Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography

This book focuses on remote sensing for urban deformation monitoring. In particular, it highlights how deformation monitoring in urban areas can be carried out using Persistent Scatterer Interferometry (PSI) and Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Several contributions show the capabilities of Interferometric SAR (InSAR) and PSI techniques for urban deformation monitoring. Some of them show the advantages of TomoSAR in un-mixing multiple scatterers for urban mapping and monitoring. This book is dedicated to the technical and scientific community interested in urban applications. It is useful for choosing the appropriate technique and gaining an assessment of the expected performance. The book will also be useful to researchers, as it provides information on the state-of-the-art and new trends in this fiel

Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models

© Copyright © 2020 Elmahdy, Ali, Mohamed, Howari, Abouleish and Simonet. Mangrove forests are acting as a green lung for the coastal cities of the United Arab Emirates, providing a habitat for wildlife, storing blue carbon in sediment and protecting shoreline. Thus, the first step toward conservation and a better understanding of the ecological setting of mangroves is mapping and monitoring mangrove extent over multiple spatial scales. This study aims to develop a novel low-cost remote sensing approach for spatiotemporal mapping and monitoring mangrove forest extent in the northern part of the United Arab Emirates. The approach was developed based on random forest (RF), Kernel logistic regression (KLR), and Naive Bayes Tree machine learning algorithms which use multitemporal Landsat images. Our results of accuracy metrics include accuracy, precision, and recall, F1 score revealed that RF outperformed the KLR and NB with an F1 score of more than 0.90. Each pair of produced mangrove maps (1990–2000, 2000–2010, 2010–2019, and 1990–2019) was used to image difference algorithm to monitor mangrove extent by applying a threshold ranges from +1 to −1. Our results are of great importance to the ecological and research community. The new maps presented in this study will be a good reference and a useful source for the coastal management organization

Study of groundwater properties and behaviour using geospatial techniques

Groundwater contributes a significant proportion of the earth’s freshwater and is essential to sustain life on earth, but its availability in spatial and temporal dimensions is not uniform. With the advent of efficient pumps and rural electrification, global groundwater extraction increased from 312 km3/year in the 1960s to 800 km3/year in 2000s; approximately 70% of this extraction is used for agriculture. About half of domestic human water consumption in urban areas is from groundwater. The ever-increasing dependence on groundwater has led to its depletion across various parts of the world. This trend must be reversed to sustain the critical role of groundwater. Groundwater monitoring based on validated data can provide information that can guide decision making to decrease groundwater stress on local and global scales. This thesis aims to monitor spatio-temporal changes in groundwater and related phenomena (like land subsidence) using geospatial techniques like InSAR, GRACE, GIS, data analysis and data visualisation. The over-extraction or rebound of groundwater can lead to land deformation because of the change in effective stress of underground sediments. Groundwater-induced land movement can cause damage to property and resources, and hence it must be monitored for the safety and economics of a city. This thesis explores the suitability of Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) to measure land deformation and different senor-software for InSAR processing. The groundwater quantity variation and resulting land deformation for London using InSAR and Gravity Recovery and Climate Experiment (GRACE) between 2002-2010 were analysed. Long-term, decreasing, complex, non-linear patterns in the spatial and temporal domains from both InSAR and GRACE datasets were observed. The land movement velocities varied from -6 to +6 mm/year, and their reliability was validated with observed GNSS data by conducting a two-sample t-test. The average groundwater loss estimated from GRACE was found to be 9.003 MCM/year. The results demonstrate that InSAR and GRACE complement each other and can be an excellent source of monitoring groundwater for hydrologists. Then groundwater induced subsidence for London and the National Capital Territory of Delhi (NCT-Delhi) between 2016 and 2020 were studied. The land movement velocities were found to vary between -24 mm/year to +24 mm/year for London and between -18 mm/year to +30 mm/year for NCT-Delhi. This land movement was compared with observed groundwater levels and spatio-temporal variation of groundwater. A 1-D mathematical model was used to quantify land deformation for a given change in groundwater level. It was broadly observed that when large volumes of groundwater are extracted, it leads to land subsidence, and when groundwater is recharged, surface uplift is witnessed. However the local geology, did play an important role in the extent of subsidence, which was considered in the mathematical model. The increased pressure on groundwater can cause spatio-temporal changes in its quality because of various atmospheric stimulations, varied geology, variation in subsurface mineralogy and factors controlling residence times. Moreover, the variation of groundwater quality is vital for the sustainable management and safety of groundwater. Thus, the variation in groundwater quality is analysed from observed data for London between 2000 and 2020. The data samples were used from 500 wells in the London basin, and the data is provided in the free open access domain by Environment Agency. The overall groundwater in London was found to be dominant magnesium bicarbonate type which typically represents shallow fresh groundwater, and spatio-temporal variations of hardness, sodium, and dissolved oxygen (DO) were also studied. Significant variations in the range of each constituent were found, which was attributed to variation in the geology of the London Palaeogene aquifers and anthropogenic activities. All the case studies help better understand the phenomenon of spatio-temporal variation in groundwater behaviour and associated land deformation for urban cities. The research presented in this thesis can be used to determine whether groundwater is available and suitable for its intended purpose, discover pollutants, examine any spatio-temporal variations, and monitor land subsidence

