Landslide inventories (geomorphological, seasonal, and event-based) are crucial for susceptibility, vulnerability, and risk assessments. Despite the high frequency of landslides in Brazil, no guidelines, or common procedures for the creation of inventories exist. Object-based image analysis (OBIA) is a well-established method for mapping natural hazards. However, the application of this technique for landslide mapping in Brazil is still incipient. The use of OBIA could improve the creation of landslide inventories in the country. Thus, this study aims to identify rainfall-induced shallow landslides using the OBIA method and compare the parameters used in the classification rule set in two study areas in Brazil: Itaóca (São Paulo state), and Nova Friburgo (Rio de Janeiro state). Both study areas were strongly affected by high-magnitude mass movements in 2014 (Itaóca) and 2011 (Nova Friburgo). The analysis was performed using RapidEye satellite images (5 m resolution), dated 2014/01/30 (Itaóca) and 2011/01/20 (Nova Friburgo), in the eCognition 10.0 (Trimble) software. The classification considered spectral, spatial, and contextual information. The mapping accuracy was assessed by comparison to a shallow landslide inventory created through expert interpretation. The results indicate the good applicability of the OBIA method in the tropical environments of Brazil. In Itaóca, shallow landslides of varied sizes occurred, whereas in Nova Friburgo large landslides were more common, with a medium size of 1.900 m² and 3.200 m² and a median of 725 m² and 950 m², respectively. The rule set applied for both study areas included the same processes but with slightly modified parameters, for example, the mean NDVI and slope were adapted according to the local environmental and geomorphological conditions. The results confirm the transferability of the approach in Brazil, although minor adaptations in the main rule set are required for better results.

How to cite: Dias, H. C., Hölbling, D., and Grohmann, C. H.: An object-based approach for semi-automated shallow landslide mapping: suitability and comparison in Itaóca (SP) and Nova Friburgo (RJ), southeastern Brazil, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-158, https://doi.org/10.5194/egusphere-egu23-158, 2023.

Tropical cyclones exert a strong driving force for change in mangrove systems. The potential increase in cyclone intensity globally is concerning as this may lead to significant changes in species composition, forest complexity and loss of ecosystem services, including important climate mitigation services. This has implications for the efficacy of blue carbon offsetting in the tropics. There is an urgent need to understand and quantify the effects of cyclones on mangrove ecosystems and the services they provide. The Landsat archive within Digital Earth Australia provides an unprecedented opportunity to quantify cyclone impacts at a national scale and beyond. The aim of this study was to quantify the short- and long-term impacts of Category 3-5 cyclones on mangroves in Australia. This was achieved using wind-field modelling (Geoscience Australia’s Tropical Cyclone Risk Model) and the Landsat archive to a)  establish the degree of recovery in canopy cover of mangroves following cyclones of different category strength; b) quantify the differential response on mangroves, accounting for varying cyclone intensity, mangrove composition, and recovery time since cyclone; and c) conceptualise the likely impacts of future cyclones given predictions of future change, including that associated with anthropogenic driven climatic fluctuation. Windspeeds > 165-224 km/hr, typical of category 3 cyclones, caused the most widespread damage, suggesting a critical windspeed threshold was exceeded. Patterns of short-term damage reflected location and exposure, with the greatest damage observed along open coastlines and fringing forests. Assessments indicated persistent loss of forests when the impact was high over the short-term. Areas experiencing a minor reduction in cover, and to a lesser extent major reduction in cover, exhibited signs of recovery, but the duration of recovery may be prolonged (>10 years). Where cyclones were frequent recovery was impeded by subsequent cyclones, and this may lead to a shift in ecosystem type. The approach used Geoscience Australia’s Open Data Cube and Jupyter Notebooks, which have been published online as open-source code to allow users to repeat the assessments for future cyclones in their area of interest. This is an important feature as Data Cubes are developed and operationalised globally. This analysis demonstrates the utility of Data Cubes for assessing impacts of coastal natural hazards and provides crucial information regarding the long-term resilience of mangroves and their ecosystem services, particularly in the context of a changing climate and variation in cyclone intensity and frequency.

How to cite: Asbridge, E., Lucas, R., Rogers, K., Phillips, C., Krause, C., and Lymburner, L.: Characterising the impact of tropical cyclones on mangroves using a multi-decadal Landsat archive, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-932, https://doi.org/10.5194/egusphere-egu23-932, 2023.

The application of non-contact technologies is herein presented as a test for a scientific procedure aimed at providing a technological solution for the rock mass survey in areas affected by poor logistics for field campaigns. Either high cliff sectors, or sub-vertical coastal cliffs or, more simply, areas that cannot be reached for a field rock mass survey, represent a challenge to retrieve geostructural data in the perspective of a geomechanical characterization or stability analysis. For this study, two technologies were coupled to achieve a reliable model of rock masses. In particular, applied technologies are the aerial photogrammetric survey by Unmanned Aerial Vehicle (UAV) and the Infrared Thermography (IRT). The first represents a reference for applications aimed at rockfall stability studies, while the second is a relatively pioneering methodology exploiting the thermal radiation emitted by the rock mass and falling within the infrared portion of the electromagnetic spectrum. A three-dimensional model of the surveyed rock masses was built starting from the definition of dense point clouds and related elaborations. Thanks to the preliminary georeferencing of the UAV survey, discontinuity spatial orientation could be extracted by employing different algorithms, thus achieving their dip-dip direction values to be plotted on stereograms and statistically grouped. IRT surveys allowed the study of the distribution of the surface temperatures along the framed rock face. This is linked both to the different rock mass conditions (wet or weathered rock, presence of vegetation) and to the main geomechanical features of discontinuities such as persistence and aperture. Based on IRT outcomes, the innovative geomechanical parameters of Thermal spacing and Thermal RQD (Rock Quality Designation) were estimated with the aim of finding a potential, non-contact, alternative to the conventional procedures for the evaluation of the loose rock volumes. By matching the information achieved by the two surveying methodologies, a geomechanical model of the remotely surveyed rock mass was achieved proving the good adherence of the remote data to reality. Such outcome represents the implementation of the scientific experience on a key geomechanical topic, as well as a step forward in the integration of methodologies based on different principles but well matching if focused on a common scope.

How to cite: Mineo, S., Caliò, D., and Pappalardo, G.: Non-contact rock mass survey by means of airborne photogrammetry and Infrared Thermography, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1125, https://doi.org/10.5194/egusphere-egu23-1125, 2023.

On November 4^th, 2020 a deadly civil war broke out in Tigray, Ethiopia displacing close to 2 million people internally and more than 48,000 refugees in neighboring Sudan by August 2021[1]. Given agriculture is the livelihood of millions of people in Tigray, evaluation of the conflict’s impact on cultivated land and the consequent crop production is critical for government and non-government disaster relief institutions. Unfortunately, such evaluation is extremely challenging as the conflict is characterized by communication blackout leaving the region without access to cellphone or internet.

In this study, we used Sentinel-2 and Planet satellite imagery data to map loss of well cultivated land in 2021 due to the war. We developed multiple cultivation detection criteria based on the peak and falling limb characteristics of Normalized Difference Vegetation Index (NDVI) time series, validated using limited field observations of fallow and cultivated plots from the wet season in 2021 and 2022. We employed object detection machine learning model to identify harvest piles as an additional parameter to detect farming activity.

Our predicted change in cultivation map from 2019/20 to 2021 showed that the density of conflict incidents was positively correlated to the mean net loss of well cultivated land with R² of 0.7 in Tigray highlands (elevation > 1200 m). Sub-regions with high estimated net loss of cultivated land due to abandonment of reported internally displaced people also resulted in high predicted loss of well cultivated land using NDVI based criteria in our study. In the absence of extensive in situ data, we demonstrate how satellite imagery along with good understanding of local farming practices can provide timely and useful information to assist humanitarian management efforts in times of crisis and recovery phase.

[1] Annys, Sofie, Tim Vanden Bempt, Emnet Negash, Lars De Sloover, Robin Ghekiere, Kiara Haegeman, Daan Temmerman, and Jan Nyssen. Tigray: Atlas of the Humanitarian Situation (version 2.2). Zenodo, 2021. https://doi.org/10.5281/zenodo.5805687.

How to cite: Weldegebriel, L., Lobell, D., Negash, E., and Nyssen, J.: Eyes in the sky to the rescue - Monitoring the impact of armed conflict on cultivated land using satellite imagery in Tigray, Ethiopia., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1561, https://doi.org/10.5194/egusphere-egu23-1561, 2023.

Landslides pose a destructive geohazard to people and infrastructure that results in hundreds of deaths and billions of dollars in damages every year. China is one of the countries worst affected by landslides in the world, and great efforts have been made to detect potential landslides over wide regions. However, a recent government work report shows that 80% of the newly formed landslides occurred outside the areas labelled as potential landslides, and 80% of them occurred in remote rural areas with limited capability of disaster prevention and mitigation. In this presentation, a multi‐source remote sensing technical framework is demonstrated to detect potential landslides over wide regions.

How to cite: Li, Z., Zhang, C., Chen, B., Du, J., Ding, M., Zhu, W., Song, C., Yu, C., Zhan, J., and Peng, J.: A multi‐source remote sensing technical framework for wide-area landslide detection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2080, https://doi.org/10.5194/egusphere-egu23-2080, 2023.

Earthquakes are natural hazards leading to the greatest human and economic losses, which are mostly due to structural collapses. Rapid identification and assessment of earthquake-induced damage to structures is therefore an essential component of the emergency response, and instrumental to effective reconstruction plans. Typically, structural damage assessment is conducted through building-by-building inspections during post-earthquake field reconnaissance missions. These missions are expensive and time-consuming, especially if large areas need to be investigated. Remote sensing techniques provide a relatively low-cost, wide-area alternative to in-situ monitoring. Classification and change detection based on pre- and post-event optical and synthetic aperture radar (SAR) satellite images are the most used approaches to detect damaged structures after earthquakes. However, these techniques only provide qualitative observations of collapsed or severely damaged structures. In this work, we present a new approach for the quantitative assessment of earthquake-induced structural damage based on displacement measurements acquired by Airborne Light Detection And Ranging (LiDAR). The approach is based on the integration between LiDAR-based observations and structural indicators of damage. The application to the analysis of 684 buildings affected by the 2014 Napa earthquake, in California, demonstrates a good agreement between the LiDAR-based results and independent in-situ observations. This work sets the basis for the innovative exploitation of remote sensing data in disaster management.  

