The location and type of buildings are incredibly useful data in any phase of the disaster management cycle. During the prevention and preparedness phases, the exposure and vulnerability of the population to natural hazards can be identified, using building inventories. To get to know the extent of damage in the recovery phase, one needs to know the state of the buildings before the disaster. In the response phase, knowledge about the exact population distribution can prove crucial. This can be derived from the knowledge of building locations and how and when they are populated. The common spots for shelter, such as hospitals and schools, should be identified immediately after a disaster has struck. 
Humanitarian mapping has been a key support for disaster relief. Usually, the mapping is channeled through OpenStreetMap (OSM). By MapSwipe and automated completeness assessments  it can quickly become clear where data is lacking. Volunteers often map areas through Mapathons organized by the Humanitarian OpenStreetMap Team (HOT) or Missing Maps. It is partly per these endeavors that OSM contains almost 600 million buildings as of 1 January 2024.
Recently, datasets using AI methods, largely based on Earth Observation (EO) data have been created to identify the world’s buildings. The Google Open Buildings dataset, the Microsoft Global ML Building Footprints contain semi-automatically generated building footprints. Both are of near-global extent and they contain respectively 1.8 and 1.3 billion buildings, but neither is fully complete. 
Unfortunately, unlike OSM, these datasets lack building attributes. The Microsoft dataset does include height in some limited areas, such as the USA and parts of Europe. There are datasets that contain more information, but they span a much smaller area. For example the USA Structures dataset defines occupancy types of buildings based on land use. However, in an ideal situation the rich information structure found in OSM is combined with the extensiveness of building footprints from the EO-derived datasets. 
We investigated the wide range of features and attributes available from OSM, such as land use, amenities and points of interest and used these to classify all building footprints found in OSM itself and the EO-derived Google and Microsoft datasets. This resulted in three datasets of together 3.7 billion buildings. However, many of these buildings are overlapping. Therefore, a grid has been established on a resolution of roughly 100x100 meter. Each tile in the grid contains buildings exclusively from one of the three datasets, with the priorities from high to low: OSM, Google, Microsoft.
If we use the quantity of the EO-derived datasets with the elaborate OSM tagging scheme, we can make better data-informed decisions during all phases of the disaster management cycle: (1) A detailed high-resolution global building inventory leads to better risk forecasting models. (2) Knowing both location and type of buildings results in a broad understanding of the common shelter spots and better estimates of the population distribution at the time of the disaster. (3) post-disaster situations can be better analyzed in scenario-based damage assessments.

How to cite: Oostwegel, L. J. N., Evaz Zadeh, T., and Schorlemmer, D.: From Shelters to Skyscrapers: A Worldwide Exploration of Buildings and Building Types Using Volunteered Geographic Information and Earth Observation Datasets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14888, https://doi.org/10.5194/egusphere-egu24-14888, 2024.

Wildfire hazard is a prominent issue in subtropical forests as climate change and extreme drought events increase in frequency. Stand-level fuel load and forest structure are determinants of forest fire occurrence and spread. However, the current fuel management often lacks the detailed vertical fuel distribution, limiting accurate fire risk assessment and effective fuel policy implementation. In this study, backpack laser scanning (BLS) is used to estimate several 3D structural parameters, including canopy height, crown base height, canopy volume, stand density, vegetation area index (VAI) and vegetation coverage, to characterize the fuel structure characteristics and the vertical density distribution variation in different stands of subtropical forests in China. Through standard measurement by BLS point cloud data, we found that canopy height, crown base height, stand density, and VAI in the lower and middle height strata differed significantly among stand types. Comapre to vegetation coverage, LiDAR derived VAI can better show significant stratified changes in fuel density in the vertical direction among stand types. Among the stand types, conifer-broadleaf mixed forest and C. lanceolata had higher VAI in surface strata than other stand types, while P. massoniana and conifer-broadleaf mixed forests were particularly unique in having higher VAI in the lower and middle height strata, corresponding to the higher surface fuel and ladder fuel in the stand respectively. To provide more informative support for forest fuel management, BLS LiDAR data combined with other remote sensing data was advocated to facilitates the visualization of fuel density distribution and the development of fire risk assessment. 

How to cite: Li, S., Wu, Z., and Huang, C.: Revealing 3D Variations in Forest Fuel Structures in Subtropical Forests through Backpack Laser Scanning , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2597, https://doi.org/10.5194/egusphere-egu24-2597, 2024.

