NH6.1

EDI
Remote sensing big data analysis and applications in geosciences

Remote sensing techniques, such as radar (e.g., synthetic aperture radar - SAR), optical, Lidar and hyperspectral imagery, together with hydroclimatic, geological, and geophysical data, as well as in-situ observations, have been widely employed for monitoring, and responding to natural and anthropogenic hazards and assessing environmental resources. Especially with the unprecedented spatio-temporal resolution and the rapid accumulation of remote sensing data collections from various spaceborne and airborne missions, we have much more opportunities to exploit hazard- and environmental- related signals, to classify the associated spatio-temporal surface changes such as deformations and landform alterations, and to interpret the primary and secondary driving mechanisms. Yet, when archiving, processing, and analyzing abundant remote sensing data, the ad hoc artificial intelligence (AI), like machine/deep learning and computer vision, is urgently required.
In this session, we welcome contributions that focus on new AI-based algorithms to retrieve remote sensing products related to environmental resources and hazards in an accurate, automated, and efficient framework. We particularly welcome contributions for applications in (1) mining, oil/gas production, fluid injection/extraction, civil infrastructure, sinkholes, land degradation, peatlands, glaciers, permafrost, and coastal subsidence; (2) emergency response based on remote sensing data to landslides, floods, winter storms, wildfires, pandemics, earthquakes, and volcanoes; and (3) mathematical and physical modeling of the remote sensing products for a better understanding on the surface and subsurface processes.

Public information:

"Enter Zoom Meeting" button for the session will show up 8:15 am (CEST), 15 minutes before the start time. Our solicited speaker Dr. Sigrid Roessner is unable to participate in EGU. Instead, Prof. Ramon Hanssen from Delft University of Technology will give us a talk entitled “InSAR time series ambiguity resolution using recurrent neural networks” to start our session today. Looking forward to "seeing" you :-)

Co-organized by ESSI1/GI3
Convener: Ling ChangECSECS | Co-conveners: Xie HuECSECS, Mahdi Motagh, Ramon Hanssen, X. X. Zhu
Presentations
| Fri, 27 May, 08:30–11:50 (CEST)
 
Room 1.31/32
Public information:

"Enter Zoom Meeting" button for the session will show up 8:15 am (CEST), 15 minutes before the start time. Our solicited speaker Dr. Sigrid Roessner is unable to participate in EGU. Instead, Prof. Ramon Hanssen from Delft University of Technology will give us a talk entitled “InSAR time series ambiguity resolution using recurrent neural networks” to start our session today. Looking forward to "seeing" you :-)

Presentations: Fri, 27 May | Room 1.31/32

Chairpersons: Mahdi Motagh, Ling Chang
08:30–08:35
08:35–08:45
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EGU22-12269
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solicited
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Highlight
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Presentation form not yet defined
Sigrid Roessner, Robert Behling, Mahmud Haghshenas Haghighi, and Magdalena Vassileva

The Earth’s surface hosts a large variety of human habitats being subject to the simultaneous influence of a wide range of dynamic processes. The resulting dynamics are mainly driven by a complex interplay between geodynamic and hydrometeorological factors in combination with manifold human-induced land use changes and related impacts. The resulting effects on the Earth’s surface pose major threats to the population in these areas, especially under the conditions of increasing population pressure and further exploitation of new and remote regions accompanied by ongoing climate changes. This situation leads to significant changes in the type and dimension of natural hazards that have not yet been observed in the past in many of the affected regions.

This situation has been leading to an increasing demand for systematic and regular large area process monitoring which cannot be achieved by ground based observations alone. In this context, the potential of satellite remote sensing has already been investigated for a longer period of time as an approach for assessing dynamic processes on the Earth’s surface for large areas at different spatial and temporal scales. However, until recently these attempts have been largely hampered by the limited availability of suitable satellite remote sensing data at a global scale. During the last years new globally available satellite remote sensing data sources of high spatial and temporal resolution (e.g., Sentinels and Planet) have been increasing this potential to a large extent.

During the last decade, we have been pursuing extensive methodological developments in remote sensing based time series analysis including optical and radar observations with the goal of performing large area and at the same time detailed spatiotemporal analysis of natural hazard prone regions affected by a variety of processes, such as landslides, floods and subsidence. Our methodological developments include among others large-area automated post-failure landslide detection and mapping as well as assessment of the kinematics of pre- and post-failure slope deformation.  Our combined optical and radar remote sensing approaches aim at an improved understanding of spatiotemporal dynamics and complexities related to the evolution of these hazardous processes at different spatial and temporal scales.  We have been developing and applying our methods in a large variety of natural and societal contexts focusing on Central Asia, China and Germany.

We will present selected methodological approaches and results for a variety of hazardous surfaces processes investigated by satellite remote sensing based time series analysis. In this we will focus on the potential of our approaches for supporting the needs and requirements imposed by the disaster management cycle representing a widely used conceptual approach for disaster risk reduction and management including, rapid response, long-term preparedness and early warning.

How to cite: Roessner, S., Behling, R., Haghshenas Haghighi, M., and Vassileva, M.: Time series analysis using global satellite remote sensing data archives for multi-temporal characterization of hazardous surface processes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12269, https://doi.org/10.5194/egusphere-egu22-12269, 2022.

08:45–08:51
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EGU22-1082
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ECS
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Highlight
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On-site presentation
Richa Marwaha and Matthew Saunders

Peatlands cover ~3% of the global land area and are under threat from a land-use change such as drainage for peat extraction, and conversion to agriculture and commercial forestry. Historically, peatlands in Ireland have been used for industrial peat extraction and domestic turf cutting. One such example is Cavemount bog, County Offaly, Ireland a former raised bog where peat extraction started in the 1970s and ceased in 2015. After 2015,  a programme of rehabilitation commenced by rewetting the site to raise water levels and to promote the establishment of wetland habitats. Some of the key species associated with the vegetation communities that have been developing across the site include Betula pubescens, Calluna vulgaris, Eriophorum angustifolium, Typha latifolia and Phragmites australis.

To monitor the progress of the colonisation of natural vegetation as part of the rehabilitation plan, reliable habitat maps are required. Google Earth Engine (GEE) is a cloud computing platform where satellite images can be processed to obtain cloud-free composite images. GEE was used to develop an automated approach to map the habitats at Cavemount using multispectral satellite imagery (Sentinel-2) and a machine-learning model i.e. random forest classifier. In this study 9 habitat classes were used which included bare peat, coniferous trees, heather, heather and scrub, open water, pioneer open cutaway habitats, scrub pioneer open cutaway habitats, wetland and mosaic of wetland and scrub. Cloud-free composites for the growing season (May to September) using satellite imagery from 2018-2021 were used to get spectral indices such as NDVI (normalised difference vegetation index), NDWI (normalised difference water index), mNDWI (modified normalised difference water index), red-edge vegetation index, EVI (enhanced vegetation index) and BSI (bare soil index). To extract open water, a seasonal composite of mNDWI was used which could differentiate water from bare peat. The seasonal composite of mNDWI was also used to monitor flooding over winter periods due to increased rainfall and was compared with summer conditions. These indices along with 10 spectral bands (10-20 m resolution) were used as an input to a random forest model, and a yearly habitat map from 2018 to 2021 was developed. The overall accuracy for the testing data from 2018, 2019, 2020 and 2021 was 87.42%, 86.81%, 87.16% and 87.50% and kappa coefficient was 0.81, 0.80, 0.81 and 0.81 respectively. Over time, the former peat extraction area showed a transformation from bare peat to a mosaic of wetland vegetation. This methodology will provide a useful tool for the long-term monitoring of the habitats at this site and to evaluate the effect of rehabilitation on the ecological composition of the site. The final habitat map will also be integrated with the eddy covariance data from the site to provide further insight into the carbon and greenhouse gas dynamics of each habitat in the future.   

