GI5.5

Global estimates for future growth indicate that city inhabitation will increase by 13% due to a gradual shift in residence from rural to urban areas. The continuous increase in urban population has caused many cities to upgrade their infrastructures and embrace the vision of a “smart-city”. Data collection through sensors represents the base layer of every smart-city solution. Large datasets are processed, and relevant information is transferred to the police, local authorities, and the general public to facilitate decisions and to optimize the performance of cities in areas such as transport, health care, safety, natural resources and energy. The objective of the GEObyIT project is to provide a real-time risk reduction system in an urban environment by applying machine learning methodologies to automatically identify and categorise different types of geodata, i.e., seismic events and geological structures. The project focusses on the city of Oslo, Norway, addressing the common need of two departments of the municipality, i.e., the Emergency Department and the Water and Sewage Department. In the present work, we focus on passive seismic records acquired with the objective to quickly locate urban events as well as to continuous monitor changes in the near surface. For this purpose, a seismic network of Raspberry Shake 3D sensors connected to GSM modems, to facilitate real-time data transfer, was deployed in target areas within the city of Oslo in 2021. We present preliminary results of three approaches applied to the continuous data: (1) automatic detection of metro trains, (2) automatic identification of outlier events such as construction and mining blasts, and (3) noise interferometry to monitor the near sub-surface in an area with quick clay. We use a supervised method based on convolutional neural networks trained with visually identified seismic signals on three sensors distributed along a busy metro track (1). Application to continuous data allowed us the reliably detect trains as well as their direction, while not triggering other events. Further development of this approach will be useful to either sort out known repeating seismic signals or to monitor traffic in an urban environment. In approach (2) we aim to detect rare or unusual seismic events using an outlier detection method. A convolutional autoencoder was trained to create dense features from continuous signals for each sensor. These features are used in a one-class support vector machine to detect anomalies. We were able to identify a series of construction and mine blasts, a meteor signal as well as two earthquakes. Finally, we apply seismic noise interferometry to close-by sensor pairs to measure temporal variations in the shallow ground (3). We observe clear seismic velocity variations during periods of strong frost in winter 2021/2022. This opens up for the potential to detect also non-seasonal changes in the ground, for example related to instabilities in quick clay deposits located within the city of Oslo.  

How to cite: Myklebust, E., Köhler, A., and Dichiarante, A. M.: Towards an automatic real-time seismic monitoring system for the city of Oslo , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3601, https://doi.org/10.5194/egusphere-egu22-3601, 2022.

Urban forest provides several important ecosystem services to cite residents and city environment, by which most functions were related to trees’ canopy biomass. To understand the dynamics of canopy biomass affecting the ecosystem services, this study applied and compared two approaches in predicting canopy biomass of Koelreuteria elegans street trees in the city of Taipei in Taiwan. The first approach extracted vegetation indices (VI) from time series data of the 2018 Sentinel-2 satellite images, to represent signals of tree canopy variation, including Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), image classification based on VI time series data was processed to extract pixels with high canopy covers, and examined the associated phenological activities. In contrast, the other approach applied a system dynamic model to capture changes of canopy phenological activities in different seasons by factors of canopy size, leaf duration, and phenology events, all controlled by an accumulated temperature function to characterize green up and defoliation mechanisms. The growth temperature and growth rate of new leaves were calibrated with the phenological records. Results found good correlations between satellite-extracted vegetation indices approach and a temperature-driven phenological modelling. Reconstructed by NDVI and EVI, both indices caught the start of spring growth of Koelreuteria elegans in March to a full-sized canopy in April, with the whole growing season extended to the end of September, and a beginning of main defoliation from October to the lowest canopy size in January and February next year. Built from the image classification results for pure canopy cover, the maximum value of NDVI and EVI was 0.443 and 0.486, while the minimum was 0.08 and 0.163, respectively. In comparison, results from the canopy phenological modelling showed similar trends that canopy biomass reached its lowest point in February, entered to a rapid growth phase in March and reached full canopy size in April. Although the canopy phenological model also predicted a main growing season lasted until October, during the defoliation period, the leaves of the Koelreuteria elegans never completely fell off, due to the actual monthly minimum average temperature in the city of Taipei was higher than 10^oC as the threshold of the controlled temperature. Based on these results, we suggest that when ground tree survey and inventory data are available, both satellite-extracted vegetation indices and modelling approach can provide useful predictions for landscape planning and urban forestry management.

