Remote sensing has been widely used in disaster management. However, application of optical imageries in damage detection is not always feasible for immediate damage assessment. In the case of the Sichuan earthquake in 2008, the damaged areas were covered by cloud and fog for most of the time. The all weather SAR imageries could instead provide information of the damaged area. Therefore, more efforts are needed to explore the usability of SAR data. In regards to this purpose, this research focuses on studying the ability of using various SAR data in damage identification through image classification, and furthermore the effectiveness of fusion of various sensors in classification is evaluated.   Three different types of SAR imagery were acquired over the heavily damaged zone Qushan town in the Sichuan earthquake. The 3 types of SAR data are ALOS PALSAR L-band, RADARSAT-1 C-band and the TerraSAR-X X- band imageries.   Maximum likelihood classification method is applied on the imageries.  Four classes: Water, collapsed area, built-up area and landslide area are defined in the study area. The ability of each band in identifying these four classes is studied and the overall classification accuracy is analysed. Furthermore, fusion of these 3 types of imageries is performed and the effectiveness and accuracy of image fusion classification are evaluated.   The results show that classification accuracy from individual SAR imagery is not ideal. The overall accuracy which PALSAR gives is 30.383%, RADARSAT-1 is 31.268% while TerraSAR-X only achieves 37.168%. Accuracy statistics demonstrate that TerraSAR-X performs the best in classifying these four classes.   SAR image fusion shows a better classification result. Double image fusion of PALSAR and RADARSAT-1, PALSAR and TerraSAR-X, and RADARSAT-1 and TerraSAR-X give an overall classification accuracy of 41.88%, 42.478% and 37.758% respectively. The result from triple image fusion even reaches 52.507%. They are all higher than the result given by the individual images.   The study illustrates that the VHR TerraSAR X band SAR data has a higher ability in classification of damages, and fusion of different band can improve the classification accuracy.

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>

Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing-based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction.

The method for developing a soil moisture inversion algorithm using Radar data can be approached in two ways: the multiple-incident angle approach and the change detection method. This thesis discusses how these two methods can be used to predict surface soil moisture. In the multiple incident angle approach, surface roughness can be mapped, if multiple incident angle viewing is possible and if the surface roughness is assumed constant during data acquisitions. A backpropagation neural network (NN) is trained with the data set generated by the Integral Equation Method (IEM) model. The training data set includes possible combinations of backscatter obtained as a result of variation in dielectric constant within the period of data acquisitions. The inputs to the network are backscatter acquired at different incident angles. The outputs are correlation length and root mean square height (rms). Once the roughness is mapped using these outputs, dielectric constant can be determined. Three different data sets, (backscatter acquired from multiplerequencies, multiple-polarizations, and multiple-incident angles) are used to train the NN. The performance of the NN trained by the different data sets is compared. The next approach is the application of the change detection concept. In this approach, the relative change in dielectric constant over two different periods is determined from Radarsat data using a simplified algorithm. The vegetation backscatter contribution can be removed with the aid of multi-spectral data provided by Landsat. A method is proposed that minimizes the effect of incident angle on Radar backscatter by normalizing the acquired SAR images to a reference angle. A quantitative comparison of some of the existing soil moisture estimation algorithms is also made

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.

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