes were investigated for detailed urban land cover mapping. For the object-based approaches, Support Vector Machine (SVM) and rule-based classification method were combined to evaluate the classification capacities of various polarimetric features. Classification efficiencies of various multitemporal data combination forms were assessed. For the pixel-based approach, a temporal-spatial Stochastic Expectation-Maximization (SEM) algorithm was proposed. With an adaptive Markov Random Field (MRF) analysis and multitemporal mixture models, contextual information was explored in the classification process. Moreover, the fitness of alternative data distribution assumptions of multi-look PolSAR data were compared for detailed urban mapping by this algorithm.   Both the object-based and pixel-based classifications could produce the finer urban structures with high accuracy. The superiority of SVM was demonstrated by comparison with the Nearest Neighbor (NN) classifier in object-based cases. Efficient polarimetric parameters such as Pauli parameters and processing approaches such as logarithmically scaling of the data were found to be useful to improve the classification results. Combination of both the ascending and descending data with appropriate temporal span are suitable for urban land cover mapping. The SEM algorithm could preserve the detailed urban features with high classification accuracy while simultaneously overcoming the speckles. Additionally the fitness of the G0p and Kp distribution assumptions were demonstrated better than the Wishart one. / <p>QC 20110315</p>

Résumé : Le contexte récent d’accélération de la fonte des glaciers et calottes glaciaires (GCG) de l’archipel arctique canadien, jumelé aux difficultés de suivi des GCG de cette région, rendent essentiels le développement et l’utilisation de nouvelles approches innovatrices de suivi. Le potentiel de la stéréo radargrammétrie (SRG) RADARSAT-2 est ici caractérisé pour l’extraction d’élévations et le calcul de changements d’élévation et de bilans de masse (historiques et récents) sur les calottes glaciaires Barnes et Penny (Nunavut, Canada). Par la méthode semi-automatisée de recherche de corrélation à partir de couples stéréoscopiques RADARSAT-2 de 2013 (mode wide ultra-fin; résolution spatiale de 3 m; taille d’image de 50 km x 50 km), une précision verticale de ~7 m (LE68) est mesurée sur la terre ferme, et cette valeur de précision est possiblement légèrement supérieure sur la calotte Barnes, étant donné la variabilité de profondeur de pénétration. Par captage 3D, une précision altimétrique de ~3-4 m (LE68) est mesurée par différents photo-interprètes à partir de couples RADARSAT de 2012 en zone d’ablation de la calotte Penny. Sur la calotte Barnes, les changements d’élévation mesurés par rapport aux premiers modèles numériques de terrain disponibles permettent de mesurer un bilan de masse spécifique historique (1960-2013) de -0,49 ± 0,20 m w.e./année, pour un bilan de masse total de -2,9 Gt/année. Entre 2005 et 2013, le bilan de masse spécifique de cette calotte augmente significativement à -1,20 ± 0,86 m w.e./année, pour un bilan de masse total de -7 Gt/année. En zone d’ablation de la calotte Penny, un changement d’élévation annuel moyen de -0,59 m/année est mesuré entre 1958 et 2012. Parallèlement, plusieurs aspects méthodologiques et techniques sont discutés et analysés. Des profondeurs de pénétration nulles (bande C) sont mesurées à partir des images acquises sur la calotte Barnes à la toute fin de la saison d’ablation (fin septembre/début octobre), alors que cette profondeur augmente à ~2,5-3 m pour des images acquises à la fin octobre/début novembre (période de gel). Nos résultats suggèrent aussi que le modèle de fonction rationnelle, lorsqu’utilisé avec des images RADARSAT-2 en mode wide ultra-fin, permet d’obtenir des précisions plus constantes que le modèle hybride de Toutin. De par son indépendance des conditions météorologiques, son utilisation possible sans point de contrôle et sa simplicité de traitement, la SRG RADARSAT-2 s’avère donc être une excellente alternative aux technologies actuelles pour le suivi de GCG situés dans des régions affectées par des contraintes opérationnelles importantes. / Abstract : Given the recent melt acceleration of the Canadian arctic archipelago’s ice caps and the monitoring difficulties of this remote region, the development of new innovative monitoring tools has become essential. Here, the potential of the RADARSAT-2 stereo radargrammetry (SRG) is characterized for