Monitoring permafrost environments with Synthetic Aperture Radar (SAR) sensors

Permafrost occupies approximately 24% of the exposed land area in the Northern Hemisphere. It is an important element of the cryosphere and has strong impacts on hydrology, biological processes, land surface energy budget, and infrastructure. For several decades, surface air temperatures in the high northern latitudes have warmed at approximately twice the global rate. Permafrost temperatures have increased in most regions since the early 1980s, the averaged warming north of 60°N has been 1-2°C. In-situ measurements are essential to understanding physical processes in permafrost terrain, but they have several limitations, ranging from difficulties in drilling to the representativeness of limited single point measurements. Remote sensing is urgently needed to supplement ground-based measurements and extend the point observations to a broader spatial domain. This thesis concentrates on the sub-arctic permafrost environment monitoring with SAR datasets. The study site is selected in a typical discontinuous permafrost region in the eastern Canadian sub-Arctic. Inuit communities in Nunavik and Nunatsiavut in the Canadian eastern sub-arctic are amongst the groups most affected by the impacts of climate change and permafrost degradation. Synthetic Aperture Radar (SAR) datasets have advantages for permafrost monitoring in the Arctic and sub-arctic regions because of its high resolution and independence of cloud cover and solar illumination. To date, permafrost environment monitoring methods and strategies with SAR datasets are still under development. The variability of active layer thickness is a direct indication of permafrost thermal state changes. The Differential SAR Interferometry (D-InSAR) technique is applied in the study site to derive ground deformation, which is introduced by the thawing/freezing depth of active layer and underlying permafrost. The D-InSAR technique has been used for the mapping of ground surface deformation over large areas by interpreting the phase difference between two signals acquired at different times as ground motion information. It shows the ability to detect freeze/thaw-related ground motion over permafrost regions. However, to date, accuracy and value assessments of D-InSAR applications have focused mostly on the continuous permafrost region where the vegetation is less developed and causes fewer complicating factors for the D-InSAR application, less attention is laid on the discontinuous permafrost terrain. In this thesis, the influencing factors and application conditions for D-InSAR in the discontinuous permafrost environment are evaluated by using X- band and L-band data. Then, benefit from by the high-temporal resolution of C-band Sentinel-1 time series, the seasonal displacement is derived from small baseline subsets (SBAS)-InSAR. Landforms are indicative of permafrost presence, with their changes inferring modifications to permafrost conditions. A permafrost landscape mapping method was developed which uses multi-temporal TerraSAR-X backscatter intensity and interferometric coherence information. The land cover map is generated through the combined use of object-based image analysis (OBIA) and classification and regression tree analysis (CART). An overall accuracy of 98% is achieved when classifying rock and water bodies, and an accuracy of 79% is achieved when discriminating between different vegetation types with one year of single-polarized acquisitions. This classification strategy can be transferred to other time-series SAR datasets, e.g., Sentinel-1, and other heterogeneous environments. One predominant change in the landscape tied to the thaw of permafrost is the dynamics of thermokarst lakes. Dynamics of thermokarst lakes are developed through their lateral extent and vertical depth changes. Due to different water depth, ice cover over shallow thermokarst ponds/lakes can freeze completely to the lake bed in winter, resulting in grounded ice; while ice cover over deep thermokarst ponds/lakes cannot, which have liquid water persisting under the ice cover all winter, resulting in floating ice. Winter ice cover regimes are related to water depths and ice thickness. In the lakes having floating ice, the liquid water induces additional heat in the remaining permafrost underneath and surroundings, which contributes to further intensified permafrost thawing. SAR datasets are utilized to detect winter ice cover regimes based on the character that liquid water has a remarkably high dielectric constant, whereas pure ice has a low value. Patterns in the spatial distribution of ice-cover regimes of thermokarst ponds in a typical discontinuous permafrost region are first revealed. Then, the correlations of these ice-cover regimes with the permafrost degradation states and thermokarst pond development in two historical phases (Sheldrake catchment in the year 1957 and 2009, Tasiapik Valley 1994 and 2010) were explored. The results indicate that the ice-cover regimes of thermokarst ponds are affected by soil texture, permafrost degradation stage and permafrost depth. Permafrost degradation is difficult to directly assess from the coverage area of floating-ice ponds and the percentage of all thermokarst ponds consisting of such floating-ice ponds in a single year. Continuous monitoring of ice-cover regimes and surface areas is recommended to elucidate the hydrological trajectory of the thermokarst process. Several operational monitoring methods have been developed in this thesis work. In the meanwhile, the spatial distribution of seasonal ground thaw subsidence, permafrost landscape, thermokarst ponds and their winter ice cover regimes are first revealed in the study area. The outcomes help understand the state and dynamics of permafrost environment.Der Permafrostboden bedeckt etwa 24% der exponierten Landfläche in der nördlichen Hemisphäre. Es ist ein wichtiges Element der Kryosphäre und hat starke Auswirkungen auf die Hydrologie, die biologischen Prozesse, das Energie-Budget der Landoberfläche und die Infrastruktur. Seit mehreren Jahrzehnten erhöhen sich die Oberflächenlufttemperaturen in den nördlichen hohen Breitengraden etwa doppelt so stark wie die globale Rate. Die Temperaturen der Permafrostböden sind in den meisten Regionen seit den frühen 1980er Jahren gestiegen. Die durchschnittliche Erwärmung nördlich von 60° N beträgt 1-2°C. In-situ-Messungen sind essentiell für das Verständnis der physischen Prozesse im Permafrostgelände. Es gibt jedoch mehrere Einschränkungen, die von Schwierigkeiten beim Bohren bis hin zur Repräsentativität