Search

Three-Dimensional Ground Penetrating Radar (3D GPR) detects subsurface targets non-destructively, rapidly, and continuously. The complex environment around urban roads affects the positioning accuracy of 3D GPR. The positioning accuracy directly affects the data quality, as inaccurate positioning can lead to distortion and misalignment of 3D GPR data. This paper proposed a multi-level robust positioning method to improve the positioning accuracy of 3D GPR in dense urban areas in order to obtain more accurate underground data. In environments with good GNSS signals, fast and high-precision positioning can be achieved based on GNSS data using differential GNSS technology; in scenes with weak GNSS signals, high-precision positioning of subsurface data can be achieved by using GNSS and IMU as well as using GNSS/INS tightly coupled solution technology; in scenes with no GNSS signals, SLAM technology is used for positioning based on INS data and 3D point cloud data. In summary, this method ensures a positioning accuracy of 3D GPR better than 10 cm and high-quality 3D images of underground urban roads in any environment. This provides data support for urban road underground structure surveys and has broad application prospects in underground disease detection and prevention. [ABSTRACT FROM AUTHOR]

Mini–unmanned aerial vehicles (mini-UAVs) are emerging as a promising platform for through-wall radar to sense the enclosed space in cities, especially high-rise buildings, due to their excellent maneuverability. However, due to unavoidable environmental interference such as airflow, mini-UAVs are prone to trajectory deviation thus degrading their sensing accuracy. Most of the existing approaches model the impact of trajectory deviation into a polynomial phase error on the received signal, which cannot fit the space-variant motion error well. Moreover, the large trajectory deviations of UAVs introduce the unavoidable envelope error. This article proposes an autofocusing algorithm based on the back projection (BP) image, which directly estimates the trajectory deviations between the actual and measured track. Thus, the problem of the 2D space variability of the motion error can be circumvented. The proposed method mainly consists of two steps. First, we estimate the trajectory deviation in the line-of-sight (LOS) direction by exploring the underlying linear property of the wall embedded in the BP imaging result. Then, the estimated trajectory deviation in the LOS direction is compensated for to obtain an updated BP image, followed by a Particle Swarm Optimization (PSO) approach to estimate the trajectory deviation along the track through focusing targets behind the wall. Simulations and practical experiments show that the proposed algorithm can accurately estimate the serious trajectory deviations larger than the range resolution, improving the sensing robustness of UAV-borne through-wall radar greatly. [ABSTRACT FROM AUTHOR]

The country of Libya, situated on the Mediterranean fault zone, has a distinctive geodynamic regime due to the interplay between the Eurasian and African plates, which governs its tectonic evolution. In addition to its seismological significance, Libya is characterized by numerous subsidence and slope instabilities in regions with steep terrain. These geological phenomena have significant consequences for the built environment, as they pose an immediate danger to entire towns and essential infrastructure. Furthermore, infrequent weather phenomena, such as intense precipitation and thunderstorms, when coupled with the geological characteristics of some regions and the presence of seismically active terrain, have the potential to trigger landslide and land subsidence, resulting in significant harm to vital infrastructure. The current study utilizes the DInSAR technology to identify distinct subsidence occurrences that were induced by intense precipitation in coastal regions of Libya, specifically in Derna. These areas experienced significant flooding resulting in collapses during September 2023. A total of six pairs of co-event Interferometric Synthetic Aperture Radar (SAR) were utilized to generate displacement maps in the vertical, north-east, and north-west directions for the purpose of analysing the deformations. The aforementioned activities are conducted via Sentinel-1A images, which is freely accessible through the Copernicus program. Additionally, flood-prone zones were defined using Sentinel-1 GRD imagery. The Interferometric processing revealed multiple areas of subsidence. Subsidence rates of up to −14 cm were found in Derna city's urban cores after flood. The findings suggest that subsidence may have an effect on the flood-proneness of the region of Derna City as Ground subsidence also occurred in the period immediately before the earthquake, at a rate of −14 cm. [ABSTRACT FROM AUTHOR]

This study focuses on analyzing and monitoring urban subsidence, particularly in the city of Palermo, Italy. Land subsidence, induced by natural and human factors, poses threats to infrastructure and urban safety. Remote sensing (RS), specifically synthetic-aperture radar interferometry (In-SAR), is employed due to its ability to detect ground displacements over large areas with great precision. The persistent scatterer InSAR (PS-InSAR) technique is utilized to identify stable targets and track millimeter-level surface deformations. This research spans from October 2014 to October 2021, using Sentinel-1 satellite data to capture ground deformation from various angles. The findings are integrated into an accessible web app (ArcGIS) for local authorities that could be used aiding in urban planning and enhancing safety measures. This study's results offer updated deformation maps, serving as an operational tool to support decision-making and community resilience, emphasizing risk awareness and responsible practices. This study highlights that the exponential expansion of urban areas, which does not take into account historical information, can gravely jeopardize both the integrity of urban infrastructure and the well-being of its inhabitants. In this context, remote sensing technologies emerge as an invaluable ally, used in monitoring and safeguarding the urban landscape. [ABSTRACT FROM AUTHOR]

Mining activities represent one of the main causes leading to subsidence in the natural and urban environment. Sustainable urban planning and detection of potential hazards in mining areas involve the use of adequate instruments such as the continuous monitoring of land subsidence. The complexity of urban environment demands the utilization of new methods for monitoring and quantifying the effects of the mining processes. In the last decades, considering the technological developments from the remote sensing domain, the Synthetic Aperture Radar Interferometry (InSAR) techniques offer the opportunity for early detection and continuous monitoring of subsidence in mining areas, including urban centers. Considering various parameters of mining subsidence monitoring, a review of several tens of studies realized in different mining sites, based on InSAR techniques, is presented. As mining subsidence in urban areas has a direct impact on the quality of life, the review is completed with demographic indicators assessment, followed by a study case on the dynamics of the population in an urban mining area from Romania, Ocnele Mari. [ABSTRACT FROM AUTHOR]

Observations of the vertical wind profile in Chongqing, a typical mountainous city in China, are important, but they are sparse and have low resolution. To obtain more wind profile data, this study matched the Aeolus track with ground-based wind observation sites in Chongqing in 2021. Based on the obtained results, verification and quality control studies were conducted on the wind observations of a wind profile radar (WPR) with radiosonde (RS) data, and a comparison of the Aeolus Mie-cloudy and Rayleigh-clear wind products (Aeolus winds measured in cloudy and aerosol-rich atmospheric conditions from Mie-channel-collected data and winds measured in clear-air conditions from Rayleigh-collected data) with WPR data was then performed. The conclusions can be summarized as follows: (1) a clear correlation between the wind observations of WPR and RS was found, with a correlation coefficient (R) of 0.71. Their root mean square deviation increased with height but decreased at heights between 3 and 4 km. (2) After quality control using Gaussian filtering (GF) and empirical orthogonal function construction (EOFc; G=87.23 %) of the WPR data, the R between the WPR and RS reached 0.83 and 0.95, respectively. The vertical distribution showed that GF could better retain the characteristics of WPR wind observations but with limited improvement in decreasing deviations, whereas EOFc performed better in decreasing deviations but considerably modified the original characteristics of the wind field, especially regarding intensive vertical wind shear in strong convective weather processes. (3) In terms of the differences between the Aeolus and WPR data, 56.0 % and 67.8 % deviations were observed within ±5 m s -1 for Rayleigh-clear and Mie-cloudy winds (Aeolus winds measured in cloudy and aerosol-rich atmospheric conditions from Mie-channel-collected data and winds measured in clear-air conditions from Rayleigh-collected data) vs WPR winds, respectively. Vertically, large mean differences of both Rayleigh-clean and Mie-cloudy winds versus WPR winds appeared below 1.5 km, which is attributed to the prevailing quiet and small winds within the boundary layer in Chongqing; in this case the movement of molecules and aerosols is mostly affected by irregular turbulence. Additionally, large mean differences at a height range between 4 and 8 km for Mie-cloudy versus WPR winds may be related to the high content of cloud liquid water in the middle troposphere of Chongqing. (4) The differences in both Rayleigh-clear and Mie-cloudy versus WPR winds had changed. Deviations of 58.9 % and 59.6 % were concentrated within ±5 m s -1 for Rayleigh-clear versus WPR winds with GF and EOFc quality control, respectively. In contrast, 69.1 % and 70.2 % of deviations appeared within ±5 m s -1 for Rayleigh-clear versus WPR and EOFc WPR winds, respectively. These results shed light on the comprehensive applications of multi-source wind profile data in mountainous cities or areas with sparse ground-based wind observations. [ABSTRACT FROM AUTHOR]