How to cite: Marwaha, R. and Saunders, M.: Monitoring of rehabilitation of a raised bog in Ireland using a machine learning model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1082, https://doi.org/10.5194/egusphere-egu22-1082, 2022.

08:51–08:57
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EGU22-2114
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ECS
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Virtual presentation
Xiao Yu and Xie Hu

NOAA reported that the sea level has risen by 203-228 mm since 1880 and the rates accelerated to 3.556 mm/year during 2006-2015. Coastal regions, home to about half of the world’s population (~3 billion), are subject to erosion from wind and waves and subsidence from natural compaction and artificial explication of subsurface resources, and are at high risks of floods from accidental storms and inundations from prolonged sea level rise. The vertical land motion (VLM) directly determines the relative sea level rise. To be specific, locally upward VLM can help alleviate the risks while locally downward VLM may hasten the arrival of inundation. Therefore, monitoring coastal VLM is fundamental in coastal resilience and hazard mitigation. 

One 12-floor building, Champlain Towers South, in the Miami suburb of Surfside collapsed catastrophically and claimed 98 lives on June 24th, 2021. No confident conclusion has been drawn on the cause of the collapse, but it might be related to multiple processes from the ground floor pool deck instability, concrete damage, and land subsidence.

Subsidence has been noted in populous Surfside since 1990s. However, we still lack a detailed mapping of the contemporary coastal subsidence. Here we focus on multi-source Synthetic Aperture Radar (SAR) datasets from C-band Sentinel-1 and X-band TerraSAR-X satellite imagery.

We use the time-series SAR interferometry of ascending Sentinel-1 path 48 to extract the VLM from 2015 to 2021. A comparatively stable GPS station ZMA1 obtained from the Nevada Geodetic Laboratory acts as the reference site to calibrate InSAR results. Long-wavelength atmospheric phase screen and orbit errors are approximated by the low-order polynomial fitting. The average subsidence rates derived from stacking can help reduce the temporarily high-frequency noise. A comparison with the GPS network solution can help verify InSAR measurements. Beyond that, we will also rely on high-resolution X-band TerraSAR-X data (Path 36, strip_014) to elaborate VLM details in the building clusters. Beyond that, NOAA reported that the relative sea level increase in Florida is 2.97 mm/year from 1931 to 2020, i.e., >0.3 m in one century. The 2019 Unified Sea Level Rise Projection in Southeast Florida predicted that the sea level in 2024 will rise by 254 to 432 mm in Florida compared to the level in 2000. We aim to extract the high-accuracy VLM to provide scientific evidence for more safe urban planning and effective adaptation strategies in coastal cities, for an ultimate goal of coastal resilience during global climate change.

How to cite: Yu, X. and Hu, X.: Multi-annual InSAR solution of vertical land motion in 2021 lethal building collapse site in Miami , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2114, https://doi.org/10.5194/egusphere-egu22-2114, 2022.

08:57–09:03
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EGU22-7803
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ECS
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On-site presentation
Teo Beker, Homa Ansari, Sina Montazeri, and Qian Song

TecVolSA (Tectonics and Volcanoes in South America) is a project with a goal of developing intelligent Earth Observation (EO) data processing and exploitation for monitoring various geophysical processes in central south American Andes. Large amount of Sentinel-1 data over the period of about 5 years has been processed using mixed Permanent Scatterer and Distributed Scatterer (PS/DS) approaches. The received products are velocity maps with InSAR relative error in the order of 1 mm/yr on a large scale (>100km). The second milestone of the project was automatic extraction of information from the data. In this work, the focus is on detecting volcanic deformations. Since the real data prepared in such manner is limited, to train a deep learning model for detection of volcanic deformations, a synthetic training set is used. Models are trained from scratch and InceptionResNet v2 was selected for further experiments as it was found to give best performance among the tested models. The explainable AI (XAI) techniques were used to understand and analyze the confidence of the model and to understand how to improve it. The models trained on synthetic training set underperformed on real test set. Using GradCAM technique, it was identified that slope induced signal and salt lake deformations were mistakenly identified as volcanic deformations. These patterns are difficult to simulate and were not contained in synthetic training set. Bridging this distribution gap was performed using hybrid synthetic-real fine-tuning set, consisting of the real slope induced signal data and synthetic volcanic data. Additionally, false positive rate of the model is reduced using low-pass spatial filtering of the real test set, and finally by adjustments of the temporal baseline received from a sensitivity analysis. The model successfully detected all 10 deforming volcanoes in the region, ranging from 0.4 - 1.8 cm/yr in deformation.

How to cite: Beker, T., Ansari, H., Montazeri, S., and Song, Q.: Detection of Volcanic Deformations in InSAR Velocity Maps - a contribution to TecVolSA project, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7803, https://doi.org/10.5194/egusphere-egu22-7803, 2022.

09:03–09:09
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EGU22-3291
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ECS
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Virtual presentation
Wei Tang, Zhiqiang Gong, Jinbao Jiang, and Zhicai Li

Liaohe River Delta (LRD) is one of the major centers for hydrocarbon production, agriculture, and fisheries in Northeastern China. Liaohe Oilfield, located in the deltaic region, is China’s third-largest oilfield with an annual production capacity of 10 million tons of crude oil and 800 million m3 of natural gas. Since its operation in 1970, Liaohe Oilfield had produced more than 480 million tons of crude oil and 88 billion m3 of natural gas by the end of 2019.

Pore pressure drawdown due to oil/gas production has resulted in reservoir compaction and surface subsidence above the reservoir. This compaction and subsidence can cause significant damages to production and surface facilities. Main concerns are related to low-lying coastal areas in the context of eustatic sea-level rise (SLR), where land subsidence contributes to relative SLR and exacerbates flooding hazards. In addition, regional and local land subsidence have combined with global SLR to cause wetland loss in the LRD.

Our main aim in this study is to investigate time-dependent land subsidence induced by reservoir depletion in LRD, by analyzing Synthetic Aperture Radar (SAR) images from Sentinel-1 satellite. We retrieved vertical land subsidence and horizontal displacements through processing and merging multi-geometry images from two ascending and two descending tracks covering the area over the 2017 to 2021 time span. We observed significant local subsidence features in several active production oilfields, and the areal extent of subsidence is basically consistent with the spatial extent of production wells. The most prominent subsidence is occurring in the Shuguang oilfield. Due to reservoir depletion, it forms a land subsidence bowl in an elliptical shape with a major axis of ~6.3 km and a minor axis of ~3.2 km, and the maximum subsidence rate is exceeding 230 mm/yr. Because of the large depth D relative to the areal extent L, that is, a relatively small ratio L/D, the displacement field caused by oil production is three-dimensional. An inward, symmetrical, east-west horizontal movement was observed around the subsidence bowl in Shuguang oilfield, with an average eastward movement rate of ~40 mm/yr and an average westward rate of ~30 mm/yr. This three-dimensional deformation is well reproduced by a cylindrical reservoir compaction/subsidence model.

In September 2021, a storm surge accompanied by heavy rainfall caused water levels to rise by 50-130 cm in Liaodong Bay, resulting in extreme flooding in oilfields along the coast. The most severe flooding hazard was occurring in the Shuguang oilfield with the highest land subsidence rate. Our new InSAR-derived surface subsidence associated with the oilfield operations raises the question of the potential impact of land subsidence on the flood severity. This work highlights the importance of incorporating reservoir depletion-induced subsidence into flood management to ensure the security of the oil and gas industry along the coastal regions.