elevations extraction, as well as for elevation changes/mass balances calculations (historical and recent) on Barnes and Penny ice caps (Nunavut, Canada). Using the semi-automatic approach of correlation search from RADARSAT-2 stereoscopic couples of 2013 (wide ultra-fine mode; spatial resolution of 3 m; coverage of 50 km x 50 km), a vertical precision of ~7 m (LE68) is measured on ice-free terrain and this precision is possibly slighty worse on the ice cap because of the penetration depth’s variability. On the other hand, the 3D vision extraction approach reveals an altimetric precision of ~3-4 m (LE68) on the ablation area of the Penny Ice Cap. On the Barnes Ice Cap, elevation changes calculated relative to the oldest digital elevation models available allows to calculate an historical specific mass balance (1960-2013) of -0,49 ± 0,20 m w.e./year, resulting in a total annual mass balance of -2,9 Gt/year. Between 2005 and 2013, the specific mass balance of this ice cap increases to -1,20 ± 0,86 m w.e./year, which equals to a total annual mass balance f -7 Gt/year. On Penny Ice Cap’s ablation area, an average elevation change of -0,59 m/year is measured between 1958 and 2012. As also suggested in the literature, the recent melt acceleration is highly linked to warmer summer temperatures. Methodological and technical aspects are also presented and analyzed. No penetration depth (C band) is perceived on elevations derived from late ablation season images (late September/beginning of October), while a penetration of ~2,5-3 m is measured from images acquired in late October/beginning of November (freeze period). Our results also suggest the superiority and better consistency of the rational function model for geometrical correction of wide ultra-fine mode RADARSAT-2 images, compared to the hybrid Toutin’s model. Because of its all-weather functionality, its possible use without any ground control point and the simplicity and facility of its treatment, the RADARSAT-2 SRG represents a really good technology for glacier monitoring in regions affected by serious operational constraints.

MODIS est une méthode fiable et précise utilisée couramment pour suivre l'évolution du couvert nival au-dessus de bassins versants alpins. Toutefois, cette méthode de télédétection possède quelques limitations importantes, tel que l'inhabilité à distinguer la neige humide de la neige sèche, qui pourrait être mieux prise en compte par l'utilisation d'une méthode de télédétection complémentaire telle que l'imagerie par radar à synthèse d'ouverture (RSO). Le site d'étude utilisé pour le projet est le bassin versant de la rivière Nechako, situé dans la chaîne Côtière de la Colombie-Britannique, qui est caractérisé par un manteau neigeux pouvant atteindre plusieurs mètres d’épaisseur en montagne. Quinze images RADARSAT-2 en mode ScanSAR Wide ont été obtenues en polarisation VV et VH entre les mois de mars et juillet 2012. Elles ont été traitées à l'aide d'un algorithme basé sur la méthode de Nagler et Rott pour distinguer la neige humide de la neige sèche, mais qui utilise un seuil graduel plutôt que le seuil de -3 dB fréquemment utilisé. Les cartes de neige humide qui découlent de cette technique correspondent mieux aux incertitudes retrouvées sur le bassin en raison de la présence importante de forêts de conifères et de régions montagneuses. Les cartes ont été combinées au produit de neige de MODIS, afin d'utiliser son habileté à détecter le couvert nival avec précision pour corriger les zones de bruit des images RSO, causées entre autres par des sols gorgés en eau. Afin d'aider l'analyse des images RSO, une modélisation fine du manteau neigeux a été effectuée avec le logiciel Crocus afin de procéder à une analyse détaillée de l’évolution des caractéristiques du manteau neigeux, notamment du contenu en eau liquide de la neige, tout au long de l’hiver. La modélisation a été effectuée à l'emplacement de trois coussins à neige sur le bassin versant et est réalisée grâce à l'utilisation de données du North American Regional Reanalysis (NARR). À partir des résultats du modèle Crocus et de l'équivalent en eau observé aux coussins à neige, une relation a été établie entre la détection de neige humide en montagne par RADARSAT-2 et le ruissellement reçu au réservoir de la rivière Nechako. Avec le jeu de données actuel, le ruissellement maximal reçu au réservoir a été prévu avec une précision de 10 jours. Il est prévu que davantage d'années d’images radar pourraient permettre de confirmer et de réduire cet intervalle.