begrenzter Einzelpunktmessungen reichen. Fernerkundung ist dringend benötigt, um bodenbasierte Messungen zu ergänzen und punktuelle Beobachtungen auf einen breiteren räumlichen Bereich auszudehnen. Diese Dissertation konzentriert sich auf die Umweltbeobachtung der subarktischen Permafrostböden mit SAR-Datensätzen. Das Untersuchungsgebiet wurde in einer typischen diskontinuierlichen Permafrostzone in der kanadischen östlichen Sub-Arktis ausgewählt. Die Inuit-Gemeinschaften in den Regionen Nunavik und Nunatsiavut in der kanadischen östlichen Sub-Arktis gehören zu den Gruppen, die am stärksten von den Auswirkungen des Klimawandels und Permafrostdegradation betroffen sind. Synthetische Apertur Radar (SAR) Datensätze haben Vorteile für das Permafrostmonitoring in den arktischen und subarktischen Regionen aufgrund der hohen Auflösung und der Unabhängigkeit von Wolkendeckung und Sonnenstrahlung. Bis heute sind die Methoden und Strategien mit SAR-Datensätzen für Umweltbeobachtung der Permafrostböden noch in der Entwicklung. Die Variabilität der Auftautiefe der aktiven Schicht ist eine direkte Indikation der Veränderung des thermischen Zustands der Permafrostböden. Die Differential-SAR-Interferometrie(D-Insar)-Technik wird im Untersuchungsgebiet zur Ableitung der Bodendeformation, die durch Auftau- / und Gefriertiefe der aktiven Schicht und des unterliegenden Permafrostbodens eingeführt wird, eingesetzt. Die D-InSAR-Technik wurde für Kartierung der Landoberflächendeformation über große Flächen verwendet, indem der Phasenunterschied zwischen zwei zu verschiedenen Zeitpunkten als Bodenbewegungsinformation erfassten Signalen interpretiert wurde. Es zeigt die Fähigkeit, tau- und gefrierprozessbedingte Bodenbewegungen über Permafrostregionen zu detektieren. Jedoch fokussiert sich die Genauigkeit und Wertschätzung der D-InSAR-Anwendung bis heute hauptsächlich auf kontinuierliche Permafrostregion, wo die Vegetation wenig entwickelt ist und weniger komplizierte Faktoren für D-InSAR-Anwendung verursacht. Das diskontinuierliche Permafrostgelände wurde nur weniger berücksichtigt. In dieser Dissertation wurden die Einflussfaktoren und Anwendungsbedingungen für D-InSAR im diskontinuierlichen Permafrostgebiet mittels X-Band und L-Band Daten ausgewertet. Dann wurde die saisonale Verschiebung dank der hohen Auflösung der C-Band Sentinel-1 Zeitreihe von „Small Baseline Subsets (SBAS)-InSAR“ abgeleitet. Landformen weisen auf die Präsenz des Permafrosts hin, wobei deren Veränderungen auf die Modifikation der Permafrostbedingungen schließen. Eine Kartierungsmethode der Permafrostlandschaft wurde entwickelt, dabei wurde Multi-temporal TerraSAR-X Rückstreuungsintensität und interferometrische Kohärenzinformationen verwendet. Die Landbedeckungskarte wurde durch kombinierte Anwendung objektbasierter Bildanalyse (OBIA) und Klassifikations- und Regressionsbaum Analyse (CART) generiert. Eine Gesamtgenauigkeit in Höhe von 98% wurde bei Klassifikation der Gesteine und Wasserkörper erreicht. Bei Unterscheidung zwischen verschiedenen Vegetationstypen mit einem Jahr einzelpolarisierte Akquisitionen wurde eine Genauigkeit von 79% erreicht. Diese Klassifikationsstrategie kann auf andere Zeitreihen der SAR-Datensätzen, z.B. Sentinel-1, und auch anderen heterogenen Umwelten übertragen werden. Eine vorherrschende Veränderung in der Landschaft, die mit dem Auftauen des Permafrosts verbunden ist, ist die Dynamik der Thermokarstseen. Die Dynamik der Thermokarstseen ist durch Veränderungen der seitlichen Ausdehnung und der vertikalen Tiefe entwickelt. Aufgrund der unterschiedlichen Wassertiefen kann die Eisdecke über den flachen Thermokarstteichen/-seen im Winter bis auf den Wasserboden vollständig gefroren sein, was zum geerdeten Eis führt, während die Eisdecke über den tiefen Thermokarstteichen/-seen es nicht kann. In den tiefen Thermokarstteichen/-seen bleibt den ganzen Winter flüssiges Wasser unter der Eisdecke bestehen, was zum Treibeis führt. Das Wintereisdeckenregime bezieht sich auf die Wassertiefe und die Eisdicke. In den Seen mit Treibeis leitet das flüssige Wasser zusätzliche Wärme in den restlichen Permafrost darunter oder in der Umgebung, was zur weiteren Verstärkung des Permafrostauftauen beiträgt. Basiert auf den Charakter, dass das flüssige Wasser eine bemerkenswert hohe Dielektrizitätskonstante besitzt, während reines Eis einen niedrigen Wert hat, wurden die SAR Datensätzen zur Erkennung des Wintereisdeckenregimes verwendet. Zunächst wurden Schemen in der räumlichen Verteilung der Eisdeckenregimes der Thermokarstteiche in einer typischen diskontinuierlichen Permafrostregion abgeleitet. Dann wurden die Zusammenhänge dieser Eisdeckenregimes mit dem Degradationszustand des Permafrosts und der Entwicklung der Thermokarstteiche in zwei historischen Phasen (Sheldrake Einzugsgebiet in 1957 und 2009, Tasiapik Tal in 1994 und 2010) erforscht. Die Ergebnisse deuten darauf, dass die Eisdeckenregimes der Thermokarstteiche von der Bodenart, dem Degradationszustand des Permafrosts und der Permafrosttiefe beeinflusst werden. Es ist schwer, die Permafrostdegradation in einem einzelnen Jahr direkt durch den Abdeckungsbereich der Treibeis-Teiche und die Prozentzahl aller aus solchen Treibeis-Teichen bestehenden Thermokarstteiche abzuschätzen. Ein kontinuierliches Monitoring der Eisdeckenregimes und -oberflächen ist empfehlenswert, um den hydrologischen Verlauf des Thermokarstprozesses zu erläutern. In dieser Dissertation wurden mehrere operativen Monitoringsmethoden entwickelt. In der Zwischenzeit wurden die räumliche Verteilung der saisonalen Bodentauabsenkung, die Permafrostlandschaft, die Thermokarstteiche und ihre Wintereisdeckenregimes erstmals in diesem Untersuchungsgebiet aufgedeckt. Die Ergebnisse tragen dazu bei, den Zustand und die Dynamik der Permafrostumwelt zu verstehen

Monitoring permafrost environments with Synthetic Aperture Radar (SAR) sensors

