This study proposes an innovative approach to develop a regional-scale landslide forecasting model based on rainfall thresholds optimized for operational early warning. In particular, it addresses ...two main issues that usually hinder the operational implementation of this kind of models: (i) the excessive number of false alarms, resulting in civil protection system activation without any real need, and (ii) the validation procedure, usually performed over periods too short to guarantee model reliability. To overcome these limitations, several techniques for reducing the number of false alarms were applied in this study, and a multiple validation phase was conducted using data from different sources. An intensity-duration threshold system for each of the five alert zones composing the Liguria region (Italy) was identified using a semiautomatic procedure called MaCumBA, considering three levels of criticality: low, moderate, and high. The thresholds were developed using a landslide inventory collected from online newspapers by a data mining technique called SECaGN. This method was chosen to account for only those events that echo on the Internet and therefore impact society, ignoring landslides occurred in remote areas, not of interest for civil protection intervention, which would adversely affect the model performance because they would result in false alarms. A calibration phase was performed to minimize the impact of false alarms, allowing at least one false alarm per year over the moderate criticality level. In addition, an innovative approach to include antecedent rainfall as the third dimension of the intensity-duration thresholds was applied, generating a consistent reduction in false alarms. The results were validated through an independent landslide inventory and were compared with (i) the alert issued by the regional civil protection agency to observe the improvements achieved with the proposed model and to evaluate to what extent the proposed model is consistent with the assessments of the civil protection and (ii) a dataset of the national states of emergency to verify the suitability of the developed thresholds for alerting citizens. The thresholds obtained showed high predictive capabilities, confirming their suitability for implementation in an operational landslide early warning system.
Technological progress in remote sensing has enabled digital representation of terrain through new techniques (e.g. digital photogrammetry) and instruments (e.g. 3D laser scanners). However, the use ...of old aerial images remains important in geosciences to reconstruct past landforms and detect long-term topographic changes. Administrations have recently expressed growing interest in sharing photogrammetric datasets on public repositories, providing opportunities to exploit these resources and detect natural and anthropogenic topographic changes. The SfM-MVS photogrammetric technique was applied to scanned historical black and white aerial photos of the Serra de Fontcalent (Alicante, Spain), as well as to recent high-quality digital aerial photos. Ground control points (GCPs) extracted from a LiDAR-derived three-dimensional point cloud were used to georeference the results with non-linear deformations. Two point clouds obtained with SfM-MVS were compared with the LiDAR-derived reference point cloud. Based on the result, the quality of the models was analysed through the comparison of the stages on stable areas, i.e., lands where no variations were detected, and active areas, with quarries, new infrastructures, fillings, excavations or new buildings. This study also indicates that errors are higher for old aerial photos (up to 5 m on average) than recent digital photos (up to 0.5 m). The application of SfM-MVS to open access data generated 3D models that enhance the geomorphological analysis, compared to stereophotogrammetry, and effectively detected activities in quarries and building of landfills.
•Existing techniques enable the use of historical and recent aerial photos to reconstruct DEMS•An area of interest is reconstructed using historical printed and digital aerial photos, obtained from readily available resources, through Structure-from-Motion•An airbone LiDAR derived point cloud was used as a benchmark and for the extraction of ground control points•Three different stages of the landform were analysed and compared•The quality of the reconstruction and the capability of monitoring changes were analysed
Abstract
The Tuscany region of Italy is widely affected by subsidence, landslides and floods, which severely impact buildings and infrastructure. In particular, Firenze-Prato-Pistoia basin has a long ...experience of ground deformation related to groundwater withdrawal. European remote-sensing satellite (ERS) data collected since 1992 have revealed the presence of several subsiding areas in the basin such as the south-eastern portion of the city of Pistoia. Sentinel-1 persistent scatterer interferometry (PSI) measurements for 2015–2018 confirmed the long-term subsidence of this area, associated with intense horticulture (plant nurseries). At the same time, Sentinel-1 data revealed the unexpected movement of Pistoia historic center, which has always been considered stable in the past. To identify the complex relationship between aquifer conditions and ground displacement, a hydrogeologic model of the Pistoia aquifers was developed, applying an integrated modelling procedure. Hydrodynamic-parameter distributions, calibrated and validated by means of Sentinel-1 PSI measurements, suggest that subsidence in Pistoia area is probably related with the combined impacts of groundwater extraction and highly compressible aquitards. To evaluate the potential evolution of ground displacement, numerical simulations were extended until 2050, using regional and global climate model data, analyzing three different pumping-rate scenarios. This led to the development of several subsidence hazard maps of the city of Pistoia that display the influence of groundwater extraction in controlling land subsidence in the area. This study emphasizes the importance of developing proper groundwater management policies, especially in alluvial aquifers made of fine compressible sediments, in order to sustainably utilize underground freshwater resources and to avoid related side effects.