Urban land cover mapping represents one of the most important remote sensing applications in the context of rapid global urbanization. In recent years, high resolution spaceborne Polarimetric Synthetic Aperture Radar (PolSAR) has been increasingly used for urban land cover/land-use mapping, since more information could be obtained in multiple polarizations and the collection of such data is less influenced by solar illumination and weather conditions.  The overall objective of this research is to develop effective methods to extract accurate and detailed urban land cover information from spaceborne PolSAR data. Six RADARSAT-2 fine-beam polarimetric SAR and three RADARSAT-2 ultra-fine beam SAR images were used. These data were acquired from June to September 2008 over the north urban-rural fringe of the Greater Toronto Area, Canada. The major landuse/land-cover classes in this area include high-density residential areas, low-density residential areas, industrial and commercial areas, construction sites, roads, streets, parks, golf courses, forests, pasture, water and two types of agricultural crops. In this research, various polarimetric SAR parameters were evaluated for urban land cover mapping. They include the parameters from Pauli, Freeman and Cloude-Pottier decompositions, coherency matrix, intensities of each polarization and their logarithms.  Both object-based and pixel-based classification approaches were investigated. Through an object-based Support Vector Machine (SVM) and a rule-based approach, efficiencies of various PolSAR features and the multitemporal data combinations were evaluated. For the pixel-based approach, a contextual Stochastic Expectation-Maximization (SEM) algorithm was proposed. With an adaptive Markov Random Field (MRF) and a modified Multiscale Pappas Adaptive Clustering (MPAC), contextual information was explored to improve the mapping results. To take full advantages of alternative PolSAR distribution models, a rule-based model selection approach was put forward in comparison with a dictionary-based approach.  Moreover, the capability of multitemporal fine-beam PolSAR data was compared with multitemporal ultra-fine beam C-HH SAR data. Texture analysis and a rule-based approach which explores the object features and the spatial relationships were applied for further improvement. Using the proposed approaches, detailed urban land-cover classes and finer urban structures could be mapped with high accuracy in contrast to most of the previous studies which have only focused on the extraction of urban extent or the mapping of very few urban classes. It is also one of the first comparisons of various PolSAR parameters for detailed urban mapping using an object-based approach. Unlike other multitemporal studies, the significance of complementary information from both ascending and descending SAR data and the temporal relationships in the data were the focus in the multitemporal analysis. Further, the proposed novel contextual analyses could effectively improve the pixel-based classification accuracy and present homogenous results with preserved shape details avoiding over-averaging. The proposed contextual SEM algorithm, which is one of the first to combine the adaptive MRF and the modified MPAC, was able to mitigate the degenerative problem in the traditional EM algorithms with fast convergence speed when dealing with many classes. This contextual SEM outperformed the contextual SVM in certain situations with regard to both accuracy and computation time. By using such a contextual algorithm, the common PolSAR data distribution models namely Wishart, G0p, Kp and KummerU were compared for detailed urban mapping in terms of both mapping accuracy and time efficiency. In the comparisons, G0p, Kp and KummerU demonstrated better performances with higher overall accuracies than Wishart. Nevertheless, the advantages of Wishart and the other models could also be effectively integrated by the proposed rule-based adaptive model selection, while limited improvement could be observed by the dictionary-based selection, which has been applied in previous studies. The use of polarimetric SAR data for identifying various urban classes was then compared with the ultra-fine-beam C-HH SAR data. The grey level