Application of Differential and Polarimetric Synthetic Aperture Radar (SAR) Interferometry for Studying Natural Hazards

In the following work, I address the problem of coherence loss in standard Differential Interferometric SAR (DInSAR) processing, which can result in incomplete or poor quality deformation measurements in some areas. I incorporate polarimetric information with DInSAR in a technique called Polarimetric SAR Interferometry (PolInSAR) in order to acquire more accurate and detailed maps of surface deformation. In Chapter 2, I present a standard DInSAR study of the Ahar double earthquakes (Mw=6.4 and 6.2) which occurred in northwest Iran, August 11, 2012. The DInSAR coseismic deformation map was affected by decorrelation noise. Despite this, I employed an advanced inversion technique, in combination with a Coulomb stress analysis, to find the geometry and the slip distribution on the ruptured fault plane. The analysis shows that the two earthquakes most likely occurred on a single fault, not on conjugate fault planes. This further implies that the minor strike-slip faults play more significant role in accommodating convergence stress accumulation in the northwest part of Iran. Chapter 3 presents results from the application of PolInSAR coherence optimization on quad-pol RADARSAT-2 images. The optimized solution results in the identification of a larger number of reliable measurement points, which otherwise are not recognized by the standard DInSAR technique. I further assess the quality of the optimized interferometric phase, which demonstrates an increased phase quality with respect to those phases recovered by applying standard DInSAR alone. Chapter 4 discusses results from the application of PolInSAR coherence optimization from different geometries to the study of creep on the Hayward fault and landslide motions near Berkeley, CA. The results show that the deformation rates resolved by PolInSAR are in agreement with those of standard DInSAR. I also infer that there is potential motion on a secondary fault, northeast and parallel to the Hayward fault, which may be creeping with a lower velocity

Rice Crop Height Inversion from TanDEM-X PolInSAR Data Using the RVoG Model Combined with the Logistic Growth Equation

The random volume over ground (RVoG) model has been widely used in the field of vegetation height retrieval based on polarimetric interferometric synthetic aperture radar (PolInSAR) data. However, to date, its application in a time-series framework has not been considered. In this study, the logistic growth equation was introduced into the PolInSAR method for the first time to assist in estimating crop height, and an improved inversion scheme for the corresponding RVoG model parameters combined with the logistic growth equation was proposed. This retrieval scheme was tested using a time series of single-pass HH-VV bistatic TanDEM-X data and reference data obtained over rice fields. The effectiveness of the time-series RVoG model based on the logistic growth equation and the convenience of using equation parameters to evaluate vegetation growth status were analyzed at three test plots. The results show that the improved method can effectively monitor the height variation of crops throughout the whole growth cycle and the rice height estimation achieved an accuracy better than when single dates were considered. This proved that the proposed method can reduce the dependence on interferometric sensitivity and can achieve the goal of monitoring the whole process of rice height evolution with only a few PolInSAR observations.This research was funded in part by the National Natural Science Foundation of China (grant nos. 41820104005, 42030112, 41904004) and in part by the and the Spanish Ministry of Science and Innovation (grant no. PID2020-117303GB-C22)

Single-pass polarimetric SAR interferometry for vessel classification

This paper presents a novel method for vessel classification based on single-pass polarimetric synthetic aperture radar (SAR) interferometry. It has been developed according to recent ship scattering studies that show that the polarimetric response of many types of vessels can be described by trihedral- and dihedral-like mechanisms. The adopted methodology is quite simple. The input interferometric data are decomposed in terms of the Pauli basis, and hence, one height image is derived for each simple mechanism. Then, the local maxima of these images are isolated, and a 3-D map of scatters is generated. The correlation of this map with the scattering distribution expected for a set of reference ships provides the final classification decision. The performance of the proposed method has been tested with the orbital SAR simulator developed at Universitat PolitÈcnica de Catalunya. Different vessel models have been processed with a sensor configuration similar to the incoming TanDEM-X system. The analysis of diverse vessel bearings, vessel speeds, and sea states shows that the map of scatters matches reasonably the geometry of ships allowing a correct identification even for adverse environmental conditions.Peer Reviewe

Land cover and forest mapping in boreal zone using polarimetric and interferometric SAR data