Résumé
La région de la Toscane en Italie est. grandement affectée par la subsidence, les glissements de terrain et les inondations, qui ont des impacts sévères sur les bâtiments et infrastructures. Le bassin de Firenze-Prato Pistoia a notamment une longue expérience des déformations du sol liées aux prélèvements d’eau souterraine. Les données collectées depuis 1992 par le satellite ERS (European Remote Sensing) ont mis en évidence plusieurs zones de subsidence dans le bassin, comme la partie sud-est de la ville de Pistoia. Les mesures d’interférométrie par la technique de suivi des réflecteurs persistants (PSI) opérées par Sentinel 1 de 2015 à 2018 ont confirmé la subsidence de ce secteur sur le long terme en lien avec l’horticulture intensive (pépinières). Concomitamment, les données de Sentinel 1 ont montré le déplacement inattendu du centre historique de Pistoia, qui avait toujours été considéré comme stable par le passé. Afin d’identifier la relation complexe entre les conditions hydrogéologiques et les mouvements de terrain, un modèle hydrogéologique des aquifères de Pistoia a été développé, en appliquant une modélisation intégrée. La distribution des paramètres hydrodynamiques, calibrée et validée sur la base des mesures de Sentinel 1, suggère que la subsidence du secteur de Pistoia serait liée à la conjonction des impacts des prélèvements d’eau souterraine et de la forte compressibilité des aquitards. Afin d’estimer l’évolution potentielle des déplacements de terrain, les simulations numériques ont été projetées jusqu’en 2050, en utilisant les modèles climatiques régionaux et globaux, et en analysant trois scénarios de pompage différents. Ceci conduit à élaborer plusieurs cartes de l’aléa de subsidence sur la ville de Pistoia, qui illustrent l’influence des prélèvements d’eau souterraine sur le contrôle de la subsidence dans le secteur. La présente étude met en avant l’importance du développement de vraies politiques de gestion des eaux souterraines, notamment dans des aquifères alluviaux constitués de sédiments fins et compressibles, pour garantir un usage pérenne des ressources en eau douce souterraine, sans effets collatéraux.
Resumen
La región de la Toscana en Italia está muy afectada por subsidencias, deslizamientos de tierra e inundaciones, que afectan gravemente a los edificios y la infraestructura. En particular, la cuenca de Firenze-Prato-Pistoia tiene una larga experiencia de deformación del suelo relacionada con la extracción de aguas subterráneas. Los datos del satélite European Remote-Sensing (ERS) reunidos desde 1992 han revelado la presencia de varias zonas de subsidencia en la cuenca, como la parte sudoriental de la ciudad de Pistoia. Las mediciones de interferometría de dispersión persistente (PSI) del Sentinel-1 para 2015–2018 confirmaron la subsidencia a largo plazo de esta zona, asociada a la intensa horticultura (viveros de plantaciones). Al mismo tiempo, los datos de Sentinel-1 revelaron el movimiento inesperado del centro histórico de Pistoia, que siempre se ha considerado estable en el pasado. Para identificar la compleja relación entre las condiciones del acuífero y el desplazamiento del suelo, se elaboró un modelo hidrogeológico de los acuíferos de Pistoia, aplicando un procedimiento de modelado integrado. Las distribuciones de parámetros hidrodinámicos, calibrados y validados por medio de las mediciones del Sentinel-1 PSI, sugieren que la subsidencia en la zona de Pistoia está probablemente relacionada con los impactos combinados de la extracción de aguas subterráneas y acuíferos altamente comprimibles. Para evaluar la posible evolución del desplazamiento del terreno, se ampliaron las simulaciones numéricas hasta 2050, utilizando datos de modelos climáticos regionales y mundiales, analizando tres escenarios diferentes de velocidad de bombeo. Esto condujo a la elaboración de varios mapas de peligro de subsidencia de la ciudad de Pistoia que muestran la influencia de la extracción de aguas subterráneas en el control de la subsidencia del terreno en la zona. En este estudio se destaca la importancia de elaborar políticas adecuadas de gestión de las aguas subterráneas, especialmente en los acuíferos aluviales formados por sedimentos finos comprimibles, a fin de utilizar de manera sostenible los recursos de agua subterránea dulce y evitar los efectos secundarios conexos.
