This PDF file contains the front matter associated with SPIE Proceedings Volume 11863, including the Title Page, Copyright form and the Table of Contents

A warm welcome to all participants of our conference! My name is Karsten Schulz. I am the conference chair of Earth Resources and Environmental Remote Sensing & GIS Applications. I am glad that I am supported by my two excellent Co-Chairs Konstantinos Nikolakopoulos and Ulrich Michel. We all are deeply impressed by our faithful community. Already last year I expressed my gratitude about our faithful community because despite these horrible pandemic situation we had a stable number of submissions and very interesting scientific contributions. I could not imagine that we will receive nearly 50% more submissions this year. Our Program includes more than 50 scientific contributions submitted by researchers from 22 countries. 14 countries from Europe and the other covers nearly all parts of the world. So I hope you all will use the chance to hear these interesting high quality talks on demand in the next days. Especially our involved young scientists deserve your attention. So, it is all the more regrettable that we have again not the chance to meet us in person. I really miss to meet you all - our faithful participant - and our lively discussion of the presented contribution - face to face. - and the discussions how we could improve our conference. Nevertheless, also in these difficult times, we extended our topics and organized a special session with more than 10 scientific contributions. I am referring to the session "Satellite Remote Sensing and Ground-based Nondestructive Technologies in Civil and Environmental Engineering". Chaired by Luca Bianchini Ciampoli from the University of Roma Tree and (Prof.) Fabio Tosti from the University of West London. Perhaps you remember Valerio Gagliardi also associated with Roma Tree. After his interesting talk concerning InSAR last year we discussed to introduce this new session and I have to confess he and his colleagues' were very successful to organize it and we have many interesting contributions for this topic. But I really have to talk with them about the length of their session name. It is really a pity that we again will not have complete live sessions and that instead we will again meet us only in an about 2 hours long networking event. Under the impression that we heard all Security Defence Conferences and most remote sensing conferences choosed networking sessions and also the Optical System Design Conference will have only one streaming event we also thought it would be better to go this way again. One problem was that due to limited time for a complete live streaming event all presentation should have been shortened to about 8 minutes. But the uploaded long version of about 20 minutes would still have been necessary. I thought that would be very confusing. However, I hope that you all will help me to organize a successful networking event. I will invite all session chairs to tell us a little bit about the developments in their session and to identify one or two topics in their session which we can discuss during the networking event. At the end of the session we will discuss the future development of our conference and I hope we will see lively discussions. My best wishes for you all and I hope I will see you in our networking event! And please take care and stay healthy!

Earthquake disaster damage detection and mapping is one of the oldest challenges for remote sensing scientists. The usefulness of almost every type of active and passive sensor deployed on air or spaceborne platforms has been examined in the past. The advent and the development of unmanned aerial vehicles (UAVs) during the last decade have opened up many new opportunities for earthquake damage mapping. The main advantages of UAVs are the high spatial resolution, the possibility to acquire stereo images and produce both orthophotos and Digital Surface Models, and the flexibility of the platform. Earthquake-induced strain and rupture traces are expressed on the surface and imprinted in the topography on the landscapes of fault zones. UAVs provide an efficient and flexible solution for the acquisition of multi-angle imagery in order to reconstruct in fine scale the fault zone topography. The combination with RTK GNSS measurements provide the necessary accuracy to the final maps. A characteristic post-earthquake response based on UAV, GNSS and TLS technologies is presented in the current study. On March 3, 2021 (10:16:10 UTC) an earthquake with magnitude of 6.3 struck Thessaly, central Greece. The earthquake occurred in a region primarily characterized by active NW-SE trending normal faults, which belong to the Northern Thessaly fault zone. On March 4, 2021, (18:38:19 UTC), another earthquake struck the same area with magnitude 5.9. The University of Patras team detected innumerable lateral spreading, and liquefaction sand boils in close proximity with the active fault trace in the earthquake epicentral area. All these secondary earthquake environmental effects were mapped with UAV and traced with RTK GNSS. Co-seismic surface offset on the fault trace measured over than 25 cm. Our team mapped several places showing clear tectonic deformatin, although the intense agricultural modifications due to human activities disturbed the near surface stratigraphy. Therefore, only one favorable site for trenching was found. Two trenches 10 m long and 2.0 m wide with a maximum depth of almost 2.5 m were excavated and then mapped both by terrestrial photogrammetry and by a Terrestrial Laser Scanner.