co-occurrence matrix textures generated from the ultra-fine-beam C-HH SAR data were found to be more efficient than the corresponding PolSAR textures for identifying urban areas from rural areas. An object-based and pixel-based fusion approach that uses ultra-fine-beam C-HH SAR texture data with PolSAR data was developed. In contrast to many other fusion approaches that have explored pixel-based classification results to improve object-based classifications, the proposed rule-based fusion approach using the object features and contextual information was able to extract several low backscatter classes such as roads, streets and parks with reasonable accuracy. / <p>QC 20121112</p>

Mapping ice and open water in ocean bodies is important for numerous purposes including environmental analysis and ship navigation. The Canadian Ice Service (CIS) currently has several expert ice analysts manually generate ice maps on a daily basis. The CIS would like to augment their current process with an automated ice-water discrimination algorithm capable of operating on dual-pol synthetic aperture radar (SAR) images produced by RADARSAT-2. Automated methods can provide mappings in larger volumes, with more consistency, and in finer resolutions that are otherwise impractical to generate. We have developed such an automated ice-water discrimination system called MAGIC. The algorithm first classifies the HV scene using the glocal method, a hierarchical region-based classification method. The glocal method incorporates spatial context information into the classification model using a modified watershed segmentation and a previously developed MRF classification algorithm called IRGS. Second, a pixel-based support vector machine (SVM) using a nonlinear RBF kernel classification is performed exploiting SAR grey-level co-occurrence matrix (GLCM) texture and backscatter features. Finally, the IRGS and SVM classification results are combined using the IRGS approach but with a modified energy function to accommodate the SVM pixel-based information. The combined classifier was tested on 61 ground truthed dual-pol RADARSAT-2 scenes of the Beaufort Sea containing a variety of ice types and water patterns across melt, summer, and freeze-up periods. The average leave-one-out classification accuracy with respect to these ground truths is 95.8% and MAGIC attains an accuracy of 90% or above on 88% of the scenes. The MAGIC system is now under consideration by CIS for operational use.

Mapping ice and open water in ocean bodies is important for numerous purposes including environmental analysis and ship navigation. The Canadian Ice Service (CIS) currently has several expert ice analysts manually generate ice maps on a daily basis. The CIS would like to augment their current process with an automated ice-water discrimination algorithm capable of operating on dual-pol synthetic aperture radar (SAR) images produced by RADARSAT-2. Automated methods can provide mappings in larger volumes, with more consistency, and in finer resolutions that are otherwise impractical to generate. We have developed such an automated ice-water discrimination system called MAGIC. The algorithm first classifies the HV scene using the glocal method, a hierarchical region-based classification method. The glocal method incorporates spatial context information into the classification model using a modified watershed segmentation and a previously developed MRF classification algorithm called IRGS. Second, a pixel-based support vector machine (SVM) using a nonlinear RBF kernel classification is performed exploiting SAR grey-level co-occurrence matrix (GLCM) texture and backscatter features. Finally, the IRGS and SVM classification results are combined using the IRGS approach but with a modified energy function to accommodate the SVM pixel-based information. The combined classifier was tested on 61 ground truthed dual-pol RADARSAT-2 scenes of the Beaufort Sea containing a variety of ice types and water patterns across melt, summer, and freeze-up periods. The average leave-one-out classification accuracy with respect to these ground truths is 95.8% and MAGIC attains an accuracy of 90% or above on 88% of the scenes. The MAGIC system is now under consideration by CIS for operational use.

