Thèses (M.Sc.)--Université de Sherbrooke (Canada), 1999. / Titre de l'écran-titre (visionné le 20 juin 2006). Publié aussi en version papier.

Pressured and ridged ice is a dangerous hazard facing ships in the Arctic. Ships can become stuck or beset in these conditions, which is environmentally and economically costly. Understanding where and when ridges form as a result of pressured ice is important for ensuring safe winter shipping operations; however there have been few studies to date regarding the distribution of ridges and their impacts within a geographic region. The Hudson Strait, which connects Hudson Bay and the Atlantic Ocean, is the site of ongoing winter shipping, where vessels frequently encounter pressured ice. This thesis addresses two questions: where and when do ridges occur in the Hudson Strait and what are their impacts on an ice strengthened vessel traveling through the Strait. To answer the first question, ridges were manually identified in RADARSAT-1 and -2 images during the winter months (December to May) from 1997-2012. Ridge counts and densities for each winter season were calculated and their spatial distribution was mapped. A 30-year sea ice climatology of the Hudson Strait was also created in order to understand ongoing trends in freeze-up and breakup timing in the region. Recurring patterns in the location and timing of ridging were found in the Hudson Strait, specifically in areas where shearing and bottlenecks created pressure. Ridge densities were correlated with sea level pressure, air temperature and wind NCEP reanalysis data to look to for connections between these factors and ridge densities. Some connections were found between freeze-up dates, sea level pressure and ridge densities. The second half of this thesis focuses on how ridges impact the voyage of an ice-strengthened vessel. Log books from the MV Arctic, a cargo ship that makes two winter transits through the Hudson Strait every year, were used to plot the movement of the ship and where and when it became beset. These data were examined for temporal and spatial patterns in besetting events. Most besetting events took place in February and March. They typically occurred in the eastern and western ends of the Strait. These voyages were compared to ridge data from the first half of thesis, and there were good correlations between the presence of high ridge densities and ship besetting events, demonstrating that ridge densities identified in satellite imagery can act as a proxy when forecasting hazardous ice conditions. This research fills an important knowledge gap in understanding where and when pressured ice forms in the Hudson Strait and what factors play a role in creating this hazardous ice condition. It also addresses the impacts that ridges have on ship transits through the Strait.

Thèses (M.Sc.)--Université de Sherbrooke (Canada), 2001. / Titre de l'écran-titre (visionné le 20 juin 2006). Publié aussi en version papier.

Information about the distribution of surface soil moisture can greatly benefit the management of agriculture and natural resource. However, direct measurement of soil moisture over larger areas can be impractical and expensive, which has led scientists to develop satellite based remote sensing techniques for soil moisture assessments. Retrieving soil moisture from radar satellite imagery often associated with the collection and use of ancillary field data on surface roughness. However, field data that is meant to characterize surface roughness is often unreliable, is expensive to collect and is nearly impossible to acquire for large scale applications. These issues represent barriers to the adoption and of radar data for mapping soil moisture over large areas.The research presented in the dissertation is aimed at the development of an operational soil moisture assessment system based solely on radar satellite data and a radar model, eliminating the field data requirements altogether. The research is directed towards a so-called equation-based solution of the problem as an alternative to the approach that requires the use of extensive field-data sets on surface roughness. This approach is based on the concept that if the number of equations are equal to the number of unknowns, then explicit solutions of all unknowns are possible. My research derived the necessary equations to solve for soil moisture and surface roughness. The derivation of the equations and how to use them to estimate soil moisture without using ancillary field data was demonstrated by my research. Validation results showed that the equation-based method that was developed is capable of providing more precise estimates of surface soil moisture than that of ancillary field-data supported method.