Subsidence is a highly destructive natural hazard, which can be caused by both natural and anthropogenic causes. Its impacts include the creation of cracks and fissures, damages to buildings and infrastructures, a decrease in storage capacity of aquifer systems, and an increase of the susceptibility to flooding. In this study, Persistent Scattered Interferometry (PSI) has been used to process Synthetic Aperture Radar (SAR) images, for the detection and analysis of ground deformation and subsidence processes in the island of Mallorca. The study database is composed of 120 images captured by the Sentinel 1A and 1B satellites (between May 2016 and December 2019), from which we derived a map of accumulated displacement rates occurred during a 3 years and a half period. The results show important subsidence processes of up to 30 mm year-1 in large areas of the sedimentary basin of Palma, and of lesser magnitude (between 10 and 20 mm year-1) in locations of the Central Depression and in basins in the Serra de Tramuntana, such as the Torrent Major at Sóller and the Torrent de Gorg Blau. A significant relationship has also been observed between the thickness of the Quaternary sediment and the observed subsidence rates. The results highlight the high degree of geomorphological dynamism at very short time scales that characterizes Mallorca, and the vulnerability of certain urban areas, such as the city of Palma (? 400000 inhabitants), and agricultural areas, such as the Central Depression, facing the risk of subsidence and associated damages. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study to determine the potential of radar imaging to detect classes of built-up areas defined in the Urban Atlas (UA) spatial database. The classes are distinguished by function and building density. In addition to the reflectance value itself, characteristics such as building density or spatial layout can improve the identification of these classes. In order to increase the classification possibilities and better exploit the potential of radar imagery, a grey-level co-occurrence matrix (GLCM) was generated to analyse the texture of built-up classes. Two types of synthetic-aperture radar (SAR) images from different sensors were used as test data: Sentinel-1 and ICEYE, which were selected for their different setup configurations and parameters. Classification was carried out using the Random Forests (RF) and Minimum Distance (MD) methods. The use of the MD classifier resulted in an overall accuracy of 64% and 51% for Sentinel-1 and ICEYE, respectively. In ICEYE, individual objects (e.g. buildings) are better recognised than classes defined by their function or density, as in UA classes. Sentinel-1 performed better than ICEYE, with its texture images better complementing the features of urban area classes. This remains a significant challenge due to the complexity of urban areas in defining and characterising urban area classes. Automatic acquisition of training fields directly from UA is problematic and it is therefore advisable to independently obtain reference data for built-up area categories. [ABSTRACT FROM AUTHOR]

Observations of vertical wind in Chongqing, a typical mountainous city in China, are important, but sparse and have low resolution. To obtain more wind profile data, this study matched the Aeolus track with ground-based wind observation sites in Chongqing in 2021. Based on the obtained results, verification and quality control studies were conducted on the wind observations of a wind profile radar (WPR) with radiosonde (RS) data, and a comparison of the Aeolus Mie-cloudy and Rayleigh-clear wind products with WPR data was then performed. The conclusions can be summarized as follows: (1) A clear correlation between the wind observations of WPR and RS was found, with a correlation coefficient (R) of 69.92%. Their root-mean-square deviation increased with height but decreased by 3 - 4 km. (2) After quality control of Gaussian filtering (GF) and empirical orthogonal function construction (EOFc, G = 87.23%) of the WPR data, the R between the WPR and RS reached 76.00% and 95.44%, respectively. The vertical distribution showed that GF could better retain the characteristics of WPR wind observations, but with limited improvement in decreasing deviations, whereas EOFc performed better in decreasing deviations, but considerably modified the original characteristics of the wind field, especially regarding intensive vertical wind shear in strong convective weather processes. (3) In terms of the differences between the Aeolus and WPR data, 56.0% and 67.8% deviations were observed between ± 5 m/s for Rayleigh-clear and Mie-cloudy winds vs. WPR winds, respectively. Vertically, the mean differences of both Rayleigh-clean and Mie-cloudy winds versus WPR winds appeared below 1.5 km, which is attributed to the prevailing quiet and small winds within the boundary layer in Chongqing, such that the movement of molecules and aerosols is mostly affected by irregular turbulence. Additionally, large mean differences of 4-8 km for Mie-cloudy versus WPR winds may be related to the high content of cloud liquid water in the middle troposphere, influenced by the topography of the Tibetan Plateau. (4) The differences in both Rayleigh-clear and Mie-cloudy versus WPR winds had changed. Deviations of 58.9% and 59.6% were concentrated between ±5m/s for Rayleigh-clear versus WPR winds with GF and EOFc quality control, respectively. In contrast, 69.1% and 70.2% of deviations appeared between ± 5 m/s for Rayleigh-clear versus WPR and EOFc WPR winds, respectively. These results shed light on the comprehensive applications of multi-source wind profile data in mountainous cities or areas with sparse ground-based wind observations. [ABSTRACT FROM AUTHOR]

Extracting buildings in urban scenes from remote sensing images is crucial for the construction of digital cities, urban monitoring, urban planning, and autonomous driving. Traditional methods generally rely on shadow detection or stereo matching from multi-view high-resolution remote sensing images, which is cost-intensive. Recently, machine learning has provided solutions for the estimation of building heights from remote sensing images, but challenges remain due to the limited observation angles and image quality. The inherent lack of information in a single modality greatly limits the extraction precision. This article proposes an advanced method using multi-source remote sensing images for urban building height estimation, which is characterized by multi-level cross-fusion, the multi-task joint learning of footprint extraction and height estimation, and semantic information to refine the height estimation results. The complementary and effective features of synthetic aperture radar (SAR) and electro-optical (EO) images are transferred through multi-level cross-fusion. We use the semantic information of the footprint extraction branch to refine the height estimation results, enhancing the height results from coarse to fine. Finally, We evaluate our model on the SpaceNet 6 dataset and achieve 0.3849 and 0.7231 in the height estimation metric δ 1 and footprint extraction metric D i c e , respectively, which indicate effective improvements in the results compared to other methods. [ABSTRACT FROM AUTHOR]

Ground deformation is one of the crucial issues threatening many cities in both societal and economic aspects. Interferometric synthetic aperture radar (InSAR) has been widely used for deformation monitoring. Recently, there has been an increasing availability of massive archives of SAR images from various satellites or sensors. This paper introduces Multi-Satellite SBAS that exploits complementary information from different SAR data to generate integrated long-term ground displacement time series. The proposed method is employed to create the vertical displacement maps of Almokattam City in Egypt from 2000 to 2020. The experimental results are promising using ERS, ENVISAT ASAR, and Sentinel-1A displacement integration. There is a remarkable deformation in the vertical direction along the west area while the mean deformation velocity is −2.32 mm/year. Cross-validation confirms that the root mean square error (RMSE) did not exceed 2.8 mm/year. In addition, the research findings are comparable to those of the previous research in the study area. Consequently, the proposed integration method has great potential to generate displacement time series based on multi-satellite SAR data; however, it still requires further evaluation using field measurements. [ABSTRACT FROM AUTHOR]