How to cite: Foroughnia, F., Macchiarulo, V., Berg, L., DeJong, M., Milillo, P., Hudnut, K. W., Gavin, K., and Giardina, G.: Rapid assessment of earthquake-induced building damage using remote sensing LiDAR data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3464, https://doi.org/10.5194/egusphere-egu23-3464, 2023.

In the last 50 years, drastic change in the spatio-temporal variation of climate patterns have been observed over the Indian Himalayan Region (IHR) resulting increase in the frequency of Extreme Weather Events (EWE) such as cloud burst, flash flooding, land slide etc. Change in climate parameters has direct impact and consequence on the Himalayan ecosystem, which in turn adversely affects livelihood of people living in the high altitude area. Also, alteration in Land Use Land Cover (LULC) have occurred rapidly in the already vulnerable IHR for the last few decades. Few studies have carried out to analyse the impact of land cover changes and regional climate variability on mountain hazard susceptibility in different parts of the world. But there’s a lack of such type of study in high altitude areas of IHR. Here, we have made an attempt to assess the impact of regional climate variability (Precipitation, Air Temperature, Soil Moisture, Relative Humidity) as well as spatio-temporal variations in LULC on the increasing frequency of EWE in Beas river basin of Kullu district, India. For this purpose, using multi-temporal LANDSAT data and high resolution Terra Climate monthly data, temporal Land Cover Changes as well as climate variability over the period of 21 years, i.e., from the year 2000 to 2020 for Beas valley of Kullu district, India have been assessed. Disaster data highlights a drastic increase of 378% in the average occurrence of EWE during present years (i.e., 2016 to 2020) than that of last 16 years (2000 to 2015). Socio-economic survey have been carried out in the disaster prone villages of Beas basin to study people perception.  68.6% respondents believe that the increase in EWE is due to change in climate pattern. It is observed from LULC change detection that a massive increase in Agricultural land, including orchard expansion, of 123 % occurred during the year 2020 than that of 2000 in Beas Valley. Also, there’s a sharp increase of 40.63 % in settlement areas which includes the tourism activities such as hotels, restaurants, etc. during the year 2020 than that of 2000. The average rise in average air temperature is observed as 0.53° Celsius in study area over the period of 21 years. Annual precipitation shows a decrease of 76 mm to 325 mm during the year 2020 than that of year 2000 whereas the number of extreme rainfall days increases by 33.3% within the same interval. Outcome of the paper will be helpful in better understanding the impact of land cover dynamics and regional climate variability on the frequency of EWE in the Beas Valley of Kullu district, Himachal Pradesh, India.

Keywords

Indian Himalayan Region, Extreme Weather Events, LULC, Climate Variability, Livelihood Security

How to cite: Ghosh, S., Lata, R., and Gouda, K. C.: Monitoring the Role of Temporal Land Cover Changes on Mountain Hazard Susceptibility in Beas Valley, Himachal Pradesh, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3919, https://doi.org/10.5194/egusphere-egu23-3919, 2023.

The vigorous development of geospatial information technology has not only achieved good results in land monitoring, but has also been gradually extended to other application fields. Hazards monitoring is one of the important applications. Geospatial information can be obtained through surveying and mapping technology, and through multi-temporal geospatial data, the production, migration and migration of debris deposits can be quantitatively evaluated in a reasonable time and space in catchment scale. In recent years, the development and integration of MEMS technology has contributed to the rapid development of UAV measurement. This goal can be achieved due to the advantages of UAVs, such as efficiency, timeliness, low cost, and easy operation in severe weather conditions. The real-time, clear and comprehensive low- and middle-altitude photos of the area can be used as the most basic and important spatial information for research and analysis. Based on the aforementioned technologies, we selected two specific landslide-prone areas for UAV photogrammetry data acquisition. The two sites of forestry road and nursery situated in the high mountainous forestry in southern Taiwan. In order to evaluate potential hazards and hazard monitoring, the multi-temporal high precision terrain geomorphology in different periods is essential, so as to assessment the hazards and for early warring. For these purposes, we try to integrate several technologies, especially by unmanned aircraft system imageries and existed airphotos, to acquire and to establish the geoinfomatic datasets. The methods, including, (1) Remote-sensing images gathered by UAS and by aerial photos taken in different periods; (2) field in-situ ground control points and check points installation and geomatic measurement; (3) 3D geomorphological virtual reality model construction; (4) Geologic, morphotectonic and landslide micro-geomorphologic analysis; (5) DTM of difference from multi-temporal dataset to evaluate the topographic and environment changes. The main achievements are four: a) UAV photogrammetry and accuracy analysis in the study area; b) Construction of a 3D terrain model in the study area; c) Geological survey of collapsed areas; d) Multi-year spatial information variation analysis. According to the findings, landslides activated continuously in different periods and different areas. In different sections of the riverbeds, different degrees of siltation or erosion have been identified, so regular monitoring and potential hazard assessment are still necessary.

How to cite: Chang, K.-J., Huang, M.-J., Tseng, C.-W., and Wahyuni, A.: Environmental evolution and landslide hazard assessment in high mountainous areas based on UAV multi-sensors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4344, https://doi.org/10.5194/egusphere-egu23-4344, 2023.

Earth observation data are helpful in analyzing how climate-related variables affect geomorphological processes. This work aims at evaluating the influence of rainfall on slow-moving landslides, by means of a quantitative procedure for (i) identifying clusters of pixels measuring satellite-based surface displacements indicating landslide activity, (ii) comparing them with rainfall series, and (iii) applying statistical tests to assess their relationships at the regional scale. The methodology was developed within the framework of the OT4CLIMA project (Development of Innovative Earth Observation Technologies for the Study of Climate Change and Its Impacts on the Environment), funded by the Italian Ministry of Education, University and Research. The project aimed to develop advanced Earth observation technologies and methodologies for improving the capabilities to understand the effects of climate change at regional and sub-regional scale.

The procedure presented here was applied in the Basento river basin, within the Basilicata region, southern Italy.  The Basento catchment, with an area of 1535 km² and a NW-SE trend, falls within the domain of the Apennine chain in the western part, and within the Bradanic Trough in the eastern part. The stratigraphic and structural setting of the basin plays a significant role in determining landslide occurrence and distribution.

Rainfall series were gathered from rain gauges (http://www.centrofunzionalebasilicata.it/it/) and analyzed to evaluate the presence of temporal trends. Ground displacements were obtained by applying the P-SBAS (Parallel Small BAseline Subset) technique to three datasets of Sentinel-1 images: T146 ascending orbit, and T51 and T124 descending orbits, for the period 2015–2020. The displacement series of the pixels located in areas mapped as landslides by the Italian Landslide Inventory (IFFI database, https://idrogeo.isprambiente.it/app/) and sited within rain gauge influence regions (defined as 10 km circular buffers) were studied.

Two slow-moving landslides were selected and investigated in detail. The average displacement series of the landslides were analyzed and compared to the rainfall series to search for relations, by employing statistical and non-parametric tests. More in detail, the Kendall rank correlation coefficient and the Maximal Information Coefficient were adopted in the analysis. Significant results were obtained for the T124 descending orbit for both landslides, for a 3-day cumulative rainfall and a 7-day delay of the slope response. Given the procedure’s replicability it can be applied to study areas with different physiographic and climatic features. Other applications might involve satellite- or radar-based rainfall estimates.

How to cite: Ardizzone, F., Gariano, S. L., Volpe, E., Antronico, L., Coscarelli, R., Manunta, M., and Mondini, A. C.: Quantitative comparison between DInSar-derived surface displacements on slow-moving landslides and ground-based rainfall series, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5139, https://doi.org/10.5194/egusphere-egu23-5139, 2023.

InSAR  time-series analysis is a powerful remote sensing technique that can measure ground deformation with high spatial and temporal resolution. It has been widely used in various applications, such as the detection and monitoring of land subsidence, earthquakes, landslides, and other geohazards. In recent years, there has been significant development in InSAR time-series analysis, including the development of new algorithms and techniques, the availability of new and improved satellite data, and the application of InSAR in new and innovative ways. In this research, we propose the use of improved combined scatterers interferometry with optimized point scatterers (ICOPS) time-series analysis for InSAR application in the continuous monitoring of surface deformation. The key features of ICOPS include the optimization of point scatterers, the use of combined scatterers, and the optimization based on machine learning. These features enable ICOPS to effectively handle non-linear deformation, slow rate deformations, and to provide more accurate and reliable deformation estimates. The ICOPS method can be briefly explained by optimizing the measurement points obtained based on combined scatterers interferometry of persistent scatterers (PS) and distributed scatterers (DS) using machine learning and statistical based approaches. Distributed scatterers (DS) are used as a complement to persistent scatterers which provide increased spatial coverage with an assessment of pixel quality. After the measurement points are obtained, then the optimization process begins with support vector regression (SVR) which can handle non-linear cases and is capable of processing large data. Then, optimization results using machine learning will then be maximized using the optimized hot-spot analysis (OHSA) method to obtain spatially clustered deformation maps. In practically, we applied ICOPS to InSAR data of a surface deformation case study in the several regions and compared the results with those obtained using other InSAR methods. For the application, we applied the ICOPS in the Yellowstone National Park, USA, for detecting surface deformation around Yellowstone caldera related with volcanic activity. Then, the application for ICOPS was used in land subsidence in coastal city in Semarang, Indonesia. We also try the ICOPS for measure the surface deformation related with the construction activity in reclaimed area in Dangjin, South Korea. The results showed that ICOPS can accurately and reliably monitor surface deformation, even in complex and challenging scenarios. In summary, our study demonstrates the potential of ICOPS time-series analysis for InSAR application in the continuous monitoring of surface deformation and highlights its advantages over other methods. This work can contribute to the development of more effective and robust InSAR-based monitoring systems for surface deformation and support the sustainable and resilient management of our built and natural environments.