Snow avalanches are a natural hazard that incur great cost to both property and to human welfare. In some countries they are known to cause more fatalities than both earthquakes and landslides. They also pose a threat to transportation corridors such as year-round highways and railroads that must pass through mountainous regions. There are two categories of avalanche formation that must be recognized when considering slope failure: loose and slab. The former occurs when there is little cohesion in the snowpack and a localized failure progresses downslope. This takes place when the slope angle is steeper than the angle of repose, making failure circumstances comparatively easy to predict. In contrast, slab avalanches occur when a cohesive slab of snow is released over an extended plane of weakness. This happens when a stress, such as the loading of fresh or windblown snow, or the weight of a person, is introduced to a slab layer which has formed on top of a weak layer. The formation of the weak layer that governs slab releases is much more difficult to predict, making this category of avalanche more hazardous. The plane of the weak layer may be comprised of different types of crystals (e.g., hoar and faceted). These are formed either at the surface or at a subsurface depth through morphological processes involving the transport of heat and vapour pressure gradients through the snowpack. These formations are weak since they exhibit poor intergranular bonding and lack shear strength. Even though it has been recognized as a factor in a significant fraction of failure events, the formation of near-surface faceted crystal layers has not been studied extensively. Elucidating the formation of subsurface faceted crystals will advance the current understanding about the formation of snow slabs, which in turn, could be used in the prediction of slope failure. The formation process of subsurface faceted crystals is tied to the penetration of solar radiation into the snowpack. More specifically, absorbed radiation provides the energy that gives rise to the morphological processes governing crystal growth. Consequently, the quantification of light penetration through snow is of interest for studies on the formation of the weak layers associated with snow failure. Despite its importance, investigations of light penetration through snow are still scarce in the literature, and the datasets obtained from field work are affected by experimental limitations. To overcome these limitations and to advance the understanding of light penetration into near-surface layers of snow, we employed a predictive in silico experimental setup. Our findings demonstrate that snow grain size and sample density must be carefully accounted for when estimating the quantity of solar radiation contributing to the subsurface morphological processes that form faceted crystals. In addition, our in silico experiments provide a detailed assessment of the hyperspectral transmission profiles at different depths. To the best of our knowledge, such an assessment has not been reported in the related literature to date.

Rock-falls are catastrophic phenomena, which have been studied widely through remote sensing data and special software analysis. Rock-fall investigation is quite crucial and especially the rock-fall prediction, as these phenomena cause serious damages and fatalities that could be avoided with a correct susceptibility mapping. In this paper, the RocFall software was chosen aiming to achieve a rock-fall simulation. Kinetic energy illustration, velocity calculation, end points detection and bounces heights depiction can be calculated by the specific program. A well-studied landslide in Moira settlement, near to the city of Patras (Western Greece) was selected for the simulation as many geo-data sets already exist. Slope maps and elevation profiles were extracted, in GIS environment, from different Digital Surface Models (DSMs). Those derived products were implemented in RocFall software for further simulations. More specifically, free available DSMs such as DSM from the Greek Cadastral DSM, ALOS AW3D30 DEM, ASTER GDEM, SRTM30 DEM, SRTM90 DEM, TanDEM_X as well as UAV DSM created by field campaigns were used for slope profile extraction. The results were assessed based on the spatial resolution of DSMs and were validated with in situ observations and measurements. The current study has two objectives: Firstly, to evaluate RocFall software outcomes with the field measurements and secondly, to estimate the influence of the DSM spatial resolution and accuracy to rock-fall simulation. According to previous studies, the spatial resolution is affecting the vertical accuracy of the DSMs. Among the aforementioned DSMs, UAV DSM was proved more appropriate for landslides simulation.