Urban areas around the world are changing constantly and therefore it is necessary to update urban land cover maps regularly. Remote sensing techniques have been used to monitor changes and update land-use/land-cover information in urban areas for decades. Optical imaging systems have received most of the attention in urban studies. The development of SAR applications in urban monitoring has been accelerated with more and more advanced SAR systems operating in space.   This research investigated object-based and rule-based classification methodologies for extracting urban land-cover information from high resolution SAR data. The study area is located in the north and northwest part of the Greater Toronto Area (GTA), Ontario, Canada, which has been undergoing rapid urban growth during the past decades. Five-date RADARSAT-1 fine-beam C-HH SAR images with a spatial resolution of 10 meters were acquired during May to August in 2002. Three-date RADARSAT-2 ultra-fine-beam C-HH SAR images with a spatial resolution of 3 meters were acquired during June to September in 2008.   SAR images were pre-processed and then segmented using multi-resolution segmentation algorithm. Specific features such as geometric and texture features were selected and calculated for image objects derived from the segmentation of SAR images. Both neural network (NN) and support vector machines (SVM) were investigated for the supervised classification of image objects of RADARSAT-1 SAR images, while SVM was employed to classify image objects of RADARSAT-2 SAR images. Knowledge-based rules were developed and applied to resolve the confusion among some classes in the object-based classification results.   The classification of both RADARSAT-1 and RADARSAT-2 SAR images yielded relatively high accuracies (over 80%). SVM classifier generated better result than NN classifier for the object-based supervised classification of RADARSAT-1 SAR images. Well-designed knowledge-based rules could increase the accuracies of some classes after the object-based supervised classification. The comparison of the classification results of RADARSAT-1 and RADARSAT-2 SAR images showed that SAR images with higher resolution could reveal more details, but might produce lower classification accuracies for certain land cover classes due to the increasing complexity of the images. Overall, the classification results indicate that the proposed object-based and rule-based approaches have potential for operational urban land cover mapping from high-resolution space borne SAR images. / QC 20101209

Les milieux humides remplissent plusieurs fonctions écologiques d’importance et contribuent à la biodiversité de la faune et de la flore. Même s’il existe une reconnaissance croissante sur l’importante de protéger ces milieux, il n’en demeure pas moins que leur intégrité est encore menacée par la pression des activités humaines. L’inventaire et le suivi systématique des milieux humides constituent une nécessité et la télédétection est le seul moyen réaliste d’atteindre ce but. L’objectif de cette thèse consiste à contribuer et à améliorer la caractérisation des milieux humides en utilisant des données satellites acquises par des radars polarimétriques en bande L (ALOS-PALSAR) et C (RADARSAT-2). Cette thèse se fonde sur deux hypothèses (chap. 1). La première hypothèse stipule que les classes de physionomies végétales, basées sur la structure des végétaux, sont plus appropriées que les classes d’espèces végétales car mieux adaptées au contenu informationnel des images radar polarimétriques. La seconde hypothèse stipule que les algorithmes de décompositions polarimétriques permettent une extraction optimale de l’information polarimétrique comparativement à une approche multipolarisée basée sur les canaux de polarisation HH, HV et VV (chap. 3). En particulier, l’apport de la décomposition incohérente de Touzi pour l’inventaire et le suivi de milieux humides est examiné en détail. Cette décomposition permet de caractériser le type de diffusion, la phase, l’orientation, la symétrie, le degré de polarisation et la puissance rétrodiffusée d’une cible à l’aide d’une série de paramètres extraits d’une analyse des vecteurs et des valeurs propres de la matrice de cohérence. La région du lac Saint-Pierre a été sélectionnée comme site d’étude étant donné la grande diversité de ses milieux humides qui y couvrent plus de 20 000 ha. L’un des défis posés par cette thèse consiste au fait qu’il n’existe pas de système standard énumérant l’ensemble possible des classes physionomiques ni d’indications précises quant à leurs caractéristiques et dimensions. Une grande attention a donc été portée à la création de ces classes par recoupement de sources de données diverses et plus de 50 espèces végétales ont été regroupées en 9 classes physionomiques (chap. 7, 8 et 9). Plusieurs analyses sont proposées pour valider les hypothèses