GIS, proximal, and remote sensing applications for natural hazards processes and impacts analysis and mitigation 

The increase of climate-related hazards has been driven by climate change, increasing human activities and infrastructure development, particularly in vulnerable areas. More efforts should be directed towards effective disaster risk management to reduce damages and losses, focusing on hazard, vulnerability, and risk mapping. Remote Sensing (RS) and Geographic Information Systems (GIS) are powerful tools in mapping change and rate of change concerning natural hazards, particularly in data-scarce environments, thanks to the great advantage of sensing extended areas at low cost and with regular revisit capability. Furthermore, satellite RS offers the opportunity to gain fresh insights into biophysical environments through satellite systems' spatial, temporal, spectral, and radiometric resolutions. The advantages of RS are further supported by the analytical and geospatial data integration capabilities of GIS.
On the other hand, proximal RS offers a unique opportunity to observe processes characterized by fast dynamics and complex geometries and provides data at ultra-high temporal and spatial resolution. The number of proximal RS solutions currently adopted to study and monitor natural hazards has progressively increased in the last decades. Nowadays, UAV, terrestrial radar interferometry, and digital photogrammetry are among the most diffuse proximal systems adopted to identify precursor elements for detailed hazard assessment and support decision-makers during emergencies. In particular, the use of these systems helps create high-resolution 3D models of the study area and monitor natural hazards. The adoption of multi-scale and multi-sensor approaches can be beneficial for studying the same phenomenon from different points of view and can support a detailed description of the studied process and the most critical parameters that can be adopted for its characterization. The availability of many technical solutions represents an additional value, but the lack of defined methodologies can limit these systems' standardized use, particularly during emergencies. This session aims to explore the use of satellite and proximal RS techniques and GIS analysis in different scenarios related to natural hazards and impact analysis and mitigation, including the preliminary characterization of potential dangerous processes, the evaluation of the elements at risk, the management of the emergency phase and the support of recovery and post-emergency reconstruction.

Co-organized by G3/GI3/GM10
Convener: Daniele Giordan | Co-conveners: Oriol Monserrat, Francesco Nex, Niccolò Dematteis, Dimitrios Alexakis, Raffaele Albano, Maria Ferentinou, Christos PolykretisECSECS
| Wed, 25 May, 17:00–18:30 (CEST)
Room 1.31/32

Presentations: Wed, 25 May | Room 1.31/32

Chairpersons: Daniele Giordan, Oriol Monserrat, Niccolò Dematteis
Landslides and debris flows
On-site presentation
Marcel Hürlimann, Roger Ruiz-Carulla, José Moya, Ona Torra, Felipe Buill, and M. Amparo Núñez-Andrés

Debris flow and related processes strongly affect the morphology of headwater catchments and deliver large amounts of sediments into the drainage network. The Rebaixader monitoring site, which is situated in the Central Pyrenees, is a perfect location to analyse different slope mass-wasting processes and to quantify the sediment yield in this headwater catchment. Two types of data are available: first, yearly photogrammetric surveys by Uncrewed Aerial Vehicle (UAV) have been performed since 2016, and second, an instrumental monitoring system is operational since 2009. Therefore, six years of data can be compared by these two approaches. While the UAV surveys produce point-clouds, Digital Surface Models (DSM) and orthophotos, the monitoring system determines the total volume of each torrential flow by flow-depth sensors, geophones and video cameras. Therefore, the volumes of the torrential flows determined by the instrumental monitoring system were compared and contrasted with those obtained from the DoD (Dem of differences) of photogrammetric reconstructions from UAV flights.

The final values of the sediment yield are between 0.1 and 0.2 m3/m2/y, which shows that this torrential catchment has a very high erosion activity.

The experience from this study shows that the applied monitoring techniques make it possible to i) quantify the sediment yield, ii) identify the different phenomena, and iii) determine the spatial distribution of each process. Regarding the UAV-datasets, the appropriateness of using DoD or advantages of comparing directly the different 3D point clouds are other conclusions derived from this study that will be discussed.

How to cite: Hürlimann, M., Ruiz-Carulla, R., Moya, J., Torra, O., Buill, F., and Núñez-Andrés, M. A.: Multi-temporal sediment-yield estimates in a steep headwater catchment using UAV and sensor measurements. Challenges and results from the Rebaixader debris-flow monitoring site (Pyrenees)., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2660, https://doi.org/10.5194/egusphere-egu22-2660, 2022.

