Structural health monitoring of engineered structures using a space-borne synthetic aperture radar multi-temporal approach: from cultural heritage sites to war zones

Structural health monitoring (SHM) of engineered structures consists of an automated or semi-automated survey system that seeks to assess the structural condition of an anthropogenic structure. The aim of an SHM system is to provide insights into possible induced damage or any inherent signals of deformation affecting the structure in terms of detection, localization, assessment, and prediction. During the last decade there has been a growing interest in using several remote sensing techniques, such as synthetic aperture radar (SAR), for SHM. Constellations of SAR satellites with short repeat time acquisitions permit detailed surveys temporal resolution and millimetric sensitivity to deformation that are at the scales relevant to monitoring large structures. The all-weather multi-temporal characteristics of SAR make its products suitable for SHM systems, especially in areas where in situ measurements are not feasible or not cost effective. To illustrate this capability, we present results from COSMO-SkyMed (CSK) and TerraSAR-X SAR observations applied to the remote sensing of engineered structures. We show how by using multiple-geometry SAR-based products which exploit both phase and amplitude of the SAR signal we can address the main objectives of an SHM system including detection and localization. We highlight that, when external data such as rain or temperature records are available or simple elastic models can be assumed, the SAR-based SHM capability can also provide an interpretation in terms of assessment and prediction. We highlight examples of the potential for such imaging capabilities to enable advances in SHM from space, focusing on dams and cultural heritage areas. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Wetland mapping and monitoring using polarimetric and interferometric synthetic aperture radar (SAR) data and tools

Wetlands are home to a great variety of flora and fauna species and provide several unique environmental functions, such as controlling floods, improving water-quality, supporting wildlife habitat, and shoreline stabilization. Detailed information on spatial distribution of wetland classes is crucial for sustainable management and resource assessment. Furthermore, hydrological monitoring of wetlands is also important for maintaining and preserving the habitat of various plant and animal species. This thesis investigates the existing knowledge and technological challenges associated with wetland mapping and monitoring and evaluates the limitations of the methodologies that have been developed to date. The study also proposes new methods to improve the characterization of these productive ecosystems using advanced remote sensing (RS) tools and data. Specifically, a comprehensive literature review on wetland monitoring using Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) techniques is provided. The application of the InSAR technique for wetland mapping provides the following advantages: (i) the high sensitivity of interferometric coherence to land cover changes is taken into account and (ii) the exploitation of interferometric coherence for wetland classification further enhances the discrimination between similar wetland classes. A statistical analysis of the interferometric coherence and SAR backscattering variation of Canadian wetlands, which are ignored in the literature, is carried out using multi-temporal, multi-frequency, and multi-polarization SAR data. The study also examines the capability of compact polarimetry (CP) SAR data, which will be collected by the upcoming RADARSAT Constellation Mission (RCM) and will constitute the main source of SAR observation in Canada, for wetland mapping. The research in this dissertation proposes a methodology for wetland classification using the synergistic use of intensity, polarimetry, and interferometry features using a novel classification framework. Finally, this work introduces a novel model based on the deep convolutional neural network (CNN) for wetland classification that can be trained in an end-to-end scheme and is specifically designed for the classification of wetland complexes using polarimetric SAR (PolSAR) imagery. The results of the proposed methods are promising and will significantly contribute to the ongoing efforts of conservation strategies for wetlands and monitoring changes. The approaches presented in this thesis serve as frameworks, progressing towards an operational methodology for mapping wetland complexes in Canada, as well as other wetlands worldwide with similar ecological characteristics

Monitoring wetlands and water bodies in semi-arid Sub-Saharan regions

Surface water in wetlands is a critical resource in semi-arid West-African regions that are frequently exposed to droughts. Wetlands are of utmost importance for the population as well as the environment, and are subject to rapidly changing seasonal fluctuations. Dynamics of wetlands in the study area are still poorly understood, and the potential of remote sensing-derived information as a large-scale, multi-temporal, comparable and independent measurement source is not exploited. This work shows successful wetland monitoring with remote sensing in savannah and Sahel regions in Burkina Faso, focusing on the main study site Lac Bam (Lake Bam). Long-term optical time series from MODIS with medium spatial resolution (MR), and short-term synthetic aperture radar (SAR) time series from TerraSAR-X and RADARSAT-2 with high spatial resolution (HR) successfully demonstrate the classification and dynamic monitoring of relevant wetland features, e.g. open water, flooded vegetation and irrigated cultivation. Methodological highlights are time series analysis, e.g. spatio-temporal dynamics or multitemporal-classification, as well as polarimetric SAR (polSAR) processing, i.e. the Kennaugh elements, enabling physical interpretation of SAR scattering mechanisms for dual-polarized data. A multi-sensor and multi-frequency SAR data combination provides added value, and reveals that dual-co-pol SAR data is most recommended for monitoring wetlands of this type. The interpretation of environmental or man-made processes such as water areas spreading out further but retreating or evaporating faster, co-occurrence of droughts with surface water and vegetation anomalies, expansion of irrigated agriculture or new dam building, can be detected with MR optical and HR SAR time series. To capture long-term impacts of water extraction, sedimentation and climate change on wetlands, remote sensing solutions are available, and would have great potential to contribute to water management in Africa