de cette thèse (chap. 9). Des analyses de sensibilité par diffusiogramme sont utilisées pour étudier les caractéristiques et la dispersion des physionomies végétales dans différents espaces constitués de paramètres polarimétriques ou canaux de polarisation (chap. 10 et 12). Des séries temporelles d’images RADARSAT-2 sont utilisées pour approfondir la compréhension de l’évolution saisonnière des physionomies végétales (chap. 12). L’algorithme de la divergence transformée est utilisé pour quantifier la séparabilité entre les classes physionomiques et pour identifier le ou les paramètres ayant le plus contribué(s) à leur séparabilité (chap. 11 et 13). Des classifications sont aussi proposées et les résultats comparés à une carte existante des milieux humide du lac Saint-Pierre (14). Finalement, une analyse du potentiel des paramètres polarimétrique en bande C et L est proposé pour le suivi de l’hydrologie des tourbières (chap. 15 et 16). Les analyses de sensibilité montrent que les paramètres de la 1re composante, relatifs à la portion dominante (polarisée) du signal, sont suffisants pour une caractérisation générale des physionomies végétales. Les paramètres des 2e et 3e composantes sont cependant nécessaires pour obtenir de meilleures séparabilités entre les classes (chap. 11 et 13) et une meilleure discrimination entre milieux humides et milieux secs (chap. 14). Cette thèse montre qu’il est préférable de considérer individuellement les paramètres des 1re, 2e et 3e composantes plutôt que leur somme pondérée par leurs valeurs propres respectives (chap. 10 et 12). Cette thèse examine également la complémentarité entre les paramètres de structure et ceux relatifs à la puissance rétrodiffusée, souvent ignorée et normalisée par la plupart des décompositions polarimétriques. La dimension temporelle (saisonnière) est essentielle pour la caractérisation et la classification des physionomies végétales (chap. 12, 13 et 14). Des images acquises au printemps (avril et mai) sont nécessaires pour discriminer les milieux secs des milieux humides alors que des images acquises en été (juillet et août) sont nécessaires pour raffiner la classification des physionomies végétales. Un arbre hiérarchique de classification développé dans cette thèse constitue une synthèse des connaissances acquises (chap. 14). À l’aide d’un nombre relativement réduit de paramètres polarimétriques et de règles de décisions simples, il est possible d’identifier, entre autres, trois classes de bas marais et de discriminer avec succès les hauts marais herbacés des autres classes physionomiques sans avoir recours à des sources de données auxiliaires. Les résultats obtenus sont comparables à ceux provenant d’une classification supervisée utilisant deux images Landsat-5 avec une exactitude globale de 77.3% et 79.0% respectivement. Diverses classifications utilisant la machine à vecteurs de support (SVM) permettent de reproduire les résultats obtenus avec l’arbre hiérarchique de classification. L’exploitation d’une plus forte dimensionalitée par le SVM, avec une précision globale maximale de 79.1%, ne permet cependant pas d’obtenir des résultats significativement meilleurs. Finalement, la phase de la décomposition de Touzi apparaît être le seul paramètre (en bande L) sensible aux variations du niveau d’eau sous la surface des tourbières ouvertes (chap. 16). Ce paramètre offre donc un grand potentiel pour le suivi de l’hydrologie des tourbières comparativement à la différence de phase entre les canaux HH et VV. Cette thèse démontre que les paramètres de la décomposition de Touzi permettent une meilleure caractérisation, de meilleures séparabilités et de meilleures classifications des physionomies végétales des milieux humides que les canaux de polarisation HH, HV et VV. Le regroupement des espèces végétales en classes physionomiques est un concept valable. Mais certaines espèces végétales partageant une physionomie similaire, mais occupant un milieu différent (haut vs bas marais), ont cependant présenté des différences significatives quant aux propriétés de leur rétrodiffusion. / Wetlands fill many important ecological functions and contribute to the biodiversity of fauna and flora. Although there is a growing recognition of the importance to protect these areas, it remains that their integrity is still threatened by the pressure of human activities. The inventory and the systematic monitoring of wetlands are a necessity and remote sensing is the only realistic way to achieve this goal. The primary objective of this thesis is to contribute and improve the wetland characterization using satellite polarimetric data acquired in L (ALOS-PALSAR) and C (RADARSAT-2) band. This thesis is based on two hypotheses (Ch. 1). The first hypothesis stipulate that classes of plant physiognomies, based on plant structure, are more appropriate than classes of plant species because they are best adapted