Drought is a climate-related slow-onset hazard event and has a significant impact on agricultural production and ecosystem health. Mainland Southeast Asia is a tropical and subtropical region of major cropland and vegetation ecosystems, and this region is increasingly vulnerable to drought-related hazards. This study assessed space-time variability of vegetation dynamics and their drought impacts using satellite-based vegetation condition time series and multi-temporal drought indices from 2000 to 2022 over the MSEA region. Specifically, we examined the vegetation dynamics and their responses to multi-temporal (short-term and long-term) drought indices in consideration of different land cover types, land-use transitions, and elevation characteristics. We also used an explanable machine learning method to quantify the impacts of multi-faceted droughts on natural and undisturbed vegetation ecosystems. Our results revealed that vegetation in the MSEA region suffered from multi-year drought-induced stress, but overally nearly 70% of the region experienced a greening trend over the study period. Most declining vegetation areas are observed in forest and rainfed croplands in Cambodia and southern Laos whereas Vietnam witnessed a greening trend. Vegetation-drought analysis indicated that recent land-use transitions and lower altitude areas had higher responses of vegetation to droughts. In natural and undisturbed ecosystems, short-term drought disturbances had the largest impact on vegetation, accounting for nearly 93% of observed variations. The largest influential factors among the examined drought indices was identified as the SPEI-3 and TCI, accounting for around 35% and 20% of the observed changes in vegetation, respectively. Notably, the SPEI-3 highlights that favorable wet conditions can result in an enhancement of vegetation condition by up to 15%, while severe drought occurrences can lead to a significant reduction of up to 20% in vegetation condition.

How to cite: Ha Van, T., Uereyen, S., and Kuenzer, C.: Space-time variability of vegetation and their multi-faceted drought impacts in the tropical and subtropical regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7988, https://doi.org/10.5194/egusphere-egu24-7988, 2024.

Frequent climate changes and intense human activities increase the risk of vegetation destruction. Spectral index-based change detection can identify vegetation destruction from multi-temporal images, providing valuable insights for vegetation management and post-disaster recovery efforts. However, we still face the challenge of the spectral diversity of vegetation destruction and the complexity of the background environment. Existing spectral indices (VIs) often struggle to accurately detect vegetation destruction in complex scenarios. These VIs focus on specific aspects of vegetation, such as leaf, canopy or water content, limiting their effectiveness in capturing vegetation dynamics. In addition, they are susceptible to background environment changes. To overcome these challenges, this study proposes a new metric called Slope Vegetation Index (SVI) using bands that are sensitive vegetation leaf, canopy, and water content (i.e., green, near-infrared (NIR), and short-wave infrared (SWIR) bands). The performance of SVI was verified by the dual time-phase difference method, and five widely used VIs were selected for detailed comparison. In addition, the performance of SVI was evaluated using PROSAIL simulation data, various vegetation change scenarios, and real vegetation destruction cases. Moreover, we assessed the applicability of SVI to other multispectral sensors. The results showed that compared with existing VIs, SVI exhibited the highest sensitivity to vegetation changes under different chlorophyll and water content conditions. In various vegetation change scenarios and vegetation destruction cases, SVI consistently had the best performance, with Producer’s Accuracy (PA), User’s Accuracy (UA), and F1 scores all exceeding 0.90. In complex scenarios, SVI could better highlight vegetation changes while suppressing background environment changes. Additionally, SVI performed well on other Landsat-8/9 images, with an F1 score exceeding 0.89. This study confirms that SVI is valid for vegetation destruction detection and has potential for large-scale and high-frequency vegetation monitoring.

How to cite: Zhao, C., Pan, Y., Ren, S., Ma, G., Gao, Y., Wu, H., and Zhu, Y.: Improving the detection accuracy of vegetation destruction events using bands sensitive to vegetation foliage, canopy and water content, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1217, https://doi.org/10.5194/egusphere-egu24-1217, 2024.