摘要
意大利Tuscany地区受到沉降,山体滑坡和洪水的广泛影响,严重影响了建筑物和基础设施。特别是,Firenze-Prato-Pistoia盆地长期受到与地下水开采相关的地面变形。自1992年以来收集到欧洲遥感卫星(ERS)数据显示盆地中存在多个沉降区,例如Pistoia市的东南部。 2015–2018年的Sentinel-1持久散射干涉测量(PSI)测量证实了该地区的长期沉降,与强烈的园艺(苗圃)有关。同时,Sentinel-1数据显示了皮斯托亚历史中心的意外移动,该移动在过去一直被认为是稳定的。为了确定含水层条件与地面位移之间的复杂关系,采用了集成的建模程序,建立了Pistoia含水层水文地质模型。通过Sentinel-1 PSI测量校准和验证的水动力参数分布表明,Pistoia地区的沉降可能与地下水开采和可压缩性高的隔水层的综合影响有关。为了评估地面位移的潜在演变,利用区域和全球气候模型数据,分析了三种不同的开采情景,将数值模拟扩展到2050年。这导致了绘制Pistoia市数个沉降灾害图,这些图显示了地下水开采对控制该地区土地沉降的影响。这项研究强调了制定适当的地下水管理政策的重要性,特别是在由细的可压缩沉积物组成的冲积含水层中,从而可持续地利用地下淡水资源并避免相关的副作用。
Resumo
A região da Toscana, na Itália, é amplamente afetada por subsidência, deslizamentos de terra e inundações, que impactam com severidade edifícios e infraestrutura. A bacia hidrográfica Firenze-Prato-Pistoia, em particular, tem uma longa experiência de deformação do solo relacionada à retirada de água subterrânea. Os dados de sensoriamento remoto por satélite (European Remote-Sensing satellite - ERS) coletados desde 1992 revelam a presença de várias áreas afundadas na bacia, como a porção sudeste da cidade de Pistoia. As medições da interferometria de dispersão persistente (PSI) Sentinel-1 de 2015–2018 confirmam a subsidência de longo prazo desta área, associada à horticultura intensa (viveiros de plantas). Ao mesmo tempo, os dados do Sentinel-1 revelaram o movimento inesperado do centro histórico de Pistoia, que sempre foi considerado estável no passado. Para identificar a complexa relação entre as condições do aquífero e a movimentação do solo, foi desenvolvido um modelo hidrogeológico dos aquíferos de Pistoia, aplicando um procedimento de modelagem integrado. Os parâmetros hidrodinâmicos obtidos, calibrados e validados por meio de medições Sentinel-1 PSI, sugerem que a subsidência na área de Pistoia é resultado da combinação entre extração de água subterrânea e a ocorrência de aquitardos altamente compressíveis. Para avaliar a evolução potencial de deslocamento do solo da região, simulações numéricas foram estendidas até 2050, usando dados de modelos climáticos regionais e globais, analisando três diferentes cenários de taxas de extração de água. Isso levou ao desenvolvimento de vários mapas de perigo de subsidência para a cidade de Pistoia, que mostram a influência da extração de água subterrânea no controle de subsidência do terreno na área. Este estudo enfatiza a importância do desenvolvimento de políticas adequadas de gestão das águas subterrâneas, especialmente em aquíferos aluviais formados por sedimentos compressíveis finos, para a utilização sustentável dos recursos hídrico subterrâneos evitando-se os efeitos colaterais relacionados.