How to cite: Pan, W.-C. and Cheng, S.-T.: Predicting and comparing canopy biomass by satellite-extracted vegetation indices and a temperature-driven phenological modelling approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7094, https://doi.org/10.5194/egusphere-egu22-7094, 2022.

In this work, the A-DInSAR techniques are applied in Central Asturias (N Spain). In this area, the presence of the most important cities in the region is remarkable, as well as industry and port infrastructures and a dense road network. Moreover, this region is specially known for their historical coal exploitation, which was developed mainly on the Central Coal Basin for almost 2 centuries, and is being abandoned from the beginning of the 21^st. The main aim of this study is detecting and analysing deformations associated to this underground coal mining activity. For this, the following methodology was realised: 1) Acquisition and processing of 113 SAR images, provided by Sentinel-1A and B in descending trajectory between January 2018 and February 2020, by means of PSIG software; 2) Obtaining of Line of Sight mean deformation velocity map (in mm year^-1) and deformation time series (in mm); 3) Analysis of detected terrain displacements and definition of mining impact. The results show a Velocity Line of Sigh (VLOS) range between -18.4 and 37.4 mm year^-1, and accumulated ground displacements of -69.1 and 75.6 mm. The analysis, interpretation and validation of these ground motion allow us to differentiate local sectors with recent deformation related to subsidence and uplift movements with maximum VLOS of -18.4 mm year^-1 and 9.5 mm year^-1. This study represents an important contribution to improve the knowledge about deformations produced by impact of coal mining activity in a mountain and urban region. In addition, this work corroborates the reliability and usefulness of the A-DInSAR techniques like powerful tools in the study and analysis of geological hazards at regional and local scales for the monitoring and control of underground mining infrastructures.

How to cite: Cuervas-Mons, J., Domínguez-Cuesta, M. J., Mateos-Redondo, F., Monserrat, O., and Barra, A.: Mining impact in a coal exploitation under an urban area: detection by Sentinel-1 SAR data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8095, https://doi.org/10.5194/egusphere-egu22-8095, 2022.

The continuous acquisition of CO₂ soil flux data has been started on Mt Etna in November 2021, with the aim of assessing a first balance between CO₂ from volcanic and biological origin. Our long-term goal is an interdisciplinary study of volcanic, biological, ecological, biogeochemical, climatic and biogeographical aspects, including the anthropogenic impact on the environment. All aspects are integrated in the study of the so-called Critical Zone, i.e. the layer between the deep rock and the top of the vegetation where the main biological, hydrological and geological processes of the ecosystem take place. The new research activity at Mt Etna is performed within the framework of the PON-GRINT project for infrastructure enhancement (EU, MIUR), and it adds up to activities going on at Grand Paradiso National Park (Italian Alps), and Ny Alesund (Svalbard, NO, High Arctic) in the framework of the IGG-CNR Critical Zone Observatories.

During the first phase of the project, two fixed stations were installed in two sites at Piano Bello (Valle del Bove, Milo), in an area where the endemic Genista aetnensis grows. An Eddy Covariance system for net CO₂ ecosystem exchange measurement and a weather station will be installed in 2022. Carbon stable isotopes data will be acquired periodically using in-situ instrumentation (i.e. Delta Ray).  The installation sites are selected after CO₂ soil flux surveys around the volcano using a portable accumulation chamber. The two stations installed at Piano Bello consist of an automatic accumulation chamber fixed to the ground, a mobile lid with a diffusion infrared sensor for measuring CO₂, a data logger and a sensor for measuring soil moisture and temperature. The accumulation chambers are programmed to acquire data on ecosystem respiration every hour for all day. Data are transmitted to the IGG data collection center. The new IGG-CNR Mt Etna CZO will contribute investigating CO₂ fluxes at the soil-vegetation-atmosphere interface in different geological and environmental contexts. We benefit from the collaboration with the National Institute of Geophysics and Volcanology (INGV), the Ente Parco dell'Etna, and the Dipartimento Regionale dello Sviluppo Rurale e Territoriale di Catania.

How to cite: Pennisi, M., D'Incecco, S., Baneschi, I., Lelli, M., Provenzale, A., and Raco, B.: Investigating the carbon biogeochemical cycle at Mt Etna, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8156, https://doi.org/10.5194/egusphere-egu22-8156, 2022.