Lineament mapping is a genuine issue for deciphering the tectonic setting, geological history, stability evaluation, engineering constructions localizations, prospecting orogenic gold deposits, prospecting polymetallic vein-type mineralization,….etc. Consequently, the substantial objective of this study is to compare various remote sensing data sets concerning with their potency in lineaments elicitation to recommend the superior for future usage. Toward this aim, approximately most of the common remote sensing datasets, are tackled in this study, including optical sensors (Landsat 8 OLI, ASTER, Advanced Land Imager, Sentinel 2A), active radar data (Sentinel 1), digital elevation models (ALOS PALSAR, SRTM, NASA, ASTER V3). In this scope, an entirely automatic lineament derivation environment is created through integration of edge detection algorithm, line-linking algorithm, as well as digitizing the pre-existing lineaments from geological maps. Subsequently, thematic comparison, visual interpretation, geostatistical analysis and accuracy assessment are performed. Results revealed that the used optical sensors are less efficient than DEMs having the same spatial resolution. Also, sentinel 1 radar data (C-band, f = 5.4 GHz) is more competent than optical data sources. ALOS PALSAR DEM is more eligible than any other utilized data type. Wholly, DEMs built from L-band (f =1.27 GHz) radar data (PALSAR DEM) proved their leverage in automatic lineament extraction to a limit that can deviate from the well-known relationship between number of extracted lineaments and pixel size.

Several raw materials for “green” energy production, such as high purity quartz, lithium, rare earth elements, beryllium, tantalum, and caesium, can be sourced from a rock type known as pegmatite. The GREENPEG project (https://www.greenpeg.eu/), started in May 2020, is developing and testing new and advanced exploration technologies and algorithms to be integrated and upscaled into flexible, ready-to-use economically efficient and sustainable methods for finding buried pegmatites and their “green” technology raw materials. One of the tasks of this project aims to apply different image processing techniques to different satellite images (Landsat, ASTER, and Sentinel-2) in order to automatically identify pegmatite bodies. In this work, we will present the preliminary results, regarding the application of machine learning algorithms (ML), more specifically, random forests (RF) and support vector machines (SVM) to one of the study areas of the project in Tysfjord, northern Norway, to identify pegmatite bodies. To be able to determine the classes that would make up the study area, geological data of the region, such as lithological maps, aeromagnetic data, and high-resolution aerial photographs, were used to define the four classes (1. pegmatites, 2. water bodies, 3. vegetation, 4. granite). All training locations were randomly selected, with 25% of the samples split into testing, and the remaining 75% split for training. The SVM algorithm presented more promising results in relation to overfitting and final image classification than RF. Testing the algorithms with several variables of parameters was able to make the process more efficient. Acknowledgments This study is funded by European Union’s Horizon 2020 innovation programme under grant agreement No 869274, project GREENPEG: New Exploration Tools for European Pegmatite Green-Tech Resources.

The use of vegetation forest fires (FF) is known as one of the most important criteria in global change, especially because of their events on changes in land cover and atmospheric chemistry. Fire and climate are related with each other, meaning that the fire regime responds quickly to changes in climate. There are many environmental factors that affect the spread of fire, as ignition agents, topography, vegetation, landscape fragmentation, etc. Several of them can be obtained by Remote Sensing (RS) data/techniques. Fire risk (FR) assessment is one of the main components when determining the right approach to protect and prevent a region against FF. An analysis of the variables that influence FR was performed by applying a Geographical Information System (GIS) methodology to obtain a final FR map. The methodology used was based on the Chuvieco FR model. It was necessary to consider the fire danger (FD) and the fire vulnerability (FV). In this study, the main objective was to estimate the FR map, which is composed by two maps: ignition map, propagation map; and several variables, such as Fuel Moisture Content (FMC), Human Risk Index (HRI), vegetation indices (estimated through Sentinel-2 images), among others. The results were validated with the data relative to the fire occurrences in Valongo (Porto, Portugal) in 2019. It was demonstrated that the model is sufficiently accurate to apply in Portugal and in other areas with the respective local adjustments. In the future, this model will be implemented in a GIS plugin, under QGIS software.