Differential Radar Interferometry Applied to the Detection and Monitoring of Geological Hazards

We live in a constantly changing environment, characterized by climate changes, extreme weather events and the occurrence of more frequent geological hazards that have a strong negative impact on the territory and society, interrupting services, damaging buildings and infrastructure and jeopardizing the life of millions of people worldwide. For this reason, there is the need to build a society resilient to natural-hazards, which can understand how the natural system behaves and responds to natural and human-induced modifications and can adapt to these changes. The monitoring of the territory is necessary to comprehend the triggering factors and the mechanisms of geological hazards and to plan the most suitable actions to prevent and mitigate the risk. The monitoring of geological hazards with conventional ground-based techniques such as Global Positioning System (GPS) and levelling is usually expensive and time consuming, which limits the number of measured points and the overall duration of the surveys. One of the best way to overcome to these problems is to use remote-sensing techniques to monitor large portion of territory reducing operating costs and time. Advanced Differential Synthetic Aperture Radar Interferometry (A-DInSAR) is one of the best tool to monitor and study ground displacements over very large portions of territory in a cost-effective way. In this Doctoral Thesis, we applied A-DInSAR to the monitoring of the geological instabilities occurring in different areas characterized by unique geological and environmental features. The selected areas include different environments such as vegetate territories, low and steep topography, coastal areas, salty deserts, urbanized land, each of them affected by hazards of natural and anthropic origin such as landslides, subsidence and karstic activity. In each case study, the monitoring activity presented its own challenges that were overcome adopting specific technical solutions in the data processing and management. The aim of this work is to give an overview of the potential of A-DInSAR techniques when applied to the study of geological hazards in different environments. This can be useful to show to local Authorities that A-DInSAR can be fully integrated as part of the activities carried out to manage the territory and to prevent and mitigate the risk related to geological hazards

Lokaalstatistikute kasutamine rohumaade ja metsade kaugseires

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKäesolev doktoritöö analüüsib lokaalstatistikute kasutamist rohumaade ja metsade kaugseires. Töö esimene osa käsitleb rohumaade monitoorimist tehisava-radari (synthetic aperture radar (SAR)) abil ning teine osa metsade kaugseiret kasutades optilisi sensoreid. Analüüsides rohumaade niitmise ja C- laineala tehisava-radari interferomeetrilise koherentsuse seoseid leiti, et selle parameetri kasutamisel on potentsiaali niitmise tuvastamise algoritmide ja rakenduste väljaarendamiseks. Tulemused näitavad, et pärast niitmist on VH ja VV polarisatsiooni 12-päeva interferomeetrilise koherentsuse mediaan väärtused statistiliselt oluliselt kõrgemad võrreldes niitmise eelse olukorraga. Koherentsus on seda kõrgem, mida väiksem on ajaline vahe niitmise ja pärast seda üles võetud esimese interferomeetrilise mõõtmise vahel. Hommikune kaste, sademed, põllutööde teostamine, näiteks külv või kündmine, kõrgelt niitmine ja kiire rohu kasv pärast niitmist vähendavad koherentsust ja raskendavad niitmise sündmuste eristamist. Selleks, et eelpoolnimetatud mõjusid leevendada tuleks tulevikus uurida 6-päeva koherentsuse ja niitmise sündmuste vahelisi seoseid. Käesolevas doktoritöös esitatud tulemused loovad siiski tugeva aluse edasisteks uuringuteks ja arendusteks eesmärgiga võtta C-laineala tehisava-radari andmed niitmise tuvastamisel ka praktikas kasutusele. Lisaks näidati, et ortofotodel põhinevate metsa kaugseire hinnangute andmisel on abi lokaalstatistikute kasutamisest. Analüüsides kaugseire hinnangut riigimetsa takseerandmete (national forest inventory) kohta leiti, et näidistel põhinev järeldamine (case-based reasoning (CBR)) sobib hästi selliste kaugseire ülesannete empiirilisteks lahendusteks, kus sisendandmetena on kasutatavad väga paljud erinevad andmeallikad. Leiti, et klasteranalüüsi saab kasutada kaugseire tunnuste eelvaliku meetodina. Võrreldes erinevaid tekstuuri statistikuid näidati, et lokaalselt arvutatud keskväärtus on kõige väärtuslikum tunnus. Järeldati, et nii statistiliste kui ka struktuursete lokaalstatistikute kasutamisega saab lisada pikslipõhistele kaugseire hinnangutele olulist andmestikku.This thesis studies approaches for remote sensing of grasslands and forests based on local statistics. The first part of the thesis focuses on monitoring of grasslands with SAR and the second part to monitoring of forests with optical sensors. It is shown that there is potential to develop mowing detection algorithms and applications using C-band SAR temporal interferometric coherence. The results demonstrate that after a mowing event, median VH and VV polarisation 12-day interferometric coherence values are statistically significantly higher than those from before the event. The sooner after the mowing event the first interferometric acquisition is taken, the higher the coherence. Morning dew, precipitation, farming activities, such as sowing or ploughing, high residual straws after the cut and rapid growth of grass are causing the coherence to decrease and impede the distinction of a mowing event. In the future, six-day interferometric coherence should also be analysed in relation to mowing events to alleviate some of these factors. Nevertheless, the results presented in this thesis offer a strong basis for further research and development activities towards the practical use of spaceborne C-band SAR data for mowing detection. Further, it was shown that local statistics can be useful for estimation of forest parameters from ortophotos and they could also provide helpful ancillary information to conduct a photo-interpretation tasks over forested areas. It was demonstrated that cluster analysis can be used as pre-selection method for the reduction of remote sensing features. Additionally, it was shown that case-based reasoning (a machine learning method) is well suited for empirical solutions of remote sensing tasks where there are many different data sources available. It was concluded that the use of local statistics adds valuable data to pixel-based remote sensing estimations