Permafrost occupies approximately 24% of the exposed land area in the Northern Hemisphere. It is an important element of the cryosphere and has strong impacts on hydrology, biological processes, land surface energy budget, and infrastructure. For several decades, surface air temperatures in the high northern latitudes have warmed at approximately twice the global rate. Permafrost temperatures have increased in most regions since the early 1980s, the averaged warming north of 60°N has been 1-2°C. In-situ measurements are essential to understanding physical processes in permafrost terrain, but they have several limitations, ranging from difficulties in drilling to the representativeness of limited single point measurements. Remote sensing is urgently needed to supplement ground-based measurements and extend the point observations to a broader spatial domain. This thesis concentrates on the sub-arctic permafrost environment monitoring with SAR datasets. The study site is selected in a typical discontinuous permafrost region in the eastern Canadian sub-Arctic. Inuit communities in Nunavik and Nunatsiavut in the Canadian eastern sub-arctic are amongst the groups most affected by the impacts of climate change and permafrost degradation. Synthetic Aperture Radar (SAR) datasets have advantages for permafrost monitoring in the Arctic and sub-arctic regions because of its high resolution and independence of cloud cover and solar illumination. To date, permafrost environment monitoring methods and strategies with SAR datasets are still under development. The variability of active layer thickness is a direct indication of permafrost thermal state changes. The Differential SAR Interferometry (D-InSAR) technique is applied in the study site to derive ground deformation, which is introduced by the thawing/freezing depth of active layer and underlying permafrost. The D-InSAR technique has been used for the mapping of ground surface deformation over large areas by interpreting the phase difference between two signals acquired at different times as ground motion information. It shows the ability to detect freeze/thaw-related ground motion over permafrost regions. However, to date, accuracy and value assessments of D-InSAR applications have focused mostly on the continuous permafrost region where the vegetation is less developed and causes fewer complicating factors for the D-InSAR application, less attention is laid on the discontinuous permafrost terrain. In this thesis, the influencing factors and application conditions for D-InSAR in the discontinuous permafrost environment are evaluated by using X- band and L-band data. Then, benefit from by the high-temporal resolution of C-band Sentinel-1 time series, the seasonal displacement is derived from small baseline subsets (SBAS)-InSAR. Landforms are indicative of permafrost presence, with their changes inferring modifications to permafrost conditions. A permafrost landscape mapping method was developed which uses multi-temporal TerraSAR-X backscatter intensity and interferometric coherence information. The land cover map is generated through the combined use of object-based image analysis (OBIA) and classification and regression tree analysis (CART). An overall accuracy of 98% is achieved when classifying rock and water bodies, and an accuracy of 79% is achieved when discriminating between different vegetation types with one year of single-polarized acquisitions. This classification strategy can be transferred to other time-series SAR datasets, e.g., Sentinel-1, and other heterogeneous environments. One predominant change in the landscape tied to the thaw of permafrost is the dynamics of thermokarst lakes. Dynamics of thermokarst lakes are developed through their lateral extent and vertical depth changes. Due to different water depth, ice cover over shallow thermokarst ponds/lakes can freeze completely to the lake bed in winter, resulting in grounded ice; while ice cover over deep thermokarst ponds/lakes cannot, which have liquid water persisting under the ice cover all winter, resulting in floating ice. Winter ice cover regimes are related to water depths and ice thickness. In the lakes having floating ice, the liquid water induces additional heat in the remaining permafrost underneath and surroundings, which contributes to further intensified permafrost thawing. SAR datasets are utilized to detect winter ice cover regimes based on the character that liquid water has a remarkably high dielectric constant, whereas pure ice has a low value. Patterns in the spatial distribution of ice-cover regimes of thermokarst ponds in a typical discontinuous permafrost region are first revealed. Then, the correlations of these ice-cover regimes with the permafrost degradation states and thermokarst pond development in two historical phases (Sheldrake catchment in the year 1957 and 2009, Tasiapik Valley 1994 and 2010) were explored. The results indicate that the ice-cover regimes of thermokarst ponds are affected by soil texture, permafrost degradation stage and permafrost depth. Permafrost degradation is difficult to directly assess from the coverage area of floating-ice ponds and the percentage of all thermokarst ponds consisting of such floating-ice ponds in a single year. Continuous monitoring of ice-cover regimes and surface areas is recommended to elucidate the hydrological trajectory of the thermokarst process. Several operational monitoring methods have been developed in this thesis work. In the meanwhile, the spatial distribution of seasonal ground thaw subsidence, permafrost landscape, thermokarst ponds and their winter ice cover regimes are first revealed in the study area. The outcomes help understand the state and dynamics of permafrost environment.Der Permafrostboden bedeckt etwa 24% der exponierten Landfläche in der nördlichen Hemisphäre. Es ist ein wichtiges Element der Kryosphäre und hat starke Auswirkungen auf die Hydrologie, die biologischen Prozesse, das Energie-Budget der Landoberfläche und die Infrastruktur. Seit mehreren Jahrzehnten erhöhen sich die Oberflächenlufttemperaturen in den nördlichen hohen Breitengraden etwa doppelt so stark wie die globale Rate. Die Temperaturen der Permafrostböden sind in den meisten Regionen seit den frühen 1980er Jahren gestiegen. Die durchschnittliche Erwärmung nördlich von 60° N beträgt 1-2°C. In-situ-Messungen sind essentiell für das Verständnis der physischen Prozesse im Permafrostgelände. Es gibt jedoch mehrere Einschränkungen, die von Schwierigkeiten beim Bohren bis hin zur Repräsentativität begrenzter Einzelpunktmessungen reichen. Fernerkundung ist dringend benötigt, um bodenbasierte Messungen zu ergänzen und punktuelle Beobachtungen auf einen breiteren räumlichen Bereich auszudehnen. Diese Dissertation konzentriert sich auf die Umweltbeobachtung der subarktischen Permafrostböden mit SAR-Datensätzen. Das Untersuchungsgebiet wurde in einer typischen diskontinuierlichen Permafrostzone in der kanadischen östlichen Sub-Arktis ausgewählt. Die Inuit-Gemeinschaften in den Regionen Nunavik und Nunatsiavut in der kanadischen östlichen Sub-Arktis gehören zu den Gruppen, die am stärksten von den Auswirkungen des Klimawandels und Permafrostdegradation betroffen sind. Synthetische Apertur Radar (SAR) Datensätze haben Vorteile für das Permafrostmonitoring in den arktischen und subarktischen Regionen aufgrund