How to cite: Tang, W., Gong, Z., Jiang, J., and Li, Z.: Land subsidence in Liaohe River Delta, China due to oil and gas withdrawal, measured from multi-geometry InSAR data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3291, https://doi.org/10.5194/egusphere-egu22-3291, 2022.

09:09–09:15
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EGU22-4618
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ECS
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On-site presentation
Anurag Kulshrestha, Ling Chang, and Alfred Stein

Recently, we have shown that sinkholes can be characterized at an early stage by precursory deformation patterns from InSAR time series [1]. These patterns are often related to sudden changes in deformations or deformation velocities. With such a priori information, accurate deformation modelling and early detection of precursory patterns is feasible. It is still a challenge, however, to scale up methods for classifying larger numbers of sinkholes over large areas that may contain tens of thousands of InSAR observations. To address this, we explore the use of Long Short-Term Memory (LSTM) Networks to classify multi-temporal datasets by learning unique and distinguishable hidden patterns in the deformation time series samples.

We propose to design a two-layered Bi-directional LSTM model and use a supervised classifier to train the model for classifying sinkhole-related anomalous deformation patterns and non-anomalous deformation time series. Samples for linear, Heaviside, and Breakpoint deformation classes are extracted by applying Multiple Hypothesis Testing (MHT) [2] on deformation time series and are used to compile the training dataset. These samples are randomly divided into a training set and a testing set, and associated with a target label using one-hot encoding method. Hyperparameters of the model are tuned over a broad range of commonly used values. Using categorical cross-entropy as the loss function the model is optimized using the Adam optimizer.

We tested our method on an oil extraction field in Wink, Texas, USA, where sinkholes have been continuously evolving since 1980 and a recent sinkhole occurred in mid-2015. We used 52 Sentinel-1 SAR data acquired between 2015 and 2017. The results show that the supervised LSTM model classifies linear deformation samples with an accuracy of ~98%. The accuracy for classifying Heaviside and Breakpoint classes is ~75% at the most. Temporal periodicity was observed in the occurrence of anomalies, which may be related to the frequency of oil extraction and water injection events. Heaviside anomalies were observed to be clustered in space, with a higher density close to the sinkhole location. Breakpoint class anomalies were much more uniformly distributed. Close to the sinkhole spot, we found that two InSAR measurement points were classified into the Breakpoint class, and have considerable changes in deformation velocities (~60o velocity-change angle) shortly before the occurrence of this sinkhole. It is likely associated with the sinkhole-related precursory patterns. Through this study we conclude that our supervised LSTM is an effective classification method to identify anomalies in time. The classification map in terms of InSAR deformation temporal behavior can be used to identify areas which are vulnerable to sinkhole occurrence in the future and require further investigation. In the future, we plan to further develop methods to increase the classification accuracy of anomalous classes.

References:

[1] Anurag Kulshrestha, Ling Chang, and Alfred Stein. Sinkhole Scanner: A New Method to Detect Sinkhole-related Spatio-temporal Patterns in InSAR Deformation Time Series. Remote Sensing, 13(15), 2021.

[2] Ling Chang and Ramon F. Hanssen. A Probabilistic Approach for InSAR Time-Series Postprocessing. IEEE Transactions on Geoscience and Remote Sensing, 54(1):421–430, 2016.

How to cite: Kulshrestha, A., Chang, L., and Stein, A.: Supervised LSTM Modelling for Classification of Sinkhole-related Anomalous InSAR Deformation Time Series, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4618, https://doi.org/10.5194/egusphere-egu22-4618, 2022.

09:15–09:21
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EGU22-4800
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ECS
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Virtual presentation
Laura Pedretti, Massimiliano Bordoni, Valerio Vivaldi, Silvia Figini, Matteo Parnigoni, Alessandra Grossi, Luca Lanteri, Mauro Tararbra, Nicoletta Negro, and Claudia Meisina

The availability of Sentinel-1 dataset with high-temporal resolution of measures (6-12 days) and long time period, can be considered as a “near-real-time monitoring” since it provides a sampling frequency enough to track the evolution of some ground deformations (e.g. landslides, subsidence) if compared to other sensors. However, the analysis and elaborations of such huge dataset, covering large areas, could be tricky and time-consuming without a first exploitation to identify areas of potential interest for significant ground deformations. The A-InSAR Time Series (TS) interpretation is advantageous to understand the relation between ground movement processes and triggering factors (snow, heavy rainfall), both in areas where it is possible to compare A-InSAR TS with in-situ monitoring instruments, and in areas where in situ instruments are scarce or absent. Exploiting the availability of Sentinel-1 data, this work aims to develop a new methodology ("ONtheMOVE" - InterpolatiON of SAR Time series for the dEtection of ground deforMatiOneVEnts) to classify the trend of TS (uncorrelated, linear, non-linear); to identify breaks in non-linear TS; to provide the descriptive parameters (beginning and end of the break, length in days, cumulative displacement, the average rate of displacement) to characterize the magnitude and timing of changes in ground motion. The methodology has been tested on two Sentinel-1 datasets available from 2014 to 2020 in Piemonte region, in northwestern Italy, an area prone to slow-moving slope instabilities. The methodology can be applied to any type of satellite datasets characterized by low or high-temporal resolution of measures, and it can be tested in any areas to identify any ground instability (slow-moving landslides, subsidence) at local or regional scale. The thresholds used for event detection should be calibrated according to geological and geomorphological processes and characteristics of a specific site or regional site. This innovative methodology provides a supporting and integrated tool with conventional methods for planning and management of the area, furnishing a further validation of the real kinematic behaviour of ground movement processes of each test-site and where it is necessary doing further investigation. In addition, elaboration applied to Sentinel-1 data is helpful both for back analysis and for near real-time monitoring of the territory as regards the characterization and mapping of the kinematics of the ground instabilities, the assessment of susceptibility, hazard and risk.

How to cite: Pedretti, L., Bordoni, M., Vivaldi, V., Figini, S., Parnigoni, M., Grossi, A., Lanteri, L., Tararbra, M., Negro, N., and Meisina, C.: A methodology for the analysis of InSAR Time Series for the detection of ground deformation events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4800, https://doi.org/10.5194/egusphere-egu22-4800, 2022.

09:21–09:27
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EGU22-6822
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Virtual presentation
Maoyang Bai

Abstract: Accurate spatial extent changes in urban built-up areas are essential for detecting urbanization, analyzing the drivers of urban development and the impact of urbanization on the environment. In recent years, nighttime light images have been widely used for urban built-up areas extraction, but traditional extraction methods need to be improved in terms of accuracy and automation. In this experiment, a U-Net model was built and trained with the NPP-VIIRS and MOD13A1 data in 2020. We used the optimal tuning model to inverse the spatial extent of built-up areas in China from 2012 to 2021. Through this model, we analyzed the changing trend of built-up areas in China from 2012 to 2021. The results showed that U-Net outperformed random forest (RF) and support vector machine (SVM), with an overall model accuracy (OA) of 0.9969 and mIOU of 0.7342. Built-up areas growth rate is higher in the south and northwest, but the largest growth areas are still concentrated in the east and southeast, which is consistent with China's economic development and urbanization process. This experiment produced a method to extract China's urban built-up areas effectively and rapidly, which provides some reference value for China's urbanization.

How to cite: Bai, M.: Detecting China's urban built-up areas expansion over the last decade based on the deep learning through NPP-VIIRS images, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6822, https://doi.org/10.5194/egusphere-egu22-6822, 2022.