Résumé : En raison de sa grande étendue, le Nord canadien présente plusieurs défis logistiques pour une exploitation rentable de ses ressources minérales. La TéléCartographie Prédictive (TCP) vise à faciliter la localisation de gisements en produisant des cartes du potentiel géologique. Des données altimétriques sont nécessaires pour générer ces cartes. Or, celles actuellement disponibles au nord du 60e parallèle ne sont pas optimales principalement parce qu’elles sont dérivés de courbes à équidistance variable et avec une valeur au mètre. Parallèlement, il est essentiel de connaître l'exactitude verticale des données altimétriques pour être en mesure de les utiliser adéquatement, en considérant les contraintes liées à son exactitude. Le projet présenté vise à aborder ces deux problématiques afin d'améliorer la qualité des données altimétriques et contribuer à raffiner la cartographie prédictive réalisée par TCP dans le Nord canadien, pour une zone d’étude située au Territoire du Nord-Ouest. Le premier objectif était de produire des points de contrôles permettant une évaluation précise de l'exactitude verticale des données altimétriques. Le second objectif était de produire un modèle altimétrique amélioré pour la zone d'étude. Le mémoire présente d'abord une méthode de filtrage pour des données Global Land and Surface Altimetry Data (GLA14) de la mission ICESat (Ice, Cloud and land Elevation SATellite). Le filtrage est basé sur l'application d'une série d'indicateurs calculés à partir d’informations disponibles dans les données GLA14 et des conditions du terrain. Ces indicateurs permettent d'éliminer les points d'élévation potentiellement contaminés. Les points sont donc filtrés en fonction de la qualité de l’attitude calculée, de la saturation du signal, du bruit d'équipement, des conditions atmosphériques, de la pente et du nombre d'échos. Ensuite, le document décrit une méthode de production de Modèles Numériques de Surfaces (MNS) améliorés, par stéréoradargrammétrie (SRG) avec Radarsat-2 (RS-2). La première partie de la méthodologie adoptée consiste à faire la stéréorestitution des MNS à partir de paires d'images RS-2, sans point de contrôle. L'exactitude des MNS préliminaires ainsi produits est calculée à partir des points de contrôles issus du filtrage des données GLA14 et analysée en fonction des combinaisons d’angles d'incidences utilisées pour la stéréorestitution. Ensuite, des sélections de MNS préliminaires sont assemblées afin de produire 5 MNS couvrant chacun la zone d'étude en totalité. Ces MNS sont analysés afin d'identifier la sélection optimale pour la zone d'intérêt. Les indicateurs sélectionnés pour la méthode de filtrage ont pu être validés comme performant et complémentaires, à l’exception de l’indicateur basé sur le ratio signal/bruit puisqu’il était redondant avec l’indicateur basé sur le gain. Autrement, chaque indicateur a permis de filtrer des points de manière exclusive. La méthode de filtrage a permis de réduire de 19% l'erreur quadratique moyenne sur l'élévation, lorsque que comparée aux Données d'Élévation Numérique du Canada (DNEC). Malgré un taux de rejet de 69% suite au filtrage, la densité initiale des données GLA14 a permis de conserver une distribution spatiale homogène. À partir des 136 MNS préliminaires analysés, aucune combinaison d’angles d’incidences des images RS-2 acquises n’a pu être identifiée comme étant idéale pour la SRG, en raison de la grande variabilité des exactitudes verticales. Par contre, l'analyse a indiqué que les images devraient idéalement être acquises à des températures en dessous de 0°C, pour minimiser les disparités radiométriques entre les scènes. Les résultats ont aussi confirmé que la pente est le principal facteur d’influence sur l’exactitude de MNS produits par SRG. La meilleure exactitude verticale, soit 4 m, a été atteinte par l’assemblage de configurations de même direction de visées. Par contre, les configurations de visées opposées, en plus de produire une exactitude du même ordre (5 m), ont permis de réduire le nombre d’images utilisées de 30%, par rapport au nombre d'images acquises initialement. Par conséquent, l'utilisation d'images de visées opposées pourrait permettre d’augmenter l’efficacité de réalisation de projets de SRG en diminuant la période d’acquisition. Les données altimétriques produites pourraient à leur tour contribuer à améliorer les résultats de la TCP, et augmenter la performance de l’industrie minière canadienne et finalement, améliorer la qualité de vie des citoyens du Nord du Canada. / Abstract : Due to its vast extent, Northern Canada faces several logistical challenges for a profitable exploitation of its mineral resources. Remote Predictive Mapping (RPM) aims to help in targeting mineral deposits through the production of geological potential maps. Elevation data is necessary for the generation of these maps. However, the currently available elevation data north of the 60th parallel are not optimal primarily because it has been derived from contours with values at a metric precision. Additionally, exact knowledge of the vertical accuracy of elevation data is essential to insure a suitable use, within its accuracy constraints. This project aimed to improve the quality of elevation data and to contribute to the refinement of RPM products for a study site located in the Northwest Territories. The first objective was to generate control points to evaluate vertical accuracy with precision. The second objective was to generate an improved elevation model for the study site. First, a filtering method for Global Land and Surface Altimetry Data (GLA14) from the ICESat (Ice, Cloud and land Elevation SATellite) mission is presented. This filtering is based on indicators, derived from information available in GLA14 data and terrain conditions, which are then applied successively to remove potentially contaminated elevation points. The points are filtered based on the attitude calculation, signal saturation, equipment noise, atmospheric conditions, slope and number of peaks. Next, a method to generate an improved Digital Surface Models (DSM) using StereoRadarGrammetry (SRG) with Radarsat-2 (RS-2) images is described. In the first part of the adopted methodology, DSM are stereorestituted from RS-2 image pairs, without control point. Then, the vertical accuracy of the DSM is calculated using the control points resulting from the filtering of GLA14 data, and analysed according to the incidence angles combination used for the stereorestitution. Next, selections from the preliminary DSM are assembled to generate 5 DSM, each covering entirely the study site. Finally, the DSM are analysed to identify the optimal selection for the area of interest. The selected indicators were found to be efficient and complementary, except for the indicator based on the noise/signal ratio. Otherwise, all indicators allowed to filter out points exclusively. A 19% reduction of the elevation mean square error was achieved with the filtering method, when compared to Canadian Digital Elevation Data (CDED). The initial density of the GLA14 allowed maintaining a spatially homogeneous distribution of the post-filtering elevation points despite a 69% rejection rate. From the analysis of the 136 preliminary DSM, no specific combination of the acquired RS-2 images incidence angles stood out as being ideal with SRG due to high variability in vertical accuracy. Nonetheless, the analysis showed that images should be ideally acquired at sub-zero temperatures to minimize radiometric discrepancies between scenes. Results also showed that the slope is the main factor influencing the accuracy of DSM generated with SRG. The best vertical accuracy (4 m) was achieved with same-side view configurations. Opposite-side view configurations, despite achieving a vertical accuracy of 5 m, allowed a 30% reduction in the amount of images initially acquired. Therefore, the use of opposite-side view configurations could help to improve the efficiency of SRG projects by reducing considerably the acquisition period. Elevation data generated using the proposed method could help to improve results from RPM and increase the efficiency of the mining industry in Northern Canada and finally contribute to the betterment of the lives of Northern Canada’s citizens.