How to cite: Lee, C., Muhammad, F. F., and Wahyu, L. H.: Analysis of land surface changes based on time-series data interferometric synthetic aperture radar with the application of improved combined scatterers interferometry with optimized point scatterers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6349, https://doi.org/10.5194/egusphere-egu23-6349, 2023.

In the last few decades, the efforts of the scientific community to search earthquake signatures in the Earth atmosphere, ionosphere and magnetosphere have grown rapidly. The increasing amount of good quality data from ground stations and satellites allowed the detection of specific signatures with high statistical significance such as ionospheric plasma density perturbations and/or atmospheric temperature and pressure changes. In addition, the recent development of a magnetospheric–ionospheric–lithospheric coupling (M.I.L.C.) analytical model has provided promising results in the identification of causal links between the observed anomalies and their possible seismic origin. With the aim of statistical validating such a model, we have performed a multi-instrument analysis of a mid-latitude seismic event, including also the investigation of atmospheric activity, in order to validate the identification of confounders and possibly explain any observed anomalous signal. Specifically, we have investigated the earthquake (Mw 4.2) occurred in Italy (Marche) on November 20th 2022 by using high-quality data from both ground-based detectors and satellites, preserving their statistical significance, that we have compared with the predictions of the M.I.L.C. model.

How to cite: Piersanti, M., D'Angelo, G., Battiston, R., Bertello, I., Cicone, A., Diego, P., Papini, E., Parmentier, A., Recchiuti, D., and Ubertini, P.: Statistical validation of the Magnetospheric–Ionospheric–Lithospheric Coupling model on occasion of the Marche Earthquake (Mw 4.2) main shock occurred on November 20th 2022, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7008, https://doi.org/10.5194/egusphere-egu23-7008, 2023.

Satellite remote sensing is frequently used for spatial and temporal detection of geomorphic hazards (GH) such as landslides and flash floods. Establishing regional-scale inventories of GH events is crucial to understanding their behavior in both space and time, particularly in the tropics, where GH are under-researched, and impact is disproportionally high. Recently, an increased focus is seen on the use of machine- (ML) and deep learning (DL) methodologies for accurate detection of GH. These methodologies however, have in common that they rely on accurate information on either the GH location (training samples) or the GH timing (pre- and post-event imagery), making them practically unusable in unseen areas without any information on GH occurrences. Here, we aim to develop a methodology that allows for regional multitemporal GH event detection, providing both location and (semi-accurate) timing of GH events without any prior knowledge on GH event occurrence. We additionally test the suitability of our results as input for a more conventional ML-based classifier – ALADIM. We develop a pixel-based methodology using the open-access, high spatial resolution (10m) Copernicus Sentinel-2 time series from 2016 to 2021. Our methodology uses the peak of the cumulative difference from the mean for a multitude of spectral indices (NDVI, NBR, BI, SAVI, etc.) and allows us to create a map per Sentinel-2 tile that identifies impacted pixels and their related timing. We applied our methodology on six Sentinel-2 tiles in the tropical East African Rift and were able to successfully identify 29 GH events. From these, we chose 12 GH events, with a total of ~ 3900 landslide and flash flood features that occurred in different parts of the time series, in different landscapes and contained different GH event compositions (e.g. GH size, landslide to flash flood ratio). For these GH events, we validated the automatically derived GH event timing, and we used our results to automatically create training samples that served as input for the ALADIM classifier. Estimated GH event timing has on average a ~2-weeks difference from the last available cloud-free pre-event image and a ~ 6-weeks difference from the first available cloud-free post-event image. A general increase in pixel-by-pixel detection accuracy is seen when implementing our output in the ALADIM classifier, with some exceptions for GH event inventories that contain a large amount of small-sized GH features. The detection accuracies are influenced by the amount of cloud cover (less impacted pixels identified in highly cloudy regions), differences in landscapes (low noise levels in pristine forests, and high noise levels in densely cultivated landscapes) and the size distribution of GH events (lower accuracies for GH events that contained a lot of small sized features). Our methodology is working in varying landscapes, shows potential for transferability and in combination with ML-based classifiers allows to better automatize the GH event detection process. Additionally, it is highly optimized in terms of computation time allowing to process large regions of interest, within a relative short time span.

How to cite: Deijns, A., Deprez, A., Michéa, D., Dewitte, O., Kervyn, F., Thiery, W., and Malet, J.-P.: Regional Detection of Landslide and Flash Flood Events in the East African Rift, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7832, https://doi.org/10.5194/egusphere-egu23-7832, 2023.

As infrastructure faces the consequences of climate change there is an urgent need for monitoring methodologies that can provide accurate and timely information to the stakeholders and decision makers in order to mitigate the risk and ensure the safety. According to the World Meteorological Organization the last decade has been recorded as the warmest period in human history. As a consequence, particularly high temperatures and frequent weather extremes (drought, floods, etc.) jeopardize the infrastructure safety. At the same time, the need for reliable, cost-efficient and globally applied infrastructure monitoring methodologies is even more crucial in areas with high seismicity and or volcanic activity.

In this framework the current project, named “PROION”, focuses on the infrastructure monitoring in the “Enceladus” Hellenic supersite. Τhis very active tectonic and seismic area includes geographically:

• the urban centers of Athens, Corinth and Patras (> 50% of the country's population),
• some of the most important archaeological monuments (Ancient Olympia and Mycenae) and
• some very important infrastructures such as Mornos and Evinos dams, Rio-Antirrio Bridge etc.

The Enceladus Supersite area presents the highest seismicity in Europe, the highest recorded ground acceleration in Greece(0.77g) and a very high frequency of landslides.

The aim of the project is the development of a platform for the continuous monitoring of high importance infrastructures such as public buildings and dams. The methodology combines instrumental and remote sensing measurements along with fuzzy logic networks methods and machine learning algorithms. Specifically, measurements obtained by three-axis accelerometers, low cost GNSS receivers and Persistent Scatterer Interferometry are fused and validated with high-precision 3D reference data derived from TLS surveys and UAV campaigns. The processing is based on soft computing algorithms while very accurate deformation maps are utilized for making decision about the current and the future state of each infrastructure. “PROION” project is financially supported by the European Union and the Hellenic government. 

«Acknowledgment:  This research has been co‐financed  by the European Union and Greek  national funds  through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call  RESEARCH – CREATE – INNOVATE (project code: T2EΔK-02396 Μultiparametric monitoring platform with micro-sensors of eNceladus hellenIc supersite)».

How to cite: Nikolakopoulos, K., Kyriou, A., Sokos, E., Bousias, S., Strepelias, E., Groumpos, P., Mpelogianni, V., Roumelioti, Z., Serpetsidaki, A., Paliatsas, D., Ganas, A., Charalampoulou, V. (., and Athanasopoulos, T.: Synergy of accelerometer, GNSS, InSAR and TLS measurements in the light of PROION Project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8367, https://doi.org/10.5194/egusphere-egu23-8367, 2023.

One of the main sources of noise affecting the Differential Synthetic Aperture Radar Interferometry (DInSAR) products is represented by the Atmospheric Phase Screen (APS) signals which are caused by the temporal and spatial variability of atmospheric conditions between the interferometric radar image pairs. Accordingly, it can be challenging to discriminate atmospheric phase delay signals from the deformation ones, and this is particularly difficult in volcanic areas which are often characterized by the presence of both significant topography and displacements.

In this work we present an extensive analysis based on the exploitation of the ECMWF ERA-5 data [1] to filter out the APS contribution from DInSAR products. In particular, we focus on the impact of the ERA-5 corrections on DInSAR deformation time series, as well as on single interferograms. The generation of the exploited DInSAR products is performed through the P-SBAS advanced DInSAR approach [2]. To filter out the APS signal component from the retrieved deformations we have developed an automating processing chain that exploits:

• the PyAPS Python software implementing the approach described in [3], for the APS evaluation;
• an ad-hoc developed IDL code, for correcting the generated interferograms and deformation time series.

The presented experimental analysis has been carried out by taking into account large Sentinel-1 (S-1) datasets acquired both from ascending and descending orbits over several volcanic areas, which are of particular interest for the presence of both significant atmospheric phenomena and remarkable deformations:

• Etna (Sicily, Italy);
• La Palma island (Canary, Spain);
• Stromboli (Sicily, Italy);
• Mauna Loa (Hawaii, United States).

In these sites, the lateral variation of pressure, temperature and humidity, jointly with a topography-correlated component due to the variation of the atmospheric parameters with height, makes the APS interferometric component difficult to be distinguished from the deformation one. This makes the exploitation of auxiliary data crucial.

Moreover, an additional analysis has been carried out by considering the first DInSAR results obtained by exploiting the L-band SAR images acquired by the SAOCOM-1 satellites, over the Ischia island (Campania, Italy).

Our results show that the ERA-5 based APS correction is capable to effectively filter out, for the considered sites, the atmospheric phase contributions relevant to the typical seasonal oscillations as well as those correlated with topography. As expected, because of the coarse spatial resolution of the input ERA-5 data (30 km on the horizontal grid), it is indeed less effective for removing the small spatial scales (turbulent) component of the atmospheric phase signals, which requires different filtering approaches to be corrected.

[1]       https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5

[2]     Manunta, M. et al., The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment, IEEE Trans. Geosci. Remote Sens., 2019.

[3]       R. Jolivet et al, "Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data," Geophysical Research Letters, vol. 38, no. 17, 2011.

How to cite: Lanari, R., Zinno, I., Casamento, F., Casu, F., and De Luca, C.: Exploiting ERA-5 data for the atmospheric filtering of DInSAR deformation products in volcanic areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9995, https://doi.org/10.5194/egusphere-egu23-9995, 2023.