The standard Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique, which is commonly used for surface motion analysis, assumes linear deformation velocities. While effective for monitoring urban subsidence over short periods, it falls short when dealing with non-linear, earthquake-related deformations over extended timeframes. To address this limitation, we use a non-linear PSInSAR technique, which is an enhancement of PSInSAR, to identify non-linear deformation patterns. We processed Sentinel-1A images from ascending and descending orbits in the Islamabad/Rawalpindi region from December 2015 to January 2023 using non-linear PSInSAR. By calculating the differences in deformation, we analyzed surface movements and assessed the impact of the 2017 earthquake on urban areas. Our findings reveal that the earthquake significantly increased the deformation in ascending and descending orbit tracks, with an average deformation of up to 70 mm/yr and a line-of-sight movement of up to 30 mm/yr. Our observations indicate that the deformation is directed towards the line of sight in the north and south of the deformed area, suggesting subsidence between the two uplifting faults, potentially linked to a concealed fault line along the deformation zone boundary. This contradicts previous arguments, suggesting that water extraction is the leading cause of deformation. Our analysis with non-linear PSInSAR demonstrates that tectonics play a significant role in deformation, providing valuable insights into tectonic-activity-induced deformations in urban areas over the long term. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) survey can be used to image geological hazards such as cavities, faults, fissures and also for identifying buried utilities. Sinkholes formed in the Secunderabad-Patny stretch road, Hyderabad, India were speculated to have been caused due to leaking from the sewer pipe. GPR survey using 200 MHz antenna was used in order to study the sinkholes formed in the collapsed area. GPR profiles were taken both parallel to and perpendicular to the road section to get a complete 3D visualization. Subsurface images acquired the marked position of the manholes, cavities, loose soil, different utilities, and sewer pipeline in the area. The results showed that there was no leaking in the sewer pipeline, instead, a water pipeline was damaged due to poor maintenance. Thus, GPR can be efficiently used to delineate the subsurface infrastructure and has further played an important role in identifying, delineating the zone of sinkhole in a busy street in Secunderabad. [ABSTRACT FROM AUTHOR]

Ulva prolifera (U. prolifera), a global eco-environmental issue, has been recurring annually in the Yellow Sea of China since 2007, leading to significant impacts on the coastal ecosystem and the economies of coastal cities. To enhance the frequency of daily monitoring for U. prolifera and to advance the multi-source remote sensing monitoring system, a combination of the Sentinel-1 SAR remote sensing satellite and the Sentinel-3 OLCI and NPP VIIRS optical remote sensing satellites was employed. This comprehensive analysis encompassed the examination of Sentinel-1 C band characteristics, the range of influence of U. prolifera, and the migration trajectory of its enrichment zones. On 6 June 2021, three satellite images depicted the northwest drift of U. prolifera, followed by a southward movement after making contact with the coast of Qingdao, China, on 12 June. The most extensive impact area caused by U. prolifera was observed on 18 June. Subsequently, the images revealed a contraction and enrichment of U. prolifera in an eas–-west direction. The amalgamation of radar and optical remote sensing satellites in a multi-frequency monitoring approach allows for a continuous all-weather surveillance mechanism for U. prolifera. This mechanism serves to provide timely alerts for the prevention and management of U. prolifera outbreaks. [ABSTRACT FROM AUTHOR]

A timely and accurate spatial mapping of built-up areas (BA) is crucial in making cities and human settlements safe, resilient, and sustainable. Synthetic Aperture Radar (SAR) data are useful for BA mapping due to strong coherent backscatter from diverse human-made targets, distinct texture patterns, and sensitivity to its geometric characteristics. However, BA mapping using SAR data is still challenging due to various geometrical and physical factors that often lead to misinterpretations. Therefore, this study presents a novel method to map BA using single-date dual polarized Sentinel-1 SAR data to overcome such challenges in complex built-up scenarios. Here, we propose three normalized descriptors derived from the Stokes vector elements. These three descriptors are found to be vital in characterizing different types of BA structures. We then consider the symmetric quadratic mean of the normalized descriptors to propose a novel dual polarimetric radar built-up index (DpRBI). The proposed index provides information about the average normalized polarized power, which is useful for mapping diverse types of BA or their combination in an image. We assess the proposed methodology utilizing the Sentinel-1 image acquired over three major cities: Delhi, Milan, and Barcelona. The BA map accuracy for all three cities was found to be ≈ 85 % with a low BA omission error. We compared the mapping results with available BA/land cover products: World Settlement Footprint 2019 (WSF-19) and World cover 2020 (WC-20). The BA map obtained using the proposed index was better than WSF-19 and WC-20, particularly for BA omission errors. For example, we observed a relatively low BA omission error of 22.34 % using DpRBI compared to 31.86 % and 41.79 % for WC-20 and WSF-19, respectively, over Milan, Italy. The code is available at: https://github.com/navv37/DpRBI [ABSTRACT FROM AUTHOR]

Over the past several decades, subsidence has emerged as a significant issue affecting urban, coastal, and mining areas worldwide. This phenomenon has been exacerbated by a decrease in groundwater resources, which has become a major problem on a global scale. Insufficient availability of surface water has led to excessive exploitation of the earth's groundwater system through unplanned processes. As a result, serious problems have arisen, including economic challenges, hazardous activities, and damage to the Earth's surface. One of the most significant and impactful consequences of excessive groundwater extraction is land subsidence. During a comprehensive literature review focusing on land displacement, 40 research papers were analyzed and discussed. These studies explored various techniques employed to monitor subsidence, such as synthetic aperture radar (SAR), interferometry synthetic aperture radar (InSAR), persistent scatterer interferometry synthetic aperture radar (PS-InSAR), and differential synthetic aperture radar (D-InSAR). Researchers have consistently found a strong correlation between the over extraction of underground fluids, such as water, oil, and gas, and land subsidence. One prominent cause of land subsidence identified by researchers is the over extraction of underground fluids. When excessive amounts of water, oil, or gas are extracted from underground reservoirs, the resulting voids and reduced pressure cause the overlying land to sink and compact. This process can have severe consequences for human settlements, infrastructure, and ecosystems in affected areas. The depletion of groundwater reserves, in particular, has become a pressing concern because of its vital role in supporting agricultural, industrial, and domestic water needs. Furthermore, the underground construction of tunnels has also been identified as a significant contributor to land subsidence. The excavation of tunnels creates voids and alters the natural balance of underground structures, leading to ground settlement and subsidence. This issue is particularly relevant in urban areas where extensive tunneling projects are undertaken to facilitate transportation, utilities, and infrastructure development. To monitor and assess land subsidence, various methods and processes are employed. SAR, InSAR, PS-InSAR, and D-InSAR techniques are valuable tools in this regard. SAR uses satellite-based radar systems to capture high-resolution images of the Earth's surface, enabling the detection of subtle changes over time. In contrast, InSAR employs two or more radar images to measure ground deformation by analyzing interference patterns. PS-InSAR focuses on persistent scatterers, which are specific targets that exhibit stable radar reflections over time, providing precise measurements of subsidence. D-InSAR combines two radar images acquired at different times to accurately calculate differential ground movements. By using advanced monitoring methods, scientists can assess the extent of subsidence and develop strategies to mitigate its negative impacts. It is crucial to adopt sustainable practices and ensure careful management of groundwater resources to effectively address this pressing issue. [ABSTRACT FROM AUTHOR]

A tornado hit the northeastern suburbs of Hamburg, Germany, on 7 June 2016. It had an estimated strength of upper end F1 on the Fujita scale and was short-lived with an approximate duration of only 13 min and a path length of just about 1.3 km. We demonstrate that such a small-scale, extreme event can be observed and forecasted accurately by a low-cost radar and by an atmospheric model with low computational costs, respectively. Observations from a low-cost single polarized X-band radar covering the urban area of Hamburg with 60 m spatial and 30 s temporal resolution are analyzed with respect to their ability to capture the development as well as the track of the tornado. In contrast to the national C-band radar network, the X-band radar is capable of capturing the hook echo of the tornado as well as the circular pattern in rain rates, because of its higher resolution in space and time. High-resolution forecasts of the tornado event are conducted with the computational efficient Conformal Cubic Atmosphere Model (CCAM) in order to test the capability of predicting the tornado with a lead time of a few hours. A three step downscaling method is used to obtain a spatial resolution of 1 km with initial conditions taken from the NCEP analysis. Calculated severe weather indices clearly indicate a potential for a tornado. CCAM cannot explicitly resolve small scale tornadic features but the model simulates a strong convective cell only a few kilometers apart from the tornadic thunderstorm observed by the radar. [ABSTRACT FROM AUTHOR]