Remote sensing offers a wide range of instruments suitable to meet the growing need for consistent, timely and cost-effective monitoring of land cover and forested areas. One of the most important instruments is synthetic aperture radar (SAR) technology, where transfer of advanced SAR imaging techniques from mostly experimental small test-area studies to satellites enables improvements in remote assessment of land cover on a global scale. Globally, forests are very suitable for remote sensing applications due to their large dimensions and relatively poor accessibility in distant areas. In this thesis, several methods were developed utilizing Earth observation data collected using such advanced SAR techniques, as well as their application potential was assessed. The focus was on use of SAR polarimetry and SAR interferometry to improve performance and robustness in assessment of land cover and forest properties in the boreal zone. Particular advances were achieved in land cover classification and estimating several key forest variables, such as forest stem volume and forest tree height. Important results reported in this thesis include: improved polarimetric SAR model-based decomposition approach suitable for use in boreal forest at L-band; development and demonstration of normalization method for fully polarimetric SAR mosaics, resulting in improved classification performance and suitable for wide-area mapping purposes; establishing new inversion procedure for robust forest stem volume retrieval from SAR data; developing semi-empirical method and demonstrating potential for soil type separation (mineral soil, peatland) under forested areas with L-band polarimetric SAR; developing and demonstrating methodology for simultaneous retrieval of forest tree height and radiowave attenuation in forest layer from inter-ferometric SAR data, resulting in improved accuracy and more stable estimation of forest tree height

Radar interferometry techniques for the study of ground subsidence phenomena: a review of practical issues through cases in Spain

Subsidence related to multiple natural and human-induced processes affects an increasing number of areas worldwide. Although this phenomenon may involve surface deformation with 3D displacement components, negative vertical movement, either progressive or episodic, tends to dominate. Over the last decades, differential SAR interferometry (DInSAR) has become a very useful remote sensing tool for accurately measuring the spatial and temporal evolution of surface displacements over broad areas. This work discusses the main advantages and limitations of addressing active subsidence phenomena by means of DInSAR techniques from an end-user point of view. Special attention is paid to the spatial and temporal resolution, the precision of the measurements, and the usefulness of the data. The presented analysis is focused on DInSAR results exploitation of various ground subsidence phenomena (groundwater withdrawal, soil compaction, mining subsidence, evaporite dissolution subsidence, and volcanic deformation) with different displacement patterns in a selection of subsidence areas in Spain. Finally, a cost comparative study is performed for the different techniques applied.The different research areas included in this paper has been supported by the projects: CGL2005-05500-C02, CGL2008-06426-C01-01/BTE, AYA2 010-17448, IPT-2011-1234-310000, TEC-2008-06764, ACOMP/2010/082, AGL2009-08931/AGR, 2012GA-LC-036, 2003-03-4.3-I-014, CGL2006-05415, BEST-2011/225, CGL2010-16775, TEC2011-28201, 2012GA-LC-021 and the Banting Postdoctoral Fellowship to PJG

Growing stock volume estimation in temperate forsted areas using a fusion approach with SAR Satellites Imagery

Forest monitoring plays a central role in the context of global warming mitigation and in the assessment of forest resources. To meet these challenges, significant efforts have been made by scientists to develop new feasible remote sensing techniques for the retrieval of forest parameters. However, much work remains to be done in this area, in particular in establishing global assessments of forest biomass. In this context, this Ph.D. Thesis presents a complete methodology for estimating Growing Stock Volume (GSV) in temperate forested areas using a fusion approach based on Synthetic-Aperture Radar (SAR) satellite imagery. The investigations which were performed focused on the Thuringian Forest, which is located in Central Germany. The satellite data used are composed of an extensive set of L-band (ALOS PALSAR) and X-band (TerraSAR-X, TanDEM-X, Cosmo-SkyMed) images, which were acquired in various sensor configurations (acquisition modes, polarisations, incidence angles). The available ground data consists of a forest inventory delivered by the local forest offices. Weather measurements and a LiDAR DEM complete the datasets. The research showed that together with the topography, the forest structure and weather conditions generally limited the sensitivity of the SAR signal to GSV. The best correlations were obtained with ALOS PALSAR (R2 = 0.61) and TanDEM-X (R2 = 0.72) interferometric coherences. These datasets were chosen for the retrieval of GSV in the Thuringian Forest and led with regressions to an root-mean-square error (RMSE) in the range of 100─200 m3ha-1. As a final achievement of this thesis, a methodology for combining the SAR information was developed. Assuming that there are sufficient and adequate remote sensing data, the proposed fusion approach may increase the biomass maps accuracy, their spatial extension and their updated frequency. These characteristics are essential for the future derivation of accurate, global and robust forest biomass maps