Virtual presentation
Andrey Medvedev, Natalia Telnova, Natalia Alekseenko, Arseny Kudikov, Bashir Kuramagomedov, and Yaroslav Grozdov

Specific features of current semiarid landscape along the Eastern Caucasus foothills (so-called Dagestan extra-mountain region) are badlands formed on loess and clay deposits. The active piping, erosional and gravitational processes present a direct hazard for extensive grazing activities and infrastructure facilities accommodated here. The badlands topography is complicated with the abundance of diverse pseudokarst forms such as blind valleys, caverns, different sized and shaped sinkholes. Such typical patterns as chains of elongated sinkholes, marking the direction of underground flow along the bottoms of erosional forms, are rather distinguishable on satellite imagery with submeter spatial resolution. However, the real density and morphometric analysis of surface pseudokarst forms can be well mapped and analyzed only by means of remote sensing data with ultrahigh spatial and vertical resolution (about several decimeters). For the area in study we used UAV-derived data from 100 m altitude of survey to produced Digital Terrain Model (DTM) with resolution of 20 cm. The automatic extraction of DTM’s for semiarid badland with sparse desert steppe vegetation was rather simple but there is obvious limitations of using UAV data for morphometric analysis of the badland were manifested in the formation of the so-called "dead zones" in case of the large and deep sinkholes. For a complete three-dimensional reconstruction of the badland topography, the terrestrial laser scanning data were additionally involved.

As a result of the analysis of the DTM with very high resolution, derived highly-detailed morphometric and hydrological models were built, reflecting the complex structure and genesis of the badland topography. Automatic identification and mapping of sinkholes reveal the prevalence of large sinkholes with a diameter of 5-15 m and a depth of 1-3 m along the erosional valleys for the study area. Along the slopes more smaller sinkholes forms (up to 0.3 m in diameter and up to 1 m in depth) were identified, the complex network of gullies and micro-terraces pattern were clearly reconstructed. Identification and mapping of sites with high susceptibility to current processes of different genesis was done: in particular, the identified closed catchment micro-basins are areas of predominance of piping processes, while the escarpments in the upper parts of the steep slopes of the badlands are most affected by erosional processes with formation of micro-gullies.

Under regular monitoring of piping, erosional and gravitational processes remodeling the badland topography, it is necessary to carry out multitemporal UAV surveys at low altitudes along with terrestrial laser scanning data. Such complex approach will make it possible to identify more reliably the current ratio of surface and groundwater runoff, and to early allocate and warn the hazardous geomorphological processes.

How to cite: Medvedev, A., Telnova, N., Alekseenko, N., Kudikov, A., Kuramagomedov, B., and Grozdov, Y.: The use of UAV-derived ultrahigh resolution data for the assessment of semiarid badland exposure to hazardous geomorphological processes: case of the Eastern Caucasus foothills, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9087, https://doi.org/10.5194/egusphere-egu22-9087, 2022.

Virtual presentation
Othmar Frey, Charles Werner, Andrea Manconi, and Roberto Coscione
Terrestrial radar interferometry (TRI) has become an operational tool to measure slope surface displacements [1,2]. The day-and-night and all-weather capability of TRI together with the ability to measure line-of-sight displacements in the range of sub-centimeter to sub-millimeter precision are strong assets that complement other geodetic measurement techniques and devices such as total stations, GNSS, terrestrial laser scanning, and close/mid-range photogrammetric techniques.

(Quasi-)stationary TRI systems are bound to relatively high frequencies (X- to Ku-band or even higher) to obtain reasonable spatial resolution in azimuth and yet the azimuth resolution is typically only in the order of tens of meters for range distances beyond a few kilometers. These aspects are limiting factors to obtain surface displacement maps at high spatial resolution for areas of interest at several kilometers distance and also for (slightly) vegetated slopes due to the fast temporal decorrelation at high frequencies.
Recently, we have implemented and demonstrated car-borne and UAV-borne repeat-pass interferometry-based mobile mapping of surface displacements with an in-house-developed compact L-band FMCW SAR system which we have deployed 1) on a car and 2) on VTOL UAVs (Scout B1-100 and Scout B-330) by Aeroscout GmbH [3,4]. The SAR imaging and interferometric data processing is performed directly in map coordinates using a time-domain back-projection (TDBP) approach [5,6] which precisely takes into account the 3-D acquisition geometry.