Copernicus DEM is the new global dataset with the desired coverage and resolution to become the standard elevation dataset to replace SRTM. SRTM represents the Earth’s terrain at the level of February 2000, with a spatial resolution of 1’ and a feature resolution that can cover medium-scale landform features. The GLO-30 Copernicus DEM is based on the WorldDEM topographic data, which has an original resolution of 0.4”, obtained from TerraSAR-X and TanDEM-X SAR interferometry data. The WorldDEM sources were acquired between December 2010 and January 2015, and every surface was acquired twice. GLO-30 dataset is resampled from 0.4” to 1”. The global validation against ICEsat data revealed a RMSE of 1.68 m, a standard deviation of 1.68 m, and an absolute vertical accuracy linear error at 90% confidence interval of 2.17 m.

In the present approach, LiDAR data at 0.5 and 1 m spatial resolution covering two regions over Eastern and Western Romania were used to evaluate the accuracy in landform representation of the GLO-30 Copernicus DEM. The results show that the deformations due to the RADAR acquisition (shortening and layover) are lower than for the SRTM dataset, but the number of voids is bigger, especially in the mountainous areas. The resolution of the geomorphic landforms is superior to SRTM data, with river channels, gullies, and landslides features being very often recognizable in non-forested areas.

In conclusion, GLO-30 Copernicus DEM outperforms SRTM and can be used as a new source of global elevation data, but care is needed when certain types of landforms are targeted by the analysis, especially in forested areas. The most affected by the inconsistencies due to the RADAR acquisition technology is the hydrological features, especially in mountainous areas with forest cover.

How to cite: Niculita, M.: Copernicus DEM vs. LiDAR: assessment of landform accuracy representation at regional scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10090, https://doi.org/10.5194/egusphere-egu23-10090, 2023.

Shallow landslides and debris flows triggered by high-intensity continuous precipitation are widespread in the  western part of Hubei province, China. Studies have also shown that most of the landslides in western Hubei belong to the category of  slow-moving landslides that impact infrastructures and can eventually turn into catastrophic failures. Although many landslides cannot be prevented, some possible scientific early warning detection and landslide evolution analysis before and after the failure can help in risk management practices. Under favorable conditions, optical and Synthetic Aperture Radar (SAR) satellite remote sensing data  play a key role in robust characterization of life cycles of slow moving landslides. In this study, we utilized various SAR and optical sensors to investigate kinematic evolution and volumetric change related to the 21 July, 2020, the Shaziba landslide that occurred in Mazhe County, Hubei Province, China (N 30° 36′ 5′′, E 109° 29′ 85′′). The catastrophic failure happened  following a record precipitation that reached a historical peak of 442.3 mm in the month of failure, exceeding the previous historical peaks in the same month. This landslide caused the collapse of more than 60 homes, the destruction of village roads, the destruction of electrical infrastructure and agriculture, the evacuation of more than 1000 individuals, and the pollution of Enshi City's water supply source with silt. Fortunately, there were no fatalities. The pre-failure ground deformation using Sentinel-1 data from June 2016 to July 2020 indicates ground motions at average rates of 30 mm/yr. The co-failure estimation from Planet and Sentinel-2 shows that horizontal displacements in the eastern part of the landslide up to 30 m. The landslide eroded to 4.93 million m3 meters, with DEMs generated from TanDEM-X data before and after the failure. The post-failure ground deformation analysis performed using Sentinel-1 and TerraSAR-X data between August 2020 to July 2021 indicated the instability of the marginal scarps above the crown of landslide and eastern, with an LOS displacement rate of approximately -30 - -10 mm/year. 

How to cite: Wang, W., Motagh, M., Mirzaee, S., Li, T., Zhou, C., Tang, H., and Roessner, S.: Lifecycle of the 21 July, 2020 Shaziba landslide investigated using multi-source remote sensing observation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10116, https://doi.org/10.5194/egusphere-egu23-10116, 2023.

In the last decades, Differential Synthetic Aperture Radar Interferometry (DInSAR) has demonstrated to be an effective tool to detect and follow Earth surface deformations with a centimeter to millimeter accuracy in different hazard scenarios. In particular, the DInSAR techniques are nowadays playing an important role to study ground deformation phenomena, such as volcanic eruptions and seismic events, thanks to their capability to provide dense measurements over wide areas and at relatively low cost.

The increasing diffusion of the use of DInSAR is also due to the large availability of huge and easily accessible SAR data archives, as those acquired, since late 2014, by the Copernicus Sentinel-1 constellation, which is routinely providing C-band SAR data with a defined repeat-pass frequency at a rather global scale. Therefore, such a constant and reliable availability of data allowed us to move from single event analysis to monitoring tasks, particularly in natural hazard prone areas.

In this work we present the DInSAR related activities that are carried out at the Institute of Electromagnetic Sensing of Environment of National Research Council of Italy (IREA-CNR) to support the Italian Department of Civil Protection (DPC) for volcanoes and seismic areas studying and monitoring.

First, by exploiting the Sentinel-1 data archives and the publicly accessible earthquake catalogues, we implemented an automatic service that generates the DInSAR co-seismic displacement maps, once an earthquake that likely produces ground deformation occurs. Although originally developed to monitor the Italian territory, the service has been extended to operate at global scale and the generated products are made freely available to the scientific community through the European Plate Observing System Research Infrastructure (EPOS-RI).

Furthermore, by also exploiting Sentinel-1 data, we developed a second service which is devoted to volcano ground displacement monitoring. The designed system is fully automatic and the process is triggered by the availability of a new SAR data in the Sentinel-1 catalogues acquired from both ascending and descending passes, for every monitored volcano site. The data, per each orbit, are automatically ingested and then processed through the well-known Parallel Small BAseline Subset (P-SBAS) DInSAR technique that allows generating the displacement time series and the corresponding mean displacement velocity maps relevant to the overall observation period. The so-retrieved Line of Sight (LOS) measurements are then combined to compute the Vertical and East-West components of the retrieved deformation, which are straightforward understandable by most of the end users. This service is currently operative for the main active Italian volcanoes (Campi Flegrei caldera, Mt. Vesuvius, Ischia, Mt. Etna, Stromboli and Vulcano), but it can be easily extended to include other volcanic areas on Earth.

Finally, thanks to the availability of an airborne platform which is equipped with a X-band and L-band SAR sensor, we implemented a pre-operative infrastructure that, in conjunction with the already mentioned spaceborne systems, allows us to provide further information on the areas under study, particularly during emergency scenarios.

This work is supported by the CNR-IREA and Italian DPC agreement, the CNR-IREA/MiTE-DGISSEG agreement, the H2020 EPOS-SP (GA 871121), the ASI DInSAR-3M project.

How to cite: Monterroso, F. and Casu, F. and the IREA CNR Team: Spaceborne and airborne DInSAR products generation and analysis to support Civil Protection activities in volcanic and seismic regions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14140, https://doi.org/10.5194/egusphere-egu23-14140, 2023.

Satellite remote sensing data provide key information needed to understand the dynamic behavior of our planet as well as to prepare for, respond to, and recover from disasters. The Observational Products for End-Users from Remote Sensing Analysis (OPERA) project at the Jet Propulsion Laboratory, in partnership with the US Geological Survey and the University of Maryland, starts releasing near-global products that are based on Harmonized Landsat-8 Sentinel-2 A/B (HLS) optical datasets in February 2023: (1) Dynamic Surface Water eXtent (DSWx-HLS) and (2) Land Surface Disturbance (DIST-HLS) product suites. These derived products have applications including  monitoring and guiding future hazard management and recovery efforts. While OPERA does not have an urgent response requirement for disasters, the project will process and deliver the data to end-users as soon as possible. HLS has 2-3 day revisit frequency at the equator allowing the potential for OPERA products to help provide analysis ready data from before, during, and after some events to aid disaster response and recovery efforts. All the DSWx and DIST products will be freely available to the public through various Distributed Active Archive Centers (PO.DAAC for DSWx, https://podaac.jpl.nasa.gov/; LPDAAC for DIST, https://lpdaac.usgs.gov/) and NASA’s Earthdata Search platform based on their scheduled operational release.

Here, we present applications of the first provisional products from the DSWx-HLS and DIST-HLS suites to monitor changes in water bodies and vegetation cover due to droughts, floods, and wildfires. In particular, we focus our analysis on: (a) drastic extent changes in reservoirs,  such as for Lake Mead from 2014-present, (b) mapping flood extents such as the 2020 dam failures in Midland, Michigan, and (c) mapping burned areas due to wildfires such as the 2022 wildfires in New Mexico and in California. We develop open-source tutorials using GIS software and Jupyter Notebooks to visualize and showcase these applications. Both the provisional data and the tutorials are available on the OPERA website (https://www.jpl.nasa.gov/go/opera) to ensure broad access and reproducibility. 

How to cite: Bato, M. G., Devlin, K., Dhillon, R., Bonnema, M., Sangha, S., Niemoeller, S., Pickens, A., Shiroma, G. H. X., Handwerger, A. L., Jones, J. W., Hansen, M., Osmanoglu, B., Venkataramani, K., Fattahi, H., Bekaert, D., Song, Z., and Chan, S.: A first look at the OPERA Surface Water eXtent and Land Surface Disturbance products and their applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10200, https://doi.org/10.5194/egusphere-egu23-10200, 2023.