La télédétection possède plusieurs applications potentielles pour le suivi de l’humidité de surface du sol (0 à 5 cm de profondeur). Un suivi de l’humidité du sol à période régulière permettrait de nombreuses applications en hydrologie, climatologie, suivi d’événements météorologiques et agriculture de précision. Le signal radar à synthèse d’ouverture (RSO) en bande C tel que celui de RADARSAT-2 est sensible aux variations des paramètres du sol et de la végétation selon certaines conditions. L’inversion de modèles de rétrodiffusion linéaire a permis l’estimation de l’humidité du sol en zone agricole, mais pour des domaines de validité très restreints. Diverses missions satellitaires en cours ou futures permettent l’acquisition d’images radars polarimétriques. Les variables cohérentes déduites de ces images permettent de mieux décrire les cibles observées et elles ont permis l’estimation de l’humidité du sol pour un sol nu. Toutefois, le potentiel d’utilisation de la polarimétrie pour des cibles couvertes de végétation est encore mal connu. L’objectif de ce projet est d’évaluer le potentiel de la polarimétrie pour l’inversion de l’humidité du sol en zone agricole à partir d’images RSO en bande C. La campagne SMAPVEX12 menée à l’été 2012 au Manitoba a permis l’acquisition simultanée d’images polarimétriques RADARSAT-2, ainsi que des conditions du sol et de la végétation pour des champs de blé. La rétrodiffusion radar, en polarisation linéaire ou circulaire, est très sensible à l’humidité du sol avant l’épiaison du blé. Après, la végétation domine le signal. La calibration du modèle semi-empirique des canaux linéaires de rétrodiffusion, développé par Gherboudj et al. (2011) n’a pu correctement représenter les relations de la rétrodiffusion avec les caractéristiques agricoles observées. L’information de phase conservée par le capteur de RADARSAT-2 permet l’extraction de variables polarimétriques telles que la différence de phase HH-VV et la hauteur de socle, l’anisotropie A et l’entropie H issues de la décomposition de Cloude-Pottier dont la sensibilité à l’humidité du sol sera étudiée. Des modèles empiriques simples, calibrés par régression linéaire multiple de termes utilisant de 2 à 6 variables polarimétriques, ont été développés et ont permis d’estimer l’humidité du sol sur 5 champs de blé pour toute leur période de croissance avec une erreur RMSD de 0,074 m³/m³ en expliquant plus de 53.5% (R2) de la variance des valeurs d’humidité du sol observées, contre une erreur de 0.098 m³/m³ et une variance expliquée de 19.0% pour un modèle empirique basé que sur les variables incohérentes. / Abstract: Remote sensing has been widely researched toward estimation of soil conditions over agricultural fields. Monitoring of surface soil moisture mv would benefit many applications in hydrology, climatology, precision agriculture and risk reduction applied to meteorological events. C-band synthetic aperture radar (SAR) signal’s, such as that of RADARSAT-2, is sensitive to soil and vegetation characteristics. Backscattering coefficients obtained from those sensors allowed the estimation of mv by inverting empirical or semi-empirical models, under very strict conditions that limit their applicability. Many on-going or future missions provides polarimetric SAR images. However, the potential of polarimetric SAR sensors operated in c-band is not yet fully understood for soil moisture estimation over vegetated fields. This paper study the effects of soil and vegetation characteristics on polarimetric RADARSAT-2 images and proposes a simple empirical model based on polarimetric parameters extracted from RADARSAT-2 imagery to retrieve surface soil moisture (0-5 cm) over agricultural fields. The data used in this study was obtained during the SMAPVEX12 campaign, which occurred on the summer of 2012 between june 6th and july 17th in Manitoba, Canada. Fully polarimetric RADARSAT-2 images were acquired over 13 wheat fields over their whole growth cycle while their soil and vegetation conditions were monitored. Linear backscattering showed significant correlations for all polarizations before crops flowering. Sensitivity analysis of the extracted polarimetric variables to soil moisture demonstrated distinct correlations before and after the beginning of the crops flowering stage. The calibrated semi-empirical model proposed by Gherboudj et al. (2011) showed poor representation of the observed relationships between linear backscattering channels and crop conditions. The phase information, obtained by the RADARSAT-2 sensor, allowed extraction of polarimetric variables. Among those, phase difference HH-VV, the pedestal height and both the anisotropy H and entropy H obtained from Cloude-Pottier decomposition, showed significant correlations to soil moisture. A simple empirical model, calibrated with multiple linear regression from 2 to 6 polarimetric variables, allowed to retrieve soil moisture with a RMSD of 0,074 m³/m³ while it explained more than de 53.5% (R2) of observed soil moisture variability, while a simple linear model based only on incoherent variables could only estimate soil moisture with a RMSD of 0.098 m³/m³ and a R2 value of 19.0%.