We have meanwhile further consolidated our experience with the repeat-pass SAR interferometry data acquisition, SAR imaging, interferometric
processing, and surface displacement mapping using the car-borne and UAV-borne implementations of our InSAR system based on a number of repeat-pass interferometry campaigns. In our contribution, we present the capabilities of this new InSAR-based mobile mapping system and we discuss the lessons learned from our measurement campaigns.

[1] Caduff, R., Schlunegger, F., Kos, A. & Wiesmann, A. A review of terrestrial radar interferometry for measuring surface change in the geosciences. Earth Surface Processes and Landforms 40, 208–228 (2015).
[2] Monserrat, O., Crosetto, M. & Luzi, G. A review of ground-based SAR interferometry for deformation measurement. ISPRS Journal of Photogrammetry and Remote Sensing 93, 40–48 (2014).
[3] O. Frey, C. L. Werner, and R. Coscione, “Car-borne and UAV-borne mobile mapping of surface displacements with a compact repeat-pass interferometric SAR system at L-band,” in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2019, pp. 274–277.
[4] O. Frey, C. L. Werner, A. Manconi, and R. Coscione, “Measurement of surface displacements with a UAV-borne/car-borne L-band DInSAR system: system performance and use cases,” in Proc. IEEE Int. Geosci. Remote Sens. Symp.IEEE, 2021, pp.628–631.
[5] O. Frey, C. Magnard, M. Rüegg, and E. Meier, “Focusing of airborne synthetic aperture radar data from highly nonlinear flight tracks,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 6, pp. 1844–1858, June 2009.
[6] O. Frey, C. L. Werner, and U. Wegmuller, “GPU-based parallelized time-domain back-projection processing for agile SAR platforms,” in Proc. IEEE Int. Geosci. Remote Sens. Symp., July 2014, pp. 1132–113.

How to cite: Frey, O., Werner, C., Manconi, A., and Coscione, R.: High-resolution mobile mapping of slope stability with car- and UAV-borne InSAR systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8587, https://doi.org/10.5194/egusphere-egu22-8587, 2022.

On-site presentation
Marek Ewertowski and Aleksandra Tomczyk

Slope-related mass movements and erosional processes are common in all regions on Earth and especially dangerous in mountain areas, where they can rapidly transfer material, threatening human lives and infrastructure. However, the characteristics and activity of small scale (< 1000 m2) events in highly elevated tropical mountains remain poorly understood, even though these areas are often populated. The morphological characterization and investigation of the short-term dynamics of different types of mass movement and erosional processes can help infer about slope processes and take appropriate actions to limit associated hazards. This contribution aims:(1) To recognize the different processes that contribute to overall slope dynamics; (2) To document the morphology and short-term (annual dynamics) of geohazards-related landforms (e.g. small landslides, erosional rills and gullies); (3) To investigate the relationships between the characteristics and dynamics of geohazard sites and the landscape properties; (4) To develop a model of mass wasting mechanisms as agents of slopes development in tropical mountains.

The study areas were located in South America in Cordillera Vilcanota (Willkanuta) in Peruvian Andes and Eje Cafetero region in Colombian Andes. We documented and investigated the morphology and annual spatial pattern of activity of 15 sites representing different types of geohazards. Topographic analyses were based on time series of data captured using an unmanned aerial vehicle (UAV). Where possible, we investigated the observed dynamics of slope processes in combination with data on anthropogenic use to identify the main possible hazards. We identified four main types of processes responsible for transforming the land surface within studied sites: landslides, debris flows, falling, accelerated soil erosion. The morphological expression of these processes included the formation of erosional rills and gullies, landslide head scarps and lobes, debris flow channels, and avalanche deposits. In addition, we identified two main processes that control the activity of small geohazard sites. First, road works often caused activation of mass movements because of undercutting roadsides and associated anthropogenic earth movements. Second, the topographic properties of slopes (mainly slope and aspect) can increase the landscape response to direct anthropogenic pressure. Documented activity often follows a pattern of initiation of movements at the bottom of the site and its further propagation towards the upper escarpment. These results suggest that the dynamics of small geohazard sites strongly depend on local conditions and direct human impacts. While individual events are hard to predict, the presence of fine-scale rills and furrows might be helpful as indicators of probable increase in activity of slope processes. Over the longer time scales, that can be used to identify the most hazardous elements of the slope systems.