Following last deglaciation, glacially eroded hillslopes of the Tröllaskagi peninsula in N-Iceland experienced intensive rockslide activity. In the Almenningar site, located on the outermost part of the peninsula, several rockslide features show active surface displacement, which have repeatedly caused major damages and hazardous conditions to a road crossing the area. Since 1977 the Icelandic Road and Coastal Administration (IRCA) has maintained regular measurements and in 2022, GNSS stations were installed for real time monitoring. These measurements indicate deformation rates up to 70 cm per year. Monitoring results further suggest a relationship between deformation and hydroclimatological factors. Furthermore, the front of the deformation area reaches the coast forming up to 60 m high cliffs where clear indications of extensive coastal erosion can be found. Whereas traditional landslide monitoring and field measurements can be expensive and time consuming, remote sensing methods such as Differential satellite Interferometric Synthetic Aperture Radar (D-InSAR) time series analysis has proven to be a valuable tool for large-scale slope deformation detection. In our study, we implement multitemporal D-InSAR methods, which decrease limiting factors like atmospheric delay, orbital errors and decorrelation. We use the Copernicus Sentinel-1 SAR satellite constellation with its 6 day (since 2022: 12 day) revisit time over Iceland. Besides the monitored Almenningar site, two additional sites in Tröllaskagi, Stífla and Siglufjarðarfjall, were selected for slope deformation detection due to their landform similarity and possible threat to infrastructure. The displacement time series was generated for the summers of 2016 through 2022 to (i) detect the spatio-temporal slope deformation patterns in Almenningar, Stífla and Siglufjarðarfjall, (ii) analyse if these landforms have similar deformation response to hydroclimatological conditions and/or seismic episodes and (iii) if there is noticeable difference in deformation rates at the coast or inland. The results of this research will be presented here. In conclusion, D-DInSAR provides useful information to analyse other rockslide features in Tröllaskagi and correlate detected deformation to hydroclimatological conditions and seismic episodes. Results will serve as an important support for hazard and risk assessment and contribute to further research on triggering factors.

How to cite: Adalsteinsdottir, Á. D., Sæmundsson, Þ., Buchelt, S., and Ullmann, T.: Detection of slope deformation at the Tröllaskagi peninsula, N-Iceland, using Sentinel-1 D-InSAR time series (2016-2022), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12538, https://doi.org/10.5194/egusphere-egu23-12538, 2023.

Taiwan result in the active collision of both Eurasian and Philippine Sea Plates characterized by an annual average convergence rate close to 10 cm.y^-1. The Longitudinal Valley is parallel and eastward of the Central (Backbone) Range which is made of metamorphic rocks, and is also situated to the west of the Coastal Range (of volcanic affinity). In between both, lay the Longitudinal Valley (125km long and N020°E trending) which behave as the active crustal suture zone. The latter presents both inter-seismic creeping displacement (Champenois et al., 2013, Deffontaines et al., 2018) and was hit by 7 major earthquakes of magnitudes larger than 5 during the last 70 years which highlights its high seismic hazards.

We combine herein a preseismic UAS survey (May 20, 2015) with one done immediately after the last large earthquake on the eastern Central Range (Oct 07, 2022). We therefore study both (1) the differences from a quantitative point of view; and (2) from a morpho-structural qualitative analysis point of view.

We acquired so many high-resolution photographs using several drones flying at 350 meters above the ground. After photogrammetric processing, we calculate both (1) a high-resolution Digital Elevation Model (UAS-HR-DSM) that takes into account buildings and vegetations, and deduce (2) a Digital Terrain Model (UAS-HR-DSM) corresponding to the ground. Our ground validation (GCP’s) leads us to get a 7cm planimetric resolution (X, Y) and below 40cm vertical accuracy.

This UAS-HR-DSM combined with field work and the preliminary PSInSAR (PALSAR-JAXA) processing led us to better characterize the active tectonic features through a detailed morphostructural analysis. It also permit us to map into much details the active structures and consequently to up-date the pre-existing geological mappings (e.g. CGS geological maps, Lin et al., 2009; Shyu et al., 2005, 2006, 2007, 2008). Then we up-date and combined our new structural scheme with geodetic data (levelings, GPS…) and PALSAR PSInSAR results acquired during the same monitoring time period to locate, characterize and quantify the active tectonic structures, taking into account previous works (e.g. Yu et al., 1997; Lee et al., 2008; Hsu et al., 2009; Huang et al., 2010…). We then precise structural geometries and some geological processes as well as the location of active folds and active faults during the PSInSAR monitoring time-period.

This may lead us to better constrain the seismic hazards and the earthquake cycles of the place.

How to cite: Deffontaines, B., Chang, K.-J., Li, R.-F., Lin, C.-W., Pasquali, P., Magalhaes, S., and Fortunato, G.: Active tectonics from UAS-HR-DSM combined with PSInSAR: Case example along the Longitudinal Valley - Eastern Taiwan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12543, https://doi.org/10.5194/egusphere-egu23-12543, 2023.

Windthrow is one of the major disturbances for forests and can affect large areas, causing extensive economic and ecological impacts across the different types of forests. Remote sensing is an effective tool with potential to cost-efficiently map large wind-affected regions, with a specific revisit time and spatial resolution, depending on the sensors used. Windthrow detection relies mostly on spatiotemporal changes of forest reflectance. However, one of the main drawbacks in using optical images is the cloud cover, and thus the availability of cloud-free images, especially during the cool season, when most of the windthrow events occur. For this reason, relying solely on the twin satellite Sentinel-2 may not be enough as data with 5-day revisit time are only available since 2016. On the other hand, different approaches have been developed to deploy radar images for scene changes detection, nonetheless, for forests, both L- and C-bands shall be integrated to capture the changes in the different layers of the forest, rendering the process very complicated. In this study, we developed two different approaches for windthrow detection based on the difference between the surface reflectance composite of the image’s (Sentinel-2 and Landsat-8/9) bands, before and after a windthrow event. First, a global machine learning model was developed using multiple windthrow events across Europe in order to classify windthrow events at continental scale. Then, a local machine learning model was developed using samples of damaged areas and non damaged areas of the same forest type in order to classify windthrow events within the same satellite image. Overall, our preliminary results showed that the global model presented relatively lower accuracy and F1 score. This finding is most probably due to the different types of forests, which present different spectral signatures and hamper the correct classification of the affected areas. Conversely, the local model presented higher accuracy and F1 score due to the homogeneity in the selected forest type. Our preliminary results thus indicate that windthrow detection at large scales is still challenging and local models may be a reliable alternative for assessing the wind-affected forests.

How to cite: Galizia, L., Nasrallah, A., Elkhoury, C., Coutu, S., Castet, C., and Voituron, Q.: Windthrow detection with moderate to high resolution optical imageries across the European forests, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12623, https://doi.org/10.5194/egusphere-egu23-12623, 2023.

Rock glaciers represent a typical landform characterizing high mountain periglacial terrains. They are composed by a mixture of frozen debris and interstitial ice and when are affected by downslope displacement related to the permafrost deformation or melting, they are called active rock glaciers. The observation of their changes over time is a critical indicator of the state of water resources and permafrost distribution and is therefore of great importance for risk scenario definition and related natural hazards management, as well as can be related to the climate change. 

Synthetic Aperture Radar (SAR) data have proven to be a useful tool to estimate surface displacement phenomena in mountainous areas that can be difficult to access. In particular, Differential Interferometry can measure displacement in the line of site direction with high accuracy. However, sometimes the characteristics of the areas in terms of displacement rate and number of available images lead to some limitations, which can be overcome by using other complementary tools such as SAR offset-tracking (OT) or by integrating both techniques together.

In the OT case, the estimation of the deformation in azimuth and range directions is obtained maximizing the normalized cross-correlation between pairs of images and the minimum detectable displacement depends on the spatial resolution of SAR images, in the sense that a finer spatial resolution allows estimating a lower minimum detectable displacement. Consequently, these techniques can be profitably used for medium- or long-term displacement measurements [1].

In this work, OT is used to monitor the active alpine Lazaun rock glacier, located in the Ötztal in South Tyrol (Italy). Lazaun has a snow cover free period limited typically to 3 months per year, covers an area of about 0.12 km², and its altitude is between 2480 and 2700 m a.s.l.. Previous measurements performed by using GPS have detected displacements reaching ca. 1.5 m per year [2]. Different kind of data (TerraSAR-X, Cosmo-Skymed First and Second generation, Sentinel-1 and SAOCOM) characterized by different wavelengths, exposure and resolutions have been tested to estimate the rock glacier displacements and the results have been compared with GPS measurements executed in 2016-2018 and 2022. The results show that considering the limited size of the area of interest and the displacement rate, the spatial resolution of the data is of fundamental importance and only using spotlight SAR data is possible to estimate the displacement on both seasonal and inter-annual temporal scale.

This research is part of the 2021-2023 project ‘CRIOSAR: Applicazioni SAR multifrequenza alla criosfera’, funded by ASI under grant agreement n. 2018-12-U.0. TerraSAR-X data were provided by the European Space Agency, Project Proposal id 34722, © DLR, distribution Airbus DS Geo GmbH, all rights reserved.

[1] Strozzi, Tazio, et al. "Glacier motion estimation using SAR offset-tracking procedures." IEEE Transactions on Geoscience and Remote Sensing 40.11 (2002): 2384-2391.

[2] Krainer, Karl, et al. "A 10,300-year-old permafrost core from the active rock glacier Lazaun, southern Ötztal Alps (South Tyrol, northern Italy)." Quaternary Research 83.2 (2015): 324-335.

How to cite: Cuozzo, G., Heredia, M. S., De Gregorio, L., Callegari, M., Mejia-Aguilar, A., Bertone, A., and Notarnicola, C.: Deformation monitoring of the Alpine Lazaun rock glacier using offset tracking and multi-source SAR data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12787, https://doi.org/10.5194/egusphere-egu23-12787, 2023.

Drylands cover approximately 40% of the Earth’s land surface and are home to over a quarter of the global population. Despite the deficit of surface water, rare but strong precipitation events are the fundamental driver for geomorphic activity in arid regions. A quantification of the frequency and magnitude of episodic river discharge is essential for a robust characterization of flood hazards and, thus, better understanding of the poorly studied hydromorphodynamics in deserts. However, observation data from gauges are sparsely distributed and, if existent, often do not cover a sufficiently long seamless time series or feature extensive gaps. This applies, for instance, to the remote Northwest Namibia, where more than a dozen ephemeral rivers drain the Kunene Highlands towards the Skeleton Coast, yet daily river flow data for a period of several decades is only available from the Hoanib.