Landslides are one of the most dangerous natural hazards, causing every year fatalities, considerable damage and relevant economic losses. Early warning systems (EWS) for rainfall-induced landslides represent an useful tool for mitigating the impact of such hazard. The Umbria Regional Civil Protection Service developed a system able to take into account also the soil moisture conditions over the regional territory based on set of soil moisture-based thresholds. By identifying the soil saturation conditions before and after the rainfall event (obtained through a hydrological model), it has been seen that most of the activations occurred when the soil reached saturation. In this way, an alert can be issued when the amount of rainfall needed by the soil to reach saturation is observed. The amount of rainfall needed to reach saturation has been calculated through the definition of soil hydraulic parameters and the saturation degree at the start of the rainfall event. The obtained threshold is based on soil characteristics and it is independent by the input data (no need for recalibration or threshold adjustment). The proposed methodology is able to identify correctly most of the proposed events with a very limited amount of false alarms considering all the rainfall events occurred during the 1989-2022 period. Moreover, the use of high-resolution rainfall and soil moisture satellite-derived products has been tested for a limited time window to test whether these new sources of information can be used with benefit, even for operational purposes.

How to cite: Ciabatta, L., Galeazzi, S., Brocca, L., and Ponziani, F.: Operational soil moisture-based threshold for the assessment of landslide occurrence over Umbria region, central Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8452, https://doi.org/10.5194/egusphere-egu24-8452, 2024.

How to cite: Muraleedharan, S., Kezhkepurath Gangadhara, K., and Raja, B.: Comparison of precipitation datasets for representing flood characteristics in Malaysia , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9361, https://doi.org/10.5194/egusphere-egu24-9361, 2024.

The geologically recent Himalayas, characterized by fragile slopes and active tectonics, are inherently susceptible to natural hazards, including frequent earthquakes and associated secondary hazards. Northwest Indian states like Jammu & Kashmir, Ladakh, Himachal Pradesh, and Uttarakhand experience these events with varying intensities, posing significant risks to infrastructure and livelihoods. Among these hazards are earthquakes-induced landslides (EIL), which often modify the landscapes and affect communities. While the existing inventory of EIL provides valuable insights, limitations require further refinement. For instance, the point-based inventory by Barnard et al. (2001) for the Chamoli region mapped 56 EIL within the 226 sq. km. of the region from the epicentre. The lack of landslide geometry in the inventory restricts detailed analysis and hampers robust landslide modelling and risk assessment. To bridge this gap, this study presents an approach to transition from the conventional point-based inventory to a more comprehensive polygon-based inventory for EILs triggered by the 1999 Chamoli earthquake (Mw 6.8). Utilizing pre- and post-event Landsat-5 imagery, the study employs multi-spectral analysis techniques like Pseudo Colour Transform (PCT), Normalized Difference Vegetation Index (NDVI), and image differencing. By integrating these analyses with visual interpretation (shape of the landslide), the study accurately delineates the spatial extent and geometry of EILs in the Chamoli and Rudraprayag districts of Uttarakhand, India. By providing detailed information and spatial distribution of landslides, this approach allows for enhanced risk assessment. Future research will utilize high-resolution (5m) IRS-1C/1D panchromatic imagery to (1) identify smaller-scale landslides, (2) monitor land-use/land-cover changes within existing landslide zones for a five-year period, and (3) analyze the resulting landscape dynamics in the aftermath of the Chamoli earthquake. This analysis will shed light on the intricate relationship between land-use modifications and post-seismic landscape evolution.

How to cite: Joshi, S. and Subramanian, S. S.: The Chamoli Earthquake (1999): Transitioning from Point-Based to Polygon-Based Landslide Inventory in Uttarakhand, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9998, https://doi.org/10.5194/egusphere-egu24-9998, 2024.

Slow-moving landslides (SML; mm year⁻¹ to 100 m year⁻¹) can be a ubiquitous geomorphic process in tropical mountain landscapes. Yet, answer to crucial questions such as what landscape characteristics exert the most important control on their spatial distribution (e.g., slope, connection to rivers, climate, lithology, tectonic setting, recent deforestation, degree of anthropogenic activity, etc.), or how does their dynamic behaviour responds to landscape changes (urbanisation, deforestation, etc.), remains elusive – and is typically relying on information collected on single or a few landslide(s). Intrinsically complex, obtaining large-scale datasets with dense surface displacement measurements is even more so in the tropics, where field access is typically difficult, and rapid vegetation changes and persistent cloud cover hamper the use of satellite remote sensing. In this work, we attempt to overcome these limitations by exploiting synergies between spaceborne sensors (i.e., radar and optical) and deformation measurement techniques (i.e., interferometry and sub-pixel image correlation), to obtain multi-year datasets of the activity of SML in the western branch of the East African Rift (wEAR). Characterised by a large natural landscape and climatic diversity, the wEAR is exemplative of many tropical mountain regions, i.e., i) affected by large-scale land use changes and ii) disproportionately high landslide impacts and iii) largely overlooked in landslide research.  We collected a spatio-temporal inventory containing characterised by varying level of activity and behaviours, and located in contrasting environments. This regional-scale dataset will form the foundation for untangling the intricate influences of climate, lithology, tectonics and man-made environmental changes on the occurrence and activity of SML. By investigating their interaction with river system, we also aim at estimating how they contribute to controls on river sediment budgets, regional erosion rates, channel network evolution and flooding patterns – key for our understanding of landscape evolution, sediment budgets and geo-hydrological hazards. Overall, this work aims at moving forward our understanding of a key geomorphic process in severely under-researched types of environments subject to rapid changes. This is not only essential for a better hazard assessment, but also for comprehending how (human-induced and/or natural) environmental changes affect these landscapes and the sediment dynamics.

