This paper presents an advanced workflow for processing radar imagery stacks using Persistent Scatterer and Distributed Scatterer Interferometry (PSDS) to enhance spatial coherence and improve displacement detection accuracy. The workflow leverages Level 2 Coregistered Single Look Complex (L2-CSLC) images generated by the open-source COMPASS (Coregistered Multi-temporal Sar SLC) framework in combination with the Combined eigenvalue maximum likelihood Phase Linking (CPL) approach implemented in MiaplPy. Starting the analysis directly from Level 2 products offers a significant advantage to end-users, as they simplify processing by being pre-geocoded and ready for immediate analysis. Additionally, the open-source nature of the workflow and the use of L2-CSLC products simplify the processing pipeline, making it easier to distribute directly to users for practical applications in monitoring infrastructure stability in dynamic environments. The ISCE3-MiaplPy workflow is compared against ISCE2-MiaplPy and the European Ground Motion Service (EGMS) to assess its performance in detecting infrastructure deformations in dynamic environments, such as the Algeciras port. The results indicate that ISCE3-MiaplPy delivers denser measurements, albeit with increased noise, compared to its counterparts. This higher resolution enables a more detailed understanding of infrastructure stability and surface dynamics, which is critical for environments with ongoing human activity or natural forces.

This paper presents an advanced methodology for processing radar imagery stacks using Persistent Scatterer and Distributed Scatterer Interferometry (PSDS) to enhance spatial coherence and improve displacement detection accuracy. The workflow leverages Level 2 Coregistered Single Look Complex (L2-CSLC) images generated by the open-source COMPASS (COregistered Multi-temPorAl Sar SLC) framework in combination with the Combined eigenvalue maximum likelihood Phase Linking (CPL) approach as implemented in MiaplPy. Starting the analysis directly from Level 2 products offers a significant advantage to end-users, as they simplify processing by being pre-geocoded and ready for immediate analysis. Additionally, the open-source nature of the workflow and the use of L2-CSLC products simplify the processing pipeline, making it easier to distribute directly to users for practical applications in monitoring infrastructure stability in dynamic environments. The ISCE3-MiaplPy workflow is compared against ISCE2-MiaplPy and the European Ground Motion Service (EGMS) to assess its performance in detecting infrastructure deformation in dynamic environments, such as the Algeciras port. Results indicate that ISCE3-MiaplPy delivers significantly denser measurements, albeit with increased noise, compared to its counterparts. This higher resolution enables a more detailed understanding of infrastructure stability and surface dynamics, which is critical for environments with ongoing human activity or natural forces.

Port infrastructures are critical nodes in global supply chains that demand routine, comparable, and traceable surveillance to manage risk and operability effectively. Among remote sensing technologies, Interferometric Synthetic Aperture Radar (InSAR) offers unique potential for continuous geometric monitoring of coastal assets. However, port operators face significant barriers to adopting this technology: the difficulty of interpreting satellite data in complex coastal environments with heterogeneous reclaimed platforms, mixed construction typologies, and variable surface conditions that complicate generation of reliable displacement maps aligned with specific engineering assets. This Ph.D. thesis addresses this gap by establishing a decision-oriented, port-wide observatory of geometric evolution. The methodology integrates four core innovations: (1) a standardized unit of analysis based on Vulnerable Areas of Interest (VAI) that groups surfaces with homogeneous function and exposure, enabling cross-terminal benchmarking; (2) an open, port-adapted Multi-Temporal InSAR workflow utilizing Phase-Linking on standardized L2-CSLC products to maximize measurement density in challenging marine environments; (3) LiDAR-enhanced spatial attribution of InSAR reflectors that resolves radar target ambiguity through whitening-based nearest-neighbor association, ensuring deformation signals are assigned to specific structural components; and (4) statistical trend-change detection using Multiple Hypothesis Testing with Baarda's B-method for rigorous error control, allowing detection of regime shifts without relying on brittle generic thresholds. Key findings demonstrate the efficacy of the proposed framework. The LiDAR-enhanced spatial attribution significantly reduces positional ambiguity and enables reliable association of radar scatterers with specific structural elements. Multi-geometry analysis reveals complex deformation fields that would be misinterpreted using single-geometry approaches. The MHT-based change detection successfully differentiates between steady basin-wide consolidation and localized anomalies, identifying specific stress events such as the impact of Storm Gloria in Castellón and differential settlement patterns in Huelva. For dynamic operational areas such as container yards, where precise displacement retrieval is limited by decorrelation from container restacking, coherence analysis along crane rails provides qualitative indicators of yard operational status. The research provides a comprehensive framework for risk-based surveillance of port geometry. By ensuring that geometric deviations are traced to specific assets, statistically validated through MHT, and standardized through the VAI concept, the proposed workflow transforms routine satellite archives into auditable signals that populate the Vulnerability component of the standard port risk vector R = P | V | C, where P represents probability of failure, V the degree of structural affection, and C economic consequences. This provides port authorities with observational evidence to inform maintenance prioritization and probabilistic reliability assessments, bridging the gap between remote sensing measurements and operational risk management. RESUMEN Las infraestructuras portuarias son nodos críticos en las cadenas de suministro globales que exigen una vigilancia rutinaria, comparable y trazable para gestionar el riesgo y la operatividad de manera efectiva. Entre las tecnologías de teledetección, el Radar de Apertura Sintética Interferométrico (InSAR) ofrece un potencial único para el monitoreo geométrico continuo de activos costeros. Sin embargo, los operadores portuarios enfrentan barreras significativas para adoptar esta tecnología: la dificultad de interpretar datos satelitales en entornos costeros complejos, donde plataformas ganadas al mar heterogéneas, tipologías constructivas mixtas y condiciones superficiales variables complican la generación de mapas de desplazamiento fiables y alineados con activos específicos. Esta tesis doctoral aborda esta brecha mediante el establecimiento de un observatorio de evolución geométrica a escala portuaria orientado a la decisión. La metodología integra cuatro innovaciones fundamentales: (1) una unidad de análisis estandarizada basada en Áreas de Interés Vulnerables (VAI) que agrupa superficies con función y exposición homogéneas, permitiendo la comparación entre terminales; (2) un flujo de trabajo MT-InSAR abierto y adaptado al entorno portuario, utilizando Phase-Linking sobre productos L2-CSLC estandarizados para maximizar la densidad de medidas en ambientes marinos desafiantes; (3) atribución espacial mejorada con LiDAR de los reflectores InSAR, que resuelve la ambigüedad de los blancos radar mediante asociación de vecinos más cercanos basada en blanqueamiento, asegurando que las señales de deformación se asignen a componentes estructurales específicos; y (4) detección estadística de cambios de régimen mediante Contrastación de Hipótesis Múltiples (MHT) con el método B de Baarda para control riguroso del error, permitiendo detectar cambios de régimen sin depender de umbrales genéricos frágiles. Los hallazgos clave demuestran la eficacia del marco propuesto. La atribución espacial mejorada con LiDAR reduce significativamente la ambigüedad posicional y permite asociar de manera fiable los dispersores radar con elementos estructurales específicos. El análisis multi-geometría revela campos de deformación complejos que serían malinterpretados utilizando enfoques de geometría única. La detección de cambios basada en MHT distingue con éxito entre la consolidación constante a escala de dársena y las anomalías...