Wind turbine (WT) ground motion emissions have a significant influence on sensitive measuring equipment like seismic monitoring networks. WTs permanently excite ground motions at certain constant frequencies due to the eigen modes of the tower and blades as well as the motion of the blades. The emitted waves have frequencies mainly below 10 Hz which are relevant for the observation of, e.g., local tectonic or induced seismicity. Furthermore, frequencies proportional to the blade passing frequency can be observed in ground motion data above 10 Hz, closely linked to acoustic emissions of the turbines. WTs are often perceived negatively by residents living near wind farms, presumably due to low frequency acoustic emissions. Therefore, similarities in ground motion and acoustic data provide constraints on the occurrence of such negatively perceived emissions and possible counter-measures to support the acceptance of WTs.

We study ground motion signals in the vicinity of two wind farms on the Swabian Alb in Southern Germany consisting of three and sixteen WTs, respectively, which are of the same turbine type, accompanied by acoustic measurements and psychological surveys. A part of the measurements is conducted in municipalities near the respective wind farms where residents report that they are affected by emissions. Additional measurements are conducted in the forests surrounding the WTs, and within WT towers. The wind farms are located on the Alb peneplain at 700-800 m height, approximately 300 m elevated compared to the municipalities. Results indicate that WTs are perceived more negatively in the location where the wind farm is closer to the municipality (ca. 1 km) and where other environmental noise sources like traffic occur more frequently. At the location more distant to the WT (ca. 2 km), even though more WTs are installed, residents are affected less. To improve the prediction of ground motion emissions, instruments are set up in profiles to study the amplitude decay over distance, which is linked to the local geology.

This study is supported by the Federal Ministry for Economic Affairs and Energy based on a resolution of the German Bundestag (03EE2023D).

How to cite: Gassner, L. and Ritter, J.: Ground motion emissions due to wind turbines: Results from two wind farms on the Swabian Alb, SW Germany, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8472, https://doi.org/10.5194/egusphere-egu22-8472, 2022.

In-situ monitoring of slope is crucial for recognizing and recording the occurrence of landslide. Figuring out the correlation between monitoring data and hillslope displacement would help early warning for landslide-induced disasters. Xinzhuang potential deep-seated landslide area has been identified by Taiwan executive authority where is located in Kaohsiung City, southern Taiwan, it covers a 10.3 hectares’ area and 20 buildings with an average slope of 22.8 degrees. The lithology of the upper slope is sand-shale interbedded with highly sand contented, which differs from lower slope in shale with mud contented.

For conducting early warning and comprehending displacement of landslide in this study, the monitoring of ground displacement was carried out using the tiltmeter and the GNSS RTK (Real Time Kinematic), and the hydrology data (rainfall and ground water level) were recorded every 10 minutes by automatic gauges. Furthermore, we executed manual borehole inclinometer measurement to obtain the possible sliding position of subsurface.

This study has been conducted for two years, the results shows that (1) The local shallow creep (4-5 meters underground) in the central deep-seated landslide area was recorded by the tiltmeter, GNSS and borehole inclinometer measurement. (2) The groundwater level is the significant factor for displacements of creep in this site. (3) The velocity of the displacement would be accelerated when the groundwater level was higher than 2.1 meters. (4) The 6-hours displacement has a highly correlation with accumulative rainfall and ground water level. Moreover, the results have been applied to the landslide early-warning system of Taiwan authority.

How to cite: Wang, J.-S., Lai, T.-Y., Hsu, Y.-C., Fu, G.-L., Tsai, C. H., and Huang, T.-Y.: Preliminary Analysis on Multi-Devices Monitoring of Potential Deep-Seated Landslide in Xinzhuang, Southern Taiwan, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11008, https://doi.org/10.5194/egusphere-egu22-11008, 2022.

Very often many new construction and operating embankment dams need to be evaluated in terms of the slope stability. The necessity of considering body forces, pore-water pressures, and a variety of soil types in the analysis vitiates the application of methods that are well founded in the mechanics of continua and employ representative constitutive equations.

This study comparing stability analysis using total stress after the end of construction with effective stress couple of years later after the first impounding. Studies have indicated the advantages to be obtained employing an effective stress failure criterion (Bishop, 1952, Henkel and Skempton, 1955 and Bishop, 1960) for analysis and design of embankment dams. Pore-water pressure are determined from piezometer readings during the construction until the dam was operated.

This paper presents the results of stability analysis of embankments dam with both parameters and conditions, resulting that pore water pressures influence slope stability of the embankment.