An insight in cloud computing solutions for intensive processing of remote sensing data

The investigation of Earth's surface deformation phenomena provides critical insights into several processes of great interest for science and society, especially from the perspective of further understanding the Earth System and the impact of the human activities. Indeed, the study of ground deformation phenomena can be helpful for the comprehension of the geophysical dynamics dominating natural hazards such as earthquakes, volcanoes and landslide. In this context, the microwave space-borne Earth Observation (EO) techniques represent very powerful instruments for the ground deformation estimation. In particular, Small BAseline Subset (SBAS) is regarded as one of the key techniques, for its ability to investigate surface deformation affecting large areas of the Earth with a centimeter to millimeter accuracy in different scenarios (volcanoes, tectonics, landslides, anthropogenic induced land motions). The current Remote Sensing scenario is characterized by the availability of huge archives of radar data that are going to increase with the advent of Sentinel-1 satellites. The effective exploitation of this large amount of data requires both adequate computing resources as well as advanced algorithms able to properly exploit such facilities. In this work we concentrated on the use of the P-SBAS algorithm (a parallel version of SBAS) within HPC infrastructure, to finally investigate the effectiveness of such technologies for EO applications. In particular we demonstrated that the cloud computing solutions represent a valid alternative for scientific application and a promising research scenario, indeed, from all the experiments that we have conducted and from the results obtained performing Parallel Small Baseline Subset (P-SBAS) processing, the cloud technologies and features result to be absolutely competitive in terms of performance with in-house HPC cluster solution

Data Fusion and Synergy of Active and Passive Remote Sensing; An application for Freeze Thaw Detections

There has been a recent evolvement in the field of remote sensing after increase of number satellites and sensors data which could be fused to produce new data and products. These efforts are mainly focused on using of simultaneous observations from different platforms with different spatial and temporal resolutions. The research dissertation aims to enhance the synergy use of active and passive microwave observations and examine the results in detection land freeze and thaw (FT) predictions. Freeze thaw cycles particularly in high-latitude regions have a crucial role in many applications such as agriculture, biogeochemical transitions, hydrology and ecosystem studies. The dielectric change between frozen ice and melted water can dramatically affect the brightness temperature (TB) signal when water transits from the liquid to the solid phase which makes satellite-based microwave remote sensing unique for characterizing the surface freeze thaw status. Passive microwave (PMW) sensors with coarse resolution (about 25 km) but more frequent observations (at least twice a day and more frequent in polar regions) have been successfully utilized to define surface state in terms of freeze and thaw in the past. Alternatively, active microwave (AMW) sensors provide much higher spatial resolution (about 100 m or less) though with less temporal resolution (each 12 days). Therefore, an integration of microwave data coming from different sensors may provide a more complete estimation of land freeze thaw state. In this regard, the overarching goal of this research is to explore estimating high spatiotemporal freeze and thaw states using the combination of passive and active microwave observations. To obtain a high temporal resolution TB, this study primarily builds an improved diurnal variation of land surface temperature from integration of infrared sensors. In the next step, a half an hourly diurnal cycle of TB based on fusion of different passive sensors is estimated. It should be mentioned that each instrument has its own footprint, resolution, viewing angle, as well as frequency and consequently their data need to be harmonized in order to be combined. Later, data from an AMW sensor with fine spatial resolution are merged and compared to the corresponding passive data in order to find a relation between TB and backscatter data. Subsequently, PMW TB map can be downscaled to a higher spatial resolution or AMW backscatter timeseries can be generalized to high temporal resolution. Eventually, the final high spatiotemporal resolution TB product is used to examine the freeze thaw state for case studies areas in Northern latitudes

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