How to cite: Dille, A., Smets, B., Vanmaercke, M., and Dewitte, O.: Combining radar and optical satellite data to gather a comprehensive regional-scale dataset of the activity of slow-moving landslides in diverse tropical landscapes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16249, https://doi.org/10.5194/egusphere-egu24-16249, 2024.

Slow-moving landslides represent a significant hazard to local communities and infrastructure in mountainous regions worldwide. Given their challenging and often inaccessible terrain, satellite imagery holds great potential for monitoring landslides from space. In this study we use optical data from Sentinel-2 and PlanetScope satellites for tracing surface displacement across slow-moving landslides through image cross-correlation. Our work particularly focuses on the variables affecting measurement precision, including orthorectification errors and mismatches due to variable shading or seasonal snow cover. Erroneous measurements can be reduced when image pairs are carefully selected based on their view angles and sun positions. This practice, however, severely limits the number of potential image pairs, resulting in disconnected networks of displacement maps. This in turn poses problems when solving for a displacement time series using an inversion technique. Here, we evaluate the effect of network connectivity and measurement noise on inversion results using both synthetic and real-world data. Our findings support the extraction of accurate displacement estimates from remotely sensed data, advancing the detection potential of landslides and their dynamic behaviors. 

How to cite: Mueting, A. and Bookhagen, B.: Satellite-based tracking of slow-moving landslides: challenges and perspectives, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11256, https://doi.org/10.5194/egusphere-egu24-11256, 2024.

Nepal is one of the most susceptible countries to landsliding, with much of the country characterised by steep topography, annual monsoon rainfall and active tectonics. Current understanding of landslides in Nepal is predominantly based on static, catchment-scale landslide inventories or centred around data from specific events, such as the 2015 Gorkha earthquakes. Whilst static inventories provide a useful snapshot of past landslide characteristics, we cannot use these to infer how long landslides persist or how the hazard posed by landslides may evolve through time. In addition, the large number of small-scale inventories that currently exist cannot be readily compared, making it difficult to assess whether trends observed in specific catchments can be applied on a national scale. In this study, we aim to utilise advances in openly accessible remote sensing of large geospatial datasets, namely Google Earth Engine, to record the spatial and temporal evolution of landslides across the full extent of Nepal.

We build on an existing automated landslide detection algorithm in Google Earth Engine to compile a national scale landslide probability map, which is re-mapped annually. This allows us to capture changes in landslide hazard both spatially and temporally across the country. The algorithm uses NDVI differencing to identify possible new landslides. Our work seeks to refine this output by using landslide-specific information obtained from a series of existing manually mapped landslide inventories. This step includes applying spectral and object-based filters as well as using susceptibility metrics, such as topography, trained using manually mapped landslide inventories. By adding a landslide-specific filtering step, we aim to build on existing NDVI differencing approaches and improve per pixel landslide probability values. We present preliminary findings using this record of landslide hazard through time to better understand controls on slope failure evolution and persistence, to tackle questions such as whether new landslides evolve and runout from existing landslides, to consider how landslide mechanism changes through time, and how hazard translates into physical exposure through the use of metrics such as landslide proximity to roads and buildings.

How to cite: Harvey, E., Rosser, N., Kincey, M., Densmore, A., Shrestha, R., Singh Pujara, D., Dunant, A., Van Wyk de Vries, M., and Arrell, K.: Using Google Earth Engine to map landslide hazard and exposure across Nepal, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15272, https://doi.org/10.5194/egusphere-egu24-15272, 2024.