Radar polarimetry and interferometry for remote sensing of boreal forest

Forest biomass is a key parameter of the global biosphere which is linked to many fields of research. Modeling addressing climate, ecology, and economics as well as many other prediction frameworks require an accurate assessment of global forest biomass. Methods for producing forest information are rapidly developing and traditional forest inventory by visual estimation has been gradually replaced by the use of airborne and spaceborne instruments. Nevertheless, the estimation of biomass on a global basis including boreal, temperate, and tropical forests, is still a major challenge. Among other spaceborne sensors, synthetic aperture radar (SAR) is one of the most suitable tools for large scale mapping and it has also been often used for forest mapping. However, commonly used backscattering intensity based methods do not provide a satisfactory accuracy for biomass estimation; hence, the scientific radar community has been developing more accurate means based on advanced SAR imaging and analyzing techniques, such as SAR polarimetry and interferometry. The work within this thesis contributes to this effort specifically in the field of remote sensing with the emphasis on SAR polarimetry and interferometry for boreal forest applications. The study concentrates on three main topics: polarimetric SAR image analysis, retrieval of forest height by means of SAR interferometry, and modeling of radar backscattering from trees. The main contributions of this work include a new effective approach in polarimetric target decomposition, novel polarimetric visualization schemes, an improved interferometric tree height estimation method suitable for boreal forest, interferometric tree height estimation capability demonstration for X-band, a novel method for relating SAR measurements to single tree scattering modeling, and taking the scattering modeling from a pine tree to the single needle level with accurate field models. Furthermore, the forest height estimation scheme proposed in this work potentially enables tree height estimation with existing spaceborne interferometric X-band SAR systems. The proposed method uses an interferometric coherence model and a ground elevation model to produce accurate tree height maps from single polarization interferometric SAR data. The method is demonstrated with airborne SAR measurements and will be tested in the near future with satellite data. Since tree height is related to forest biomass through tree allometry, tree height measurements from space would enable more accurate global forest biomass maps

Estimativa de biomassa na região amazônica utilizando técnicas de aprendizado de máquina