Hence, we propose a workflow based on the Landsat multispectral satellite imagery archive to detect flood events and their spatial impact since 1984 in a high resolution (30 m) for the entire Kunene Region (~144 km²). To cater for the limitations related to a revisit time of 16 days and potential impracticality of scenes due to cloud cover, we calculated spectral indices allowing for the detection of both inundated areas during flooding (e.g., Normalized Difference Water Index) and effects sustained after flood recession (e.g., Tasseled Cap Wetness to detect increased soil moisture). The large remote sensing dataset is processed via cloud computing using the Google Earth Engine. As a novel approach, we try to implement a frequency analysis directly in the Google Earth Engine environment after attributing the spectral imprints of floods to their magnitudes. For this purpose, a statistical relationship is developed between the daily record of the gauging station at the Hoanib and the spatiotemporal multispectral surface characteristics along the river course and floodplains. By transferring this relationship to the other ephemeral streams, spatially highly resolved recurrence intervals for areas affected by floods of different magnitudes can be derived for the entire Kunene Region.

How to cite: Walk, J., Boemke, B., and Ullmann, T.: Spatial flood frequency analysis of ephemeral rivers in Northwest Namibia based on cloud computing of Landsat time series, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12947, https://doi.org/10.5194/egusphere-egu23-12947, 2023.

Deformation effects on surface due to active geological processes are several (e.g., uplift, subsidence and shear discontinuities), these are strictly related to the source parameters and to the geomechanical properties of the surrounding rocks. In the last 20 years, remote sensing represents a key tool for the evaluation and monitoring of the natural hazards. Disasters occur when hazard and vulnerability match.

The risk is proportional to the magnitude of the hazards and the vulnerability of the involved population. Among the deformation monitoring systems, photogrammetry technique from Unmanned Aerial Vehicles (UAVs) is spreading thanks to the high efficiency in data acquisition (time span, resource, and operators), low cost and the capability to acquire high-resolution images. The use of UAVs in contexts of natural hazard presents three main steps for risk assessment: pre-post event data acquisition, emergency support and monitoring. The mud volcano of Santa Barbara (Municipality of Caltanissetta, Sicily, Italy) represents a potentially dangerous site. On 11th August 2008 a paroxysmal event caused serious damage to infrastructures for a range of about 2 km. The main clues of mud volcano paroxysmal events are the uplift and the development of structural features with dimensions ranging from centimetre to decimetre. Here we present a methodology for monitoring of deformation processes that may be precursors of the mud volcano unrest period. This methodology is based on: i) the data collection, ii) the Structure from Motion (SfM) processing chain and iii) the M3C2-PM algorithm for the comparison between point clouds and uncertainty analysis with a statistical approach. This methodology is useful to detect hazard precursors by monitoring of deformation processes with centimetre precision and a temporal frequency of 1 - 2 months. Precision maps and the M3C2-PM algorithm are used to determine surface variations. The statistical analysis allows us to verify i) the uncertainty between the different surveys ii) the spatial variability of the accuracy; iii) the quality of the georeferencing of the surveys based on the number of GCPs (ground control points).

How to cite: De Guidi, G., Brighenti, F., Carnemolla, F., Giuffrida, S., Messina, D., and Monaco, C.: Use of UAVs for geological risk management and analysis: The case study of the mud volcano of Villaggio Santa Barbara, Caltanissetta (Sicily)., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13676, https://doi.org/10.5194/egusphere-egu23-13676, 2023.

Due to the combined effects of climate change and human activities, hazards and natural disasters are becoming more frequent and severe, with impacts on the environment, the economy and human lives. In this sense, an increasing number of institutions and political and organizational structures shift away from emergency response towards disaster risk reduction and planning. Such is the case of the Manging Authority of the Region of Central Macedonia (RCM) who funded the project “Monitoring climate change induced coastal erosion in the region of Central Macedonia with satellite and in-situ data” project, in which collocated Earth Observation and in situ data have been used to develop algorithms and models to assess hazard exposure and vulnerability to erosion for the entire coastal area of RCM.

The methodology applied included three main phases, referring to the design of the web GIS application that hosts the observatory, its services, and derived datasets (Phase A), the creation of the algorithms and tools for the calculation of all the necessary indicators (Phase B) and the evaluation of the current state and the proposal of alternatives for risk management (Phase C). The spatial databases, being re-evaluated throughout the project, host digital products created by applying specialized algorithms that processed optical images from the Sentinel-2 and Landsat-8 satellites to create timeseries of mulitiple indicators such as Chl-a and coastline alterations, Sentinel-1 SAR acquisitions to extract a low resolution Surface Deformation Rate model and OCN Component Extractor Speed and Direction; satellite altimetry observations from the Cryosat-2, Jason1/2/3, SARAL and Sentinel-3a/3b missions for monitoring Sea Level Anomalis and the variations of the Sea Surface Temperature. Over areas with high vulnerability, in-situ geodetic and bahtymetric observations of the coastal area have been collected to calculate high resolution models of the topography and bathymetry employing GNSS, UAV mapping and echo sounding.

The developed databases and indicators were not only used to estimate the correlation between the gradual change of the derived indicators with the human activity, but also to calculate 50- and 100-year simulation indicators of the vulnerability of the coastal areas of RCM to erosion under the pressure of tidal waves. Moreover, a tool for determining passive flood mapping in the case of four different sea level rise scenarios using the bathtub approach has been carried out. All the information was integrated in a web GIS application, conventionally named “Integrated Observatory System for Preventing and Managing the Risk of Coastal Erosion due to the Impact of Climate Change through the Utilization of Earth Observation Data”, designed to ensure interactivity, interoperability and exchange of information, support decision making and evaluate alternative coastal zone development strategies, fully compatible with the national Integrated Coastal Zone Management (ICZM). The observatory is used by the Department of Environment and Industry, Energy & Natural Resources of the Region of Central Macedonia since 2021. Networking activities between stakeholders and public authorities have already been carried out, regarding erosion problems highlighted from the project’s results while alternative and sustainable prevention measures have been presented to local stakeholders.

How to cite: Triantafyllou, A. I., Michailidou, A., Tzanou, E. A., Vergos, G. S., and Tsakoumis, G. M.: An Integrated Observatory System utilizing EO data to prevent and manage the Risk of Coastal Erosion due to the Impact of Climate Change in the Region of Central Macedonia, Greece, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14288, https://doi.org/10.5194/egusphere-egu23-14288, 2023.

In the last decade the area of the Szent-Mihály Hill at the Danube Bend (North Hungary) has become widely known for geohazards, e.g., due to landslide risk of the slope, etc. Numerous mass movement-related accidents have taken place, risking human life and making significant damages and interruption in railway and car traffic. The continuous formation of hillslope debris is due to the geodynamic setting: active uplift of the study area and the incision of the Danube River contribute to increasing slope angles. The near-surface debris is prone to produce voluminous gravitational movements. Due to the uninterrupted soil creep, the vegetation is also sparse, having a little role in stabilizing the slopes. Consequently, monitoring and continuous modeling of hazardous slopes is indispensable. From regular drone-borne and multispectral data acquisition, we can investigate the drainage networks and its temporal changes in order to map the high-risk areas of the slopes.

To model where sediment volume can be accumulated developing potentially dangerous hot spots for landsliding, runoff modeling has been performed. To obtain high resolution, and up-to-date DTMs of the area several UAV measurement campaigns have been made in various (leaf-on and leaf-off) seasons taken in similar acquisition angle, with 60-70% overlapping. The relative high relief of the area represents a challenge to achieve an approximately identical image resolution. The quasi-circular shape of the study area introduces the effects of different styles of shading even in one acquisition. Data processing has been made with Agisoft Metashape Professional® software package, with digital photogrammetry techniques resulting a dense point cloud for each acquisition date. Ground Control Points (GCP) were fixed on the field at various elevations, to reduce error due to uncertainty of camera locations. Using these processed data, a Digital Elevation Model (DEM) have been carried out. The obtained DEMs were filtered to get DTMs. Since part of the material flow follows the drainage network, with the processed DEM, we could investigate these features using the of flow modeling via SAGA. After creating the drainage system, we located the main pourpoints and the catchment areas, belonging to each one. By picking the biggest drainage areas, which also have the biggest risk of landslide events, potential accident causing features could be located.

Due to the huge computer capacity requirement of the applied software, we have run the processing in medium resolution for optimal procession time. To find the ideal resource-quality ratio, the same dataset has been processed multiple times, each with different quality. This way a medium and high-resolution model have been created. To compare the results, runoff modeling also has been computed with both qualities. The results show, that at least high, or ultra-high quality processing method is required to reach the necessary level of details. With this method we were able to locate the most hazardous section of the hill and numerous methods for accident prevention has been suggested.

Funding: F.V. is supported by Project TKP2021-NVA-29, support provided by the Ministry of Innovation and Technology, Hungary, National Research, Development and Innovation Fund (TKP2021-NVA funding scheme).

How to cite: Kondacs, F., Rozman, G., Vörös, F., Kovács, B., and Székely, B.: Geohazards of Szent Mihály Hill, Danube Bend, North Hungary: UAV monitoring to prevent landslide caused accidents, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14633, https://doi.org/10.5194/egusphere-egu23-14633, 2023.

In recent decades, snowfalls, snow cover and duration over Central Italy have decreased and there have been some extreme snowfall events followed by extreme avalanche activities. In this regard, the Calderone Glacier (hereinafter Calderone) represents a geographical and geomorphological element of great interest and is defined as a sentinel of climate change in central Italy, as it is going through a strong phase of reduction: fragmented into 2 glacierets since the end of the last century, it is the only glacial area in the Apennines, and the southernmost in Europe, and for its position on the summit of the Italian Gran Sasso (2912 m asl), a mountain group located in the center of the Apennine belt in the Mediterranean area.

The Italian Glaciological Committee (Comitato Glaciologico Italiano (CGI) every year with ad hoc in-situ inspections in early autumn monitor the Calderone mass balance. The mass balance of a glacier depends on the interplay between the mass gains and losses promoted by climate and those associated by the inherent flux; its monitoring is essential because it can contribute to the knowledge of the current ongoing evolution of glaciers.