This study will document the abandoned Carmelite monastery located in Polemidia, Limassol District, Cyprus. The monastery was abandoned in 1570 and recently restored by the Greek Orthodox Church, with the name of Panagia Karmiotissa. The church of Panagia Karmiotissa is the only preserved Gothic church in the Limassol area during the time of the Frankish House of Lusignan in Cyprus. To the north area of the church there is a spring, where legend states that a holy shrine of the Virgin Mary springs miraculously. In between the Church and the spring, the rubble of a separate construct is visible, which may possibly be the monk residence halls. From the end of the 12th century to the 19th century, little is known about the site. This study investigates and documents both the church and the surrounding area, using remote sensing data, aerial images and ground penetrating radar. This investigation using remote sensing techniques is an effort to uncover and understand the history of the site.

Non-invasive techniques (e.g., photogrammetry and laser scanning) are becoming crucial methods for archaeological applications, such as monitoring, revealing, and preserving buried monuments. Visual analysis techniques based on photogrammetric-derived digital models have been applied in several scientific fields, particularly in digital archaeology. Examining the capabilities of these techniques (visual analysis techniques) for archaeological prospection and enhancing the effectiveness of existing approaches are therefore fundamental themes in digital archaeology. This research aimed to evaluate the capabilities of different visual analysis techniques derived from the Structure from Motion and multi-view stereoscopic (SfM-MVS) methods in identifying archaeological remains in the ancient city of Babylon, the capital of the ancient Babylonian civilization in Mesopotamia. These techniques can produce: 1) standalone raster images, such as hillshade, slope gradient, local relief, and sky-view factor (SVF); and 2) fusible new rasters that could produce by integrateing multi-layered topographic data calculated from gridded digital models towards detecting new potential findings. In this study, raw aerial images were collected using a DJI Phantom 4 Pro drone over the ancient city of Babylon in summer 2018. The raster layers obtained from the derived SfM digital models (i.e., DSM) are applied to trace buried monuments that could reveal possible new attributes of the ancient city structure. Using more than one raster image in prospecting is critical to confirm possible new archaeological remains. Although these raster images have the same fine grain (2.19 cm/pix), they have various merits in terms of highlighting topographic features in the investigated site. The combination of different visualization tools (e.g., DSM, differential openness, and SVF layers) into a new integrated single raster could simultaneously emphasis and advance the identification of archaeological features, such as edges, circular, and linear features in comparison with a standalone raster approach. Therefore, this study revealed several potential buried monuments, such as foundations, construction remains, and paths of the archaeological site. These non-destructive approaches for prospecting and detection archaeological remains should be applied more widely by the digital archaeological community to enhance the evaluation of archaeological change and promote comprehension of understudied archaeological areas worldwide.

The use of vegetation indices to highlight archaeological features from remote sensing (RS) sensors is increasingly in demand, especially due to the possibility of obtaining high-resolution multispectral images with drones. The objective of this work is divided in two steps: i) to test techniques for the semi-automatic identification of crop marks in a study zone, in Salvada, Portugal, and; ii) to develop a Geographical Information Systems (GIS) open-source application, named ArchMarks, under QGIS software, to enhance the identification of archaeological crop marks using RS data/techniques, integrating the automatic creation of indices and the identification techniques tested before. ArchMarks aims to compute several vegetation and soil indices from multispectral imagery considering four bands (Red, Green and Blue and Near InfraRed (NIR)) for enhancing the identification of archaeological crop marks. In order to define the best approach to implement in the plugin, some tests were performed with two algorithms available in QGIS: ContrastEnhancement and WatershedSegmentation. High-resolution aerial imagery (25-50 cm spatial resolution) was obtained from Web Map Service (WMS) services in addition to Sentinel-2 satellite image, whose spatial resolution is of 10 meters in the Red Green Blue-Near InfraRed bands. The application is free and can be adapted to other region of interest. In the future, the plugin will be improved with a methodology to download remote sensing data (aerial images and satellite data) from WMS sources, band stacking, and machine learning algorithms, such as Support Vector Machine (SVM) algorithm, to automatically classify archaeological traces on the vegetation or bare soil.