Wildlife habitat mapping strongly supports applications in natural resource management, environmental conservation, impacts of anthropogenic activity, perturbed ecosystem restoration, species-at-risk recovery and species inventory. Remote sensing has long been identified as a feasible and effective technology for large-area wildlife habitat mapping. However, existing and future uncertainties in remote sensing will definitely have a significant effect on relevant scientific research, such as the limitation of Landsat-series data; the negative impact of cloud and cloud shadows (CCS) in optical imagery; and landscape pattern analysis using remote sensing classification products. This thesis adopted a manuscript-style format; it addresses these challenges (or uncertainties) and opportunities through exploring the state-of-the-art optical and radar remotely sensed data for large-area wildlife habitat mapping, and investigating their feasibility and applicability primarily by comparison either on the level of direct remote sensing products (e.g. classification accuracy) or indirect ecological model (e.g. presence/absence and frequency of use model based on landscape pattern analysis). A framework designed to identify and investigate the potential remotely sensed data, including Disaster Monitoring Constellation (DMC), Landsat Thematic Mapper (TM), Indian Remote Sensing (IRS), and RADARSAT-2, has been developed. The chosen DMC and RADARSAT-2 imagery have acceptable capability of addressing the existing and potential challenges (or uncertainties) in remote sensing of large-area habitat mapping, in order to produce cloud-free thematic maps for the study of wildlife habitat. A quantitative comparison between Landsat-based and IRS-based analyses showed that the characteristics of remote sensing products play an important role in landscape pattern analysis to build grizzly bear presence/absence and frequency of use models.