Landslides, a naturally occurring geological phenomenon, significantly threaten public safety, infrastructure, and the environment. Identifying the landslide-prone areas is essential for efficient risk mitigation and land-use planning. The main reason for concern about landslides is their potential to have disastrous effects, including property damage and casualties. Usually, landslides happen when a slope's stability fails due to natural or man-made causes like intense rain, earthquakes, etc. An area which are susceptible to landslide must be identified to prepare for disasters and take proactive mitigation measures. This study aims to use a Geographic Information System (GIS) and the weighted overlay method to create a landslip susceptibility map for the Jaintia Hills district. The main issue regarding landslide susceptibility involves three key factors: firstly, the inadequate knowledge of the geographic layout of areas vulnerable to landslides; secondly, the lack of a uniform strategy for evaluating landslide susceptibility; and thirdly, the immediate need for reliable resources to assist in land-use planning and development, to minimising the risk associated with landslides. The present study uses remote sensing and GIS techniques to address this challenge. It applies the Analytic Hierarchy Process (AHP) weighted overlay technique in GIS, incorporating eight thematic layers: elevation layer, drainage density (DD), land use/land cover (LULC), soil type, slope layer, aspect layer, geography, lineament density (LD), and geomorphology. The thematic layers are carefully selected to capture various factors influencing landslide occurrence, ensuring a robust and accurate susceptibility assessment. The AHP incorporates expert knowledge to allocate weights to each thematic layer using pairwise comparison. The overlay process combines these layers to generate a comprehensive map reflecting the potential zones of landslides in the Jaintia Hills district. The results reveal a detailed landslide susceptibility map for the Jaintia districts, highlighting areas prone to landslides. It reveals that approximately 11634 hectares are in the high landslide occurrence zone, and 52849 hectares are in the medium zone. The map compared with locations where the landslides occurred in the past and found that most of the points lie in the high-prone zone for landslides, which shows the significant accuracy of the prepared map. However, prepared map will provide valuable insights for land-use planning and risk mitigation strategies, aiding decision-makers in developing sustainable policies to safeguard both human lives and the environment in the Jaintia Hills region.

Keywords – Landslide Susceptibility, GIS, Remote sensing, Natural Hazards, AHP weighted overlay method

How to cite: Kumar, S., Choudhary, M. K., Thomas, T., and Umare, S.: Assessment of Landslide Susceptibility Map in the Jaintia Hill District Using Remote Sensing and GIS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14506, https://doi.org/10.5194/egusphere-egu24-14506, 2024.

This work aims to present and to disseminate the ALTRUIST (totAL variomeTry foR tsUnamI hazard eStimaTion) project.

ALTRUIST is one of the eight projects selected worldwide for the Joint Call by the AXA Research Fund and the Intergovernmental Oceanographic Commission of UNESCO (IOC-UNESCO) on Coastal Livelihood within the framework of the United Nations Ocean Decade [1].

ALTRUIST’s main goal is to improve the reliability and accuracy of real-time tsunami warning systems leveraging the recent findings of Global Navigation Satellite System (GNSS) remote sensing. GNSS remote sensing employs the GNSS signal to infer information about atmosphere, oceans and ground.

In detail, ALTRUIST leverages the Total Variometric Approach (TVA) methodology [2]. GNSS Variometry is based on single time differences of suitable linear combinations of GNSS carrier-phase, allowing a GNSS receiver to provide valuable real-time information in a standalone operative mode. TVA jointly employs VADASE (Variometric Approach for Displacement Analysis Stand-alone Engine) and VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithms.

TVA allows for the simultaneous and real-time estimation of ground shaking, co-seismic displacements and ionospheric Total Electron Content (TEC) disturbances, using the same real-time GNSS data stream. The joint use of the information from the ground and ionosphere can be really beneficial.  Coseismic displacements can be used to retrieve important parameters about the seismic source and the seafloor displacement. The ionospheric observation, can, in turn, give information about the seismic source and, to some extents, about the ground motion.

These data are, hence, crucial in natural hazards management and can support traditional instruments to improve the quick estimation of the tsunami hazard.

ALTRUIST is currently being tested within the GNSS network of the Observatoire Volcanologique et Sismologique de Guadeloupe of Institut de Physique du Globe de Paris (IPGP), in the Caribbeans.

In detail, ALTRUIST is built to be a scalable and modular architecture that provides the first joint ground and ionosphere real-time solutions. In detail, it provides the real-time visualization on a dashboard and allows access to information and historical solutions through API.