The paper presents the results of studies of medieval archaeological sites, the Laishevsky and Ostolopovsky settlements, and the Ostolopovsky hillfort, using multitemporal remote sensing data and modern field research methods. The studied sites are located in the zone of active bank transformation and have been destroyed since the creation of the Kuibyshev reservoir. To assess the dynamics of the coastline, multitemporal remote sensing data were used. Shoreline positioning for the historical period of the 1950s was interpreted from archival aerial images (Kazan University Library). 1975 satellite images obtained during the Corona reconnaissance space program KeyHole-9 Hexagon satellite (10.06.1975). For their georeferencing very high-resolution satellite images were used (2019). The maximum errors of georeferencing are less than 3 pixels for both sites. In 2018-2019, field surveys of the shoreline fragments at the Ostolopovsky and the Laishevskoe settlements were carried out in 2020 – at Ostolopovo hillfort placement. For field studies, a DJI Phantom 4 drone and GNSS receiver with real-time kinematic corrections were used. The Digital Shoreline Analysis System (DSAS), as an extension module of the ArcGIS software, was used to quantify shoreline displacement. Shoreline indicators such as linear retreat rate (m/year), shoreline displacement (m) were automatically calculated. A quantitative assessment of the Kuibyshev shoreline transformation makes it possible to evaluate the damage caused to archaeological sites and the risk of their further destruction.

It is widely known that water resources are facing several issues, as their limited availability has been affected by climate change, human over-exploitation and environmental pollution. The reduction of the availability of water resources along with the deterioration of water quality could have devastating consequences for human beings and natural environment. Therefore, ensuring the availability, quality and quantity of water resources is a key issue which has attracted the attention of researchers, governments and the public. The construction of dams constitutes a water storage solution in areas suffering from severe water shortage problems. Dams are artificial barriers, constructed across a river in order to hold back the rain water which falls during the wettest months, while a lake or reservoir is being formed behind them. In the current study, we map and monitor the existing water resources within the reservoir of a newly con-structed dam during its gradual filling. In that context, we acquired Sentinel-1 radar data and Sentinel-2 multispectral data which are provided free of charge to all users through the Copernicus programme. Our data sets are covering a period of one and a half year, from the completion of the dam construction and the beginning of the reservoir’s filling until today, wherein the water level is at the highest point. Sentinel-1 data were processed using random forest classifier and dividing the area into water and land sub-parts, while Modified Normalized Difference Water Index (MNDWI) was calculated for Sentinel-2 data. The multi-dated water extents were integrated in an ArcGIS environment in order to evaluate them and investigate if a combined processing of radar and multispectral data of Copernicus programme could be the right choice towards a more accurate and detailed monitoring of water resources in a dam reservoir. Validation of the results has been performed using in situ measurements and observations.

Monitoring and maintenance of protective infrastructure assests is a key aspect of common crisis prevention strategies in the alpine area. However, the current gold standard of manual inspection and surveying is labour intensive, demanding, and sometimes dangerous to the inspectors. Moreover, collected data usually is of qualitative rather than quantitative nature and downstream analysis options are relatively limited. In this work, we introduce an easy-to-use, operator-assumption-light approach to extract check dam structures from high density multi-echo aerial laserscans using a combination of 3D point cloud processing techniques and morphological filtering in the 2.5D space. The only required inputs to the anayltical pipeline are a georeferenced point cloud of the scanned area as well as the approximate position of the check dams – an information usually readily available from geographic information systems (GIS). Another strength of our method lies in its ability to cope with locally sparse or missing data as well as dense vegetation overgrowing the check dams. In general, the automatically reconstructed checkdam structures are in good agreement (deviation < 10 mm) with manually cropped data, albeit with some considerable deviations around edges of near-vertical parts of up to several meters as a result of erosive properties of the morphological filters. In addition to reconstruction of check dams and their surrounding terrain, we also estimate the overflow level of the check dams. This allows us to automatically obtain their respective upstream reservoirs from the surrounding terrain using region growing techniques. We see the digital model thus obtained as being well suited to support rapid quantitative assessment of the remaining protective properties of a given check dam and by extension maintenance related decision-making.