Continuation of the traditional type of monitoring, like the one performed by CGI, based on direct measurements of accumulation and ablation by means of a network of stakes, appears to be an unlikely prospect, because in-situ data gathering usually implies expensive field campaigns and with difficult access to the sites, resulting in limited spatial and temporal resolution. In contrast, techniques based on remotely sensed data, among several techniques, those relying on Synthetic Aperture Radar (SAR) demonstrated to be very effective due to the instrument’s capability of operating day and night independently of the weather conditions.

Differential interferometry or DInSAR can be used to estimate displacements, but due the slow-changing nature of glacier masses and the consequent temporal distance necessary to appreciate changes between two dates, DInSAR technique, in such evaluation conditions, suffers from generally low coherence values, which generally prevent accurate estimates. 

For such a reason, in this work we propose to estimate the mass balance for the Calderone through the displacement maps obtained from the difference between two Digital Elevation Models (DEM) obtained from the processing of COSMO-Skymed X band data. Each DEM is obtained from adjacent dates (w.r.t. products availability), and their generation is less subject to the low-coherence problem. In this way two DEMs, whose temporal distance is about 12 months, can be subtracted to obtain displacement maps that are subsequently compared with CGI in-situ measurements for the winter periods from 2010 to 2022. The data used in this study consist of COSMO-SkyMed satellite X-band single-look complex images in slant geometry (level 1A), Stripmap Himage mode (HH polarization) at 3m of spatial resolution. Processing includes, in addition to a canonical DEM generation process, a specific part focused on obtaining the average values, active area and total area for the calculation of the mass balance.

Preliminary results will be illustrated and discussed, pointing out potential developments and critical issues.

How to cite: Alvan Romero, N., Palermo, G., Raparelli, E., Tuccella, P., D'Aquila, P., Caira, T., and Pecci, M.: Monitoring the Calderone glacierets in Central Italy using Digital Elevation Models generated from COSMO-Skymed X band synthetic aperture radar, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15076, https://doi.org/10.5194/egusphere-egu23-15076, 2023.

SAR images can be used to measure changes in the surface of the Earth over time using Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) techniques. TS-InSAR enables the detection and measurement of very small changes in surface deformation, often on the order of millimetres or less. This makes it a powerful tool for monitoring a wide range of natural and man-made phenomena, such as tectonic activity, subsidence, ground water extraction, and the behaviour of engineered structures like buildings and bridges. While TS-InSAR provides deformation measurements, further analysis must be taken to understand the underlying cause of the deformation. In this study, a novel framework has been developed to extract the vast amount of information embedded within the large number of ground deformation Measurement Points (MPs) derived from the Small BAseline Subset (SBAS; Berardino et al., 2002) TS-InSAR technique. The proposed automatic data-mining approach begins with clusterization the TS-InSAR MPs by applying a nonlinear dimensionality-reduction technique, Uniform Manifold Approximation and Projection (UMAP; McInnes et al., 2018), prior to performing clustering with Hierarchical Density based Spatial Clustering of Applications with Noise (HDBSCAN; Campello et al. 2013) in order to group together MPs exhibiting similar deformation behaviour on a large scale. Next, every extracted cluster time series is further investigated by applying a piecewise linear function as a method to detect and quantify accelerations and decelerations of deforming areas.

A test of the method has been conducted over the Bandung Basin (Indonesia) using Sentinel-1 data from October 2015 to December 2020. Application of the method provides an objective way to identify changes in displacement rates over time and provides a wealth of information on the dynamics of surface displacement over a large area. The displacement rates, their spatial variation, and the timing and location of accelerations and decelerations can be used to investigate the physical behaviour of the deforming ground by linking the timing and location of changes in displacement rates to causal and triggering factors.

References
Berardino, P., Fornaro, G., Lanari, R., Sansosti E. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, 40 (11) (2002), pp. 2375-2383.
Campello, R.J., Moulavi, D., Sander, J. Density-based Clustering Based on Hierarchical Density Estimates. In Advances in Knowledge Discovery and Data Mining, Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining; Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu,
G., Eds.; Springer: Berlin, Germany, 2013; pp. 160–172 McInnes, L. and Healy, J. UMAP: uniform manifold approximation and projection for dimension
reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

How to cite: Rygus, M., Hussain, E., Novellino, A., Bateson, L., and Meisina, C.: A TS-InSAR clustering approach to detect spatio-temporal changes inground deformation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15507, https://doi.org/10.5194/egusphere-egu23-15507, 2023.

Deep-seated Gravitational Slope Deformations (DsGSDs) are widespread phenomena across alpine arch. Despite the slow evolution, long-lasting deformation trend of these huge phenomena can represent a relevant geo-hazard, variably affecting human settlements and infrastructures. Given the complexity and spatial heterogeneity of these phenomena, DsGSDs behavior can feature distinct deformation sectors, highlighted by distinctive morpho-structural lineaments. To assess the internal variability in terms of kinematics, deformation trends and style of activity of a deep-seated phenomenon, a local scale analysis is needed. Notoriously, spaceborne radar interferometry have proven to be suitable to characterize ground deformation displacement of very-slow phenomena as DsGSDs, although DInSAR techniques application remain challenging, especially in mountain areas due to complex topography, abundant vegetation and snow cover. In this study, a methodology for the characterization of DsGSDs, exploiting Sentinel-1 dataset on both ascending and descending orbits, is proposed. The Sentinel-1 images are processed with the multi-resolution Component extrAction and sElection SAR- Detector (CAESAR -D), which allows increasing the monitored area density via a spatially variable multilook. Subsequently, operating in a GIS environment, a post-processing and a dedicated analysis of the obtained measured points is implemented. Morpho-structural domains were mainly defined on the basis of geomorphological criteria, leveraging on DEM derivative products (e.g., slope, aspect and hillshade), orthophoto analysis and taking in account the information available in the Italian Landslide Inventory (IFFI). For each recognized domain, firstly, an analysis on the PSs coverage was performed in order to identify the proper distribution and density of the SAR-derived measurement points for a correct definition of the state of activity. Then, we operated filtering the available SAR datasets from possible anomalous values mainly related to the slope orientation to the satellite line of sight (LOS), in order to obtain suitable dataset for the ground deformation analysis. Finally, the filtered measured points were interpolated with the Inverse Distance Weighting (IDW) technique, with the aim of produce diverse ground deformation maps depending on the orientation of the analyzed phenomenon. The combination of ascending and descending geometries allowed to obtain east-west and vertical components of velocity. The projection on the V_LOS along the slope allowed to partially reduce the limitation of the topography on SAR sensitivity. This allowed us to analyze the displacement pattern of DsGSDs in more reliable way. We tested the procedure on two variably oriented DsGSDs phenomena, located in the alpine region Aosta Valley, the Croix de Fana DsGSD, mainly north-south oriented, and the Valtournenche DsGSD, mainly east-west oriented. The variations of the kinematic behavior between the morpho-structural sectors is detected, also considering any other phenomenon as secondary landslide or talus, superimposed on the DsGSD. Overall, the implemented methodology allows to a rapid and low-cost generation of ground deformation maps able to spatially analyze and characterize the morpho-structural domains of DsGSDs, providing an effective tool suitable for the definition of DsGSDs impact on the diverse anthropic elements and a proper land use planning in mountainous territories.

This research was carried out in the framework of the ASI contract n. 2021-10-U.0 CUP F65F21000630005 MEFISTO

How to cite: Cardone, D., Cignetti, M., Notti, D., Godone, D., Giordan, D., Verde, S., Calò, F., Reale, D., Sansosti, E., and Fornaro, G.: Morpho-structural domains characterization by spaceborne SAR data: application on two deep-seated phenomena in Aosta Valley (north-western Italy)., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15780, https://doi.org/10.5194/egusphere-egu23-15780, 2023.

We present WASDI, an open-source, federated multi-cloud platform for Earth Observation (EO) data that has been used successfully in several impactful international projects in the field of Natural Hazards (NH), in particular with floods and wildfires. 

WASDI helps EO developers turn their algorithms into operational services in the cloud. These can also be published in a marketplace where end users can exploit them. The platform automatically interoperates with several data providers. The accessible data includes observations (such as those from the Copernicus and Landsat programmes), derived products (such as the Copernicus DEM and the ESA World Cover), and the output of simulations (such as ECMWF ERA5 reanalysis). A single interface simplifies the access and thus the development of algorithms that operate data fusion using different sources.

Thanks to these capabilities, WASDI is used to daily monitor floods in several South East Asian countries, in the context of a World Bank initiative to promote parametric reinsurance of the financial risks associated with floods. The extent of the floods on bare soil are mapped mainly using an algorithm developed by the Luxembourg Institute of Science and Technology (LIST), using Sentinel-1 data, and integrated by an algorithm that uses Sentinel-2 data developed by the CIMA Foundation. Urban floods are mapped with great accuracy using Sentinel-1 thanks to an innovative algorithm developed by LIST. These algorithms have been deployed to the platform and are now available in its marketplace.

Recently, these technologies have been used to perform an extensive assessment of the effects caused by the very large floods in Pakistan in 2022 to support the Asian Development Bank (ADB) recovery efforts in the framework of ESA’s Global Development Assistance (GDA) Disaster Resilience program.

The platform features also other algorithms, such as those for the detection of active fires based on Sentinel-3 data, which have been used for the assessment of wildfires in the Greek island of Evia in 2022, and for mapping burned areas using couples of Sentinel-2 images, developed by CIMA Foundation and used by the Civil Protection Department of Italy and other countries. This algorithm was also  used to help assess the impact of the wildfires of 2020 in Ukraine, close to the Chernobyl power plant.

These cases demonstrate the effectiveness of Earth Observation data, algorithms and cloud technology in case of natural hazards for prevention, response and assessment.

How to cite: Nattero, C., Rudari, R., Chini, M., Campanella, P., and Menapace, M.: WASDI, a cloud platform for Earth Observation and Natural Hazards, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15882, https://doi.org/10.5194/egusphere-egu23-15882, 2023.