der hohen Auflösung und der Unabhängigkeit von Wolkendeckung und Sonnenstrahlung. Bis heute sind die Methoden und Strategien mit SAR-Datensätzen für Umweltbeobachtung der Permafrostböden noch in der Entwicklung. Die Variabilität der Auftautiefe der aktiven Schicht ist eine direkte Indikation der Veränderung des thermischen Zustands der Permafrostböden. Die Differential-SAR-Interferometrie(D-Insar)-Technik wird im Untersuchungsgebiet zur Ableitung der Bodendeformation, die durch Auftau- / und Gefriertiefe der aktiven Schicht und des unterliegenden Permafrostbodens eingeführt wird, eingesetzt. Die D-InSAR-Technik wurde für Kartierung der Landoberflächendeformation über große Flächen verwendet, indem der Phasenunterschied zwischen zwei zu verschiedenen Zeitpunkten als Bodenbewegungsinformation erfassten Signalen interpretiert wurde. Es zeigt die Fähigkeit, tau- und gefrierprozessbedingte Bodenbewegungen über Permafrostregionen zu detektieren. Jedoch fokussiert sich die Genauigkeit und Wertschätzung der D-InSAR-Anwendung bis heute hauptsächlich auf kontinuierliche Permafrostregion, wo die Vegetation wenig entwickelt ist und weniger komplizierte Faktoren für D-InSAR-Anwendung verursacht. Das diskontinuierliche Permafrostgelände wurde nur weniger berücksichtigt. In dieser Dissertation wurden die Einflussfaktoren und Anwendungsbedingungen für D-InSAR im diskontinuierlichen Permafrostgebiet mittels X-Band und L-Band Daten ausgewertet. Dann wurde die saisonale Verschiebung dank der hohen Auflösung der C-Band Sentinel-1 Zeitreihe von „Small Baseline Subsets (SBAS)-InSAR“ abgeleitet. Landformen weisen auf die Präsenz des Permafrosts hin, wobei deren Veränderungen auf die Modifikation der Permafrostbedingungen schließen. Eine Kartierungsmethode der Permafrostlandschaft wurde entwickelt, dabei wurde Multi-temporal TerraSAR-X Rückstreuungsintensität und interferometrische Kohärenzinformationen verwendet. Die Landbedeckungskarte wurde durch kombinierte Anwendung objektbasierter Bildanalyse (OBIA) und Klassifikations- und Regressionsbaum Analyse (CART) generiert. Eine Gesamtgenauigkeit in Höhe von 98% wurde bei Klassifikation der Gesteine und Wasserkörper erreicht. Bei Unterscheidung zwischen verschiedenen Vegetationstypen mit einem Jahr einzelpolarisierte Akquisitionen wurde eine Genauigkeit von 79% erreicht. Diese Klassifikationsstrategie kann auf andere Zeitreihen der SAR-Datensätzen, z.B. Sentinel-1, und auch anderen heterogenen Umwelten übertragen werden. Eine vorherrschende Veränderung in der Landschaft, die mit dem Auftauen des Permafrosts verbunden ist, ist die Dynamik der Thermokarstseen. Die Dynamik der Thermokarstseen ist durch Veränderungen der seitlichen Ausdehnung und der vertikalen Tiefe entwickelt. Aufgrund der unterschiedlichen Wassertiefen kann die Eisdecke über den flachen Thermokarstteichen/-seen im Winter bis auf den Wasserboden vollständig gefroren sein, was zum geerdeten Eis führt, während die Eisdecke über den tiefen Thermokarstteichen/-seen es nicht kann. In den tiefen Thermokarstteichen/-seen bleibt den ganzen Winter flüssiges Wasser unter der Eisdecke bestehen, was zum Treibeis führt. Das Wintereisdeckenregime bezieht sich auf die Wassertiefe und die Eisdicke. In den Seen mit Treibeis leitet das flüssige Wasser zusätzliche Wärme in den restlichen Permafrost darunter oder in der Umgebung, was zur weiteren Verstärkung des Permafrostauftauen beiträgt. Basiert auf den Charakter, dass das flüssige Wasser eine bemerkenswert hohe Dielektrizitätskonstante besitzt, während reines Eis einen niedrigen Wert hat, wurden die SAR Datensätzen zur Erkennung des Wintereisdeckenregimes verwendet. Zunächst wurden Schemen in der räumlichen Verteilung der Eisdeckenregimes der Thermokarstteiche in einer typischen diskontinuierlichen Permafrostregion abgeleitet. Dann wurden die Zusammenhänge dieser Eisdeckenregimes mit dem Degradationszustand des Permafrosts und der Entwicklung der Thermokarstteiche in zwei historischen Phasen (Sheldrake Einzugsgebiet in 1957 und 2009, Tasiapik Tal in 1994 und 2010) erforscht. Die Ergebnisse deuten darauf, dass die Eisdeckenregimes der Thermokarstteiche von der Bodenart, dem Degradationszustand des Permafrosts und der Permafrosttiefe beeinflusst werden. Es ist schwer, die Permafrostdegradation in einem einzelnen Jahr direkt durch den Abdeckungsbereich der Treibeis-Teiche und die Prozentzahl aller aus solchen Treibeis-Teichen bestehenden Thermokarstteiche abzuschätzen. Ein kontinuierliches Monitoring der Eisdeckenregimes und -oberflächen ist empfehlenswert, um den hydrologischen Verlauf des Thermokarstprozesses zu erläutern. In dieser Dissertation wurden mehrere operativen Monitoringsmethoden entwickelt. In der Zwischenzeit wurden die räumliche Verteilung der saisonalen Bodentauabsenkung, die Permafrostlandschaft, die Thermokarstteiche und ihre Wintereisdeckenregimes erstmals in diesem Untersuchungsgebiet aufgedeckt. Die Ergebnisse tragen dazu bei, den Zustand und die Dynamik der Permafrostumwelt zu verstehen

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Ground-based synthetic aperture radar (GBSAR) interferometry for deformation monitoring

Ph. D ThesisGround-based synthetic aperture radar (GBSAR), together with interferometry, represents a powerful tool for deformation monitoring. GBSAR has inherent flexibility, allowing data to be collected with adjustable temporal resolutions through either continuous or discontinuous mode. The goal of this research is to develop a framework to effectively utilise GBSAR for deformation monitoring in both modes, with the emphasis on accuracy, robustness, and real-time capability. To achieve this goal, advanced Interferometric SAR (InSAR) processing algorithms have been proposed to address existing issues in conventional interferometry for GBSAR deformation monitoring. The proposed interferometric algorithms include a new non-local method for the accurate estimation of coherence and interferometric phase, a new approach to selecting coherent pixels with the aim of maximising the density of selected pixels and optimizing the reliability of time series analysis, and a rigorous model for the correction of atmospheric and repositioning errors. On the basis of these algorithms, two complete interferometric processing chains have been developed: one for continuous and the other for discontinuous GBSAR deformation monitoring. The continuous chain is able to process infinite incoming images in real time and extract the evolution of surface movements through temporally coherent pixels. The discontinuous chain integrates additional automatic coregistration of images and correction of repositioning errors between different campaigns. Successful deformation monitoring applications have been completed, including three continuous (a dune, a bridge, and a coastal cliff) and one discontinuous (a hillside), which have demonstrated the feasibility and effectiveness of the presented algorithms and chains for high-accuracy GBSAR interferometric measurement. Significant deformation signals were detected from the three continuous applications and no deformation from the discontinuous. The achieved results are justified quantitatively via a defined precision indicator for the time series estimation and validated qualitatively via a priori knowledge of these observing sites.China Scholarship Council (CSC), Newcastle Universit