These attributes embody a key point of the ALTRUIST project: sharing historical solutions foster discussion within the scientific community, whereas the interactive dashboard empowers local communities to access additional information on natural hazards.

Finally, ALTRUIST framework is versatile and can be easily applied to the monitoring of any kind of natural hazards events such as volcanic eruptions and explosions impacting ground and ionosphere geosphere.

This is the first feasibility demonstration of the ALTRUIST real-time capabilities.

References

[1] https://axa-research.org/funded-projects/climate-environment/mitigating-tsunamis-threats-and-destructive-impacts-through-enhanced-navigation-satellite-system

[2] Ravanelli, Michela, et al. "GNSS total variometric approach: first demonstration of a tool for real-time tsunami genesis estimation." Scientific Reports 11.1 (2021): 3114

How to cite: Ravanelli, M., Astafyeva, E., Sakic, P., Baucry, R., and Crespi, M.: An insight into ALTRUIST: how to use GNSS Variometry for Natural Hazards Detecting and Monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12315, https://doi.org/10.5194/egusphere-egu24-12315, 2024.

At 07:10:39 (GMT ) on 1st January 2024, a devastating earthquake struck the west coast of Honshu, Japan. According to the Global Centroid Moment Tensor (CMT) Project, the magnitude and depth of the earthquake are respectively 7.5 Mw and 12 km; and the epicentre is located at Noto, Ishikawa Prefecture, Honshu (latitude: 37.490°N, longitude: 137.170°E). Two weeks after the earthquake (up to 15th January), the death toll had risen to 221. Besides heavy casualties, post-seismic ground deformation caused by the earthquake is intensively reported. In order to study the ground deformation caused by this large and shallow earthquake, the satellite geodetic technique, namely the synthetic aperture radar interferometry or interferometric
synthetic aperture radar, InSAR, will be adopted in this study. Moreover, Sentinel 1 Level 1 SLC product from the Copernicus Program will be utilized. An InSAR processing system based on the Generic Mapping Tools (GMT), or GMTSAR, for short, is used for data processing. Preliminary pre-seismic, co-seismic, and post-seismic results derived from GMTSAR will be shown together with the outcomes from the Sentinel Application Platform (SNAP) if applicable.

How to cite: Ng, S.-M.: Preliminary InSAR Results of t he 2024 Noto Japan Earthquake, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22244, https://doi.org/10.5194/egusphere-egu24-22244, 2024.

Volcanic eruptions are a severe threat to approximately 800 million people living around 100 km from a volcano in 86 countries. For the eruptions affecting densely populated areas it is necessary to guarantee, during the emergency, the safety of the population, which requires a precise and reliable monitoring of the evolution of the volcano and the associated geological hazards.

This work shows the application of monitoring products during an emergency. Under those circumstances some requirements like the quick availability of the satellite data, the availability of experts to generate the needed products or the accessibility of the results for the decision-making authorities are of crucial importance.

During La Palma eruption in 2021, SAR data from 4 different satellites were used to generate three SAR-derived products to monitor the evolution of the morphology of the volcanic building, the extension of lava flows and ground deformation evolution in time. The availability of data from various satellites with different characteristics allows for their comparison, analyzing and identifying which is the optimal satellite and/or SAR dataset to generate each result.

This work is part of the Spanish Grant SARAI, PID2020-116540RB-C21, funded by MCIN/AEI/ 10.13039/501100011033.

How to cite: Ezquerro Martín, P., Brú Cruz, G., Galindo, I., Monserrat, O., López-Davalillo, J. C., Sánchez, N., Montoya, I., Palamà, R., Mateos, R. M., Pérez-López, R., González-Alonso, E., Grandin, R., Guardiola-Albert, C., López-Vinielles, J., Fenández-Merodo, J. A., Herrera, G., and Béjar-Pizarro, M.: Monitoring of La Palma 2021 volcanic eruption using Interferometric and Amplitude SAR data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17922, https://doi.org/10.5194/egusphere-egu24-17922, 2024.

The northern-east part of Italy is an important wine production area, which exports high-quality wine worldwide. The territory boasts of areas that are under the protection of FAO and UNESCO, thanks to the unique relationship between landscape and agriculture. In the last two decades, extreme weather events created criticalities, especially in steep slope territories.

Future climate trends could influence the frequency of heatwaves, drought and intense rainfalls, impacting on vineyards. Therefore, identifying trends helps to understand the risks to which vineyards are subjected.