09:27–09:33
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EGU22-7215
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Highlight
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Presentation form not yet defined
Mike Sips and Daniel Eggert

We present SEVA, a scalable exploration tool that supports users in detecting land-use changes in large optical remote sensing data. SEVA addresses three current scientific and technological challenges of detecting changes in large data sets: a) the automated extraction of relevant changes from many high-resolution optical satellite observations, b) the exploration of spatial and temporal dynamics of the extracted changes, c) interpretation of the extracted changes. To address these challenges, we developed a distributed change detection pipeline. The change detection pipeline consists of a data browser, extraction, error analysis, and interactive exploration component. The data browser supports users to assess the spatial and temporal distribution of available Sentinel-2 images for a region of interest. The extraction component extracts changes from Sentinel-2 images using the post-classification change detection (PCCD) method. The error assessment component supports users in interpreting the relevance of extracted changes with global and local error metrics. The interactive exploration component supports users in investigating the spatial and temporal dynamics of extracted changes. SEVA supports users through interactive visualization in all components of the change detection pipeline.

How to cite: Sips, M. and Eggert, D.: Scalable Change Detection in Large Sentinel-2 data with SEVA, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7215, https://doi.org/10.5194/egusphere-egu22-7215, 2022.

09:33–09:39
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EGU22-7236
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Virtual presentation
Yuqi Song and Xie Hu

Landslides are general natural disasters in the world. Knowledge on the landslide distribution is fundamental for landslide monitoring, disaster mitigation and reduction. Traditional in-situ observations (e.g., leveling, GPS, extensometer, inclinometer) usually have high accuracy, but they are expensive and labor intensive and may also involve risks in the field. Alternatively, remote sensing data can capture the regional land surface features and thus are efficient in landslide mapping. Recent studies on landslide identification mainly rely on the pixel-based or object-oriented classification using optical images. Nonetheless, landslide activities are governed by multiple processes including the topography, geology, land cover, catchment, precipitation, and tectonics (e.g., dynamic shaking or aseismic creeping). Remote sensing data and products are beneficial to extract some of these critical parameters on a regional scale. Rapid development of machine learning algorithms makes it possible to systematically construct landslide inventory by interpreting multi-source remote sensing big data. The populous California suffers from high risks of landsliding. The United States Geological Survey (USGS) compiles the landslide inventory in the State and reports that California has about 86k landslides. Steep slope in the costal ranges, wet climate in the northern California, youthful materials at the surface from active tectonics of the San Andreas Fault and secondary fault systems, dynamic and aseismic movements instigated from the faults all contribute to high landslide susceptibility in California. In May 2017, the steep slopes at Mud Creek on California’s Big Sur coast collapsed catastrophically. During January and February in 2019, several landslides occurred on the southern part of Santa Monica Mountains. In January 2021, a large debris flow hit the Rat Creek in Big Sur due to extreme precipitation. In addition, a fairly complete collection of remote sensing data and products are available in California. Here we use machine learning methods to refine landslides in California using remote sensing big data, including elevation, slope, and aspect derived from SRTM digital elevation models (DEM), the normalized differential vegetation index (NDVI) derived from Landsat 8 OLI images, the hydrometeorological observations, the nearest distance to rivers and faults, the geological and land cover maps, as well as Synthetic Aperture Radar (SAR) images. We will use the archived landslide inventory for model training and testing. We plan to further explore the critical variables in determining landslide occurrences and the inferred triggering mechanisms.

How to cite: Song, Y. and Hu, X.: Application of remote sensing big data in landslide identification, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7236, https://doi.org/10.5194/egusphere-egu22-7236, 2022.

09:39–09:45
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EGU22-8948
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Highlight
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On-site presentation
Hugues Brenot, Nicolas Theys, Erwin de Donder, Lieven Clarisse, Pierre de Buyl, Nicolas Clerbaux, Simone Dietmüller, Sigrun Matthes, Volker Grewe, Sandy Chkeir, Alessandra Mascitell, Aikaterini Anesiadou, Riccardo Biondi, Igor Mahorčič, Tatjana Bolić, Ritthik Bhattacharya, Tim Winter, Adam Durant, Michel Van Roozendael, and Manuel Soler

Aviation safety can be jeopardised by multiple hazards arising from natural phenomena, e.g., severe weather, aerosols/gases from natural hazard, space weather. Furthermore, there is the anthropogenic emissions and climate impact of aviation, that could be reduced. The use of satellite sensors, ground-based networks, and model forecasts is essential to detect and mitigate the risk of airborne hazards for aviation, as flying through them can have a strong impact on engines (abrasion and damages caused by aerosols) and on the health of passengers (e.g. due to associated hazardous trace gases).

The goal of this work is to give an overview of the alert data products in development in the ALARM SESAR H2020 Exploratory Research project. The overall objective of ALARM (multi-hAzard monitoring and earLy wARning system; https://alarm-project.eu) is to develop a prototype global multi-hazard monitoring and Early Warning System (EWS), building upon SACS (Support to Aviation Control Service; https://sacs.aeronomie.be). This work presents the creation of alert data products, which have a potential use in geosciences (e.g. meteorology, climatology, volcanology). These products include observational data, alert flagging and tailored information (e.g., height of hazard and contamination of flight level – FL). We provide information about the threat to aviation, but also notifications for geoscience applications. Three different manners are produced, i.e., early warning (with geolocation, level of severity, quantification, …), nowcasting (up to 2 hours), and forecasting (from 2 to 48 hours) of hazard evolution at different FLs. Note that nowcasting and forecasting concerns SO2 contamination at FL around selected airports and the risk of environmental hotspots. This study shows the detection of 4 types of risks and weather-related phenomena, for which our EWS generates homogenised NetCDF Alert Products (NCAP) data. The first type is the near real-time detection of recent volcanic plumes, smoke from wildfires, and desert dust clouds, and the interest of combining geostationary and polar orbiting satellite observations. For the second type, ALARM EWS uses satellite and ground-based (GB) observations, and model forecasts to create NCAP related to real-time space weather activity. Exploratory research is developed by ALARM partners to improve detection of a third type of risk, i.e., the initiation of small-scale deep convection (under 2 km) around airports. GNSS data (ground-based networks and radio-occultations), lightning and radar data, are used to implement NCAP data (designed with the objective of bringing relevant information for improving nowcasts around airports). The fourth type is related to the detection of environmental hotspots, which describe regions that are strongly sensitive to aviation emissions. ALARM partners investigate the climate impact of aviation emissions with respect to the actual atmospheric synoptical condition, by relying on algorithmic Climate Change Functions (a-CCFs). These a-CCFs describe the climate impact of individual non-CO2 forcing compounds (contrails, nitrogen oxide and water vapour) as function of time, geographical location and cruise altitude.

Acknowledgements:

ALARM has received funding from the SESAR Joint Undertaking (JU) under grant agreement No 891467. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the SESAR JU members other than the Union.

How to cite: Brenot, H., Theys, N., de Donder, E., Clarisse, L., de Buyl, P., Clerbaux, N., Dietmüller, S., Matthes, S., Grewe, V., Chkeir, S., Mascitell, A., Anesiadou, A., Biondi, R., Mahorčič, I., Bolić, T., Bhattacharya, R., Winter, T., Durant, A., Van Roozendael, M., and Soler, M.: Decrease of anthropogenic emission from aviation and detection of natural hazards with potential application in geosciences using satellite sensors, ground-based networks and model forecasts in the context of the SACS/ALARM early warning system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8948, https://doi.org/10.5194/egusphere-egu22-8948, 2022.

09:45–09:51
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EGU22-10041
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ECS
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Highlight
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Virtual presentation
Graham Sadler and Gareth Rees

The Arctic region is a very remote and vulnerable ecosystem but also rich in natural resources, which have been exploited for many decades.  These ecosystems are particularly vulnerable to any industrial accident.  The Arctic has short summers, low temperatures, and limited sunlight, so it can take decades for Arctic ecosystems to recover from anthropogenic pollution.  Examples of the potential hazards when exploiting natural resources in such fragile environments and the detrimental impact on the polar ecosystem and communities are all too frequent.  In the case of the oil and gas industry, spills caused by the failure of old pipelines are a very regular occurrence.  Given the geographical isolation of these activities, remote sensing is an obvious technology to underpin any effective monitoring solution.  Increasing availability in the public domain, together with recent advances in resolution, suggest satellite imagery can play a key role in effectively monitoring oil spills and is the focus for this study.