Human environmental impact mitigation is strongly associated to the sustainability philosophy, i.e., a new economy that emphasizes the value of common goods along with a change in temporal perspectives of human activities on the Planet [1], [2]. A sustainable economy is not only good for the environment but even for the economy itself although the path of innovation is clearly crippled by contrasting interests. For example, the transition from an oil society to a green society is quite hard to be fully accomplished. According to the Oil Market report provided by the International Energy Agency (IEA) for 2022 [3], oil demand growth has been increased to 2.3 mb/d (+140 kb/d) for 2022 as a whole and in 2023 it is expected to grow further.

Within such a framework, even Italy has increased oil production in latest years to 63 TWh. Worldwide, 30% of oil production relies on offshore oil rigs making at risk the ocean environment. Hence, oil-related marine pollution induced by offshore oil field activities is a serious threat for ocean ecosystem and the whole marine environment.

In such a contradictory world, it is of paramount importance to contribute with new non-cooperative technologies to monitor the impact of such high-risk oil production infrastructures in order to both prevent oil spills and to support remediation and mitigation operations in case of accidents. In this study, two alternative approaches based on fine-resolution Synthetic Aperture Radar (SAR) polarimetric satellite images are presented and analyzed to observe offshore oil rigs. The latter allow a non-cooperative large-scale continuous monitoring of both the critical infrastructures and the related oil discharges. The presented approaches are physically-based, i.e., they rely on the extraction of the different scattering properties characterizing the oil rig and the surrounding sea surface, and result in effective near real-time processing.

References

[1] Nicholas Stern, Why Are We Waiting? -The Logic, Urgency, and Promise of Tackling Climate Change, MIT press, 2015.

[2] Maurizio Migliaccio, Andrea Buono and Matteo Alparone, “Microwave satellite remote sensing for a sustainable sea”, European Journal of Remote Sensing, vol. 55, no. 1, pp. 507–519, 2022.

[3] IEA (2022), Oil Market Report - December 2022, IEA, Paris, available at: https://www.iea.org/reports/oil-market

How to cite: Migliaccio, M., Inserra, G., Buono, A., and Nunziata, F.: SAR polarimetry to monitor offshore oil rigs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16315, https://doi.org/10.5194/egusphere-egu23-16315, 2023.

The San Bartolo lava flow field is the most recent flank eruption occurred at Stromboli volcano about 2 ka ago on the NE flank of the island. Despite its importance in being the most recent example of flank activity outside the barren Sciara del Fuoco slope, where the recent activity concentrated, the eruption duration, its volume, and the sequence of events has not yet been reconstructed. In this paper, we present a new survey of the lava flow field, carried out both on the field and photogrammetric and LIDAR surveys from UASs, in order to estimate the erupted lava volume and to reconstruct the sequence of events and infer a possible duration and impact of the eruption. The analysis of the surveys allowed us to verify the contact with previous lava flow fields, allowing a more precise lateral extension of the lava delta along the coast, as well as an estimation of the thickness and volume, at least for the subaerial portion of the lava flow field. The morphology analysis of the lava flow field allowed us to recognize structures suggesting inflation and then stationing of the lava flow fronts, features indicating a long-lasting eruption and a complex interaction with the sea. Our results can provide a useful scenario should a flank eruption occur in the future, a possibility that was close to happening in 1998, when the ground deformation stations revealed a lateral intrusion in the shallow supply system of the volcano.

How to cite: Vicari, A., Famiglietti, N., Calvari, S., Marotta, E., Avino, R., Belviso, P., Harris, A., De Luca, G., Memmolo, A., Minichiello, F., and Peluso, R.: The San Bartolo lava flow field, Stromboli volcano, Italy: Reconstruction of the most recent historic flank eruption (2 kyr) affecting the inhabited area by photogrammetric and LIDAR from UASs aimed at hazard assessment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16651, https://doi.org/10.5194/egusphere-egu23-16651, 2023.

The Russian forces invaded Ukraine on 24th February 2022 leading  to widespread disruption of Ukraine's agricultural system. Ukraine is a major exporter of crops , the invasion therefore poses a significant risk to global food security. Quantifying the extent of this impact is critical, and requires monitoring of Ukraine’s agricultural lands. Total production is one of the prime indicators in this regard. Production in turn is directly proportional to the total harvested area. 

Harvested areas at regional scales have previously been estimated from satellite data. The majority of these studies use a complete satellite derived phenological time series and make the assumption that senescence leads to harvest. Both these conditions are not applicable in this case, as harvest estimates are required in-season and all planted fields would not necessarily be harvested due to the conflict . A delayed harvest also results in a long browning phase prior to harvest, making it particularly difficult to differentiate from post-harvest signatures. 

Given these constraints and challenges, we developed a method to monitor crop harvest near-real time using high resolution Planet satellite imagery. Our method includes training a model to cluster change patterns on historic data and then identify harvest patterns in the current season. Samples used to train the model consist of information from two consecutive images. Such samples are collected across the season and spatially across four  agro-climatic zones, ensuring we capture a complete representation of change patterns that exist. Clusters are assigned as ‘harvested’ or ‘non-harvested’ by visually inspecting imagery at a higher temporal resolution, using which,  harvest can be seen as a clear change event. On clusters which are not fully separable, we apply a hierarchical approach to further separate them. Our method works in the absence of extensive training labels and does not use predefined thresholds or assumptions. We applied the method across the harvesting period for winter crops in Ukraine. 

Contrary to initial reports and expectations we found a higher percentage of harvested fields in Ukraine. In free Ukraine we found 94% of planted winter crops to be harvested and in occupied Ukraine it was 88% as of 19th September 2022. Strong visual patterns of non-harvested crops were observed along the occupation borders in eastern and southern Ukraine. Harvesting trends in the north and south were largely unaffected by the conflict. With no possibility to collect ground samples, we visually interpreted satellite imagery at a higher temporal frequency to generate statistically significant validation data for model accuracy calculation. We obtained an overall accuracy of 85% with an f1-score of 90% for the harvested class and 73% for the non-harvested class. Our assessments and analysis were directed to different organizations and agencies dealing with the Ukraine crisis and led to several key insights and derived interpretations.

Following NASA EarthObservatory article was published based on this work: https://earthobservatory.nasa.gov/images/150590/larger-wheat-harvest-in-

ukraine-than-expected  

How to cite: S Nair, S., Becker Reshef, I., Wagner, J., Sadeh, Y., Hosseini, M., Khabbazan, S., Skakun, S., Munshell, B., Baber, S., Duncan, E., Li, F., Sahajpal, R., Kalecinski, N., Baker, B., and Humber, M.: A Rapid Assessment Framework to monitor harvest progress in Ukraine, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16834, https://doi.org/10.5194/egusphere-egu23-16834, 2023.

An M7.3 seismic event occurred on the Kermadec Islands (New Zealand) on June 15, 2019. It was investigated by the high-energy electron detectors of the NOAA and METOP satellites since its destructive energy could interact with the ionosphere and the Inner Van Allen Belts. Moreover, the Tonga subduction area was affected on November 11, 2022, by a strong superficial M7.3 earthquake, near the Hunga Tonga-Hunga Ha’apai volcano, whose last eruption at the beginning of 2022 represented an exceptional lithosphere-ionosphere coupling. For all the events, particle precipitation phenomena were observed. Concerning the earthquakes, the electron bursts were measured from a few hours to some days before the events while for the eruption the electron bursts were observed subsequent to the paroxysmal phase. Since the subduction area and its neighbouring regions are intensely active, we are searching for a possible connection between ionospheric events and these tectonic events to forecast the consequent natural hazards. After the recent discovery of the connections between electron bursts and successive earthquakes in the Western and Eastern Pacific (Fidani, 2021; https://doi.org/10.3389/feart.2021.673105; Fidani. 2022; https://doi.org/10.3390/app122010528), we focus on the statistical correlation between Southern Pacific earthquakes and high-energy electrons. Due to this statistical correlation, we are able to find a conditional probability of a strong earthquake given an ionospheric observation to mitigate the associated risk.

How to cite: Soldani, M., Fidani, C., D'Arcangelo, S., De Santis, A., Perrone, L., Orlando, M., and Cianchini, G.: The possible association of high energy electron precipitations and South Pacific tectonic events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17049, https://doi.org/10.5194/egusphere-egu23-17049, 2023.

The coronavirus diseases-2019 (COVID-19) has impacted many parts of the world in various ways, in particular human activities such as recreation, living, manufacturing, and interaction with the nature, since its outbreak in early 2020. As lockdown measures are the only solution to reduce and even cease the spread of virus, their implementation is a must and, thus, stops most of the human activities. It has been demonstrated that the natural environment has been much improved with the implementation of the lockdown measures for both regions and nations, lacking investigation on the global environment. In this study, we aim to examine the influence of the lockdown measures on the global environmental vulnerability and risk by comparing the global eco-environmental vulnerability in years 2016 and 2020. The findings of the global eco-environmental vulnerability in the year 2016 by Nguyen et al. (2019) serve as a reference. The year 2020 global earth observation dataset and satellite remote sensing data derived variables are used for the assessment of eco-environmental vulnerability and risk with aid of GIS modelling and spatial analysis. The accumulated impacts of the natural and human stressors on the world’s eco-environment are presented. The outcomes are validated by using PM_2.5 data with dust removal.  Results reveal that COVID-19 pandemic with lockdown situation has significantly contributed to the overall improved eco-environmental condition. A decreasing trend in the global eco-environmental vulnerability is observed approximately by 4.72% and 2.78% for the high and very high vulnerability and risk levels, respectively, as a result of reduced human activities likely associated with the implementation of lockdown measures in response to COVID-19 pandemic.

Keywords: Global eco-environment; vulnerability and risk; spatiotemporal changes; human activities; COVID-19

How to cite: Liou, Y.-A. and Nguyen, K.-A.: How the global eco-environmental patterns change due to COVID-19, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17390, https://doi.org/10.5194/egusphere-egu23-17390, 2023.