Badlands can be defined as complex and peculiar types of erosional formations that develop in clayey environments and are mainly favoured by lithological and topographic features, as well as by ...markedly seasonal climate. This work aims at assessing badland susceptibility in Volterra municipality located in Tuscany region (Italy) by means of bivariate statistical analysis implemented in a geographic information system. The Volterra municipality is affected by intense soil erosion processes, including rill and gully erosion usually turned out as badland forms, mostly occurring on Pliocene–Pleistocene clayey sediments. Firstly, an inventory of 234 badland areas was produced on the basis of an available pre-existing database, integrated with the interpretation of aerial photographs and supported by a field survey. Badlands were distinguished in type A and type B, according to different evolutional stage, vegetation presence and consequently different landforms. Then, nine geoenvironmental factors supposed to be predisposing for badland occurrence were chosen and combined with the spatial frequency of badland areas derived from the inventory, through Information Value Statistic approach. The result was a badland susceptibility map that highlights a strong control of lithology, slope gradient and land use in conditioning badland development in the investigated area. The effectiveness of the performed model was demonstrated by a validation test computed through a receiver operating characteristics analysis. The outcomes of this work provide an updated badland database that is useful for soil erosion management and further land-use planning within the Volterra municipality.
The redaction of landslide inventory is a fundamental task for risk management and territorial planning activities. The availability of synthetic aperture radar imagery, especially after the launch ...of Sentinel-1 mission, enables to systematically update landslide inventories covering wide areas in a reduced time frame and at different scales of analysis. In this work, SAR data processed from the fully automatic P-SBAS pipeline have been adopted to update the Italian national landslide database. Specifically, a matrix has been introduced by comparing past landslide state of activity obtained with Envisat data (2003–2010) and that computed with Sentinel-1 (2014–2018). The state of activity was defined by obtaining the projected velocity along the slope dip direction. The analysis involved about 56,000 landslides which showed at least one Sentinel-1 measurement point, of which 74% were classified as dormant, having annual average velocity < 7 mm/year (considering a value of two times the standard deviation) and 26% as active (mean velocity > 7 mm/year). Furthermore, a landslide reliability matrix was introduced on the landslide inventory updated with S1 data, using the measurement point (MP) density within each landslide and the standard deviation of the mean V
slope
value of each landslide. In this case, the analysis revealed that more than 80% of landslides has values of reliability from average to very high. Finally, the 2D horizontal and vertical components were computed to characterize magnitude and direction of every type of landslides included in this work, showing that spreadings, deep-seated gravitation slope deformations, and slow flows showed a main horizontal movement, while complex and translational/rotational slides had more heterogeneity in terms of deformation direction. Hence, the work demonstrated that the application of fast and automatically nationwide Sentinel-1 MTInSAR (multi-temporal interferometry SAR) may provide a fundamental aid for landslide inventory update.
The synthetic aperture radar interferometry (InSAR) technique is an effective means to monitor ground deformation with high spatial resolution over large areas. However, it is still difficult to ...obtain the spatially continuous deformation map due to SAR decorrelation or SAR distortion, which greatly limits the usage of the InSAR deformation map, especially for spatiotemporal characterizing and mechanism inversion. Some conventional methods (e.g., spatial interpolation) rely only on the deformation measurements without considering the influence factors, leading to the inaccuracy of the deformation prediction. So, we propose a multifactor-based machine learning model, namely the K-RFR model, that combines K-means clustering and random forest regression algorithm to reconstruct a continuous deformation map, where the influence factors on ground deformation are considered, such as land use, geological engineering, and under groundwater extraction. We take the city of Xi’an, China, as the study area where SBAS-InSAR was used to obtain the ground deformation maps from 2012 to 2015. Fourteen influence factors are employed, including confined water level, change of confined water, phreatic water level, change of phreatic water, rainfall, ground fissures, stratigraphic lithology, landform, hydrogeology, engineering geology, type of land use, soil type, GDP, and DEM, where the K-means clustering method is used to reduce the influence of spatial heterogeneity. The study area is divided into three homogeneous regions and modeled independently, where the mean squared errors of region I–III are 2.9 mm, 2.3 mm, and 3.9 mm, respectively, and the mean absolute errors are 2.5 mm, 1.0 mm, and 2.8 mm, respectively. Finally, the continuous ground deformation maps of Xi’an from 2012 to 2015 are reconstructed. We compared the new method with two interpolation methods. Results show that the correlation coefficient between prediction and InSAR measurements of the new model is 0.94, whereas the ordinary Kriging method is 0.69, and the IDW method is only 0.63. This study provides an effective means to predict the continuous surface deformation over a large area.