The remote sensing of polar regions and the detection of terrestrial oil spills have both been studied previously, however, there has been little work to investigate the two in combination. The challenge is how to detect an oil spill if it is from an unknown incident or illegal activity such as discharge.  Oil spill detection by applying image processing techniques to Earth Observation (EO) data has historically focused on marine pollution.  Satellite-based Synthetic Aperture Radar (SAR), with its day/night and all-weather capability and wide coverage, has proven to be effective.  Oil spill detection with remote sensing in terrestrial environments has received less attention due to the typically smaller regional scale of terrestrial oil spill contamination together with the overlapping spectral signatures of the impacted vegetation and soils.  SAR has not proven to be very effective onshore because of the false positives and consequent ambiguities associated with interpretation, reflecting the complexity of land cover.

A number of studies have highlighted the potential of airborne hyperspectral sensors for oil spill detection either through the identification of vegetation stress or directly on bare sites, with absorption bands identified in the short-wave infrared (SWIR) range at 1730 and 2300nm.  However, unlike spaceborne sensors, these devices do not provide regular coverage over broad areas.  Several hyperspectral satellites have been launched to date but have technical constraints.  The medium spatial resolution and long revisit times of most current hyperspectral instruments limit their use for identifying smaller incidents that often occur with high unpredictability.

No single sensor currently has all the characteristics required to detect the extent, impact and recovery from onshore oil spills.  This study will look at the potential of combining medium spatial resolution imagery (Sentinel-2) for initial screening, with high spatial/temporal (WorldView-3) and high spectral (PRISMA) resolution data, both covering the key SWIR bands, for site specific analysis.

How to cite: Sadler, G. and Rees, G.: Monitoring anthropogenic pollution in the Russian sub-Arctic with high resolution satellite imagery: An oil spill case study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10041, https://doi.org/10.5194/egusphere-egu22-10041, 2022.

09:51–09:59
Coffee break
Chairpersons: Ling Chang, Ramon Hanssen
10:20–10:30
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EGU22-1004
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ECS
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solicited
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Highlight
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Presentation form not yet defined
Xie Hu, Roland Bürgmann, and Xiaohua Xu

Although scientific advances have been achieved in every individual geoscience discipline, enabled by more extensive and accurate observations and more robust models, our knowledge of the Earth’s complexity remains limited. California represents an ideal natural laboratory that hosts active tectonics processes associated with the San Andreas fault system and hydrological processes dominated by the Central Valley, which contribute to dynamic surface deformation across the state. The spatiotemporal characteristics and three-dimensional patterns of the tectonic and hydrological sources of ground motions differ systematically. Spatially, interseismic creep is distributed along several strands of the San Andreas Fault (SAF) system. The elastic deformation off the locked faults usually spreads out over tens of kilometers in a long-wavelength pattern. Hydrologically driven displacements are distinct between water-bearing sedimentary basins and the bounding fault structures. Temporarily, both displacement sources involve long-term trends such as from interseismic creep and prolonged climate change. In addition, episodic signals are due to seismic and aseismic fault slip events, seasonal elastic surface and groundwater loading, and poroelastic groundwater volume strain. The orientation of tectonic strain accumulation in California mainly represents a northwest trending shear zone associated with the right-lateral strike-slip SAF system. Hydrological processes mainly deform the Earth vertically while horizontal motions concentrate along the aquifer margins.

We used the time-series ground displacements during 2015-2019 relying on four ascending tracks and five descending tracks of the ESA’s Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) observations. We considered the secular horizontal surface velocities and strain rates, constrained from GNSS measurements and tectonic models, as proxies for tectonic processes. InSAR time series and GNSS velocity maps benefit from the Southern California Earthquake Center (SCEC) Community Geodetic Model (CGM) developments. We further extracted the seasonal displacement amplitudes from InSAR-derived time-series displacements as proxies for hydrological processes. We synergized multidisciplinary remote sensing and auxiliary big data including ground deformation, sedimentary basins, precipitation, soil moisture, topography, and hydrocarbon production fields, using an ensemble, random forest machine learning algorithm. We succeeded in predicting 86%-95% of the representative data sets.

Interestingly, high strain rates along the SAF system mainly occur in areas with a low-to-moderate vegetation fraction, suggesting a correlation of rough/high-relief coastal range morphology and topography with the active faulting, seasonal and orographic rainfall, and vegetation growth. Linear discontinuities in the long-term, seasonal amplitude and phase of the surface displacement fields coincide with some fault strands, the boundary zone between the sediment-fill Central Valley and bedrock-dominated Sierra Nevada, and the margins of the inelastically deforming aquifer in the Central Valley, suggesting groundwater flow interruptions, contrasting elastic properties, and heterogeneous hydrological units.

How to cite: Hu, X., Bürgmann, R., and Xu, X.: Remote sensing big data characterization of tectonic and hydrological sources of ground deformation in California, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1004, https://doi.org/10.5194/egusphere-egu22-1004, 2022.

10:30–10:36
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EGU22-10780
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ECS
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Virtual presentation
Jesús Octavio Ruiz Sánchez, Jesús Eduardo Méndez Serrano, Mariana Patricia Jácome Páz, Nelly Ramírez Serrato, and Nestor López Váldes

The present project aims to make a preliminary assessment of the volcanic risk represented by the Apan Volcanic Field (CVA). The methodology was divided into two parts. In the first, Digital Elevation Models (DEM) published by official sources were used to identify unreported structures and perform morphometric analysis of previously dated structures. In the second stage, a new DEM was developed from interferometric methodologies to compare the results with those obtained from official sources. Two SAR satellite images from the SENTINEL-1 satellite of ESA's Copernicus program were used. Being the first of October 14, 2021, leader image, and the second of October 26, 2021, slave image. These images were processed in ESA's SNAP software. For the morphometric analysis, volcanic structures have been classified into three major categories: Young cones (0.18 Ma - 0.5 Ma), Intermediate cones (0.5 Ma-1 Ma), and Old cones (1 Ma-3 Ma). From the official DEM analysis, 243 volcanic structures were reported within the study area with a preliminary predominance of structures that fall in the range of old cones, 4 areas with a higher concentration of volcanic structures were detected in which some highly populated localities are found. In addition, demographic parameters were used for a better preliminary risk assessment in the study area. Official and Radar images DEMs were used for the morphometric analysis and the results were compared with the previously published models. Finally, it was concluded the importance of the CVA by comparison with other two Mexican volcanic fields CVA represents a moderate volcanic risk, for which a greater number of studies and monitoring in the area is recommended.  This project provides a new understanding of the volcanic hazard and risk associated with the CVA and the development of the surrounding social environment.

How to cite: Ruiz Sánchez, J. O., Méndez Serrano, J. E., Jácome Páz, M. P., Ramírez Serrato, N., and López Váldes, N.: Morphometric analysis of volcanic structures using digital elevation models and models developed from radar images in the Apan volcanic field, México., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10780, https://doi.org/10.5194/egusphere-egu22-10780, 2022.