Improving Flood Detection and Monitoring through Remote Sensing

As climate-change- and human-induced floods inflict increasing costs upon the planet, both in terms of lives and environmental damage, flood monitoring tools derived from remote sensing platforms have undergone improvements in their performance and capabilities in terms of spectral, spatial and temporal extents and resolutions. Such improvements raise new challenges connected to data analysis and interpretation, in terms of, e.g., effectively discerning the presence of floodwaters in different land-cover types and environmental conditions or refining the accuracy of detection algorithms. In this sense, high expectations are placed on new methods that integrate information obtained from multiple techniques, platforms, sensors, bands and acquisition times. Moreover, the assessment of such techniques strongly benefits from collaboration with hydrological and/or hydraulic modeling of the evolution of flood events. The aim of this Special Issue is to provide an overview of recent advancements in the state of the art of flood monitoring methods and techniques derived from remotely sensed data

Advanced machine learning algorithms for Canadian wetland mapping using polarimetric synthetic aperture radar (PolSAR) and optical imagery

Wetlands are complex land cover ecosystems that represent a wide range of biophysical conditions. They are one of the most productive ecosystems and provide several important environmental functionalities. As such, wetland mapping and monitoring using cost- and time-efficient approaches are of great interest for sustainable management and resource assessment. In this regard, satellite remote sensing data are greatly beneficial, as they capture a synoptic and multi-temporal view of landscapes. The ability to extract useful information from satellite imagery greatly affects the accuracy and reliability of the final products. This is of particular concern for mapping complex land cover ecosystems, such as wetlands, where complex, heterogeneous, and fragmented landscape results in similar backscatter/spectral signatures of land cover classes in satellite images. Accordingly, the overarching purpose of this thesis is to contribute to existing methodologies of wetland classification by proposing and developing several new techniques based on advanced remote sensing tools and optical and Synthetic Aperture Radar (SAR) imagery. Specifically, the importance of employing an efficient speckle reduction method for polarimetric SAR (PolSAR) image processing is discussed and a new speckle reduction technique is proposed. Two novel techniques are also introduced for improving the accuracy of wetland classification. In particular, a new hierarchical classification algorithm using multi-frequency SAR data is proposed that discriminates wetland classes in three steps depending on their complexity and similarity. The experimental results reveal that the proposed method is advantageous for mapping complex land cover ecosystems compared to single stream classification approaches, which have been extensively used in the literature. Furthermore, a new feature weighting approach is proposed based on the statistical and physical characteristics of PolSAR data to improve the discrimination capability of input features prior to incorporating them into the classification scheme. This study also demonstrates the transferability of existing classification algorithms, which have been developed based on RADARSAT-2 imagery, to compact polarimetry SAR data that will be collected by the upcoming RADARSAT Constellation Mission (RCM). The capability of several well-known deep Convolutional Neural Network (CNN) architectures currently employed in computer vision is first introduced in this thesis for classification of wetland complexes using multispectral remote sensing data. Finally, this research results in the first provincial-scale wetland inventory maps of Newfoundland and Labrador using the Google Earth Engine (GEE) cloud computing resources and open access Earth Observation (EO) collected by the Copernicus Sentinel missions. Overall, the methodologies proposed in this thesis address fundamental limitations/challenges of wetland mapping using remote sensing data, which have been ignored in the literature. These challenges include the backscattering/spectrally similar signature of wetland classes, insufficient classification accuracy of wetland classes, and limitations of wetland mapping on large scales. In addition to the capabilities of the proposed methods for mapping wetland complexes, the use of these developed techniques for classifying other complex land cover types beyond wetlands, such as sea ice and crop ecosystems, offers a potential avenue for further research