This project was funded by Narodowe Centrum Nauki (National Science Centre, Poland), grant number 2015/19/D/ST10/00251

How to cite: Ewertowski, M. and Tomczyk, A.: Mapping and geomorphological characterization of small-scale slope-related geohazards in the tropical high-mountain environment: case studies from Cordillera Villcanota, Peru and Eje Cafetero, Colombia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-979, https://doi.org/10.5194/egusphere-egu22-979, 2022.

Presentation form not yet defined
Michalis Diakakis, Emmanuel Vassilakis, Spyridon Mavroulis, Aliki Konsolaki, George Kaviris, Evangelia Kotsi, Vasilis Kapetanidis, Vassilis Sakkas, John D. Alexopoulos, Efthymis Lekkas, and Nicholas Voulgaris

Mediterranean tectonically-active coastal areas are a highly-dynamic environment balancing internal tectonic dynamics with external geomorphic processes, as well as manmade influences. Especially in touristic areas characterized by high built-up pressure and land value, where these dynamics are even more concentrated, the evolution of coastal environments needs careful and high-resolution study to identify localized risk and the processes they derive from.
Recently, new advanced remote sensing techniques such as Unmanned Aerial Systems (UAS)- and Terrestrial Laser Scanners (TLS)-aided monitoring have improved our capabilities in understanding the natural processes and the geomorphic risks (i.e. mass movement phenomena).
An integrated study comprising Unmanned Aerial Vehicles (UAV) and Light Detection And Ranging (LIDAR) sensors was conducted in coastal areas of the southern Ionian Islands (Western Greece) aiming to the mitigation of earthquake-triggered landslide risk and to responsible coastal development. Located at the northwesternmost part of the Hellenic Arc, this area is characterized by high seismicity and has been affected by destructive earthquakes mainly due to the Cephalonia Transform Fault Zone (CTFZ), which constitutes one of the most seismic active structures in the Eastern Mediterranean region. One of the most common environmental effect triggered by these earthquakes are landslides distributed along fault scarps in developed and highly visited coastal areas. Furthermore, this area is highly susceptible to hydrometeorological hazards inducing intense geomorphic processes, including Medicanes among others.
These technologies allow a highly-detailed view of landslide processes, providing insights on the structures and factors controlling and triggering failures along coastal scarps as well as highlighting susceptible zones and high-risk areas with accuracy and mitigating adverse effects with precision and clarity. Overall, by providing a better understanding of the risks the approach used allows a more sustainable development of these coastal segments enhanced by risk mitigation.
The study was conducted in the framework of the project “Telemachus - Innovative Operational Seismic Risk Management System of the Ionian Islands”, co-financed by Greece and the European Union (European Regional Development Fund) in Priority Axis “Environmental Protection and Sustainable Development” of the Operational Programme “Ionian Islands 2014–2020”.

How to cite: Diakakis, M., Vassilakis, E., Mavroulis, S., Konsolaki, A., Kaviris, G., Kotsi, E., Kapetanidis, V., Sakkas, V., Alexopoulos, J. D., Lekkas, E., and Voulgaris, N.: An integrated UAS and TLS approach for monitoring coastal scarps and mass movement phenomena. The case of Ionian Islands., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7536, https://doi.org/10.5194/egusphere-egu22-7536, 2022.