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

간섭 긴밀도 모델 연구와 단일 및 다중 편파 SAR 영상을 활용한 자연 재해 탐지

학위논문 (박사)-- 서울대학교 대학원 자연과학대학 지구환경과학부, 2017. 8. 김덕진.자연 재해에 대한 빠른 대응과 복구를 위해서는 피해 지역에 대한 평가가 선행되어야 하며, 그런 의미로 피해 지역을 탐지하는 것은 매우 중요하다. SAR 시스템은 기상적 조건과 주야에 무관하게 영상을 획득할 수 있으므로, 변화 혹은 피해 지역을 탐지할 수 있는 효율적인 방법이라고 알려져 있다. 또한 SAR 시스템을 통하여 계산할 수 있는 긴밀도 (coherence)는 지표의 산란체의 움직임 혹은 유전적 성질에 변화에 매우 민감하게 반응하기 때문에 피해를 탐지하기에 적합하다고 평가되어 왔다. 그러나 긴밀도를 이용한 자연재해의 피해 탐지에는 어려움이 존재할 수 있다. 즉, 탐지하고자 하는 자연재해로 인한 피해와 비, 눈, 바람과 같은 기상현상, 혹은 식생의 자연적인 변화가 미치는 영향이 긴밀도에서는 유사하게 발생할 수 있기 때문이다. 이것은 레이더 신호의 긴밀도가 미세한 변화에도 민감하게 반응하는 특징으로부터 기인한다. 그러므로 자연 현상으로부터 발생하는 긴밀도 감소 현상은 피해 탐지 알고리즘에서 오탐지율을 증가시키는 원인이 되며, 자연 재해의 영향과 분리해야 할 필요성이 있다. 또한 다양한 지표 특성을 가지는 픽셀들은 자연 현상에 대한 각기 다른 긴밀도 특성을 가지고 있기 때문에 정확한 피해 탐지를 위해서는 각 픽셀들에서의 독립적인 평가가 필요하다. 긴밀도를 결정하는 요인들이 다양하고 복합적으로 작용하기 때문에 해석에 어려움이 있다는 점 역시 긴밀도 기반 피해 탐지 알고리즘의 한계점이다. 특히 식생이 존재하는 지역에서의 긴밀도의 변화는 더욱 복잡하게 나타날 수 있다. 그 이유는 유전적 성질을 지니고 있는 산란체들이 식생에서는 수직적으로 분포하며, 파장이 긴 레이더 신호가 이를 투과함에 따라 식생의 상층부부터 하층부 또한 지표면까지 도달되어 산란되어 긴밀도를 감소시키는 체적 긴밀도 감소 현상(volume decorrelation) 때문이다. 획득 시간이 동일하지 않은 두 장의 SAR 영상을 사용하는 repeat-pass 간섭기법에서는 각 식생의 각 부분에서 발생되는 변화 정보(temporal decorrelation)도 동시에 기록되기 때문에 해석은 더욱 어려워진다. 그러므로 본 연구에서는 다중 시기 긴밀도를 이용하여 자연 현상을 해석 할 수 있는 모델을 제작하고 이를 변화 탐지 알고리즘으로 확장하여, 적용 가능성을 평가하고 정밀한 피해 지역을 추출하는 것을 목적으로 한다. 이를 위하여 첫 번째로는 간섭 기법에서의 시간 차이(temporal baseline)이 길 때, 다중 시기 긴밀도(multi-temporal coherence)를 해석할 수 있는 모델을 제작하는 것을 목적으로 하였다. 두 번째로는 단일 편파의 다중 시기 SAR 영상에서 관측되는 긴밀도를 해석하고, 모델 파라미터를 추출하며, 결과적으로 피해를 탐지하기 위한 방법을 기술하고자 하였다. 세 번째로는 다중편파의 다중 시기 SAR 영상에 대한 해석 방법에 대한 연구를 진행하는 것을 목적으로 하였다. 2장에서는 긴밀도의 측정과 긴밀도를 결정하는 대표적 요인에 대하여 분석하였고 시계열 긴밀도 감소 모델을 수식화하였다. 긴밀도 요인 중 첫 번째는 열잡음 긴밀도 감소(thermal decorrelation)로서, 열 잡음 (thermal noise)로부터 기인되며, 각 산란체의 신호대 잡음비(signal-to-noise ratio)와 밀접한 관련이 있다. 두 번째는 기하학적 비상관성(geometric decorrelation)으로, 두 센서가 다른 위치에서 신호를 송수신할 때 지상에 투영되는 파수의 스펙트럼이 이동함에 따라 발생한다. 세 번째 요인은 일반적으로 체적 비상관성 (volume decorrelation)이라 언급되는 것으로 지상의 매질 안에 산란체가 랜덤하게 분포하고 전자파가 이를 투과할 때 발생하는 위상차이에 의하여 발생된다. 체적 비상관성은 식생에서 주로 관찰되며, 이를 설명하기 위하여 RVoG 모델이 제안되기도 하였다. RVoG 모델은 식생의 잎을 포함하는 체적 레이어와 식생 하부의 지표 레이어를 포함하는 모델로서, 두 레이어에서 결정되는 간섭기법의 위상 및 긴밀도를 설명한다. 마지막 요인은 두 영상 사이에 산란체가 변화할 때 발생하는 시간 비상관성(temporal decorrelation)이다. 픽셀 안의 산란체가 비균질하게 이동하거나, 유전체의 성질이 변화할 경우 발생한다. 일반적인 repeat-pass 간섭기법의 경우 시간 비상관성이 매우 우세하게 나타나는 경우가 많으며, 식생의 경우 체적 비상관성과 시간 비상관성이 동시에 우세하게 나타난다. 식생에서 관찰되는 체적 비상관성과 시간 비상관성을 동시에 설명하는 RMoG 모델이 제안된 바 있다. 본 연구에서는 상대적으로 긴 시간 차이를 가지고 있는 repeat-pass 간섭기법에서 관측되는 긴밀도 모델을 고안하였다. 시간 비상관성을 다루는 RMoG 모델은 두 영상의 시간 차이가 크지 않을 경우, 산란체의 이동이 시간 비상관성을 발생시키는 주된 요인이라는 가정하에 제작되었다. 그러나 일반적인 인공위성 SAR는 수 일 이상의 시간 차이를 가지고 있으며, 다중 시기의 SAR 영상을 다룰 경우, 각각의 시간 차이는 상이하게 나타난다. 이 경우 시간 비상관성을 발생시키는 요인을 산란체의 이동만으로 설명하는 기에는 어려움이 있다. 그러므로 본 연구에서 고안된 모델은 지표에서의 변화를 산란체의 이동과 유전체의 성질 변화가 결합된 상태로 가정하였으며, 식생의 체적 부분은 산란체의 움직임이 체적에서의 시간 긴밀도를 감소시키는 주된 요인으로 생각하였다. 또한 다중 시기의 SAR 영상으로부터 계산된 긴밀도는 시간 차이가 증가함에 따라 긴밀도가 감소하는 현상을 관측할 수 있다. 이러한 특징은 시간 차이가 길 경우 매우 크게 나타날 수 있지만, 이전의 모델은 시간 차이가 짧은 경우를 가정하였기 때문에 그 영향이 중요하지 않았다. 그러므로 본 모델에서는 기존 모델과는 다르게 두 영상의 시간 차이가 증가함에 따라 긴밀도가 감소하는 현상을 설명하고자 지수 형태의 함수를 지표 와 체적 레이어에 각각 도입하였고 이를 시간 종속적 긴밀도(temporally-correlated coherence). 즉, 체적과 지표의 두 레이어 상에서 각각의 시간에 따라서 감소하게 되며, 이는 특정한 시간 차이에서 긴밀도가 형성되었을 때 특별한 현상이 없을 경우 예측될 수 있는 값으로 생각할 수 있다. 반면, 예측되는 값과 실제 관측값과는 차이가 존재하므로 이는 시간 독립적 긴밀도(temporally uncorrelated-coherence)로 해석하였다. 체적과 지표의 시간 긴밀도 감소 현상은 전체 긴밀도에 영향을 주기 때문에 이를 지표와 체적의 비를 도입하여, 각각의 효과가 전체 긴밀도에 주는 영향에 대하여 정량화하였다. 3장에서는 제안된 모델을 기반으로 단일 편파의 다중 시기 SAR 영상에 대하여 긴밀도 변화 탐지 알고리즘의 해석이 고안되었다. 본 방법은 일본의 키리시마 화산의 2011년 화산 폭발로 발생하였던 화산재를 탐지 하는 것을 목적으로 하였으며, 본 목적을 위하여 단일 편파의 ALOS PALSAR 영상이 사용되었다. SAR 영상을 이용하여 시간 차이가 다양하게 긴밀도가 제작되었다. 사용한 multi-looking은 32 look으로 긴밀도의 바이어스가 비교적 작음을 의미한다. 또한 픽셀의 대부분에서의 열적 비상관성(thermal decorrelation)은 무시할 수 있을 정도로 나타났으며, 기하학적 비상관성(geometric decorrelation)은 common-wave spectral filtering을 사용하여 감소되었다. 또한 대상 화산은 식생이 분포하고 있기 때문에 체적 비상관성(volume decorrelation)을 최소화하여야 할 필요성이 있다. 체적 비상관성은 식생의 높이, 식생의 수직적인 구조, 두 레이더 센서의 기선거리(spatial baseline)등에 의하여 결정된다. 식생의 물리적인 파라미터는 연구에서 수정할 수 있는 변수가 아닌 반면, 다중 시기에서 만들어 진 영상은 다수의 기선거리를 가지고 있기 때문에 기선거리에 대한 조건이 설정함으로써 체적 비상관성을 최소화 할 수 있다. RVoG 모델을 기반으로 계산된 결과 ALOS PALSAR의 경우 약 1000m의 기선거리를 가지고 있을 때 체적 긴밀도는 약 0.94 이상이 됨을 알 수 있으며, 이는 체적 긴밀도를 고려하지 않아도 됨을 의미한다. 앞서 2장에서 제안된 긴밀도 모델의 파라미터의 추출을 위하여 자료는 화산 폭발 전의 간섭쌍과 화산폭발 전후의 간섭쌍의 두 그룹으로 나누어졌다. 우선 화산 폭발 이전의 긴밀도에 대한 해석 및 이해를 위하여 긴밀도 모델이 적용되었다. 모델 파라미터에서 중요한 것은 모델에 포함되어 있는 파라미터의 수와 관측 값의 수로, 관측값이 충분할 경우에만 정확한 모델 파라미터 추출이 가능하다. 그러나 단일 편파의 다중 시기 영상을 다루는 경우 미지수의 개수가 더 많기 때문에 정확한 모델 파라미터 추출은 어려울 수 있다. 그러나 본 연구에서는 모델의 특성을 이용한 가정을 바탕으로 모델 파라미터를 추출하고자 하였다. 모델 파라미터 추출의 첫 번째는 지표대 체적비 및 시간 종속적 긴밀도의 추정으로 이는 두 지수 형태의 곡선 적합(curve fitting)으로 수행되었다. 본 결과로부터 추출된 각 픽셀의 특징적 시간 상수(characteristic time constant)는 그 픽셀이 시간의 변화에 따라 긴밀도의 안정성을 보이는 상수로, 높을수록 긴 시간 차이에도 긴밀도가 높음을 의미한다. 일반적으로 인공적인 구조물이나, 식생이 없는 나지(bare soil)에서 높은 값을 보임을 알 수 있으며, 반면 식생이 있는 픽셀은 상대적으로 낮은 값을 보였다. 추정된 결과를 바탕으로 시간 독립적 긴밀도를 추정하였으나, 이 때 미지수가 관측 값의 개수보다 많으므로 파라미터 추정에 불확실성이 존재한다. 그러므로 본 연구에서는 지표와 체적에서의 시간 종속적 긴밀도의 비를 이용하여 각 픽셀 및 각 시간차이를 갖는 긴밀도에서 체적과 지표의 시간 비상관성 중 우세한 현상을 탐지하여 우세하지 않은 현상을 무시할 수 있다고 가정하였다. 즉, 만약 지표의 시간 종속적 긴밀도가 체적의 시간 종속적 긴밀도보다 그 효과가 크다면, 시간 독립적 긴밀도가 주로 지표로부터 기인된다고 가정하는 것이다. 일반적으로 식생의 긴밀도는 지표의 긴밀도와 체적의 긴밀도의 영향이 복합적으로 작용하여 결정된다. 이 때 체적의 긴밀도의 바람에 의하여서도 쉽게 변하기 때문에 시간이 지남에 따라 그 영향이 거의 무시할 수 있게 된다. 그러므로 시간 차이가 짧을 경우 식생이 긴밀도에 주도적으로 영향을 줄 수 있지만, 시간 차이가 긴 경우 지표가 우세하게 긴밀도에 영향을 준다. 이와 같은 가정을 통하여 시간 독립적 긴밀도를 추출하였다. 각 픽셀에서 관찰되는 긴밀도의 현상을 통계적으로 분석하기 위하여 자연 재해가 포함되지 않은 자료의 시간 종속적 파라미터의 히스토그램을 제작하였고, 이를 기반의 자연 재해가 기존에 발생하였던 자연 현상이 가능성을 계산하였다. 반대로 이 수치는 자연 현상이 아닐 확률을 의미하기도 한다. 결론적으로 ALOS 자료를 사용하여 화산재가 쌓여있을 확률도를 계산하였다. 결과의 검증은 실제 현장 조사를 통하여 획득된 화산재의 두께와 영역 밀도 (area density)와의 비교를 통하여 진행되었다. 검증 결과는 두께로 약 5 cm 이상, 영역 밀도로 약 10 kg/m2 이상의 화산재가 쌓인 지역에서 상관성을 보임을 확인하였으며, 이를 바탕으로 성공적으로 재해에 대한 변화를 탐지하였음을 알 수 있었다. 4장에서는 긴밀도 모델을 이용하여 다중 시기의 다중 편파 SAR 영상을 활용하여 자연 재해 탐지 알고리즘에 적용되었다. 본 연구를 위하여 2009년부터 2015년까지의 15장의 UAVSAR 자료가 활용되었으며, 미국 캘리포니아 주에서 발생한 2015년의 산불 중 하나인 Lake fire에 대하여 연구가 진행되었다. 