Karst as an extremely complex and heterogeneous system, that presents a great challenge for the ground penetrating radar (GPR). However, properly planed GPR surveys can provide additional information about the shallow subsurface, where most karst processes take place. Due to the specific nature of the karst terrain, the introductory part presents some obstacles and adaptive approaches to karst research. An analysis of the published literature revealed that the GPR is most commonly used for detecting caves and subsidence areas and for preventing collapses in urban areas and for construction interventions. This is followed by exploration of karst aquifers, epikarst and quarry research. Some non-established applications are also presented, such as the use of a georadar in exploration of unroofed caves and cave sediments. The main purpose of this article is to demonstrate and evaluate the possibilities of using a georadar in different karst environments, to encourage its use in some new applications, and to emphasize the necessity of an interdisciplinary approach in such studies. [ABSTRACT FROM AUTHOR]

In this article an analysis of urban rainfall from hourly to seasonal scales is conducted for the Netherlands, with a focus on its capital, Amsterdam. In addition, the potential of synoptic weather types and local wind directions to categorize extreme rainfall in Amsterdam is assessed. An analysis of gauge‐adjusted daily radar rainfall retrievals with 1 km spatial resolution for 10 years shows that rainfall is enhanced over Dutch cities compared to their rural surroundings, with a maximum of a 14.2% increase over the largest cities in winter. The annual cumulative rainfall in Amsterdam appears to be significantly higher compared to its surroundings. This is due both to the higher frequency of occurrence of urban rainfall and to the higher hourly mean intensities. Extreme hourly rainfall rates appear to be affected by urban areas only in summer. Diurnal and weekly rainfall cycles do not reveal any significant urban influence. A wind direction analysis reveals that extreme rainfall events can primarily be attributed to westerly and next to southerly air masses. An analysis of the Jenkinson and Collinson (JC) and the German Weather Service (Deutscher Wetterdienst, DWD) weather types with rainfall and extreme rainfall events reveals that the JC weather types are more indicative of situations associated with rainfall extremes, whereas the DWD weather types are more indicative of situations resulting in higher accumulated rainfall amounts. [ABSTRACT FROM AUTHOR]

Climate change represents an important cause of subsidence, especially in coastal cities affected by changes in surface water level and water table. This paper presents a complementary study of Interferometric Synthetic Aperture Radar (InSAR) and Ground Penetrating Radar (GPR) for the early detection of subsidence and sinkhole phenomena. The methodology was applied to a coastal urban area in Galicia, northwest Spain (humid region), showing apparent signs of subsidence and building settlement during the last two years. Two different InSAR methods are compared for the period from June 2021 to March 2022: PSI (Persistent Scatterer Interferometry) and SBAS (Small Baseline Subsets), and the average deformation velocities obtained resulted in −3.0 mm/yr and −4.1 mm/yr, respectively. Additional GPR data were collected in January 2022 to validate the InSAR results, which detected subsidence in agreement with the persistent scatters obtained from the PSI method. This is crucial information to plan preventive maintenance. [ABSTRACT FROM AUTHOR]

An extensive magnetic survey has been carried out on a large part of the Roman and late antique city of Pollentia (Alcúdia, Mallorca, Spain), combined with ground penetrating radar (GPR) and electric resistivity imaging (ER Imaging) to obtain data for a better understanding of the ancient city. For magnetic measurements, the 7‐probe fluxgate gradiometer array was used. The GPR data were collected by means of a system equipped with two dual‐frequency antenna boxes of 200 and 600 MHz, respectively. The geophysical data provide some new insights into the layout of the city, with the identification of numerous structures in areas that are still unexplored by archaeological investigation. The results fill the gap of previous partial geophysical surveys and show an interesting, unseen image of ancient Pollentia. [ABSTRACT FROM AUTHOR]

A dense system of pre-Hispanic urban centers has been found in the Upano Valley of Amazonian Ecuador, in the eastern foothills of the Andes. Fieldwork and light detection and ranging (LIDAR) analysis have revealed an anthropized landscape with clusters of monumental platforms, plazas, and streets following a specific pattern intertwined with extensive agricultural drainages and terraces as well as wide straight roads running over great distances. Archaeological excavations date the occupation from around 500 BCE to between 300 and 600 CE. The most notable landscape feature is the complex road system extending over tens of kilometers, connecting the different urban centers, thus creating a regional-scale network. Such extensive early development in the Upper Amazon is comparable to similar Maya urban systems recently highlighted in Mexico and Guatemala. [ABSTRACT FROM AUTHOR]

The use of solar energy has shown the fastest global growth of all renewable energy sources. Efforts towards careful evaluation are required to select optimal locations for the installation of photovoltaics (PV) because their effectiveness is strongly reliant on exposure to solar irradiation. Assessing the shadows cast by nearby buildings and vegetation is essential, especially at the city scale. Due to urban complexity, conventional methods using Digital Surface Models (DSM) overestimate solar irradiation in dense urban environments. To provide further insights into this dilemma, a new modeling technique was developed for integrated 3D city modeling and solar potential assessment on building roofs using light detection and ranging (LiDAR) data. The methodology used hotspot analysis to validate the workflow in both site and without-site contexts (e.g., trees that shield small buildings). Field testing was conducted, covering a total area of 4975 square miles and 10,489 existing buildings. The results demonstrate a considerable impact of large, dense trees on the solar irradiation received by smaller buildings. Considering the site's context, a mean annual solar estimate of 99.97 kWh/m2/year was determined. Without considering the site context, this value increased by 9.3% (as a percentage of total rooftops) to 109.17 kWh/m2/year, with a peak in July and troughs in December and January. The study suggests that both factors have a substantial impact on solar potential estimations, emphasizing the importance of carefully considering the shadowing effect during PV panel installation. The research findings reveal that 1517 buildings in the downtown area of Austin have high estimated radiation ranging from 4.7 to 6.9 kWh/m2/day, providing valuable insights for the identification of optimal locations highly suitable for PV installation. Additionally, this methodology can be generalized to other cities, addressing the broader demand for renewable energy solutions. [ABSTRACT FROM AUTHOR]

Tomographic synthetic aperture radar (TomoSAR) obtains elevation resolution by adding multiple baselines successively in the direction perpendicular to the line of sight, thereby realizing three-dimensional (3D) reconstruction of complex scenes and significantly promoting the development of the 3D application field. However, a large data redundancy and long mapping time in traditional 3D imaging lead to a large data transmission burden, low efficiency, and high costs. To solve the above problems, this paper proposes a distributed SAR high-resolution and wide-swath (HRWS) 3D imaging technology scheme. The proposed scheme overcomes the size limitation of traditional single-satellite antennas through the multi-channel arrangement of multiple satellites in the elevation direction to achieve HRWS imaging; meanwhile, the distributed SAR system is integrated with tomographic processing technology to realize 3D imaging of difficult areas by using the elevation directional resolution of TomoSAR systems. HRWS 3D SAR increases the baseline length and channel number by transmission in turn, which leads to excessive pulse repetition frequency and causes echoes of different pulse signals to overlap in the same receiving cycle, resulting in range ambiguity and thus seriously affecting the quality of the 3D reconstruction. To solve this problem, this paper proposes a range ambiguity resolution algorithm based on multi-beam forming and verifies it on the measured data from airborne array SAR. Compared with the traditional TomoSAR, the distributed HRWS 3D SAR scheme proposed in this paper can obtain a greater mapping bandwidth with the same resolution in a single flight, thereby enhancing the time correlation, reducing data redundancy, and greatly improving mapping efficiency. [ABSTRACT FROM AUTHOR]