Virtual presentation
Zenaida Chitu, Ionut Sandric, Viorel Ilinca, and Radu Irimia

Curvature Subcarpathians is one of Romania's most complex geological and geomorphic areas, frequently affected by landslides. The juxtaposition of snowmelt and spring rainfalls triggers significant damages to roads and buildings every few years (2018, 2021). In this context, accurately delineating the most affected areas becomes critical for evaluating landslides exposure. Aerial images have begun to be used more and more for different risk assessment phases to detect natural phenomena spread and damaged infrastructure elements. In this study, we use fully automatic detection of the landslide body and infrastructure elements (intact or collapsed buildings and roads) to support Regional Civil Protection Agencies in disaster intervention decision support. Our methodology is based on deep learning techniques for automatic detection, mapping and classification of landslide and infrastructure elements. A U-Net model was trained to detect the landslide body, and several Mask RCNN models were trained to detect the landslide features and infrastructure elements. The training accuracy for the U-Net model used for landslide body mapping is 0.86, and the validation accuracy is 0.80. The training accuracy of the Mask RCNN models is 0.76 for landslide cracks, 0.82 for roads and 0.92 for buildings. Some confusions between landslide cracks and local roads without asphalt are often seen in rural areas. The models are run on high-resolution aerial imagery collected with Unammend Aerial Vehicles after a landslide event. The data obtained from the deep learning models are further integrated with information from various sources such as aerial/satellite imagery, online GIS resources, weather forecasts, and spatial analysis techniques for providing a helpful tool to emergency management specialists. The tools have been integrated into a GIS platform that acts as a decision support system, and it can be used from a graphical user interface without the need to have programming skills.


This work was supported by a grant of the Romanian Ministry of Education and Research, CCCDI - UEFISCDI, project number PN-III-P2-2.1-PED-2019-5152, within PNCDI III (project coordinator Ionuț Șandric, https://slidemap.gmrsg.ro) and by the project PN19450103 / Core Program (project coordinator Viorel Ilinca).

How to cite: Chitu, Z., Sandric, I., Ilinca, V., and Irimia, R.: Mapping Exposure to Landslides by Means of Artificial Intelligence and UAV Aerial Imagery in the Curvature Subcarpathians, Romania, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11334, https://doi.org/10.5194/egusphere-egu22-11334, 2022.

Virtual presentation
Manuel Andreas Luck and Irena Hajnsek

Landslide mapping using Machine Learning approaches often relies on various image statistics determined by neighbourhood functions. In this presentation, the effect of a graph network for the definition of the neighbourhood of each pixel is shown on the example of the Weheka valley, New Zealand. The graph network integrates the physical properties of sliding and flowing masses into the classification process of earth observation imagery. This neighbourhood is determined by connecting nodes based on the flow direction and therefore replacing common raster formats. Both Sentinel 1 and Sentinel 2 acquisitions are used to determine the change in each pixel. From the Sentinel 1 data the Beta Nought is calculated, and the Sentinel 2 data is used to derive multiple indices (e.g., NDWI and NDVI). These products are combined in each node of the graph network. Within the neighbourhood defined by the graph network image statistics (e.g., mean, and standard deviation) are derived for each node. All data and derived products are used to train a Random Forest Classifier which is applied to three different extents of a landslide in the Weheka valley. 81.11% of the affected area is detected for the largest event with a decreasing accuracy towards the margins of the reference area.  

How to cite: Luck, M. A. and Hajnsek, I.: Integration of a Graph Network for the Definition of Neighbourhood in Landslide Detection with Machine Learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7141, https://doi.org/10.5194/egusphere-egu22-7141, 2022.

Glaciers instabilities
Presentation form not yet defined
Daniele Giordan, Niccolò Dematteis, Fabrizio Troilo, Paolo Perret, Simone Gotterdelli, and Luca Morandini

The dynamics that characterizes glaciers instabilities are often not well known because the study of these phenomena is done in many cases after their occurrence. A few examples of dedicated high resolution and high-frequency monitoring networks have been recently implemented to support risk assessment and management of glaciers affected by large potential instabilities.

The current climate trend and the rise of high mountain regions occupations by several anthropic activities have recently created areas affected by high potential risk due to the activation of glacial hazards, in particular during the summer season.

A few possible solutions are available: the substantial limitation of touristic exploitation of these areas or the management of the risk aimed to reduce the restrictions in accessing such high-value areas.

In this regard, it is required the implementation of high-resolution and high-frequency monitoring networks able to follow the evolution of the glacier and increase the knowledge of its dynamics.

In the Courmayeur municipality (Italy), the Planpincieux Glacier is a clear example of this critical condition: an active glacier with an unstable sector that could create a large ice avalanche that can reach the bottom of the valley, which is characterized by the presence of settlements and a famous touristic area.