긴밀도 영상에서 산불에 의한 긴밀도 감소 현상을 확인할 수 있었지만, 식생 지역의 자연 현상에 의한 긴밀도 감소 현상과 복합적으로 발생하였기 때문에 해석에 어려움이 있었다. 영상의 진폭 영상을 이용한 자연 재해 탐지에도 산불 탐지할 만큼 민감도가 충분하지 않았다. 3장과 마찬가지로 본 연구 지역에서 긴밀도나 진폭만을 사용해서는 정확한 피해 지도를 만들기 어려웠으며, 그러므로 긴밀도 모델을 적용한 피해 탐지 알고리즘을 적용할 필요성이 있었다. 3장에서 제안된 모델 해석 방법과는 차이점이 있는데, 그것인 본 연구에서 사용되는 UAVSAR 자료가 다중 편파를 가지고 있으며, 공간 기선 거리가 거의 0에 가깝다는 특징이 있기 때문이다. 단일 편파 자료에서는 매개 변수의 값이 관측값보다 많았지만, 다중 편파의 경우 관측값이 더 많다. 그러므로 모델 파라미터 추정에 필요했던 가정을 줄일 수 있다는 장점이 있다. 또한 공간 기선거리가 거의 0에 가깝다는 것도 체적 비상관성을 무시할 수 있다는 것을 의미한다. 그러므로 관측된 긴밀도는 거의 시간 비상관성과 관련 있다고 생각할 수 있다. 모델 파라미터를 추출하기 위한 방법은 크게 3가지로 구성되었다. 첫 번째로는 지표와 체적에 대한 긴밀도 영향을 분리하기 위하여 우선적으로 긴밀도 최적화 알고리즘을 적용하였다. 본 연구에서는 다중 시기 영상마다 다른 최적화 벡터를 상정하는 MSM 알고리즘을 적용하였다. 이 과정을 통하여 관측할 수 있는 긴밀도가 최대치가 되게 만드는 편파와 그와 수직하는 편파를 찾을 수 있으며, 모델 해석과 연관시켰을 때 최대치가 되는 긴밀도는 지표의 변화에, 최소화되는 긴밀도는 체적의 변화와 관련되어 있다고 해석할 수 있다. 두 번째 단계에서는 시간 종속적 긴밀도에 해당하는 변수인 특징적 시간 상수를 추출하였으며, 지표대 체적비 역시 계산하였다. 단일 편파 추정 방법과 다르게 다중 편파 영상에서는 모든 편파의 긴밀도를 이용하여 체적과 지표에서의 시간 종속적 긴밀도를 추정한다. 세번째 단계에서는 체적과 지표에서의 시간 독립적 긴밀도를 동시에 추정하며 3장과는 다른 것은 이 과정에서 가정이 필요하지 않다는 것이다. 본 과정을 통하여 추정된 파라미터 중 시간 독립적 긴밀도는 시간 종속적 긴밀도로부터 설명되지 않는 부분을 추가적으로 설명하는 파라미터로써 갑작스럽게 일어나는 변화를 의미한다. 그러므로 이를 이용하여 각 픽셀에서 과거 동안 발생하였던 자연 현상이 긴밀도에 미치는 영향을 파악할 수 있으며, 산불은 비교적 강한 긴밀도 감소를 발생시키기 때문에 통계적인 접근을 통하여 확률적인 피해 가능성을 분석할 수 있었다. 산불의 경계 부분의 자료와의 상대적인 비교를 통한 검증 결과을 통하여 긴밀도만을 이용하여 피해 지역을 추정하는 방법보다 오탐지률을 줄일 수 있는 것을 알 수 있었다. 4장에서 사용된 모델 파라미터 추정 결과의 검증을 위하여 이전의 검증이 진행되어 왔던 RMoG 모델과 상대 비교를 진행하였다. RMoG의 체적과 지표 부분의 시간 비상관성 함수는 본 연구에서 사용된 모델의 시간 종속적 긴밀도와 시간 독립적 긴밀도의 곱으로 표현될 수 있다. 비교한 결과는 높은 상관성을 보이는 것으로 확인되었다. 또한 단일 편파와 다중 편파를 사용한 모델 파라미터 추정 결과와 재해 탐지 결과도 비교하였다. 모델 파라미터 추정의 경우, 단일 편파에서 추정된 결과가 다소 작음이 확인되었으며, 이것은 단일 편파(HH)가 지표와 체적 사이의 산란 중심에서 기록된 것으로 그 원인을 추정해볼 수 있다. 그럼에도 불구하고 피해탐지 방법에서의 정확도는 다중 편파를 사용하는 방법에 우세하게 나타났지만, 거의 유사한 정도의 정확도를 가지고 있음을 확인할 수 있었다. 본 연구에서 제안된 피해 탐지 알고리즘은 자연 현상에서 비롯되는 긴밀도 감소 현상을 분석하여 자연 재해로부터 발생하는 현상을 구별하여 피해로 규정하였다. 이를 통해, 기존의 알고리즘 보다 정확도를 향상시킬 수 있었다. 또한 다중 편파 간섭계 SAR 자료를 사용함으로써, 다중 편파에 기록되어 있는 다른 산란 중심에서의 변화를 이용하여 체적 및 지표에서의 변화를 독립적으로 평가하여 피해를 탐지하였다. 이와 같은 알고리즘은 다수의 자연 재해에 적용될 수 있으며, 각 픽셀의 긴밀도 특성을 반영하기 때문에 다양한 지표 타입에 적용될 수 있을 것으로 기대된다. 또한 물리적인 해석을 병합하여 피해의 심각도를 정량화 할 수 있은 가능성 역시 존재 하며, 향후 발사될 인공위성의 미션에서도 적용될 수 있기 때문에 본 연구의 의의가 크다고 판단할 수 있다.For rapid response and efficient recovery, the accurate assessment of damaged area caused by the natural disaster is essential. SAR system has been known as a powerful and effective tool for estimating damaged area due to its imaging capability at night and cloudy days. One of the damage assessment methods is based on interferometric coherence generated from two or more SAR images, namely coherent change detection. The interferometric coherence is a very sensitive detector to subtle changes induced by dielectric properties and positional disturbance of scatterers. However, the conventional approaches using the interferometric coherence have several limitations in understanding the damage mechanism caused by natural disasters and providing the accurate spatial information. These limitations come from the complicated mechanism determining the coherence. A number of sources including the sensor geometry, radar parameters, and surface conditions can induce the decorrelation. In particular, the interpretation complexity of the interferometric coherence is severe over the vegetated area, due to the volumetric decorrelation and temporal decorrelation. It is a remaining problem that the decorrelation caused by the natural phenomena such as the wind, rain, and snow can come along the decorrelation caused by natural disaster. Therefore, a new accurate approach needs to be designed in order to interpret the decorrelation sources and discriminate the effect of natural disaster from that of natural phenomena. This research starts from the development of the temporal decorrelation model to interpret the interferometric coherence observed in multi-temporal SAR data. Then, the coherence model is extended to be applied to the damage mapping algorithm for single- and fully-polarimetric SAR data for detecting the damaged area caused by volcanic ash and wildfire. The coherence model is designed so that it explains the coherence behavior observed in the multi-temporal SAR data. The noticeable characteristic is that the interferometric coherence tends to decrease as the time-interval increases. Also, the coherence for multi-layer is determined by the different contributions of each layer. For example, the volume and ground layer can affect the total coherence observed in the forest area. In order to reflect the realistic condition and physically interpret the coherence, the coherence model proposed in this research includes several decorrelation sources such as temporally correlated dielectric changes, temporally uncorrelated dielectric changes and the motions in the two layersi.e. ground and volume layer. According to the proposed model, the coherent behavior of each layer is explained by exponentially decreasing coherence (temporally-correlated coherence), and the difference between the observed coherence and the temporally-correlated coherence is interpreted as the temporally-uncorrelated coherence. The ground-to-volume ratio plays an important role to determine the contributions of temporal decorrelations in ground and volume layer. Suggested model is applied into the coherent change detection for multi-temporal and single-polarized SAR data. The method is evaluated for detection of volcanic ash emitted from Kirishima volcano in 2011 using ALOS PALSAR data. The criterion of the spatial baseline is calculated based on the Random Volume over Ground model to minimize the volumetric decorrelation. The model parameters are extracted under the several assumptions, and then the historical coherence behavior is analyzed using kernel density estimation method. By comparing the changes of model parameters between the reference pairs and event pairs, the probability of surface changes caused by volcanic ash is defined. The in-situ data, which measure the depth and area density of volcanic ash, is compared with the calculated probability maps for determining the threshold and evaluating the performance. The correlation is found over the area where the depth of the volcanic ash is more than 5 cm and the area density is more than 10 kg/m2. The temporal decorrelation model is also used for change detection using multi-temporal and fully-polarimetric interferometric SAR data. By introducing polarimetric and interferometric SAR data, the assumptions used in the method for single-polarized SAR data are reduced and the changes of two layer can be