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The present research investigates the morphology and genetical mechanism of a sinkhole which occurred in 2019 in Murisengo (NW Italy). This landform is representative of several subsidence phenomena that often concern the Monferrato area (NW Italy). In concomitance with the appearance of the sinkhole at the surface, a cone of detrital material was...
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... We experimented with three different band combinations based on their characteristics. [9] 1. 4-3-2: Natural Color This band combination results in the image appearing as perceived by the human eye. ...
The utility of aerial imagery (Satellite, Drones) has become an invaluable information source for cross-disciplinary applications, especially for crisis management. Most of the mapping and tracking efforts are manual which is resource-intensive and often lead to delivery delays. Deep Learning methods have boosted the capacity of relief efforts via recognition, detection, and are now being used for non-trivial applications. However the data commonly available is highly imbalanced (similar to other real-life applications) which severely hampers the neural network's capabilities, this reduces robustness and trust. We give an overview on different kinds of techniques being used for handling such extreme settings and present solutions aimed at maximizing performance on minority classes using a diverse set of methods (ranging from architectural tuning to augmentation) which as a combination generalizes for all minority classes. We hope to amplify cross-disciplinary efforts by enhancing model reliability.
... We experimented with three different band combinations based on their characteristics. [8] 1. 4-3-2: Natural Color This band combination results in the image appearing as perceived by the human eye. ...
... New applications of remote sensing are emerging in area traditionally not related to space technology, such as archeology and cultural heritage [9,10], real estate value assessment [11,12], and even monitoring of migrant and refugee fluxes. The latter application (Fig. 2) is based on the synergy of "big data" analyses (from mobile phones, social media, etc.) and massive EO data (from the Sentinel-1 and Sentinel-2 European Union satellites, as well as from microsatellite constellations) in order to "(. ...
The importance of earth observation (EO) from space is felt today more than ever in many different fields of human activity. Governments, international organizations, military bodies, private industry, and even individuals benefit every day of the products of spaceborne remote sensing technology. In this paper, we present a brief overview of some of the main trends in the EO scenario, focusing on the emergence of a new paradigm for EO space systems namely: the ongoing, disruptive shift from large satellites to constellations of small spacecraft, fueled by the recent introduction of several key technologies, such as for instance electric propulsion. We present the outline of a tool specifically conceived to assist the system architect in the early design phase of an EO constellation of microsatellites equipped with electric thrusters. A case study is presented for the application of a multisegmented constellation, with visual, thermal infrared, and radar components to remotely monitor a national railway network. We conclude that, in spite of the complexity of modern small satellite constellations, preliminary design can be successfully performed in a simplified and effective way.
... (c) 5 mm/yr interval contour maps superimposed onto the Landsat 8 image of April 23,2018 in RGB combination of band 7, band 6 and band 4. Agriculture areas appear in shades of light green and yellow during the growth season and are located where the subsidence is the highest. Urban areas are in white, gray or light purple color ( Focareta et al., 2015 ...
The aim of this thesis is centered on the detection and monitoring of surface deformation in northwest Turkey induced by a variety of natural (such as tectonic activity, slow moving-landslides, etc.) and anthropogenic (ground water extraction, construction activities, etc.) hazards and on the analysis of the related deformation mechanisms and their environmental consequences. In this work, I computed Interferometric Synthetic Aperture Radar (InSAR) time series to examine ground deformation evolution for three different case studies associated to different geophysical phenomena and underlying processes. The focus of this thesis is two-fold : (1) to reveal and monitor the spatio-temporal characteristics of aseismic slip along the August 17, 1999 Mw 7.4 Izmit earthquake rupture, and discuss its potential relationship with lithology and geology (2) to investigate ground subsidence in urban or human-exploited areas induced by various factors, and discuss the relative roles of tectonics, lithology and anthropogenic activities in such ground motion.In the first case-study, I combined InSAR measurements, based on X-band TerraSAR-X and C-band Sentinel-1 A-B radar images acquired over the period 2011-2017, with near field GPS measurements, performed every 6 months from 2014 to 2016, as well as creep meter measurements to examine the surface velocity field around the NAF after the 1999 Izmit earthquake. In this study, the Stanford Method for Persistent Scatterers InSAR package (StaMPS) was employed to process series of Sentinel 1 A-B (acquired along ascending and descending orbits) and TerraSAR-X (ascending orbits) radar images. The InSAR horizontal mean velocity fields reveal that the creep rate on the central segment of the 1999 Izmit fault rupture continues to decay, more than 19 years after the earthquake, in overall agreement with models of postseismic afterslip rate decaying logarithmically with time. Along the fault section that experienced a supershear velocity rupture during the Izmit earthquake, creep continues with a rate up to ~ 8 mm/yr. A significant transient event with accelerating creep is detected in December 2016 