For this reason, in the last decade, an innovative monitoring network has been implemented and tested in this very complex environment. The system comprises doppler radar, ground-based interferometric SAR and optical monitoring stations. The implementation of this hybrid network is a challenging task not only for the calibration of single instruments but also for the creation of network management that can acquire the dataset of different monitoring systems to obtain a precise representation of the evolution of the glacier. This is the final step that should be implemented for an effective strategy to support decision-makers.

How to cite: Giordan, D., Dematteis, N., Troilo, F., Perret, P., Gotterdelli, S., and Morandini, L.: Close-range hybrid solutions for glaciers instabilities monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11192, https://doi.org/10.5194/egusphere-egu22-11192, 2022.

Virtual presentation
Jesús Eduardo Méndez Serrano, Jesús Octavio Ruiz Sánchez, Nelly Lucero Ramírez Serrato, Nestor López Valdés, and Mariana Patricia Jácome Paz

On September 19, 2017, Mexico was rocked by a 7.1 earthquake, causing an immense amount of damage in the states near the epicenter. This earthquake caused hundreds of damages in historical heritage, mainly in the states of Puebla, Oaxaca and Morelos. The patrimonial damages occurred were so extensive that they are prolonged till this day. Nepopualco Morelos was one of the towns that suffered great destruction by this shaking event. Their historical and main church, “Santiago the Apostle”, was  shattered in the shake, and the cleanup is still ongoing. The objective of this project was to create a 3D model of the Santiago the Apostle Church to view the process of restoration done by the National Institute of Anthropology and History (INAH). The 3D model obtained was the result of 478 images, which were captured by three different drone flights and a set of images shot on terrestrial. These flights were done by an Anafi Parrot drone, two circular flights and a double grid flight (180 and 256 images, respectively). For the purpose of obtaining a georeferenced accurate model, twelve ground control points were acquired in the field using a Emlid Reach RS+. The 3D model  presented in this project is a high-resolution model that allows the spatial analysis of the cabinet structure and represents a low-cost methodology. This model presents a centimeter resolution, while the error corresponds to 1.56%. The main contribution of this work is the obtainment of a 3D model of  Nepopualco´s historical church in which the final product shows the present stage of reconstruction done on the structural damages caused by the earthquake. The 3D reconstruction model will be delivered to the corresponding authorities of the National Institute of Anthropology and History. There is a possible consideration in creating other models that may help the INAH in the recovery process of cultural heritage affected by natural phenomena, as well as its structural mitigation. This project is the first effort on creating a digital catalog of these types of structures that make up Morelos’ historical heritage.
Thanks to Arq. Antonio Mondragón from INAH,  Arq. Aimeé Mancilla and Arq.  Fabián Bernal Orozco for their facilities and support. We also want to thank Mr. Félix García Reyes and Gilberto García Peña, the community representatives, for their assistance in opening the entrance to the church.

How to cite: Méndez Serrano, J. E., Ruiz Sánchez, J. O., Ramírez Serrato, N. L., López Valdés, N., and Jácome Paz, M. P.: 3D Reconstruction of the ancient church Santiago the Apostle, Morelos, Mexico as a follow up to the damage caused by the 2017 earthquake, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10813, https://doi.org/10.5194/egusphere-egu22-10813, 2022.

Presentation form not yet defined
Ignacio Gatti, Andrea Taramelli, Mario Martina, Serena Sapio, Maria Jimenez, Marcello Arosio, Emma Schiavon, Beatrice Monteleone, and Margherita Righini

Earth Observation (EO) environments have been increasing exponentially in the last decades. New generation of satellites are designed for monitoring climate related hazards, providing higher spatial and temporal resolution images. Hazards processes are triggered by anomalies in precipitation. The service will be able to provide information on the extent of the flood footprint. The test area is located south of the city of Milan, where the urban area of Pavia is located. There was an unexpected high runoff of the Ticino river that produced high water in the flood-plain surface, affecting the local population for three consecutive days and with a total damage estimate of 250,699 euro.

The identification of datasets counts on a broad availability of EO data processed, such as C-band Synthetic Aperture Radar (SAR) data from the Sentinel 1 satellite constellation together with X-band SAR data provided by the TerraSAR-X.  Methods include in-SAR coherence, by cross-multiplying the two SAR images or techniques like threshold with a final pixel size of Sentinel 1 of 8.9 m and 1.8 m of TerraSAR-X. Imagery from the 25th of November (Sentinel 1) with a VV (vertical transmit, vertical receive) polarization and from the 27th of November (TerraSAR-X) with a HH (for horizontal transmit and horizontal receive) polarization were selected. Different bands have different characteristics, for instance in penetration and spatial resolution.