During the last decades, synthetic aperture radar (SAR) image exploitation has matured with the launch of different satellite missions and the development of different techniques, which allow exploiting the capabilities of the radar images. Among these techniques, persistent scatterer interferometry (PSI) has proven to be a powerful tool to derive terrain deformations over urban areas. It is based on the use of a large number of images over wide areas in order to obtain terrain displacements time series. The imagery from the different SAR missions has led to an archive with data that covers up to 30 years in the past. Moreover, different methods and algorithms have been proposed in order to perform this complex task. In this line, this work aims at identifying if data from different missions and processed by different techniques can be combined in order to study the evolution of urban monitoring. Three different PSI techniques are used in order to process data from four SAR missions: European Remote Sensing (ERS)-1/2, Environmental Satellite, COSMO-SkyMed, and the recent Sentinel-1 A/B. The rapidly evolving urban area of Madrid, where numerous undergrounding works have been carried out in the last decade, has been chosen as the testing environment. The density of persistent scatterers, the deformation accuracy validated with GPS displacements and deformation trends are used as the key performance items for the assessment. [ABSTRACT FROM PUBLISHER]

Health monitoring is important for densely distributed urban infrastructures, particularly in cities undergoing rapid economic progress. Permanent scatterer interferometry (PSI) is an advanced remote sensing observation technique that is commonly used in urban infrastructure monitoring. However, the rapid construction of infrastructures may easily cause a loss of coherence for radar interferometry, inducing a low density of effective permanent scatterer (PS) points, which is the main limitation of PSI. In order to address these problems, a novel time-series synthetic aperture radar interferometry (InSAR) process based on the adaptive window homogeneous pixel selection and phase optimization (AWHPSPO) algorithm and thermal expansion linear model (TELM) is proposed. Firstly, for homogeneous point selection, information on both the time-series intensity and deformation phases is considered, which can compensate for the defects of insufficient homogeneous samples and low phase quality in traditional distributed scatterer interferometric synthetic aperture radar (DS-InSAR) processing. Secondly, the physical, thermal expansion component, which reflects the material properties of the infrastructures, is introduced into the traditional linear model, which can more rationally reflect the temporal evolution of deformation variation, and the thermal expansion coefficients can be estimated simultaneously with the deformation parameters. In order to verify our proposed algorithm, the Orange Island area in Changsha City, China, was selected as the study area in this experiment. Three years of its historical time-series deformation fields and thermal expansion coefficients were regenerated. With the use of high-resolution TerraSAR-X radar satellite images, a maximum accumulated settlement of 12.3 mm and a minor uplift of 8.2 mm were detected. Crossvalidation with small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) results using Sentinel 1A data proved the reliability of AWHPSPO. The proposed algorithm can provide a reference for the control of the health and safety of urban infrastructures. [ABSTRACT FROM AUTHOR]

In recent years, Synthetic Aperture Radar (SAR) Tomography (TomoSAR) has ascertained great potential for the three-dimensional (3-D) reconstruction of observed scenes, especially in urban areas. However, the number of proceed snapshots (observations) is usually less than that of slant height samples (unknowns) in TomoSAR inversion processes. This impairs the quality of the reconstructed vertical information. To cope with this issue and improve the reliability of reconstructed vertical information, this paper investigates the possible potential of a deterministic descriptive regularization-based method. Deterministic descriptive regularization is a well-conditioned optimization framework based on the descriptive idea of a regularization solution. This strategy can help to mitigate the effect of the ill-posed problem. Thus, it can assist SAR tomography to deal with the possible impairing issues arising from low numbers and the distribution of baselines. For this purpose, the result of the proposed strategy is compared with the outcomes from the standard TomoSAR techniques, including Beamforming, Capon, and Minimum Norm. The proposed method for reconstruction of the reflectivity function of the observed scene has been performed on a dataset acquired by the Sentinel-1 sensor in 2022 over Tehran City, Iran. The experimental results indicate that the proposed algorithm can estimate building heights with a vertical accuracy of better than 91%. These results demonstrate the great potential of the proposed method for reconstructing the full 3-D images of urban areas. [ABSTRACT FROM AUTHOR]

The radar-retrieved refractivity fields provide detailed depictions of the near-surface moisture distribution at the meso-γ scale. This study represents a novel application of the refractivity fields by examining the spatiotemporal characteristics of moisture variability in a summertime coastal region in Taiwan over 4 weeks. The physiography in Taiwan lends itself to a variety of flow features and corresponding moisture behavior, which has not been well studied. High-resolution refractivity analyses demonstrate how a highly variable moisture field is related to the complex interaction between the synoptic-scale winds, diurnal local circulations, terrain, storms, and heterogeneous land use. On average, higher refractivity (water vapor) is observed along the coastline and refractivity decreases inland toward the foothills. Under weak synoptic forcing conditions, the daytime refractivity field develops differently under local surface wind directions determined by the synoptic-scale prevailing wind and the sea-breeze fronts. High moisture penetrates inland toward the foothills with southwesterly winds, but it stalls along the coastline with southerly and northwesterly winds. The moisture distribution may further affect the occurrence of the inland afternoon storms. During the nighttime, the dry downslope wind decreases the moisture from the foothills toward the coast and forms a refractivity gradient perpendicular to the meridionally oriented mountains. Furthermore, the refractivity fields illustrate higher-resolution moisture distribution over surface station point measurements by showing the lagged daytime sea-breeze front between the urban and rural areas and the detailed nighttime heterogeneous moisture distribution related to land-use and rivers. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide. • This paper attempts to provide a feasible framework for analyzing urban building energy use by utilizing real-time urban semantic perception information and building features. • The proposed framework have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings. • The proposed methodology can obtain regional energy consumption stocks and produce spatial distribution results, thus improving the interpretability of the data. • The results show that energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphoning effect is obvious in regions with high energy consumption. [ABSTRACT FROM AUTHOR]

New scattering models are constantly emerging to extract the detailed polarization information of ground targets. They contribute to the refined interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) images. However, an increasingly prominent problem lies ahead of model decomposition methods when there are similar scattering models for a decomposition scheme. It is difficult to separate the polarization power of similar scattering mechanisms reasonably and robustly. Therefore, in this paper, we first analyze the necessity of orthogonality between scattering models. Following this, we propose two mutually orthogonal rank-1 scattering models, which can degenerate to mostly current scattering models. The orthogonality and adaptability of scattering models are considered in the model derivation. Simulated PolSAR data, real ALOS-2/PALSAR-2, GF-3, and E-SAR data are selected to validate the proposed method. As shown by the results, first, the proposed method enhances the double-bounce scattering contribution in urban areas and maintains the volume-scattering contribution in vegetation areas because it separates the polarization power of different scattering mechanisms more reasonably. Second, the proposed method is powerful in the robust interpretation of different ground targets, resulting from the orthogonality of scattering models. These two characteristics of orthogonal scattering models are expected to play a positive role in large-scale applications, especially in land-cover classification. [ABSTRACT FROM AUTHOR]

Due to the difficulty in detecting the upper breakpoint of active buried faults, high-resolution detection of active buried faults in urban areas is very important for urban planning and disaster risk mitigation. The seismic reflection technique is the most common technique used to image faults at depth, but it is difficult to precisely locate the upper breakpoint of active buried faults. Ground-penetrating radar (GPR) provides high-resolution imaging in ultrashallow exploration, but the penetration depth of traditional GPR is shallow. In this work, we conducted six GPR surveys with multifrequency (25 MHz, 50 MHz and 100 MHz) and high-power GPR antennas across the possible range of the Shuiyu fault based on seismic reflection results. We obtained the trace of the Shuiyu fault within 45 m of the surface in Datong city, and the fault was found to dip approximately 70–80° to the SE. According to the GPR results, we selected a location for trenching and excavated one trench across the Shuiyu fault. The trench results verified the accuracy of the fault imaging in the GPR profiles and helped to interpret the stratigraphic information in the GPR profiles. In the accurate detection of active buried faults in other urban areas, the seismic reflection method can be used to determine the main strike of faults at depth, and then GPR can be used to detect the near-surface distribution of the faults in detail and guide trench location selection. The application of high-power GPR antennas with different frequencies in the fast detection of the upper breakpoint of active buried faults in urban areas is of great significance to earthquake hazard analysis and urban planning in Datong city. • The GPR can quickly and effectively locate the upper breakpoint of buried faults. • The combination of reflection seismic and GPR is reliable and high resolution. • The surface location of SYF in Datong city was obtained for the first time. • The SYF was active in late Late Pleistocene, the earthquake risk cannot be ignored. [ABSTRACT FROM AUTHOR]