How to cite: Hartanto, T.: Slope stability analysis of embankment dam under total and effective pore pressure, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11541, https://doi.org/10.5194/egusphere-egu22-11541, 2022.

We demonstrate that the extended dynamic response of an engineered structure can be obtained from just a single measurement at one position if rotation is recorded in combination with translation. Such a single station approach could save significant time, effort and cost when compared with traditional structural characterization using arrays. In our contribution we will focus on the monitoring of a high-rise building by tracking its dynamic properties, e.g., natural frequencies, mode shapes and damping. We present the results of the system identification for the Prime Tower in Zurich – with a height of 126 m, this concrete frame structure is the third highest building in Switzerland. It has been continuously monitored by an accelerometer (EpiSensor) and a co-located rotational sensor (BlueSeis) located near the building center on the roof for the past year. The motion on the tower roof includes significant rotations as well as translation, which can be precisely captured by the monitoring station. More than 9 natural frequencies, including the first 3 fundamental modes, as well as the next two overtones, where translations are coupled with rotations, are observed between 0.3 – 10 Hz, a frequency band of key interest for earthquake excitation, making an investigation essential. Using temporary arrays of accelerometers located across the roof and along the length of the building to perform a traditional dynamic characterisation, we can compare the array solution with the new single location solution in terms of system identification for the Prime Tower.

How to cite: Rossi, Y., Clinton, J., Chatzi, E., Schmelzbach, C., and Rothacher, M.: System identification of a high-rise building: a comparison between a single station measuring translations and rotations, and a traditional array approach., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12263, https://doi.org/10.5194/egusphere-egu22-12263, 2022.

Real prediction of friction coefficient on the road surface is essential in order to enhance the resilience of traffic management procedures for the safety of road users. Critical weather conditions could have a significant impact on the road surface, and decrease the reliable friction coefficient in extreme conditions. Weather parameters are involved in the process of traffic management are water film thickness, ice percentage, pavement temperature, ambient temperature, and freezing point. Smart road monitoring of the road surface friction changes over time means the real-time prediction of the friction coefficient changes in the future based on the intelligent weather road-based sensor is crucial to avoid uncontrolled conditions during extreme weather conditions. For this reason, the use of intelligent data analysis such as machine learning approaches is key in order to provide a holistic robust decision-making tool to support road operators or owners for further consideration of the traffic management procedures. In this study, a machine learning approach is applied to train 18 months of data collected from the real case study in Spain, and results show a good agreement between real friction coefficient and predicted friction coefficient. The trained model has been validated with various cross-validation approaches, and the high accuracy of the model is observed.

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 769129 (PANOPTIS project).

How to cite: Rasol, M., Schmidt, F., and Ientile, S.: Road surface friction measurement based on intelligent road sensor and machine learning approaches, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11730, https://doi.org/10.5194/egusphere-egu22-11730, 2022.

Worldwide, roads cross most rivers big and small, but if nobody maps the locations, do they exist? In our experiences, the answer is no, and structures such as culverts and bridges at these road-river crossings have gone overlooked in research into the impacts that infrastructure can have on rivers and the species that depend on them. There remains a need for spatially explicit data for road-river crossings as well as identification of structure types to support research and monitoring that guides more proactive approaches to infrastructure management. Our initial focus was on mapping road-river structures in Wales, United Kingdom so to better understand how these could be impacting on nature, particularly migratory fishes. However, as we began developing the spatial dataset, we became aware of broader applications, including relevance to hazard management and movement of people and goods so to support livelihoods and well-being. In this talk, I will discuss our initial approach to tackling this problem in Wales, and how we learned from that experience and refined the approach for mapping in England, including our use of openly available remotely sensed imagery from Google and Ordnance Survey so to ensure the data can be reused and modified by others for their needs and uses. I will present a spatially explicit dataset of road-river structures in Wales, including information about surrounding environmental attributes and discuss how these can help us to better understand infrastructure vulnerability and patterns at catchment and landscape scales. I will discuss the potential for diverse applications of this road-river structure dataset, particularly in relation to supporting real-time monitoring and providing the baseline data needed for any futuer machine learning or computation modelling advances for monitoring road-river structures.

How to cite: Bristol, R., Januchowski-Hartley, S., Pawar, S., Yang, X., Shinebayar, K., Jorissen, M., Mantel, S., Pregnolato, M., and White, J.: Creating a spatially explicit road-river infrastructure dataset to benefit people and nature, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12901, https://doi.org/10.5194/egusphere-egu22-12901, 2022.