on the Sentinel-1 time series, consistent with creepmeter measurements, near the maximum creep rate location. It is associated with a total surface slip of 10 mm released in one month only. The second case study deals with the identification and measurement of secular ground deformation in Istanbul from a long-term InSAR time-series spanning almost 25 years of satellite radar observations (1992-2017). This InSAR time series was computed from radar images of multiple satellites (ERS-1, ERS-2, Envisat, Sentinel-1 A, B) in order to investigate the spatial extent and rate of ground subsidence in the megacity of Istanbul.In the third case study, InSAR time-series analysis is calculated for quantifying the subsidence of the Bursa Plain (southern Marmara region of Turkey), which has been interpreted as resulting from tectonic motions in the region. In this study, the StaMPS is employed to process series of Sentinel 1 A-B radar images acquired between 2014 and 2017 along both ascending and descending orbits. The vertical velocity field obtained after decomposition of line-of-sight velocity fields on the two tracks reveals that the Bursa plain is subsiding at rates up to 25 mm/yr. The most prominent subsidence signal in the basin forms an east-west elongated ellipse of deformation in the east, and is bounded by a Quaternary alluvial plain undergoing average vertical subsidence at ~10 mm/yr. The InSAR time series within the observation period is well correlated with changes in the depth of the ground water. These observations indicate that the recent acceleration of subsidence is mainly due to anthropogenic activities rather than tectonic motion.
... (c) 5 mm/yr interval contour maps superimposed onto Landsat 8 image of 23 April 2018, in RGB combination of bands 7, band 6, and 4. Agriculture areas appear in shades of light green and yellow during the growth season and are located where the subsidence is the highest. Urban areas are in white, gray, or light purple colors[70]. ...
We characterize and monitor subsidence of the Bursa Plain (southern Marmara region of Turkey), which has been interpreted as resulting from tectonic motions in the region. We quantify the subsidence using Interferometric Synthetic Aperture Radar (InSAR) time-series analysis. The Stanford Method for Persistent Scatterers InSAR package (StaMPS) is employed to process series of Sentinel 1 A-B radar images acquired between 2014 and 2017 along both ascending and descending orbits. The vertical velocity field obtained after decomposition of line-of-sight velocity fields on the two tracks reveals that the Bursa plain is subsiding at rates up to 25 mm/yr. The most prominent subsidence signal in the basin forms an east-west elongated ellipse of deformation in the east, and is bounded by a Quaternary alluvial plain undergoing average vertical subsidence at ~10 mm/yr. Another localized subsidence signal is located 5 km north of the city, following the Bursa alluvial fan, and is subsiding at velocities up to 25 mm/yr. The comparison between temporal variations of the subsiding surface displacements and variations of the water pressure head in the aquifer allows estimation of the compressibility of the aquifer, α. It falls in the range of 0.5 × 10 −6 − 2 × 10 −6 Pa −1 , which corresponds to typical values for clay and sand sediments. We find a clear correlation between subsidence patterns and the lithology, suggesting a strong lithological control over subsidence. In addition, the maximum rate of ground subsidence occurs where agricultural activity relies on groundwater exploitation. The InSAR time series within the observation period is well correlated with changes in the depth of the ground water. These observations indicate that the recent acceleration of subsidence is mainly due to anthropogenic activities rather than tectonic motion.
... These models have discouraged the application of design and management of protected areas. Besides, protected area models include types that allow for different gradations of sustainable use in addition to safe preservation (Kitamura and Clapp 2013;Focareta et al. 2015;Cerra et al. 2016;Rowlands and Sarris 2007;Sands and Corns 2011;East et al. 2012;Leen et al. 2012;Banerjee and Srivastava 2013). ...
Sustainable management and exploitation policies as well as suitable conservation and mitigation strategies are mandatory to preserve cultural heritage and to reduce threats, weathering phenomena, and human actions that may produce significant deterioration and alteration of cultural heritage and “its environment”. In this context, remote sensing technologies can offer useful data to timely update information and documentation and set up reliable tools for systematic monitoring of cultural properties. In this study, multi-temporal and multi-sensor satellite data from Corona, Landsat, Spot, Quickbird, and Sentinel-2A have been exploited along with spatial analysis to investigate the area of the Theban temples at west Luxor (Egypt), severely threatened by uncontrolled urban sprawl. The results from our analyses showed that the urban expansion continuously occurred during the whole investigated period causing an increasing in urban areas around (1) 1.316 km2 from 1967 to 1984, (2) 1.705 km2 from 1984 to 2000, (3) 0.978 km2 from 2000 to 2003, (4) 2.314 km2 from 2003 to 2011, and (5) 1.377 km2 from 2011 to 2017. The random urban expansion caused bad sewage networks and high groundwater depth which in turn affected the archaeological areas directly (as evident on a landscape view) and indirectly by causing changes (growing) in the level of ground water depth and increasing and accelerating weathering phenomena. The quantification and mapping of urban sprawl enabled us not only to quantify and spatially characterize urban sprawl but also to create a model to mitigate the impact and provide some operational recommendations to protect the archaeological site. Outcomes from our analysis pointed out that today the tremendous availability of advanced remote sensing data has opened new prospectives unthinkable several years ago.