Geomorphological hazards are considered as one of the environmental hazards that can be associated with many losses. Many areas, including the southern slopes of Alborz, are susceptible to a variety of geomorphological hazards, therefore, this study investigates the hazards of vertical displacement and landslides in the urban area and urban margin of Lavasan, located on the southern slopes of Alborz. This study was carried out in two stages, in the first step, to evaluate the vertical displacement of the region using the Sentinel radar image and SBAS time series method. In the second step, the geomorphological, geological and human parameters as well as the fuzzy logic model and ANP are used to evaluate the potential landslide prone areas. Thirdly, based on the results of the vertical displacement assessment as well as the potential assessment of landslide prone areas, hazardous areas of the study area have been identified. The results of the SBAS time series method show that the range of studies over the 3 year period has been about 12.4 mm uplift as well as 103.2 mm subsidence. Potential results of landslide-prone zones also indicate that limited northern study areas have great potential for landslides. Also the results of the evaluation of the hazardous areas in the study area indicate that the landslide susceptible areas of 30.1 km2, Areas with displacement more than 45 mm 26.27 km2 as well as landslide prone areas with displacement more than 45 mm 6.97 km2. [ABSTRACT FROM AUTHOR]

Polarimetric decomposition is an effective way to analyze the scattering mechanism of targets in polarimetric synthetic aperture radar (PolSAR) images. However, the analysis of urban areas is frequently a challenge. Most decomposition methods use a rotated dihedral derived via rotation matrix to model the double-bounce scattering mechanism of buildings. However, according to electromagnetic theory, the existing dihedral model is not accurate, especially when the orientation angle of the dihedral is large. Therefore, the double-bounce scattering contribution in urban areas with large orientation angles will be difficult to extract. To address this problem, based on physical optics (PO) and geometric optics (GO), the interaction process of electromagnetic waves and the rotational dihedral is analyzed, and then a modified rotational dihedral model (MRDM) is proposed for the accurate representation of the rotational double-bounce scattering mechanism. Accordingly, MRDM is introduced to a five-component decomposition method (MRDM-5SD) to analyze the scattering components in an urban area. The validity of MRDM-5SD is demonstrated using several data sets. The experimental results show that the power contributions of double-bounce scattering in urban areas with large orientation angles increase by using MRDM-5SD. Therefore, MRDM can provide support for feature extraction and target detection in urban areas. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) imagery is a vital tool for flood mapping due to its capability to acquire images day and night in almost any weather and to penetrate through cloud cover. In rural areas, SAR backscatter intensity can be used to detect flooded areas accurately; however, the complexity of urban structures makes flood mapping in urban areas a challenging task. In this study, we examine the synergistic use of SAR simulated reflectivity maps and Polarimetric and Interferometric SAR (PolInSAR) features in the improvement of flood mapping in urban environments. We propose a machine learning model employing simulated and PolInSAR features derived from TerraSAR-X images along with five auxiliary features, namely elevation, slope, aspect, distance from the river, and land-use/land-cover that are well-known to contribute to flood mapping. A total of 2450 data points have been used to build and evaluate the model over four different areas with different vegetation and urban density. The results indicated that by using PolInSAR and SAR simulated reflectivity maps together with five auxiliary features, a classification overall accuracy of 93.1% in urban areas was obtained, representing a 9.6% improvement over using the five auxiliary features alone. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) tomography (TomoSAR) has been widely used in the three-dimensional (3D) reconstruction of urban areas using the multi-baseline (MB) SAR data. For urban scenarios, the MB SAR data are often acquired by repeat-pass using the spaceborne SAR system. Such a data stack generally has long time baselines, which result in different atmospheric disturbances of the data acquired by different tracks. These factors can lead to the presence of phase errors (PEs). PEs are multiplicative noise for observation data, which can cause diffusion and defocus in TomoSAR imaging and seriously affect the extraction of target 3D information. In this paper, we combine the methods of the block-building network (BBN) and phase gradient autofocus (PGA) to propose a novel phase compensation method called BBN-PGA. The BBN-PGA method can effectively and efficiently compensate for PEs of the MB SAR data over a wide area and improve the accuracy of 3D reconstruction of urban areas. The applicability of this proposed BBN-PGA method is proved by using simulated data and the spaceborne MB SAR data acquired by the TerraSAR-X satellite over an area in Barcelona, Spain. [ABSTRACT FROM AUTHOR]

An important geological risk to which many towns in Puglia are exposed is sinking cavities in urban areas. For urban centers, studying, mapping, providing geological and speleological descriptions, classifying, and cataloging the forms and types of cavities is essential because cavities are linked to past local anthropic and natural processes at different sites. These circumstances could lead to the enhancement of existing underground cavities in urban areas through conservation and continuous monitoring. Unfortunately, in many cases, these underground cavities have been used as landfills and subsequently abandoned. In late March 2007, one of these cavities collapsed inside Gallipoli's inhabited center, causing damage to the structures but fortunately not human lives. In the area surrounding the collapsed cavity, a series of geophysical investigations were undertaken using ground penetrating radar in an attempt to delimit the area of collapse and develop possible interventions for restoration. In the same area, these measures were repeated 16 years later in December 2022 due to another collapse. The comparison between data acquired in these two periods shows that there were no strong changes apart from an increased presence of subsoil moisture in 2022. [ABSTRACT FROM AUTHOR]

Weather radar has demonstrated the ability to monitor wildfires and associated smoke plumes. However, the long-range, S-band operational radar network has severe limitations in providing high-resolution observations in the lower atmosphere due to the earth curvature. This paper presents an innovative technique of using shorter-range, low-power, fast scanning X-band radar for monitoring wildfires. Sample observations from wildfire events in southern China show that the polarimetric radar correlation coefficient of wildfire echoes varies from 0.55 to 0.75, with an average value of 0.61; the differential reflectivity varies from 2.28 to 5.08 dB with an average value of 3.99 dB; the differential propagation phase varies from 40.27° to 69.27°, with an average value of 56.66°; and all observables exhibit subtle changes at different scanning elevation angles. In addition, changes in echo intensity observed by higher elevation scans lag behind those observed by lower elevation scans, and the change of wildfire area lags behind the variation of echo intensity. Given the emerging interest in deploying such compact, cheaper X-band systems for urban hydrometeorological applications, it is expected that these radars, customized with flexible scan strategy, will provide new opportunities and advantages in wildfire detection and mitigation. • Weather radar has demonstrated the ability to monitor wildfires and associated smoke plumes • The operational long-range, S-band radar has severe limitations in providing high-resolution low-level measurements • The compact and low-power X-band dual-polarization radar is more efficient in monitoring wildfire and its evolution • The polarimetric observables, especially the correlation coefficient, can effectively identify the wildfire smoke plumes [ABSTRACT FROM AUTHOR]