Continuous monitoring of critical infrastructures is crucial to prevent catastrophic events such as collapse of viaducts and prioritising maintenance interventions. However, developing effective monitoring approaches must rely on the collection of a variety of information, such as the time series of structural deformations. In this context, various ground-based non-destructive testing (NDT) methods have been used in monitoring the structural integrity of transport infrastructures. However, these require routine and systematic application at the network level over long periods of time to build up a solid database of information, involving many efforts from stakeholders and asset owners in the sector. To this effect, satellite-based remote sensing techniques, such as the Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR), have gained momentum due to the provision of accurate cumulative structural displacements in bridges. Although the application of the InSAR monitoring technique is established, this is limited to the high required time for the interpretation of the data with high spatial and temporal density. This research aims to demonstrate the viability of the MT-InSAR techniques for the structural assessment of bridges and the monitoring of damage by structural subsidence, using high-resolution SAR datasets, integrated with complementary Ground-Based (GB) information. To this purpose, high-resolution SAR dataset of the COSMO-SkyMed (CSK) mission provided by the Italian Space Agency (ASI), were acquired and processed in the framework of the ASI-Open Call approved Project “MoTiB” (ID 742). In particular, a Persistent Scatterer Interferometry (PSI) analysis is applied to identify and monitoring the structural displacements at the Rochester Bridge, in Rochester, Kent, UK. In order to explore the viability of Machine Learning algorithms in detecting critical situations in the monitoring phases, an Unsupervised ML Clustering approach, which generates homogeneous and well-separated clusters, is implemented. Each PS data-point is located to specific cluster groups, based on the deformation-trend and the values of displacements of the historical time-series. This research paves the way for the development of a novel interpretation approach relying on the integration between remote-sensing technologies and on-site surveys to improve upon current maintenance strategies for bridges and transport assets.

In the last decades the scientific community has increasingly used satellite Synthetic Aperture Radar (SAR) data to improve the understanding of geophysics phenomena in many fields (e.g. geology, hydrology, glaciology, climatology, volcanology) as well as in civilian and environmental engineering. In this context, an additional step forward was provided by the new generation of X-Band very high-resolution SAR sensors, such as those hosted onboard the Italian COSMO-SkyMed (CSK) satellites, able to provide an exceptional capability of collecting dense interferometric data stacks in a short time interval (i.e. few months) with a resolution allowing to monitor single facilities in detail. In many case studies, the available CSK historical series of displacements highlighted the presence of localized deformation points, for example affecting cultural heritage buildings or of potential concern for the stability of bridges and railway networks. This highlights the maturity achieved by this technology widely used also by municipalities and public bodies for monitoring structures and infrastructures. Since 2008, ASI has strongly supported the exploitation of this kind of satellite data and in 2015, following the previous successful experiences, decided to encourage the international scientific community and the national Small and Medium Enterprises (SMEs) through two dedicated “COSMO-SkyMed Open Call” initiatives. In this framework, this paper presents some selected case studies for structure and infrastructure monitoring and the related results, focusing the discussion on the recent developments and observed trends for both scientific and commercial communities, at both national and international levels.