The purpose of this study is to identify extreme weather trends in the area located in Veneto and Friuli-Venezia Giulia and discover areas that are more affected by increasing and decreasing trends. The workflow started with analyzing historical climate data in different datasets in Google Earth Engine. We implemented the Land Surface Temperature dataset of the Modis satellite for surface temperatures and the CHIRPS Daily dataset for historical precipitation data. Additionally, for 2-meter temperatures and cumulative rainfall, we considered weather station data.

Based on the initial findings, the summer of 2022 reported strong heatwaves and drought. Certain areas showed an increase in surface temperature of more than +20%, compared to the mean summer temperature during the 2000-2010 period. For precipitations, otherwise, the central-east part of the region reported a negative anomaly of around -50 % compared to the summer average of the last 30 years.

Future research activities will focus on intense rainfall and more about the frequency, duration and distribution of heatwaves and drought to detect future scenarios.

The results of this research may inspire the development of sustainable initiatives focused on improving water management, aiming to reduce run-off during intense precipitations and enhancing water storage during drought seasons, as well as on supporting the insurance companies to provide tailored risk coverages against increasing climatic risks in vineyard farms.

How to cite: Baldan, V., Straffelini, E., D'Agostino, V., and Tarolli, P.: Extreme weather threatening vineyards of North-East Italy: multi-temporal satellite analysis in Google Earth Engine , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12538, https://doi.org/10.5194/egusphere-egu24-12538, 2024.

The work explores the use of Synthetic Aperture Radar (SAR) and optical sensors on board the Sentinel-1 and Sentinel-2 satellites for mapping and monitoring the Earth's surface in response to flood events. The choice of SAR proves advantageous due to its ability to penetrate clouds, ensuring continuity in acquisitions even in adverse weather conditions.  

The Google Earth Engine (GEE) platform, which provides the opportunity to customize algorithms and scripts, is used to perform the analysis. The goal is to develop an integrated methodology for early warning and impact mitigation. Two case studies are here presented: the flood that affected Ravenna in May 2023 and  the Apollo hurricane in Sicily in 2021. The results confirm the effectiveness of the proposed approach in monitoring and discriminating water-covered surfaces. Despite challenges in data processing and calibration, the approach proves to be a valuable monitoring tool. The integration of SAR and optical data provides a comprehensive view of the flood situation, confirmed by the convergence of information from both satellites and news services.

How to cite: Giannone, F., Guerrisi, E., Scaglione, M., and Di Francesco, S.: SAR and Optical Remote Sensing for Flood Mapping and Monitoring through GEE, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19476, https://doi.org/10.5194/egusphere-egu24-19476, 2024.

In the Himalayas, the melting of glaciers leads to the creation of proglacial lakes. The expansion of these glacial lakes poses a significant risk of glacial lake outburst floods (GLOFs), a serious geomorphological hazard involving the sudden release of water from the glacial lake. The Teesta basin in the Sikkim Himalaya is home to numerous glacial lakes in the high-altitude glacierized region, including one of the largest and fastest-growing, South Lhonak Lake (SLL). The SLL is a moraine-dammed glacial lake in Sikkim's northern district (27˚54.741’ N and 88 ˚11.857’E). Situated at an elevation of 5200 msl (mean sea level), the lake is east-west elongated and located in the tongue of the South Lhonak glacier. The date of October 4, 2023, marks a significant event in the state of Sikkim. Investigation reports reveal a severe outburst flood triggered due to the breach of the moraine dam embankment around SLL. This wreaked havoc in the northeastern state, causing substantial damage and loss, especially along the Lachen and Chungthang regions downstream. The present study attempts to investigate the geometric changes in the lake between 2015 and 2023 using C-band Sentinel-1 Synthetic Aperture Radar (SAR) datasets. The glacial region is studied using the land surface temperature (LST) into a moisture index obtained from Landsat-8 Operational Land Imager (OLI) imagery. The lake’s geometric information (length and areal coverage) is acquired through a coupled automated manual delineation approach on the Google Earth engine platform. Analysing the backscatter information of Sentinel-1 datasets shows that the stretch of the lake has substantially increased by 575m (reaching up to 2.946 km) in 2023 before the outburst. Its average annual rate of expansion is observed to be 0.05 km² in the last 8 years (2015 to 2022). The lake volume is calculated using the well-established empirical equation for South Lhonak Lake using lake area. The average volume of water in the lake from 2015 till 2023 (before the GLOF) is observed to be 102.4 million m³, which is 55.59% more than the lake volume in 2014-2016 (65.8 million m³). After the event, there is approximately a 79.44% decrease in the lake volume, a 59% decrease in the lake area, and a 48.40% decrease in lake length. The largest change in the area of the lake is observed between 2020 and 2021. Apart from the geometric changes, the moisture index has shown a monotonous increment since 2018, suggesting enhanced melting of the South Lhonak glacier, which can be attributed as one of the important parameters for the formation of such a dangerous glacial lake. The application of geospatial technology in this research can offer valuable insights into the changes occurring in glacial systems in the Himalayas. Implementing such investigative protocols is crucial for comprehending the development patterns of moraine-dammed glacial lakes, thereby aiding in the formulation of effective mitigation strategies.