Building height retrieval from synthetic aperture radar (SAR) imagery is of great importance for urban applications, yet highly challenging due to the complexity of SAR data. This paper addresses the issue of building height retrieval in large-scale urban areas from a single TerraSAR-X spotlight or stripmap image. Based on the radar viewing geometry, we propose that this problem be formulated as a bounding box regression problem and therefore allows for integrating height data from multiple data sources in generating ground truth on a larger scale. We introduce building footprints from geographic information system (GIS) data as complementary information and propose a bounding box regression network that exploits the location relationship between a building's footprint and its bounding box, enabling fast computation. The method is validated on four urban data sets using TerraSAR-X images in both high-resolution spotlight and stripmap modes. Experimental results show that the proposed network can reduce the computation cost significantly while keeping the height accuracy of individual buildings compared to a Faster R-CNN based method. Moreover, we investigate the impact of inaccurate GIS data on our proposed network, and this study shows that the bounding box regression network is robust against positioning errors in GIS data. The proposed method has great potential to be applied to regional or even global scales. Our code will be made publicly available at github.com/ya0-sun/bbox-SAR-building. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) is a remote sensing tool used for monitoring urban areas affected by geological hazards. Here we analysed the effectiveness of stabilization works on a slow-moving landslide in Arcos de La Frontera (Cádiz, Spain) using a persistent scatterer interferometric approach. The works consisted on jet grouting of cement-based injections and were applied locally to stabilize the most damaged neighbourhood. We processed a large stack of Sentinel-1 SAR satellite acquisitions covering the period January, 2016, to March, 2023, and obtained surface velocity and displacement trends measured along the line of sight (LOS) of the satellite on both ascending and descending orbits. The results show a clear deceleration of the landslide head after mid-2018, suggesting the local stabilization works were effective after that time. Prior to mid-2018, the maximum LOS velocity of the landslide head was 2.2 cm/year in ascending orbit and 1.3 cm/year in the descending orbit, decreasing to 0.43 cm/year and 0.23 cm/year, respectively. The InSAR results were compared to in-situ monitoring data and revealed that the extent of the stabilization has influenced a much larger area beyond the zone of the local interventions. Overall, InSAR has proved a powerful and versatile tool to be implemented in operational geotechnical monitoring. [ABSTRACT FROM AUTHOR]

Nowcasts (short-term forecasts) of heavy rainfall causing flash floods are highly valuable in densely populated urban areas. In the Collaborative Adaptive Sensing of the Atmosphere (CASA) project, a high-resolution X-band radar network was deployed in the Dallas–Fort Worth (DFW) metroplex. The Dynamic and Adaptive Radar Tracking of Storms (DARTS) method was developed as a part of the CASA nowcasting system. In this method, the advection field is determined in the spectral domain using the discrete Fourier transform. DARTS was recently extended to include a filtering scheme for suppressing small-scale precipitation features that have low predictability. Building on the earlier work, Stochastic DARTS (S-DARTS), a probabilistic extension of DARTS, is developed and tested using the CASA DFW radar network. In this method, the nowcasts are stochastically perturbed in order to simulate uncertainties. Two novel features are introduced in S-DARTS. First, the scale filtering and perturbation based on an autoregressive model are done in the spectral domain in order to achieve high computational efficiency. Second, this methodology is extended to modeling the temporal evolution of the advection field. The performance and forecast skill of S-DARTS are evaluated with different precipitation intensity thresholds and ensemble sizes. It is shown that S-DARTS can produce reliable probabilistic nowcasts in the CASA DFW domain with 250-m spatial resolution up to 45 min for lower precipitation intensities (below 2 mm h−1). For higher intensities (above 5 mm h−1), adequate skill can be obtained up to 15 min. [ABSTRACT FROM AUTHOR]

Over the past years, synthetic aperture radar (SAR) tomography (TomoSAR) has indicated significant potentials for three-dimensional (3D) reconstruction of buildings in urban areas. A large number of SAR images are thus required to perform tomographic inversions in non-parametric spectral estimators. To this end, in the case of a limited choice of SAR acquisitions, the present study aims to evaluate the capabilities of five non-parametric spectral estimation (SE) techniques, including linear prediction (LP), maximum entropy (ME), and minimum norm (MN), Capon, and beamforming (BF) in the tomographic reconstruction of urban environments. The performance analysis is carried out by using both simulated and real SAR datasets. The study results demonstrate that the proposed efficient LP estimator, minimizing the average output signal power over the antenna array elements, can separate the layover scatterers along the height direction. This low-computational SE method is thus able to clean side lobes while using a small number of observations. The proposed algorithms, as applied on TerraSAR-X strip-map images of the city of Tehran, Iran, verify the effectiveness of the non-parametric LP reconstruction technique for urban buildings. The estimated height of the scatterers also indicates that the LP estimator is similar to the field-based measurements once compared with ME, MN, Capon, and BF reconstruction methods. [ABSTRACT FROM AUTHOR]

As urban economies flourish and populations become increasingly concentrated, urban surface deformation has emerged as a critical factor in city planning that cannot be overlooked. Surface deformation in urban areas can lead to deformations in structural supports of infrastructure such as road bases and bridges, thereby posing a serious threat to public safety and creating significant safety hazards. Consequently, research focusing on the monitoring of urban surface deformation holds paramount importance. Interferometric synthetic aperture radar (InSAR), as an important means of earth observation, has all-day, wide-range, high-precision, etc., characteristics and is widely used in the field of surface deformation monitoring. However, traditional solitary InSAR techniques are limited in their application scenarios and computational characteristics. Additionally, the manual selection of ground control points (GCPs) is fraught with errors and uncertainties. Permanent scatterers (PS) can maintain high interferometric coherence in man-made building areas, and distributed scatterers (DS) usually show moderate coherence in areas with short vegetation; the combination of DS and PS solves the problem of manually selecting GCPs during track re-flattening and regrading, which affects the monitoring results. In this paper, 45 Sentinel-1B data from 16 February 2019 to 14 December 2021 are used as the data source in the urban area of Horqin District, Tongliao City, Inner Mongolia Autonomous Region, for example. A four-threshold (coherence coefficient threshold, FaSHPS adaptive threshold, amplitude divergence index threshold, and deformation velocity interval) GCPs point screening method for PS-DS, as well as a Small Baseline Subset-Permanent Scatterers-Distributed Scatterers-Interferometric Synthetic Aperture Radar (SBAS-PS-DS-InSAR) method for selecting PS and DS points as ground control points for orbit refinement and re-flattening, are proposed. The surface deformation results obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the SBAS-PS-DS-InSAR proposed in this paper were comparatively analysed and verified. The maximum cumulative line-of-sight settlements were −90.78 mm and −83.68 mm, and the maximum cumulative uplifts are 74.94 mm and 97.56 mm, respectively; the maximum annual average line-of-sight settlements are −35.38 mm/y and −30.38 mm/y, and the maximum annual average uplifts are 25.27 mm/y and 27.92 mm/y. The results were evaluated and analysed in terms of correlation, mean absolute error (MAE), and root mean square error (RMSE). The deformation results of the two InSAR methods were evaluated and analysed in terms of correlation, MAE, and RMSE. The errors show that the Pearson correlation coefficients between the vertical settlement results obtained using the SBAS-PS-DS-InSAR method and the GPS monitoring results were closer to 1. The maximum MAE and RMSE were 13.7625 mm and 14.8004 mm, respectively, which are within the acceptable range; this confirms that the monitoring results of the SBAS-PS-DS-InSAR method were better than those of the original SBAS-InSAR method. SBAS-InSAR method, which is valid and reliable. The results show that the surface deformation results obtained using the SBAS-InSAR, SBAS-PS-DS-InSAR, and GPS methods have basically the same settlement locations, extents, distributions, and temporal and spatial settlement patterns. The deformation results obtained using these two InSAR methods correlate well with the GPS monitoring results, and the MAE and RMSE are within acceptable limits. By comparing the deformation information obtained using multiple methods, the surface deformation in urban areas can be better monitored and analysed, and it can also provide scientific references for urban municipal planning and disaster warning. [ABSTRACT FROM AUTHOR]