estimated separately. The approach is applied to detect the burnt area caused by the Lake fire, in June 2015 using UAVSAR data. Even though, coherence analysis shows the loss of coherence due to the fire event, the temporal decorrelation caused by the natural changes is mixed with the signal of the event. In order to apply the coherence model and extract the model parameter, here, the three steps are proposedcoherence optimization, temporally-correlated coherence estimation, and temporally-uncorrelated coherence estimation. Then, the extracted model parameters are used for the damage assessment using the probability determination based on the history of natural phenomena. The final generated damage map shows higher performance than the damage mapping method using coherence only. Also, the comparison result with the RMoG model shows high agreement, which implies the extraction of the model parameters is reliable. One of the advantages of the proposed algorithm is that the more accurate delineation of damage area can be expected by isolating the decorrelation caused by the natural disaster from the effect of natural phenomena. Moreover, a distinguishable benefit can be obtained that the changes over ground and volume layers can be assessed separately by utilizing the multi-temporal full-polarimetric SAR data.Chapter 1. Introduction 1 1.1. Brief overview of SAR and its applications 1 1.2. Motivations 5 1.3. Purpose of Research 8 1.4. Outline 10 Chapter 2. Estimation of complex correlation and decorrelation sources 11 2.1. Estimation of complex correlation 11 2.2. Decorrelation sources 14 2.3. Derivation of coherence model assuming two layers for repeat-pass interferometry 35 Chapter 3. Damage mapping using temporal decorrelation model for single-polarized SAR data : A case study for volcanic ash 51 3.1. Description of study area 51 3.2. Data description 55 3.3. Extraction of temporal decorrelation parameters 61 3.4. Probability map generation 68 3.5. Mapping volcanic ash 73 3.6. Discussion 76 Chapter 4.Damage mapping using temporal decorrelation model for multi-temporal and fully-polarized SAR data 78 4.1. Description of Lake Fire and UAVSAR data 79 4.2. Brief analysis of SAR amplitude and interferometric coherence 82 4.3. Damage mapping algorithm using coherence model 89 4.4. Applicable conditions of damage mapping algorithm using coherence model 114 4. 5. Comparison of model inversion results and damage mapping algorithm results 120 4. 6. Discussion and conclusion 129 Chapter 5. Conclusions and Future Perspectives 132 Abstract in Korean 140 Bibliography 147Docto

Biomass Representation in Synthetic Aperture Radar Interferometry Data Sets

This work makes an attempt to explain the origin, features and potential applications of the elevation bias of the synthetic aperture radar interferometry (InSAR) datasets over areas covered by vegetation. The rapid development of radar-based remote sensing methods, such as synthetic aperture radar (SAR) and InSAR, has provided an alternative to the photogrammetry and LiDAR for determining the third dimension of topographic surfaces. The InSAR method has proved to be so effective and productive that it allowed, within eleven days of the space shuttle mission, for acquisition of data to develop a three-dimensional model of almost the entire land surface of our planet. This mission is known as the Shuttle Radar Topography Mission (SRTM). Scientists across the geosciences were able to access the great benefits of uniformity, high resolution and the most precise digital elevation model (DEM) of the Earth like never before for their a wide variety of scientific and practical inquiries. Unfortunately, InSAR elevations misrepresent the surface of the Earth in places where there is substantial vegetation cover. This is a systematic error of unknown, yet limited (by the vertical extension of vegetation) magnitude. Up to now, only a limited number of attempts to model this error source have been made. However, none offer a robust remedy, but rather partial or case-based solutions. More work in this area of research is needed as the number of airborne and space-based InSAR elevation models has been steadily increasing over the last few years, despite strong competition from LiDAR and optical methods. From another perspective, however, this elevation bias, termed here as the “biomass impenetrability”, creates a great opportunity to learn about the biomass. This may be achieved due to the fact that the impenetrability can be considered a collective response to a few factors originating in 3D space that encompass the outermost boundaries of vegetation. The biomass, presence in InSAR datasets or simply the biomass impenetrability, is the focus of this research. The report, presented in a sequence of sections, gradually introduces terminology, physical and mathematical fundamentals commonly used in describing the propagation of electromagnetic waves, including the Maxwell equations. The synthetic aperture radar (SAR) and InSAR as active remote sensing methods are summarised. In subsequent steps, the major InSAR data sources and data acquisition systems, past and present, are outlined. Various examples of the InSAR datasets, including the SRTM C- and X-band elevation products and INTERMAP Inc. IFSAR digital terrain/surface models (DTM/DSM), representing diverse test sites in the world are used to demonstrate the presence and/or magnitude of the biomass impenetrability in the context of different types of vegetation – usually forest. Also, results of investigations carried out by selected researchers on the elevation bias in InSAR datasets and their attempts at mathematical modelling are reviewed. In recent years, a few researchers have suggested that the magnitude of the biomass impenetrability is linked to gaps in the vegetation cover. Based on these hints, a mathematical model of the tree and the forest has been developed. Three types of gaps were identified; gaps in the landscape-scale forest areas (Type 1), e.g. forest fire scares and logging areas; a gap between three trees forming a triangle (Type 2), e.g. depending on the shape of tree crowns; and gaps within a tree itself (Type 3). Experiments have demonstrated that Type 1 gaps follow the power-law density distribution function. One of the most useful features of the power-law distributed phenomena is their scale-independent property. This property was also used to model Type 3 gaps (within the tree crown) by assuming that these gaps follow the same distribution as the Type 1 gaps. A hypothesis was formulated regarding the penetration depth of the radar waves within