10:36–10:42
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EGU22-10256
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Presentation form not yet defined
Yufang He, Guangzong Zhang, Hermann Kaufmann, and Guochang Xu

The small baseline subset of spaceborne interferometric synthetic aperture radar (SBAS-InSAR) technology has become a classical method for monitoring slow deformations through time series analysis with an accuracy in the centimeter or even millimeter range. Additionally, the process of calculating interferograms itself directly affects the accuracy of the SBAS-InSAR measurements, whereby the selection of high-quality interferogram pairs is crucial for SBAS data processing. Especially in the era of big data, the demand for an automatic and effective selection method of high-quality interferograms in SBAS-InSAR technology is growing. However, there are some methods including simulated annealing (SA) searching strategy, the graph theory (GT) and others. Until now, the most effective approach of high-quality interferogram selection still relies on the traditional manual method. Due to the high degree of human interaction and a large risk of repetitive work, this traditional manual method increases the instability and inconsistency of the deformation calculation.
Considering that the different qualities of interference pairs show different color characteristics, the DCNN method is adopted in this study. The ResNet50 model (one of DCNN models) has the advantages of representing a standard network structure and easy programming. The idea is based on the fact that interferograms less contaminated by different noise sources display smaller color phase changes within a certain phase range. Hence, training sets containing almost 3000 interferograms obtained from land subsidences in several subregions of Shenzhen in China with varying contaminations of noise were established. Up next, the ResNet50–DCNN model was set up, the respective parameters were determined through analysis of the data sets trained, and traditional interferogram selection methods were used to evaluate the performance. For simulation experiments and the evaluation and validation of real data, phase unwrapping interferograms obtained by the time-spatial baseline threshold method are used to classify high and low quality interferograms based on the ResNet50 model. The quantity of high quality interferograms extracted by the ResNet50–DCNN method is above 90% for the simulation experiment and above 87% concerning the real data experiment, which reflects the accuracy and reliability of the proposed method. A comparison of the overall surface subsidence rates and the deformation information of local PS points reveals little difference between the land subsidence rates obtained by the ResNet50–DCNN method and the actual simulations or the manual method. 
The proposed advanced method provides an automatized and fast interferogram selection process for high quality data, which contributes significantly to the application of SBAS-InSAR engineering. For future research, we will expand the training samples and study DCNN models to further improve the general accuracy for a wider applicability of this method.

How to cite: He, Y., Zhang, G., Kaufmann, H., and Xu, G.: Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10256, https://doi.org/10.5194/egusphere-egu22-10256, 2022.

10:42–10:48
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EGU22-10630
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ECS
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On-site presentation
Matteo Giomo, Johnny Moretto, and Luciano Fantinato

The European spruce bark beetle (Ips typographus) is one of the most detrimental insects of the European spruce forests. An effective mitigation measure consists in the removal of infected trees before the beetles leave the bark, which generally happens before the end of June. To minimize economic loss and prevent tree destruction, fast and early detection of European spruce bark beetle is therefore crucial for the future of spruce forests.

In order to detect the forest stressed regions, possibly associated to the beetle infestation, we investigated the forest vigour changes in time. One of the most damaged regions is Northern Italy in which the beetle diffusion has highly increased after the Storm Adrian of late 2018.

In this work we used Sentinel-2 images of a study area in the mountain territory of Val di Fiemme (Trento, Italy) from early 2017 to late 2021. A preliminary field investigation was necessary to localize healthy (green) and stressed (red) trees. NDVI index trends from Sentinel-2 showed an evident vigour discrepancy from green and red regions.

We therefore conceive a classification algorithm based on the slope of fitting lines of NDVI over time. Model accuracy is around 86%. The result is a classified map useful to distinguish stressed and healthy forest areas.

By using the proposed method and Google Earth Engine computational capabilities, we highlight the potential of a simple and effective model to predict and detect forest stressed areas, potentially associated with the diffusion of the European spruce bark beetle.

How to cite: Giomo, M., Moretto, J., and Fantinato, L.: Detection of forest stress from European spruce bark beetle attack in Northern Italy through a stress classification algorithm based on NDVI temporal changes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10630, https://doi.org/10.5194/egusphere-egu22-10630, 2022.

10:48–10:54
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EGU22-10962
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Presentation form not yet defined
Johnny Moretto, Matteo Giomo, Luciano Fantinato, and Roberto Rasera

Traditional fertilization techniques in crop production consist in a homogeneous distribution of inputs all over the cultivated field. Alternatively variable fertilization methods could minimize the environmental impact and increase economic benefits.

The objective of this study is to evaluate the capabilities of a Google Earth Engine code conceived to rapidly study the variability of cultivated fields, for a possible variable fertilization. The tool is semi-automatic as it requires just the field boundary and it gives few outputs ready to be inspected by the user. This work presents an application of this model in a corn field in Northern Italy (province of Venice).

Field variability is evaluated through NDVI index extracted from Sentinel-2 images from 2017 to 2021. For the purpose, the tool provides NDVI statistics, classified maps, classified area percentages, and punctual NDVI trends.

Results show that boundary regions of the field are systematically less vigour than other parts, thus crop production is not efficient. Otherwise, fertilization should be enhanced in internal parts, as they are steadily healthier.

The proposed model is a fast way to analyse field vigour status and Google Earth Engine capabilities permit to apply it nearly all over the world. Field variability and linked variable fertilization are crucial to reduce environmental and increase economic benefits, especially in extensive farming.

How to cite: Moretto, J., Giomo, M., Fantinato, L., and Rasera, R.: Application of a semi-automatic tool for field variability assessment on a cultivated field in Northern Italy to evaluate variable fertilization benefits, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10962, https://doi.org/10.5194/egusphere-egu22-10962, 2022.

10:54–11:00
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EGU22-11589
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On-site presentation
Fernando Monterroso, Andrea Antonioli, Simone Atzori, Claudio De Luca, Riccardo Lanari, Michele Manunta, Emanuela Valerio, and Francesco Casu

Differential Synthetic Aperture Radar Interferometry (DInSAR) is a key method to estimate, with centimeter accuracy, the earth surface displacements caused by natural events or anthropogenic activities. Furthermore, since 2014 the scientific community can benefit from the huge spaceborne SAR data archives acquired by the Copernicus Sentinel-1 (S1) satellite constellation, which operationally provides SAR data with a free and open data access policy at nearly global scale. By using the S1 acquisitions, an automatic and unsupervised processing tool that generates co-seismic interferograms and LOS displacement maps has been developed. This tool routinely queries two different earthquake catalogs (USGS and INGV) to trigger, in automatic way, the S1 data download and the DInSAR processing through the Parallel Small BAseline Subsets (P-SBAS) algorithm. In particular, in order to guide the algorithm to only intercept the earthquakes which may produce ground displacements detectable through the DInSAR technology, the tool starts the SAR data processing for those events with a magnitude greater than 4.0 in Europe, and greater than 5.5 at a global scale.

We first remark that, in order to optimize the extension of the investigated area, thus reducing the processing time and effectively exploiting the available computing resources, an algorithm for the estimation of the co-seismically affected area has been integrated as first step of the workflow. More specifically, by considering the moment tensors provided by public catalogs (USGS, INGV, Global CMT project), a forward modelling procedure generates the predicted co-seismic displacement field, used by the P-SBAS algorithm to optimize some of the DInSAR processing steps. In particular, the phase unwrapping (PhU) algorithm is applied only to the part of the DInSAR interferograms delimited by the area identified through the predicted scenario and not to the whole S1 scene. In addition, the presented automatic and unsupervised tool has been migrated within a Cloud Computing (CC) environment, specifically the Amazon Web Service (AWS). This strategy allows us a more efficient management of the needed computing resources also in emergency scenario.

The adopted solutions allowed the creation of a worldwide co-seismic maps database. Indeed, by benefiting of the last seven years of Sentinel-1 operation, the tool has generated approximately 6500 interferograms and LOS displacement maps, corresponding to a total of 383 investigated earthquakes.