Obtained products include urban footprint and flood detection maps. Results could provide an important decision support tool for a wide range of actors, including public authorities to support the preparedness, mitigation and response phases of the emergency management cycle. In addition, adaptation measurements, intervention and urban planning, as well as flood mitigation activities are additional benefits. Future analysis will include impact estimates and vulnerability analysis on the urban footprint area.


How to cite: Gatti, I., Taramelli, A., Martina, M., Sapio, S., Jimenez, M., Arosio, M., Schiavon, E., Monteleone, B., and Righini, M.: Flood detection products to support emergency management services in the Lombardy region, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12942, https://doi.org/10.5194/egusphere-egu22-12942, 2022.

On-site presentation
John Reige Bendijo and Maria Divina Morales

Floods are processes that significantly affect populations, the environment, economy and infrastructure. The Municipality of Saint Bernard, a rural, data-scarce locality, is one of the areas in the Philippines frequently affected by flooding. Risk Evaluation and Flood Susceptibility Mapping are critical components of flood prevention and mitigation techniques because they identify the most susceptible locations based on physiographic attributes that influence flooding propensity. The first objective of this study is to generate a flood susceptibility map for the identification of barangays or zones susceptible to flood in the Municipality of Saint Bernard based on the eight (8) physiographic maps, namely: (i) Fluvial Geomorphology, (ii) Slope, (iii) Elevation, (iv) Lithology, (v) Land cover, (vi) Topographic Wetness Index (TWI), (vii) Drainage density, and (viii) Distance from the Rivers and Streams. AHP serves to determine the weights of the aforementioned factors. The distance to rivers and streams is ranked as the essential factor for finding areas susceptible to flooding, with the highest weighted rate of 20.10%. The authors utilized a quantitative approach to validate the generated flood susceptibility map by correlating with the historical flood datasets. The quantitative validation showed an excellent agreement between the susceptibility zones and historical flood events, of which 74.6% were coincident with high or very high susceptibility levels, thus confirming the effectiveness of AHP. The second objective of this study is to evaluate the relative percentage risk of flooding in every barangays or zones and the generation of risk exposure maps, which is essential to visualize each barangays' or zones' builtups, roads, and the population at risk.

How to cite: Bendijo, J. R. and Morales, M. D.: Potential Flood-Prone Areas in the Municipality of Saint Bernard, Southern Leyte, Philippines: Risk Evaluation and Flood Susceptibility Mapping using GIS-based Analytical Hierarchy Process (AHP), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1574, https://doi.org/10.5194/egusphere-egu22-1574, 2022.

Climate change resilience
On-site presentation
Ghada Sahbeni, Peter K. Musyimi, Balázs Székely, and Tamás Weidinger

Drought is an extreme climate phenomenon that influences Earth’s water resources and energy balance. It affects hydrological cycle processes such as evapotranspiration, precipitation, surface runoff, condensation, and infiltration. Its extreme and severe occurrences threaten food security and drinking water availability for local populations worldwide. In this regard, this study uses Sentinel-3 SLSTR data to monitor drought spatiotemporal variation between 2019 and 2021 and investigate the crucial role of vegetation cover, land surface temperature, and water vapor amount in influencing drought dynamics over Kenyan’s lower eastern counties. Three essential climate variables (ECVs) of interest were extracted, namely, land surface temperature (LST), fractional vegetation cover (FVC), and total column water vapor (TCWV). These features were processed for four counties between the wettest and driest episodes in 2019 and 2021. The results showed that Makueni county has the highest FVC values of 88% in April and 76% in both periods and years. Machakos and Kitui counties had the lowest FVC estimates of 51% in September for both periods and range between 63% and 65% during dry seasons of both years. The land surface temperature has drastically changed over time and space, with Kitui county having the highest estimates of approximately 27 °C and 29 °C in April 2019 and September 2019, respectively. A significant spatial variation of TCWV was noticed across different counties, with the lowest value of 22 mm in Machakos county during the dry season of 2019, while Taita Taveta county had the highest estimates varying from 30 to 41 mm during the wettest season of 2021. Land surface temperature variation is negatively proportional to vegetation density and soil moisture content, as non-vegetated areas are expected to have lower moisture. A close link between TCWV and soil moisture content has been well established. Overall, Sentinel-3 SLSTR products depict an efficient and promising data source for drought monitoring, especially in cases where in situ measurements are scarce. ECVs produced maps will assist decision-makers in a better understanding of drought events that extremely influence agriculture in Kenya’s arid and semi-arid areas. Similarly, Sentinel-3 products can be used to interpret hydrological, ecological, and environmental changes and implications under different climatic conditions.