How to cite: Singh, M. K., Mondal, S. K., and Bharti, R.: South Lhonak Glacial System: Cascade Investigation Using Satellite Remote Sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18788, https://doi.org/10.5194/egusphere-egu24-18788, 2024.

With the increase of urbanization, the urban heat island phenomenon is becoming more noticeable and concerning. This effect occurs when the temperatures are higher in urbanized areas than in suburban and rural areas. The aim of this study is to analyze the influence of different land cover types on the land surface temperature (LST) and the surface urban heat island (SUHI) effect. The study was conducted on imagery acquired in September by Landsat 8 OLI TIRS satellite. The selected area was the city of Lucknow (India) and its surroundings. The climate is subtropical, seasons are clearly distinguishable - winters are cold, summers are hot and dry. Land cover types were determined using supervised classification and Normalized Difference Vegetation Index. Land surface temperature was calculated. The influence of different land cover on LST and SUHI phenomenon was analyzed by using visual analysis, comparing average temperatures of land cover classes, creating temperature profile and calculating Urban Thermal Field Variance Index to analyze the intensity of SUHI.

The type of land cover affects land surface temperature therefore has an impact on the SUHI phenomenon. Build-up areas show much higher land surface temperature than non-urban, vegetated areas. The spatial distribution of these forms of land cover, i.e. the cumulative built-up area forming the city and the green areas around the city, combined with the with their characteristic surface temperatures, result in an surface urban heat island effect. Average temperatures of non-natural surfaces are around 5°C higher than average temperatures of natural surfaces. UTFVI indicates that the intensity of SUHI phenomenon is the highest in build-up and bare soil land cover classes, and the lowest for vegetation and water areas. The intensity also varies within a single class, in urban areas UTVI is higher in the center and decreases toward the vegetation class. The LST values and temperature profile indicates that the higher temperature of non-natural land cover forms affects not only the increased temperature of the surface on which they are located but also the areas in the close neighborhood. Temperature within the same land cover class of agricultural areas and meadows areas is higher when measured in the vicinity of the urban class.

How to cite: Szrek, A.: Influence of different land cover types on land surface temperature - case study of the Lucknow city in India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15756, https://doi.org/10.5194/egusphere-egu24-15756, 2024.

On-orbit processing is an important way in the real-time remote sensing detection of earth's surface anomalies (ESSA). However, the existing methods cannot comprehensively utilize multidimensional remote sensing characteristics to detect multi-type ESSA in a unified manner. Meanwhile, it is also difficult to realize the comprehensive utilization of multidimensional remote sensing characteristics under the condition of limited storage and computing resources on satellites. Therefore, this study proposed a remote sensing method for detecting multi-type ESSA on orbit based on multidimensional feature space. The proposed method first selected the remote sensing characteristics reflecting the basic earth's surface elements to construct a multidimensional feature space and generated two comprehensive remote sensing characteristics. Then, the optimized storage content of the two comprehensive remote sensing characteristics were used to build a prior knowledge base reflecting the normal conditions of the earth's surfaces. Finally, through comparing the prior knowledge base and the real-time acquired data, this study completed the ESSA detection. The validation results indicated that the proposed method can effectively detect multi-type ESSA with a accuracy of over 85%. Meanwhile, the proposed method simplified the large and complex ESSA remote sensing characteristic system, which would be conducive to greatly reducing the complexity of ESSA detection methods and increasing the possibility of on-orbit processing.

How to cite: Wei, H., Jia, K., and Wang, Q.: A remote sensing detection method of the earth's surface anomalies based on multi-dimensional feature space, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1626, https://doi.org/10.5194/egusphere-egu24-1626, 2024.