Note also that the generated interferograms and displacement maps have been made available for the scientific community through the EPOS infrastructure and the Geohazards Exploitation Platform, thus helping scientists and researchers to investigate the dynamics of surface deformation in the seismic zones around the Earth also in the case they have not available specific DInSAR processing capabilities and/or skills.

How to cite: Monterroso, F., Antonioli, A., Atzori, S., De Luca, C., Lanari, R., Manunta, M., Valerio, E., and Casu, F.: New advances of the P-SBAS based automatic and unsupervised tool for the co-seismic Sentinel-1 DInSAR products generation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11589, https://doi.org/10.5194/egusphere-egu22-11589, 2022.

11:00–11:06
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EGU22-11701
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ECS
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On-site presentation
Dibakar Kamalini Ritushree, Mahdi Motagh, Shagun Garg, and Binayak Ghosh

 The current scenario of the world has witnessed extreme events of floods irrespective of the heterogeneity in the geographical context. The necessity for accurately mapping such events is more of the essence for disaster relief and recovery efforts. The role of satellite imageries from both optical and radar sensors could have immensely benefited the process due to its easier interpretability and high resolution. However, the use of optical sensors for flood extent extraction is limited by weather conditions and the presence of clouds.   In contrast,   SAR sensors have proved to be one of the most powerful tools for flood monitoring due to their potential to observe in all-weather/day-night conditions. The exploitation of SAR in conjunction with optical datasets has shown exemplary results in flood monitoring applications.

With the onset of deep learning and big data, the application of data driven approaches on training models has shown great potential in automatic flood mapping. In order to improve the efficiency of deep learning algorithms at a global scale, publicly available labelled benchmark datasets have been introduced. One of such datasets is Sen1Floods11, that includes raw Sentinel-1 imagery and classified permanent water and flood water, covering 11 flood events. The flood events had coverage from Sentinel-1 and Sentinel-2 imagery on the same day or within 2 days of the Sentinel-1 image from Aug’2016 to May’2019. The other one is WorldFloods that consists of Sentinel-2 data acquired during 119  flood events from Nov’2015 to March’2019. In this study, we make a comparative analysis to investigate the efficiency of these labelled benchmark datasets for automatic flood mapping using SAR data. Various types of flooding in different geographic locations in Europe, Australia, India and Iran  are selected and the segmentation networks are evaluated on existing Sentinel-1 images covering these events.

 

How to cite: Ritushree, D. K., Motagh, M., Garg, S., and Ghosh, B.: Comparative analysis of  the role of labelled benchmark datasets for automatic flood mapping using SAR data , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11701, https://doi.org/10.5194/egusphere-egu22-11701, 2022.

11:06–11:12
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EGU22-12127
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ECS
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On-site presentation
Micol Fumagalli, Alberto Previati, Serena Rigamonti, Paolo Frattini, and Giovanni B. Crosta

Analysis of ground deformation is particularly demanding when displacement rates are in the range of some mm/y.  This study integrates different statistical techniques to unravel the spatial and temporal patterns of vertical ground deformation in an alluvial basin. Beyond the identification of critical areas, this is also essential to delineate a conceptual model for the uplift and subsidence mechanisms in complex environments such as a layered aquifer suffering strong piezometric oscillations and land use changes due to human activities.

The study area covers about 4000 km2 in the Lombardy region (N Italy) and includes the Milan metropolitan area and a part of the Po alluvial plain between the Como and Varese lakes. In this study, Sentinel-1A (C-band) PS-InSAR data with an average revisiting time 6 days and an average PS distance of 20 m, processed by TRE-Altamira, were analysed to investigate different movement styles in the study area.

The PS-InSAR data ranges from 2015 to 2020 and reveal a wide gently subsiding area oriented in NW-SE direction (average subsiding rate of nearly -1.5 mm/yr along the line of sight). Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were applied on ground deformation and piezometric time series, showing analogue spatial patterns of the fluctuation styles. Then, from the correlations between the spatial patterns of ground motion, groundwater level changes and geological data, and between the temporal patterns of rainfall and groundwater abstraction rates, the main causes of ground motion were identified and summarized in a conceptual model.

Finally, after reconstructing the aquifer composition and the geo-hydro-mechanical properties, and by implementing the hydraulic stresses from the conceptual model, a hydro-mechanical coupled FEM numerical model was developed. This allowed verifying the hypotheses through the comparison between the simulated ground displacement and the measured one.

How to cite: Fumagalli, M., Previati, A., Rigamonti, S., Frattini, P., and Crosta, G. B.: Methodologies for surface deformations analysis at regional scale, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12127, https://doi.org/10.5194/egusphere-egu22-12127, 2022.

11:12–11:18
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EGU22-592
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ECS
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On-site presentation
Farzad Vahidi Mayamey, Navid Ghajarnia, Saeid Aminjafari, Zahra Kalantari, and Kristoffer Hylander

Accurate knowledge of local land cover and land use and their changes is crucial for many different applications such as natural resources management, environmental studies, ecological and biodiversity change evaluations, and food security. Global landcover maps can be useful datasets as a reference source and starting points, however, they usually show areas of geographical disagreements when compared to one another. Moreover, the global land cover products mostly generalize different land cover types which may not fit exactly to the specific needs of different projects and user communities. For instance, different types of forests are mostly considered as one category as they are not easy to be differentiated. In this study, we used high-resolution time-series images of Sentinel-2 to produce a local land cover for southwest Ethiopia with focusing on 8 major land cover classes: Forests, Plantations of exotic trees, Woodlands, Home Gardens, Annual crop fields, Grazing Wetlands, Urban areas, and Open water bodies. We also utilized high-resolution google map satellite imagery and the local expert knowledge on the study area to produce an observational dataset for training and validating steps. Different machine learning algorithms, land cover combinations, and seasonal scenarios were also used to produce the best local land cover map for the study area. For this purpose, a two-step approach was implemented to produce the final high-resolution land cover map. Firstly, we produced the best individual maps for each landcover class based on the highest producer accuracy among different scenarios. Then to produce the final land cover map for all land cover classes, all individual maps were combined by using the consumer accuracy index. For this, we found the most accurate land cover class for each pixel based on the highest consumer accuracy across all individually produced maps in the first step. In the end, we evaluated the results by the validation dataset and using different confusion indices. The final high-resolution land cover map produced in this study showed us the combination of remote sensing and local field-based knowledge in cloud computing platforms like google earth engine (GEE) improves the mapping of different land cover classes across southwest Ethiopia.

 

Keywords: Land cover map; Sentinel-2; High resolution; Machine Learning; Google Earth Engine; Ethiopia

How to cite: Vahidi Mayamey, F., Ghajarnia, N., Aminjafari, S., Kalantari, Z., and Hylander, K.: Producing a High-Resolution Land Cover Map for Southwest Ethiopia Using Sentinel-2 Images and Google Earth Engine, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-592, https://doi.org/10.5194/egusphere-egu22-592, 2022.

11:18–11:24
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EGU22-12271
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ECS
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Highlight
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On-site presentation
Binayak Ghosh, Shagun Garg, Mahdi Motagh, Daniel Eggert, Mike Sips, Sandro Martinis, and Simon Plank

Floods can have devastating consequences on people, infrastructure, and the ecosystem. Satellite imagery has proven to be an efficient instrument in supporting disaster management authorities during flood events. In contrast to optical remote sensing technology, Synthetic Aperture Radar (SAR) can penetrate clouds, and authorities can use SAR images even during cloudy circumstances. A challenge with SAR is the accurate classification and segmentation of flooded areas from SAR imagery. Recent advancements in deep learning algorithms have demonstrated the potential of deep learning for image segmentation demonstrated. Our research adopted deep learning algorithms to classify and segment flooded areas in SAR imagery. We used UNet and Feature Pyramid Network (FPN), both based on EfficientNet-B7 implementation, to detect flooded areas in SAR imaginary of Nebraska, North Alabama, Bangladesh, Red River North, and Florence. We evaluated both deep learning methods' predictive accuracy and will present the evaluation results at the conference. In the next step of our research, we develop an XAI toolbox to support the interpretation of detected flooded areas and algorithmic decisions of the deep learning methods through interactive visualizations.