How to cite: Sahbeni, G., Musyimi, P. K., Székely, B., and Weidinger, T.: The Relationship Between Soil Moisture and Drought Monitoring Using Sentinel-3 SLSTR Data in Lower Eastern Counties of Kenya, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6058, https://doi.org/10.5194/egusphere-egu22-6058, 2022.

On-site presentation
James Brennan, Claire Burke, Laura Ramsamy, Hamish Mitchell, and Kamil Kluza
At Climate X we are producing risk estimates for the UK to help businesses and communities mitigate and adapt for climate change related losses. Climate X provides risk scores and expected financial losses from a plethora of hazards including flooding, subsidence, landslides, drought, fire and extreme heat. To do this at the scales we need, Earth Observation (EO) and other geospatial data sets play a crucial role in both physical modelling and risk estimation. Generating rich geospatial datasets to sit as the bedrock of risk models requires intelligent use of multiple data sources, involving the fusion of EO data from synthetic aperture radar, lidar and optical instruments and across processing levels from L1 to L3. This talk will cover the generation and use of these datasets that drive physical risk models (flooding) as well as ML enabled models (Landslides and subsidence).

How to cite: Brennan, J., Burke, C., Ramsamy, L., Mitchell, H., and Kluza, K.: Using remote sensing and GIS to project climate risk for asset management users, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5942, https://doi.org/10.5194/egusphere-egu22-5942, 2022.

On-site presentation
Juan Carlos Laso Bayas, Martin Hofer, Ian McCallum, Gernot Bodner, Maxim Lamare, Olha Danylo, Victor Maus, David Luger, Linda See, and Steffen Fritz

Climate-smart agricultural practices are techniques that help crops to endure “extreme” weather events. Practices such as minimum or no tillage, crop rotations, and cover crops reduce wind and rain-driven erosion, enhance soil physical quality, and enable soil to store water for a longer time. Climate change has already led to an increased frequency of “extreme” weather events including prolonged dry spells and intense rain. From a farmer’s perspective, a clearer and more spatially explicit demonstration of how these practices can enhance the resilience of farms would support their accelerated uptake and thus result in increased food security. From a policy maker’s perspective, knowing the extent of adoption and location of these more resilient farms would enable them to produce policies that facilitate and promote the adoption of these practices, which can buffer the effects of climate change. The use of remote sensing to detect these practices would, therefore, benefit this process. Several existing remote sensing-derived indicators, such as the Normalized Difference Vegetation Index (NDVI), are already in use. They inform farmers and policy makers on, e.g., crop and nutrient status. A combination of existing and new remote sensing-derived indices is needed to facilitate and streamline the detection and promotion of climate-smart practices, but a lack of in-situ data to date has prevented the development and verification of new models of detection. The “SATFARM services” project, which brings together expertise in agriculture, remote sensing, and data analysis, aims to connect a large agricultural time-series data set, provided by the Austrian Chamber of Agriculture, with various remote-sensing derived indicators. The goal is to detect and track climate-smart practices and to display the results on a platform (https://apps.sentinel-hub.com/eo-browser/) accessible to farmers, researchers, and policy makers. This presentation will showcase the methodology employed, the initial results and the display of these indicators on the platform.

How to cite: Laso Bayas, J. C., Hofer, M., McCallum, I., Bodner, G., Lamare, M., Danylo, O., Maus, V., Luger, D., See, L., and Fritz, S.: Remote sensing detection of climate-smart practices: Enhancing farm resilience in Austria, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9513, https://doi.org/10.5194/egusphere-egu22-9513, 2022.