Tese (doutorado) — Universidade de Brasília, Instituto de Geociências, Pós-Graduação em Geociências Aplicadas, 2021.No ano de 2016 mais de 190 países participaram da 21ª Conferência das Partes das Nações Unidas sobre Mudança Climática, realizada em Paris. Apesar de intensos trabalhos visando elaborar um tratado, os resultados não atenderam às expectativas devido à falta de metodologias que medem com precisão a quantidade de biomassa florestal. Imagens de sensoriamento remoto podem ser usadas para que seja realizada uma quantificação mais precisa e viável da biomassa existente em regiões de difícil acesso, como a região amazônica, com destaque para as imagens na faixa do micro-ondas, mais especificamente as de radares. Em função da grande quantidade de dados de sensoriamento remoto disponíveis, faz-se necessário o desenvolvimento de técnicas e ferramentas que visem organizá-los e analisá-los de forma inteligente e automática, como as técnicas de Aprendizado de Máquina (Machine Learning). A presente tese tem por objetivo geral desenvolver e aplicar uma metodologia para estimar a quantidade de biomassa arbórea em uma área da região amazônica, a partir de dados de SAR, utilizando técnicas de Aprendizado de Máquina. As etapas metodológicas de tese encontra-se divididas em três artigos técnicos sequenciais que cobrem os objetivos propostos. O primeiro artigo possui como hipótese a possibilidade de ajuste da altura interferométrica, atributos de InSAR, a partir da identificação de áreas de solo exposto, isto é, onde o valor é teoricamente igual a 0 (zero). Além de inovadora, a hipótese previa o ajuste do modelo digital da região visando aprimorar a modelagem referente à estimativa de biomassa. Entretanto, como resultado, o método proposto no primeiro artigo não possibilitou a melhora significativa da estimativa de biomassa florestal, não sendo adotado nas próximas etapas do trabalho. O segundo artigo dá continuidade ao primeiro e apresenta a aplicação de técnicas de Aprendizado de Máquina sobre os atributos de SAR extraídos dos dados disponíveis. De forma inédita avalia e compara modelos de estimativa de biomassa baseados em atributos qualitativos e quantitativos. O segundo artigo conclui que as diferentes regiões da Floresta Amazônica e suas respectivas características demandam modelos e técnicas específicas, não se enquadrando em um único padrão. Neste caso não foi possível identificar uma única técnica de Aprendizado de Máquina que se mostrasse como a mais adequada ao objetivo, apesar dos melhores resultados apontarem para o uso das redes neurais artificiais. O terceiro e último artigo conclui o trabalho da presente tese por meio da análise e construção de produtos temáticos de biomassa. Neste último artigo é apresentado um sistema computacional desenvolvido que visa otimizar o processo de categorização, necessário à representação visual da geoinformação. Os resultados obtidos no terceiro artigo mostram que o algoritmo de Otimização de Categorização proposto demonstrou capacidade de encontrar novos subintervalos de categorias que aumentaram o índice de concordância Kappa. Como resultado, foram construídos produtos temáticos que apresentaram acurácia temática superior aos obtidos pelos métodos clássicos de categorização. Juntamente, do ponto de vista computacional, a heurística proposta no algoritmo possibilitou a identificação de resultados de forma eficiente, evitando os altos custos de processamento. A hipótese proposta na tese, isto é, de que a aplicação de técnicas de aprendizado de máquina sobre dados de SAR permitem obter a estimativa de biomassa da região amazônica com erros abaixo de 20%, atendendo os padrões preceituados por organismos internacionais, não foi confirmada. Os resultados obtidos nos modelos elaborados são classificados somente como moderados. Dentre os fatores que podem ter contribuído para este resultado, está a quantidade reduzida de amostras de biomassa, com pequena variação de valores, o que prejudicou o ajuste dos modelos gerados e o acesso restrito aos dados de SAR das bandas X e P, não sendo possível gerar novos atributos coerentes.In 2016, more than 190 countries participated in the 21st United Nations Conference of Parties on Climate Change, held in Paris. Despite the intense work aiming at preparing a treaty, the results did not meet expectations due to the lack of methodologies that accurately measures the amount of forest biomass. Remote sensing images can be used to make a more accurate and viable quantification of the existing biomass in regions with difficult access, such as the Amazon region, with emphasis on images in the microwave range, more specifically those from radar. Due to the large amount of remote sensing data available, it is necessary to develop techniques and tools that aims to organize and analyze them in an intelligent and automatic way, such as Machine Learning techniques. The present thesis has as general objective to develop and apply a methodology to estimate the amount of arboreal biomass in an area of the Amazon region, using SAR data and Machine Learning techniques. The thesis methodological steps are divided into three sequential technical articles that covers the proposed objectives. The first article hypothesizes the possibility of adjusting the interferometric height, InSAR feature, using the exposed soil areas identified in the image, that is, where the value is theoretically equal to 0 (zero). In addition to being innovative, the hypothesis predicted the adjustment of the region digital model in order to improve the biomass estimation modeling. However, as a result, the method proposed in the first article did not present a significant improvement in the estimation of forest biomass and was not adopted in the next stages of the work. The second article gives sequence for the first and presents the application of Machine Learning techniques over SAR features extracted from the available data. In an unprecedented way, it presents a methodology that evaluates and compares biomass estimation models based on qualitative and quantitative features. The second article concludes that the different Amazon Forest regions and their respective characteristics demands specific models and techniques, not fitting into a single pattern. In this case, it was not possible to identify a single Machine Learning technique that proved to be the most adequate for the purpose, despite the best results pointing to the use of artificial neural networks. The third and last article concludes the work of this thesis through the analysis and construction of thematic biomass products. In this last article, a computational system that aims to optimize the categorization process was developed, necessary for the visual representation of geoinformation. The results obtained in the third article shows that the proposed Categorization Optimization algorithm demonstrated the ability to find new subintervals of categories that increased the Kappa agreement index. As a result, thematic products were constructed and presented thematic accuracy superior to those obtained by the classical categorization methods. Besides that, from a computational point of view, the heuristic proposed in the algorithm enabled the identification of results in an efficient way, avoiding high processing costs. The hypothesis proposed in the thesis, that is, that the application of machine learning techniques over SAR data allows to obtain an estimate of biomass in the Amazon region with errors below 20%, attending to the standards established by international organizations, was not confirmed. The results obtained in the constructed models were classified only moderate. Among the factors that may have contributed to this result, there is the reduced amount of biomass samples, with little variation in values, which impaired the adjustment of the generated models and the restricted access to the X and P bands SAR data, not being possible to generate new coherent features

Développements algorithmiques pour l’amélioration des résultats de l’interférométrie RADAR en milieu urbain