How to cite: Ghosh, B., Garg, S., Motagh, M., Eggert, D., Sips, M., Martinis, S., and Plank, S.: Deep learning, remote sensing and visual analytics to support automatic flood detection, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12271, https://doi.org/10.5194/egusphere-egu22-12271, 2022.

11:24–11:30
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EGU22-12507
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ECS
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Presentation form not yet defined
Michelle Rygus, Giulia Tessari, Francesco Holecz, Marie-Louise Vogt, Djoret Daïra, Elisa Destro, Moussa Isseini, Giaime Origgi, Calvin Ndjoh Messina, and Claudia Meisina

High-resolution characterisation of land deformation and its spatio-temporal response to external triggering mechanisms is an important step towards improving geological hazard forecasting and management. The work presented here is part of the ResEau-Tchad project (www.reseau-tchad.org), with a focus on the city of N’Djamena. The extraction of groundwater to sustain this rapidly growing capital city has increased the pressure on water supply and urban sanitation infrastructures which are failing to meet the current water demand. In this study we exploit Synthetic-Aperture Radar (SAR) data acquired by the Sentinel-1 satellite to investigate the temporal variability and spatial extent of land deformation to assist in the development of a sustainable water management program in N’Djamena city. 

The objectives of the work are: 1) to analyse the recent evolution of land deformation using two multi-temporal differential interferometry techniques, SBAS and PS-InSAR; and, 2) to investigate the land deformation mechanism in order to identify the factors triggering surface movements. The PS-InSAR and SBAS techniques are implemented on SAR images obtained in both ascending and descending orbits from April 2015 to May 2021 to generate high resolution deformation measurements representing the total displacement observed at the surface. While the pattern of displacement indicated by the two datasets is similar, the average velocity values obtained with PS-InSAR tend to be noisier than the ones derived using the SBAS technique, particularly when the SBAS time-series shows non-linear deformation trends.

Characterisation of the subsidence areas by means of statistical analyses are implemented to reveal the surface deformation patterns which are related to different geo-mechanical processes. The integration of the spatio-temporal distribution of PS and SBAS InSAR results with geological, hydrological, and hydrogeological data, along with subsurface lithological modelling shows a relationship between vertical displacements, clay sediments, and surface water accumulation. These areas are located mostly in the surroundings of the urban area. The city centre is observed to be mostly stable, which might be the result of the removal of the surface water through the city drainage system. Investigation of the relationship between vertical displacements and seasonal groundwater fluctuations or effects due to the groundwater withdrawal is limited due to the temporally sparse piezometric dataset; however, the recent deformation rates appear to be correlated with the groundwater level trend at some locations.

How to cite: Rygus, M., Tessari, G., Holecz, F., Vogt, M.-L., Daïra, D., Destro, E., Isseini, M., Origgi, G., Ndjoh Messina, C., and Meisina, C.: Spatio-temporal analysis of surface displacements in N’Djamena, Chad derived by Persistent Scatter-Interferometric Synthetic Aperture Radar (PS-InSAR) and Small BAseline Subset (SBAS) techniques, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12507, https://doi.org/10.5194/egusphere-egu22-12507, 2022.

11:30–11:36
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EGU22-12552
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On-site presentation
Manuel Arbelo, Jose Rafael García-Lázaro, and Jose Andres Moreno-Ruiz

Africa is the continent with the highest annual burned area, with the African savanna being the most affected ecosystem. This paper presents an assessment of the spatio-temporal accuracy of three of the main global-scale burned area products derived from images from polar-orbiting satellite-borne sensors: 1) Fire_CCI 5. 1, of 250 m spatial resolution, developed by the European Space Agency (ESA) and led by the University of Alcalá de Henares; 2) MCD64A1 C6, of 500 m spatial resolution, developed by the University of Maryland; and 3) GABAM (Global Annual Burned Area Map), of 30 m spatial resolution, developed through the Google Earth Engine (GEE) platform by researchers from the Aerospace Information Research Institute of China. The first two products are based on daily images from the MODIS (Moderate-Resolution Imaging Spectroradiometer) sensor onboard NASA's Terra and Aqua satellites, and the third is based on Landsat images available on GEE. The almost total absence of reference burned area data from official sources has made it difficult to assess the spatio-temporal accuracy of these burned area products in Africa. However, the recent creation of the Burned Area Reference Database (BARD), which includes reference datasets from different international projects, opens the possibility for a more detailed assessment. The study focused on a region covering an area of approximately 29.5 million ha located in the southern hemisphere between 10oS and 15oS and bounded longitudinally by the 35oE and 40oE meridians. The results show that the Fire_CCI 5.1, MCD64A1 C6 and GABAM products present an annual distribution of burned area with an irregular pattern in the interval between 7 and 10 million ha per year (around 30% of the whole study area), but there is hardly any correlation between their time series, with correlation coefficients lower than 0.3 for the period 2000-2019. The spatio-temporal accuracy analysis was performed for 2005, 2010 and 2016, the only years for which BARD has reference perimeters. The results are highly variable, with values between 1 and 20 million ha per year depending on the product, the year and the reference set used, which does not allow definitive conclusions to be drawn on the accuracy of the burned area estimates. These results indicate that uncertainties persist both in the burned area estimates derived from remote sensing products in these regions and in the reference sets used for their evaluation, which require further research effort.

How to cite: Arbelo, M., García-Lázaro, J. R., and Moreno-Ruiz, J. A.: Assessment of global burned area satellite products in the African savannah, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12552, https://doi.org/10.5194/egusphere-egu22-12552, 2022.

11:36–11:42
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EGU22-12575
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ECS
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Virtual presentation
Moien Rangzan and Sara Attarchi

Many satellite images are corrupted by stripping; this noise degrades the visual quality of the images and inevitably introduces errors in processing. Thermal and hyperspectral images often suffer from stripping. The frequency distribution characteristic of stripe noise makes it difficult to remove such noise in the spatial domain; contrariwise, this noise can be efficiently detected in the frequency domain. Numerous solutions have been proposed to eliminate such noise using Fourier transform; however, most are subjective and time-consuming approaches.

The lack of a fast and automated tool in this subject has motivated us to introduce a Convolutional Neural Network-based tool that uses the U-Net architecture in the frequency domain to suppress the anomalies caused by stripe noise. We added synthetic noise to satellite images to train the model. Then, we taught the network how to mask these anomalies in the frequency domain. The input image dataset was down-sampled to a size of 128 x128 pixels for a fast training time. However, our results suggest that the output mask can be up-scaled and applied on the original Fourier transform of the image and still achieve satisfying results; this means that the proposed algorithm is applicable on images regardless of their size.

After the training step, the U-Net architecture can confidently find the anomalies and create an acceptable bounding mask; the results show that - with enough training data- the proposed procedure can efficiently remove stripe noise from all sorts of images. At this stage, we are trying to further develop the model to detect and suppress more complex synthetic noise. Next, we will focus on removing real stripe noise on satellite images to present a robust tool.

How to cite: Rangzan, M. and Attarchi, S.: Removing Stripe Noise from Satellite Images using Convolutional Neural Networks in Frequency Domain, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12575, https://doi.org/10.5194/egusphere-egu22-12575, 2022.

11:42–11:50