to the information content of polarimetric radar data. The second hypothesis states that polarimetric decomposition algorithms allow an optimal extraction of polarimetric information compared to a multi-polarized approach based on the HH, HV and VV channels (Ch. 3). In particular, the contribution of the incoherent Touzi decomposition for the inventory and monitoring of wetlands is examined in detail. This decomposition allows the characterization of the scattering type, its phase, orientation, symmetry, degree of polarization and the backscattered power of a target with a series of parameters extracted from an analysis of the coherency matrix eigenvectors and eigenvalues. The lake Saint-Pierre region was chosen as the study site because of the great diversity of its wetlands that are covering more than 20 000 ha. One of the challenges posed by this thesis is that there is neither a standard system enumerating all the possible physiognomic classes nor an accurate description of their characteristics and dimensions. Special attention was given to the creation of these classes by combining several data sources and more than 50 plant species were grouped into nine physiognomic classes (Ch. 7, 8 and 9). Several analyzes are proposed to validate the hypotheses of this thesis (Ch. 9). Sensitivity analysis using scatter plots are performs to study the characteristics and dispersion of plant physiognomic classes in various features space consisting of polarimetric parameters or polarization channels (Ch. 10 and 12). Time series of made of RADARSAT-2 images are used to deepen the understanding of the seasonal evolution of plant physiognomies (Ch. 12). The transformed divergence algorithm is used to quantify the separability between physiognomic classes and to identify the parameters (s) that contribute the most to their separability (Ch. 11 and 13). Classifications are also proposed and the results compared to an existing map of the lake Saint-Pierre wetlands (Ch. 14). Finally, an analysis of the potential of polarimetric parameters in C and L-band is proposed for the monitoring of peatlands hydrology (Ch. 15 and 16). Sensitivity analyses show that the parameters of the 1st component, relative to the dominant (polarized) part of the signal, are sufficient for a general characterization of plant physiognomies. The parameters of the second and third components are, however, needed for better class separability (Ch. 11 and 13) and a better discrimination between wetlands and uplands (Ch. 14). This thesis shows that it is preferable to consider individually the parameters of the 1st, 2nd and 3rd components rather than their weighted sum by their respective eigenvalues (Ch. 10 and 12). This thesis also examines the complementarity between the structural parameters and those related to the backscattered power, often ignored and normalized by most polarimetric decomposition. The temporal (seasonal) dimension is essential for the characterization and classification of plant physiognomies (Ch. 12, 13 and 14). Images acquired in spring (April and May) are needed to discriminate between upland and wetlands while images acquired in summer (July and August) are needed to refine the classifications of plant physiognomies. A hierarchical classification tree developed in this thesis represents a synthesis of the acquired knowledge (Chapter 14). Using a relatively small number of polarimetric parameters and simple decision rules, it is possible to identify, among other, three low marshes classes and to discriminate with success herbaceous high marshes from other physiognomic classes without using ancillary data source. The results obtained are comparable to those from a supervised classification using two Landsat-5 images with an overall accuracy of 77.3% and 79.0% respectively. Various classifications using the support vector machine (SVM) can reproduce the results obtained with the hierarchical classification tree. But the possible exploitation by the SVM of a higher dimensionality, with a maximum overall accuracy of 79.1%, does not allow however to achieve significantly better results. Finally, the phase of the Touzi decomposition appears to be the only parameter (in L-band) sensitive to changes in water level beneath the peat surface (Ch. 16). Therefore, this parameter offer a great potential for peatlands hydrology monitoring compared to the HH-VV phase difference. This thesis demonstrates that the Touzi decomposition parameters allow a better characterization, better separability and better classifications of wetlands plant physiognomic classes than HH, HV and VV polarization channels. The grouping of plant species into physiognomic classes is a valid concept. However, some plant species sharing a similar physiognomy, but occupying a different environment (high vs. low marshes), have presented significant differences in their scattering properties.