Le suivi des espaces urbanisés et de leurs dynamiques spatio-temporelles représente un enjeu important pour la population urbaine, autant sur le plan environnemental, économique et social. Avec le lancement des satellites portant des radars à synthèse d’ouverture de la nouvelle génération (TerraSAR-X, COSMO-SkyMed, ALOS, RADARSAT-2,Sentinel-1, Constellation RADARSAT), il est possible d’obtenir des séries temporelles d’images avec des résolutions spatiales et temporelles fines. Ces données multitemporelles aident à mieux analyser et décrire les structures urbaines et leurs variations dans l’espace et dans le temps. L’interférométrie par satellite est effectuée en comparant les phases des images RSO prises à différents passages du satellite au-dessus du même territoire. En optant pour des positions du satellite séparées d’une longue ligne de base, l’InSAR mène à la création des modèles numériques d’altitude (MNA). Si cette ligne de base est courte et à la limite nulle, nous avons le cas de l’interférométrie différentielle (DInSAR) qui mène à l’estimation du mouvement possible du terrain entre les deux acquisitions. Pour toutes les deux applications de l’InSAR, deux opérations sont importantes qui garantissent la génération des interférogrammes de qualité. La première est le filtrage du bruit omniprésent dans les phases interférométriques et la deuxième est le déroulement des phases. Ces deux opérations deviennent particulièrement complexes en milieu urbain où au bruit des phases s’ajoutent des fréquents sauts et discontinuités des phases dus à la présence des bâtiments et d’autres structures surélevées. L’objectif de cette recherche est le développement des nouveaux algorithmes de filtrage et de déroulement de phase qui puissent mieux performer que les algorithmes considérés comme référence dans ce domaine. Le but est d’arriver à générer des produits InSAR de qualité en milieu urbain. Concernant le filtrage, nous avons établi un algorithme qui est une nouvelle formulation du filtre Gaussien anisotrope adaptatif. Quant à l’algorithme de déroulement de phase, il est fondé sur la minimisation de l’énergie par un algorithme génétique ayant recours à une modélisation contextuelle du champ de phase. Différents tests ont été effectués avec des images RSO simulées et réelles qui démontrent le potentiel de nos algorithmes qui dépasse à maints égards celui des algorithmes standard. Enfin, pour atteindre le but de notre recherche, nous avons intégré nos algorithmes dans l’environnement du logiciel SNAP et appliqué l’ensemble de la procédure pour générer un MNA avec des images RADARSAT-2 de haute résolution d’un secteur de la Ville de Montréal (Canada) ainsi que des cartes des mouvements du terrain dans la région de la Ville de Mexico (Mexique) avec des images de Sentinel-1 de résolution plutôt moyenne. La comparaison des résultats obtenus avec des données provenant des sources externes de qualité a aussi démontré le fort potentiel de nos algorithmes.The monitoring of urban areas and their spatiotemporal dynamics is an important issue for the urban population, at the environmental, economic, as well as social level. With the launch of satellites carrying next-generation synthetic aperture radars (TerraSAR-X, COSMO-SkyMed, ALOS, RADARSAT-2, Sentinel-1, Constellation RADARSAT), it is possible to obtain time series of images with fine temporal and spatial resolutions. These multitemporal data help to better analyze and describe urban structures, and their variations in space and time. Satellite interferometry is performed by comparing the phases of SAR images taken at different satellite passes over the same territory. By opt-ing for satellite positions separated by a long baseline, InSAR leads to the creation of digital elevation models (DEM). If this baseline is short and, at the limit zero, we have the case of differential interferometry (DInSAR) which leads to the estimation of the possible movement of the land between the two acquisitions. In both InSAR applica-tions, two operations are important that ensure the generation of quality interferograms. The first is the filtering of ubiquitous noise in the interferometric phases and the second is the unwrapping of the phases. These two operations become particularly complex in urban areas where the phase noise is added to the frequent jumps and discontinuities of phases due to the presence of buildings and other raised structures. The objective of this research is the development of new filtering and phase unwrap-ping algorithms that can perform better than algorithms considered as reference in this field. The goal is to generate quality InSAR products in urban areas. Regarding filtering, we have established an algorithm that is a new formulation of the adaptive anisotropic Gaussian filter. As for the phase unwrapping algorithm, it is based on the minimization of energy by a genetic algorithm using contextual modelling of the phase field. Various tests have been carried out with simulated and real SAR images that demonstrated the potential of our algorithms that in many respects exceeds that of standard algorithms. Finally, to achieve the goal of our research, we integrated our algorithms into the SNAP software environment and applied the entire procedure to generate a DEM with high-resolution RADARSAT-2 images from an area of the City of Montreal (Canada) as well as maps of land movement in the Mexico City region (Mexico) with relatively medium-resolution Sentinel-1 images. Comparison of the results with data from external quality sources also demonstrated the strong potential of our algorithms