Wildlife habitat mapping strongly supports applications in natural resource management, environmental conservation, impacts of anthropogenic activity, perturbed ecosystem restoration, species-at-risk recovery and species inventory. Remote sensing has long been identified as a feasible and effective technology for large-area wildlife habitat mapping. However, existing and future uncertainties in remote sensing will definitely have a significant effect on relevant scientific research, such as the limitation of Landsat-series data; the negative impact of cloud and cloud shadows (CCS) in optical imagery; and landscape pattern analysis using remote sensing classification products. This thesis adopted a manuscript-style format; it addresses these challenges (or uncertainties) and opportunities through exploring the state-of-the-art optical and radar remotely sensed data for large-area wildlife habitat mapping, and investigating their feasibility and applicability primarily by comparison either on the level of direct remote sensing products (e.g. classification accuracy) or indirect ecological model (e.g. presence/absence and frequency of use model based on landscape pattern analysis). A framework designed to identify and investigate the potential remotely sensed data, including Disaster Monitoring Constellation (DMC), Landsat Thematic Mapper (TM), Indian Remote Sensing (IRS), and RADARSAT-2, has been developed. The chosen DMC and RADARSAT-2 imagery have acceptable capability of addressing the existing and potential challenges (or uncertainties) in remote sensing of large-area habitat mapping, in order to produce cloud-free thematic maps for the study of wildlife habitat. A quantitative comparison between Landsat-based and IRS-based analyses showed that the characteristics of remote sensing products play an important role in landscape pattern analysis to build grizzly bear presence/absence and frequency of use models.

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

Soil freezing and thawing processes are of particular importance for agricultural areas. For example, frozen soils can increase the runoff during snowmelt in the spring. Freezing and thawing also have a direct influence on the sowing and harvesting dates, as well as on the crop yield. A better understanding of those phenomena is therefore important, and several researchers focused on this topic in the past. Due to its sensitivity to changes in the state of water, microwave remote sensing is an appropriate tool for that purpose. The main objective of this study is to monitor soil freezing and thawing processes using AMSR-E and RADARSAT-2 polarimetric data acquired over an agricultural area located near Saskatoon (Saskatchewan). With AMSR-E data, the goals are to compare different combinations of frequencies for the spectral gradient's algorithm regarding their capacity for detecting frozen soils, and to analyze the temporal dynamics of the brightness temperature in order to find a new indicator of soil freezing. As for RADARSAT-2 data, several polarimetric parameters and techniques are tested in order to identify soil freezing. For the first part concerning AMSR-E data, a global precision for the discrimination of frozen and thawed soils higher than 90% was obtained with the spectral gradient's algorithm, for the combinations including high (18.7 and 36.5 GHz) and low (6.9 and 10.7 GHz) frequencies as well as for the one using only high frequencies. It is shown that, for the combination based on the 18.7 and 36.5 GHz frequencies, results are improved when a negative threshold is used for the spectral gradient. When high and low AMSR-E frequencies are combined, a null threshold is on the contrary appropriate, which constitutes an operational advantage. A new algorithm for detecting frozen soils, based on a thresholding approach applied to the spectral gradient of polarization difference and the brightness temperature at 36.5 GHz, was also proposed. The performances of the new algorithm to discriminate frozen and thawed soils are very similar to those obtained using the spectral gradient of brightness temperature (global precision around 90% and probability of detecting frozen soils between 70% and 85%). The performances are also slightly higher for the combinations including the lower AMSR-E frequencies. However, annual statistics for the spectral gradient of polarization difference are required to calculate the thresholds. The results obtained with AMSR-E data highlight the relevance of including SMOS L-band brightness temperatures for the calculation of brightness temperature and polarization difference spectral gradients. The qualitative analysis of the results obtained using RADARSAT-2 data shows that surface scattering dominates volume scattering for frozen soils, which can be explained by the rough fields in the study area, as compared to the signal's wavelength (C-band). Nevertheless, several polarimetric parameters indicate a slight increase of the volume scattering in frozen soils, which is theoretically expected. This was observed for the linear and circular depolarization ratios, the amplitude of the HHVV, RLLL and RLRR correlation coefficients, as well as for the pedestal height. Also, the entropy and [alpha overline]-angle of the Cloude-Pottier target decomposition increase slightly in frozen soils ; the same is true for the volume scattering component of the Freeman-Durden and Yamaguchi target decompositions, with an equivalent decrease of the surface scattering component. Despite these interesting observations, a quantitative analysis of the results is necessary in order to evaluate the usefulness of polarimetry regarding the detection of frozen soils. This would allow the validation of the behavior, possibly caused by soil freezing, of the mean value and the standard deviation of the HHVV phase difference and the standard deviation of the RLLL and RLRR phase differences.