... We have underlined in our previous work [5] the peculiarity of remote sensing in providing information not only on the cultural heritage but also on the surrounding vegetation and parks inside. This aspect is very recent, because more extensive research has been performed in the field of artefacts and manufacts, less instead in the field of parks and green areas related to them. ...
... We would like also to highlight that in our previous work as presented in [5] the choice of Landsat-8 satellite, to use in combination with Sentinel-1, was not made randomly but it was a carefully reasoned choice. In fact at the time of our initial research, Sentinel-2 data were not available yet and the idea to use Lansat-8 and Sentinel-1 data together was based on two main considerations: the possibility to increase the available information for land and vegetation monitoring thanks to such different data sources, and the opportunity to anticipate results and discussion that could be resumed and confirmed, in a certain manner, as soon as Sentinel-2 would have been available. ...
... In this work our study has focused mainly on green areas in order to analyse and monitoring the health state and the different types of vegetation. Yet, obviously it can be extended to different applications, considering also that we have already carried out a wide research on the use of combined data from Sentinel-1 and Landsat-8 on other different fields ( [5], [6]), by achieving very interesting results that now can be also improved with the availability of Sentinel-2 data. ...
With the entry into operation of the Sentinel-2 mission in June 2015, a new land monitoring constellation of twin satellites has been added to the Copernicus project from ESA and new insights have been derived through the combination of Sentinel-2 data with other optical/multispectral data, and with other data from satellites belonging to the same Copernicus project. To this end, the objective of this paper has been to present new added-value tools first through the integration of different satellite platforms: data from NASA Landsat-8 and ESA Sentinel-1 have been used and combined, and furthermore through the comparison of satellite data all from the same Copernicus project: data from Sentinel-1 and Sentinel-2 have been jointly processed and compared. Although data from optical/multispectral sensors, as those of Landsat-8 and Sentinel-2, and data from SAR on board of Sentinel-1, are very different, their combination provides useful and interesting results. The integration and combination of these data can find useful application in many fields from oceans to waterways, from land surfaces to fossil deposits, from vegetation to forest areas. In this works authors have focused their interest in green areas and vegetation monitoring applications, by choosing as case of interest the Royal Palace of Caserta and its gardens. The idea has started from the increasing interest in monitoring the cultural heritage monuments and in particular the surrounding vegetation with the green areas and the parks inside. Satellite images can put into evidence boundaries modifications, the vegetation state, their possible degradation, and other phenomena such as changes in the territories due both to natural and to anthropogenic causes. Data combination from different sources as above specified gives a good number of indexes very useful to analyze the vegetation state and its health in a very deep way. Many of these indexes have been calculated and discussed for investigation.
This chapter discusses the role of remote sensing (RS) in observing and monitoring our planet, with a specific focus on satellite RS and RS through the use of networked sensors (fixed or mobile). Some history of the development of these systems over the years is first presented. Next, several applications are analyzed, and the advantages and disadvantages of processing of data collected from satellite platforms or sensor networks are highlighted. A combination of heterogeneous data from different sources is also discussed. Finally, present and future trends, employing algorithms of artificial intelligence (AI) and in particular of machine learning (ML) in RS data processing, are discussed.
Remote sensing (RS) has become one of the vital approaches to get the information directly from the earth’s surface. In recent years, with the event of environmental informatics, RS information has contend a crucial role in several areas of analysis, like atmosphere science, ecology, soil pollution, etc. When monitoring, the multispectral satellite data problem are vital once. Therefore, in our analysis, automatic segmentation has aroused a growing interest of researchers over the past few years within the multispectral RS system. To beat existing shortcomings, we provide automatic semantic segmentation while not losing significant information. So, we use SOM for segmentation functions. Additionally, we’ve got planned a particle swarm improvement (PSO) algorithmic rule for directly sorting out cluster boundaries from SOM. The most objective of this work is to get a complete accuracy of over eighty fifth (OA> 85%). Deep Learning (DL) could be a powerful image process technique, together with RS image.