Traditional bathymetry LiDAR (light detection and ranging) onboard manned and/or unmanned airborne systems cannot operate in the context of narrow rivers in urban areas with high buildings and in mountainous areas with high peaks. Therefore, this study presents a prototype of a lightweight bathymetry LiDAR onboard an unmanned shipborne vehicle (called "GQ-Cor 19"). The GQ-Cor 19 system primarily includes an emitting optical module, a receiving optical module, control module, detection module, high-speed A/D sampling module, and data processing system. Considering that the "GQ-Cor 19" is extremely close to the water surface, various new technical challenges are encountered, such as significant laser scattering energy from the surface of the water, which saturates signals received by the photomultiplier tube detector. Therefore, this study presents various new technical solutions, including (1) an improved Bresenham algorithm, (2) a small and lightweight receiving optical system with a split-field method, and (3) a data acquisition module with a high-speech A/D collector. Following a series of different experimental verifications, the results demonstrate that the new generation of single-band LiDAR onboard an unmanned shipborne vehicle can swiftly measure the underwater depth, and the maximum measurement depth is more than 25 m. The measurement accuracy is better than 30 cm and the weight is less than 12 kg. [ABSTRACT FROM AUTHOR]

Changes in land-use and land-cover (LULC) in urban areas affect the natural environment, especially urban green spaces (UGS). The present study examines the loss of UGS due to LULC transformation at different periods to predict the future vulnerable zone of UGS, based on the 'Pressure-State-Response' framework. To calculate the weight of each factor, a combined Analytical Hierarchical Process and Fuzzy Comprehensive Evaluation method have been used. An integrated multilayer perceptron based artificial neural network and Markov chain (MLP-ANN-MC) model has been employed to predict the UGS vulnerable area in Kolkata. Results indicated that growth rates of built-up area, land-use dynamic degree, change intensity index, and proximity factors are the major responsible for UGS vulnerability. Applying the MLP-ANN-MC model, future vulnerable zones were identified for management and conservation of UGS. The methodology developed and demonstrated in this study expands LULC change analysis and provide a new dimension for UGS vulnerability assessment. [ABSTRACT FROM AUTHOR]

Road collapses pose dangerous threats to traffic safety in urban cities. Typically, these collapses result from under-road defects such as cavities, loose soil, and voids. With the development of multi-channel 3D ground penetrating radar (GPR) systems, GPR has become a routine method for inspecting the subsurface conditions of roads. However, the reflection characteristics of hidden defects in 3D GPR results have not been well studied due to the complexity of urban road structures. Consequently, accurately detecting all subsurface defects in GPR survey areas remains challenging. In this paper, numerical models of road structures with typical types of hidden defects are constructed, and their reflection characteristics in GPR B-scan profiles and C-scan depth slices are analyzed. Then, the field results obtained using a car-mounted 3D GPR system are presented to confirm the reflection characteristics of these hidden defects. Finally, the reflections of manhole covers and underground pipelines, which are the most prevalent forms of clutter in GPR images in urban environments, are compared with those of hidden defects. The findings of this paper will facilitate the accurate interpretation of field 3D GPR data for road inspections and the mitigation of road collapse accidents. • Reflection characteristics of typical under-road defects in 3D GPR images. • Numerical models of road structures with typical types of hidden defects. • Field case studies employing a 3D GPR system to detect under-road defects. • Comparison between under-road defects and other anomalies in 3D GPR images. • Under-road defects detection for mitigating road collapse accidents. [ABSTRACT FROM AUTHOR]

Applications of fully polarimetric synthetic aperture radar (POLSAR) have increased in the past few decades. The potential of model-based decompositions is coupled with polarimetric information extraction from the POLSAR data for target identification and classification. The coherency matrix $[T]$ with nine independent parameters, and associated with some physical scattering models, serves as input to these decompositions. This paper attempts to assign one such physical scattering model to the real part of $T_{23}$ ($\text {Re}\{{T}_{23}\}$) and develop a new scattering power decomposition model, called as the seven-component scattering decomposition (7SD). Previously developed scattering power models have eliminated $\text {Re}\{T_{23}\}$ , assuming the orientation angle compensation condition, to reduce the number of independent $[T]$ parameters. The proposed 7SD model has been tested on fully polarimetric SAR data sets acquired by the spaceborne Advanced Land Observing Satellite-2/Phased Array type L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2) and airborne F-SAR, and the results are compared with the existing scattering power decompositions. The physical scattering model for $\text {Re}\{T_{23}\}$ is derived from a particular configuration of dipoles (referred to as “mixed dipole” configuration), which gives rise to compound scattering. The mixed-dipole scattering occurs in urban areas that are highly oriented to the radar illumination direction as well as in vegetation areas. 7SD also delivers an additional mixed dipole scattering power compared to the previous six-component scattering model. The mixed-dipole scattering model reduces the contribution of volume scattering power in double-bounce predominant areas (such as oriented urban blocks), thereby imparting improved understanding of the polarimetric information contained in the coherency matrix. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a promising non-destructive evaluation technique for detecting buried underground objects in urban area. Deep learning technique is recently being applied into this field to automate the GPR data interpretation. However, there is no proper technique that can reflect the uniqueness of urban road pavements. In this study, an underground object detection technique suitable for urban road pavement is proposed by using a statistically determined threshold amplitude and a large amount of GPR B-scan image libraries. An automated thresholding technique is newly developed based on the statistical distribution of GPR data. Deep learning technique is then applied to the reconstructed GPR data to detect underground objects in urban area. The proposed method is experimentally validated by field data collected on urban roads in Seoul, South Korea. In addition, its application possibility is also tested with full-size GPR data. The proposed method successfully emphasises the feature of underground objects and classifies hyperbola, manhole cover, layer interface and subsoil background. [ABSTRACT FROM AUTHOR]

Coastal urban areas in tropical regions, such as Greater Jakarta (GJ) in Indonesia, are susceptible to flood hazards from torrential rainfall. Efforts to understand the convective mechanisms leading to this type of rainfall have been carried out extensively, especially for events occurring in the wet season. However, hydrometeorological risks persist even in the dry season, when the rainfall is more closely correlated with the diurnal wind circulation. Among the aspects of convective activities and this diurnal circulation, the details of the updraft remain the least studied and open for discussion. We investigate the role of the updraft in urban torrential rainfall over GJ, using data from a C‐band Doppler radar and reanalysis dataset during the dry season of 2018–2019. Employing back‐trajectory calculations, we locate updraft origins corresponding to the peak of daytime inland rainfall. Most updraft origins are localized at an NW–SE‐oriented front nearly parallel to the coastline on the low‐lying plain, suggesting a region of atmospheric destabilization favorable for convective activity. The dry‐season torrential rainfall over GJ may involve convective activity from this front, that from earlier updrafts originating above the densely populated area, and further enhancement coming from orographic contributions. Our findings suggest the important role of updrafts in the rainfall generation, providing insights into the convective mechanism over GJ. [ABSTRACT FROM AUTHOR]

Array interferometric synthetic aperture radar (Array InSAR) has a 3-D resolution capability and solves the layover problem in interferometric SAR (InSAR) by arranging multiple antennas in the cross-orbit direction. Airborne Array InSAR point clouds are obtained from two scans for complete building information in urban areas, resulting in very low overlapping point cloud. The existing methods are difficult to extract the identical features for the registration of Array InSAR point clouds. To this end, a robust registration approach Array InSAR point clouds in urban areas is proposed in this study. The main contribution of this article is raising the theoretically optimal transformation for achieving point cloud registration, considering the constraint from parallel facades of a certain building. Point density estimation is adopted to retain building facade points for initial registration. The facade pairs of a specific building are then matched and divided into two categories by judging whether one contains the concave–convex features or not, for performing rotation rectification and fine shift fixation, respectively. Experimental results of both simulated and real data validate the feasibility and reliability of our approach. For the simulated data, the results reach an average rotation error of about 0.01° and an average translation error of less than 0.8 m. For the real data, two evaluation criteria are designed for the lack of reference data. The results reach an average of 0.4° of the defined angle difference and less 0.8-m distance difference from the source facades center to the normal extension of the target facades. [ABSTRACT FROM AUTHOR]