Ports pose distinctive monitoring challenges due to harsh marine conditions, mixed construction typologies, and heterogeneous ground conditions. These factors complicate the routine use of satellite InSAR, especially when medium-resolution scatterers must be reliably attributed to specific assets for risk and asset management decisions. In current practice, persistent and distributed scatterer (PS/DS) points are often interpreted in map view without an explicit positional uncertainty model or systematic linkage to three-dimensional infrastructure geometry. We present an end-to-end Differential InSAR framework tailored to large ports that fuses medium-resolution Sentinel-1 Level 2 Co-registered Single-Look Complex (L2-CSLC) stacks with high-resolution airborne LiDAR at the post-processing stage. For the Port of Bahía de Algeciras (Spain), we process 123 Sentinel-1A/B images (2020–2022) in ascending and descending geometry using PS/DS time-series analysis with ETAD-like timing corrections and RAiDER tropospheric/ionospheric mitigation. LiDAR is then used to (i) derive look-specific shadow/layover masks and (ii) perform a whitening-transformed nearest-neighbor association that assigns PS/DS points to LiDAR points under an explicit range–azimuth–cross-range (RAC) uncertainty ellipsoid. The RAC standard deviations $({\sigma }_r,{\sigma }_a,{\sigma }_c)$ are derived from the effective CSLC range/azimuth resolution and from empirical height correction statistics, providing a geometry- and data-informed prior on positional uncertainty. Finally, we render dual-geometry red–green composites (ascending to R, descending to G; shared normalization) on the LiDAR point cloud, enabling consistent inspection in plan and elevation. Across asset types, rigid steel/concrete elements (trestles, quay faces, and dolphins) sustain high coherence, small whitened offsets, and stable backscatter in both looks; cylindrical storage tanks are bright but exhibit look-dependent visibility and larger cross-range residuals due to height and curvature; and container yards and vessels show high amplitude dispersion and lower temporal coherence driven by operations. Overall, LiDAR-assisted whitening-based linking reduces effective positional ambiguity and improves structure-specific attribution for most scatterers across the port. The fusion products, geometry-aware linking plus three-dimensional dual-geometry RGB, enhance the interpretability of medium-resolution SAR and provide a transferable, port-oriented basis for integrating deformation evidence into risk and asset management workflows.

Satellite-based Interferometric Synthetic Aperture Radar (InSAR) is widely used to monitor ground deformation over large areas. However, each InSAR observation measures only the scalar projection of the three-dimensional displacement vector onto the satellite line of sight. Consequently, two independent ascending and descending line-of-sight observations can determine only two independent displacement components unless an additional physical constraint or external observation is introduced. This study examines the bias and noise sensitivity of two-geometry InSAR displacement decomposition when the additional constraint is expressed in different global and local coordinate systems. We formulate the decomposition in the East–North–Vertical and the slope-oriented Upwards–Transverse–Normal reference frames and analyze how the estimated components are affected by the true displacement direction, the orientation of the constrained axis, and its relationship with the null direction of the observation geometry. Synthetic numerical experiments are used to evaluate conventional zero-North, vertical–planimetric, slope-aligned, and null-direction-aligned decompositions. The results show that a constraint aligned with the null direction prevents the unobservable displacement component from biasing the two estimated components, whereas even small coordinate-system misalignments with respect to the true displacement direction may produce substantial errors when the constrained direction is poorly oriented with respect to the null direction. An interactive dashboard is provided to support geometry-specific sensitivity analysis and the selection of physically meaningful coordinate systems for practical InSAR applications.