InSAR Analysis of Underground Gas Storage Fibbi, Gabriele; Montalti, Roberto; Del Soldato, Matteo ...
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium,
2023-July-16
Conference Proceeding
Natural gas is an important source of energy used for domestic, industrial and transport purposes. Its presence is unevenly distributed in the world, and its consumption is subject to large seasonal ...fluctuations. In addressing this challenge, Underground Gas Storage (UGS) represents an important option to create national strategic reserves and helps ensure a steady and reliable supply of natural gas. However, gas storage activities, inducing continuous stress changes, can threaten caprock integrity, possibly resulting in gas leakages. The inherent risks necessitate the development of operational protocols for effectively characterizing and monitoring UGS activities. Interferometric Satellite Aperture Radar (InSAR) data offers a comprehensive site-specific perspective, which proves valuable for enhancing the management of withdrawal and injection rates, as well as assessing environmental impacts near UGS facilities. Its utilization contributes to higher levels of operational reliability and safety.
The diagnosis of breast cancer during pregnancy represents a challenging situation for the patient, her caregivers and physicians. Pregnancy adds complexity to oncological treatment planning, as many ...therapies can be potentially dangerous to the fetus. Therefore, a multidisciplinary approach is needed to offer a proper care for obtaining the best possible outcomes for the mother and the future child. Breast surgery is feasible throughout the pregnancy while radiotherapy should be postponed after delivery. Administration of chemotherapy is considered safe and can be given during the second and third trimesters, while it is contraindicated in the first trimester due to the high risk of fetal malformations. Endocrine therapy and targeted agents are not recommended during the whole pregnancy period; however, limited data are available on the use of the majority of new anticancer drugs in this context. The aim of the current review is to provide an update on the current state of art about the management of women diagnosed with breast cancer during pregnancy.
Background
During COVID‐19 outbreak, oncological care has been reorganized. Patients with cancer have been reported to experience a more severe COVID‐19 syndrome; moreover, there are concerns of a ...potential interference between immune checkpoint inhibitors (ICIs) and SARS‐CoV‐2 pathogenesis.
Materials and methods
Between 6 and 16 May 2020, a 22‐item survey was sent to Italian physicians involved in administering ICIs. It aimed at exploring the perception about SARS‐CoV‐2‐related risks in cancer patients receiving ICIs, and the attitudes towards their management.
Results
The 104 respondents had a median age of 35.5 years, 58.7% were females and 71.2% worked in Northern Italy. 47.1% of respondents argued a synergism between ICIs and SARS‐CoV‐2 pathogenesis leading to worse outcomes, but 97.1% would not deny an ICI only for the risk of infection. During COVID‐19 outbreak, to reduce hospital visits, 55.8% and 30.8% opted for the highest labelled dose of each ICI and/or, among different ICIs for the same indication, for the one with the longer interval between cycles, respectively. 53.8% of respondents suggested testing for SARS‐CoV‐2 every cancer patient candidate to ICIs. 71.2% declared to manage patients with onset of dyspnoea and cough as infected by SARS‐CoV‐2 until otherwise proven; however, 96.2% did not reduce the use of steroids to manage immune‐related toxicities. The administration of ICIs in specific situations for different cancer types has not been drastically conditioned.
Conclusions
These results highlight the uncertainties around the perception of a potential interference between ICIs and COVID‐19, supporting the need of focused studies on this topic.
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