the canopy. It claims that the depth of penetration is simply related to the quantisation level of the radar backscattered signal. A higher level of bits per pixels allows for capturing weaker signals arriving from the lower levels of the tree crown. Assuming certain generic and simplified shapes of tree crowns including cone, paraboloid, sphere and spherical cap, it was possible to model analytically Type 2 gaps. The Monte Carlo simulation method was used to investigate relationships between the impenetrability and various configurations of a modelled forest. One of the most important findings is that impenetrability is largely explainable by the gaps between trees. A much less important role is played by the penetrability into the crown cover. Another important finding is that the impenetrability strongly correlates with the vegetation density. Using this feature, a method for vegetation density mapping called the mean maximum impenetrability (MMI) method is proposed. Unlike the traditional methods of forest inventories, the MMI method allows for a much more realistic inventory of vegetation cover, because it is able to capture an in situ or current situation on the ground, but not for areas that are nominally classified as a “forest-to-be”. The MMI method also allows for the mapping of landscape variation in the forest or vegetation density, which is a novel and exciting feature of the new 3D remote sensing (3DRS) technique. Besides the inventory-type applications, the MMI method can be used as a forest change detection method. For maximum effectiveness of the MMI method, an object-based change detection approach is preferred. A minimum requirement for the MMI method is a time-lapsed reference dataset in the form, for example, of an existing forest map of the area of interest, or a vegetation density map prepared using InSAR datasets. Preliminary tests aimed at finding a degree of correlation between the impenetrability and other types of passive and active remote sensing data sources, including TerraSAR-X, NDVI and PALSAR, proved that the method most sensitive to vegetation density was the Japanese PALSAR - L-band SAR system. Unfortunately, PALSAR backscattered signals become very noisy for impenetrability below 15 m. This means that PALSAR has severe limitations for low loadings of the biomass per unit area. The proposed applications of the InSAR data will remain indispensable wherever cloud cover obscures the sky in a persistent manner, which makes suitable optical data acquisition extremely time-consuming or nearly impossible. A limitation of the MMI method is due to the fact that the impenetrability is calculated using a reference DTM, which must be available beforehand. In many countries around the world, appropriate quality DTMs are still unavailable. A possible solution to this obstacle is to use a DEM that was derived using P-band InSAR elevations or LiDAR. It must be noted, however, that in many cases, two InSAR datasets separated by time of the same area are sufficient for forest change detection or similar applications

Elevation and Deformation Extraction from TomoSAR

3D SAR tomography (TomoSAR) and 4D SAR differential tomography (Diff-TomoSAR) exploit multi-baseline SAR data stacks to provide an essential innovation of SAR Interferometry for many applications, sensing complex scenes with multiple scatterers mapped into the same SAR pixel cell. However, these are still influenced by DEM uncertainty, temporal decorrelation, orbital, tropospheric and ionospheric phase distortion and height blurring. In this thesis, these techniques are explored. As part of this exploration, the systematic procedures for DEM generation, DEM quality assessment, DEM quality improvement and DEM applications are first studied. Besides, this thesis focuses on the whole cycle of systematic methods for 3D & 4D TomoSAR imaging for height and deformation retrieval, from the problem formation phase, through the development of methods to testing on real SAR data. After DEM generation introduction from spaceborne bistatic InSAR (TanDEM-X) and airborne photogrammetry (Bluesky), a new DEM co-registration method with line feature validation (river network line, ridgeline, valley line, crater boundary feature and so on) is developed and demonstrated to assist the study of a wide area DEM data quality. This DEM co-registration method aligns two DEMs irrespective of the linear distortion model, which improves the quality of DEM vertical comparison accuracy significantly and is suitable and helpful for DEM quality assessment. A systematic TomoSAR algorithm and method have been established, tested, analysed and demonstrated for various applications (urban buildings, bridges, dams) to achieve better 3D & 4D tomographic SAR imaging results. These include applying Cosmo-Skymed X band single-polarisation data over the Zipingpu dam, Dujiangyan, Sichuan, China, to map topography; and using ALOS L band data in the San Francisco Bay region to map urban building and bridge. A new ionospheric correction method based on the tile method employing IGS TEC data, a split-spectrum and an ionospheric model via least squares are developed to correct ionospheric distortion to improve the accuracy of 3D & 4D tomographic SAR imaging. Meanwhile, a pixel by pixel orbit baseline estimation method is developed to address the research gaps of baseline estimation for 3D & 4D spaceborne SAR tomography imaging. Moreover, a SAR tomography imaging algorithm and a differential tomography four-dimensional SAR imaging algorithm based on compressive sensing, SAR interferometry phase (InSAR) calibration reference to DEM with DEM error correction, a new phase error calibration and compensation algorithm, based on PS, SVD, PGA, weighted least squares and minimum entropy, are developed to obtain accurate 3D & 4D tomographic SAR imaging results. The new baseline estimation method and consequent TomoSAR processing results showed that an accurate baseline estimation is essential to build up the TomoSAR model. After baseline estimation, phase calibration experiments (via FFT and Capon method) indicate that a phase calibration step is indispensable for TomoSAR imaging, which eventually influences the inversion results. A super-resolution reconstruction CS based study demonstrates X band data with the CS method does not fit for forest reconstruction but works for reconstruction of large civil engineering structures such as dams and urban buildings. Meanwhile, the L band data with FFT, Capon and the CS method are shown to work for the reconstruction of large manmade structures (such as bridges) and urban buildings