Advances in data processing and the availability of large datasets from very high-resolution (VHR) SAR satellite missions are promoting the use of multi-temporal InSAR techniques for the near-real-time assessment and the health monitoring of bridges and transport infrastructures. On the other hand, the Ground-Penetrating Radar (GPR) is a Non-destructive technology widely applied to monitor the internal state of infrastructure asset by rapid electromagnetic inspections. This research aims to investigate the viability of a novel Non-Destructive Health-Monitoring Approach (ND-HMA) based on the synergistic use of satellite remote sensing and GPR techniques for structural assessment of bridges and the prevention of damages induced by structural subsidence. The analyses were developed to identify and detect structural displacements of the Olivieri Bridge, located in Salerno, Italy. To this purpose, commercial VHR COSMO-SkyMed (CSK) products, provided by the Italian Space Agency (ASI), were acquired and processed by persistent scatterers (PS) InSAR technique. The historical time-series of deformations of the scatterers, found in correspondence of critical structural elements of the structure (i.e., bridge piers and arcs), were analyzed. Furthermore, in-situ GPR inspection analysis were carried out by using multi-frequency GPR systems equipped with both air-launched and ground-coupled antennas with central frequency ranging between 200 MHz and 1000 MHz. The implementation of the integrated approach provides a technologically enhanced and reliable mechanism for the provision of "early warning" information to be more rapidly processed and conclusively actioned by asset owners and management agencies. In particular, a novel data interpretation approach is proposed based on the selection of several PS data-points with coherent deformation trends on the bridge and the analysis of GPR outputs. The outcomes of this study demonstrate that multi-temporal InSAR remote sensing techniques can be applied to complement non-destructive ground-based analyses (e.g., ground-penetrating radars), thereby paving the way for integrated approaches in the smart monitoring of infrastructure assets.

The European Directive 2014/24/EU and its recent Italian transposition law DM 560/2017 strongly encourage an extensive use of BIM-based practices in transport infrastructure design and management operations. Accordingly, a step forward from the traditional management approach towards a shared and highly integrated model capable of including the various monitoring and maintenance phases along with economic, operational, and environmental concerns, is required. This study aims at investigating the potential of an interoperable and upgradeable BIM model supplemented by non-destructive pavement survey data, such as Ground Penetrating Radar (GPR) and Mobile Laser Scanner (MLS) data, and satellite Remote Sensing information (i.e. InSAR). The main goal of the research is to contribute to the state-of-the-art knowledge on BIM applications, by testing an infrastructure management platform capable of minimizing, or in some cases totally removing, the limitations typically related to the separate observation of these assessments to the advantage of an integrated analysis including both the design information and the routinely updated results of monitoring activities. As a test to the proposed methodology, an experimental activity was conducted over a real highway infrastructure, that was parallelly inspected by GPR, MLS and InSAR. To the purpose of the study, the raw dataset from the three surveys were separately processed, scaled and converted into suitable file formats to be more rapidly implemented into the BIM environment. As a result, the synergistic use of geometric and design information with the results from monitoring activities allowed the definition of a Digital Twin Model of the investigated road infrastructure, which can be progressively updated at each new survey, thereby permitting to detect pavement distresses and to control their evolution over time, while being aware of the effect of any delay in maintenance activities. Preliminary results have shown promising viability of the data management model for supporting asset managers in the various management phases, thereby proving this methodology to be worthy for implementation in Pavement Management Systems (PMS)

Linear transport infrastructures, as bridges and viaducts, are exposed to natural hazards or endogenous events, which can affect their operation and structural integrity. Recent unpredicted bridge failures and collapses highlight the need for effective structural monitoring operations, especially for aged concrete structures. Non-Destructive Testing (NDT) methods, such as Ground Penetrating Radar (GPR) and Mobile Laser Scanner (MLS), have been used for the assessing and monitoring of such structures in recent years. Our paper reports on the outcomes of the integrated monitoring method based on the use of GPR and MLS technologies for the structural assessment of bridges and the prevention of damages induced by structural subsidence. The analyses we made aim to assess the structural integrity of the Olivieri Viaduct, located in Salerno, Italy. Designed for this, a GPR inspection was made using multifrequency GPR systems equipped with both ground-coupled and air-launched antennas. In addition, MLS surveys were carried out to analyze and quantify pavement surface irregularities. The surface and structural condition of the pavement as measured by MLS has been integrated with GPR outcomes to identify and classify potential damage sources likely responsible for layer deterioration and surface decay. This study confirms that an integrated nondestructive monitoring approach based on GPR and MLS technologies can be successfully implemented to assess the health-condition of critical assets, clearing the way for integrated approaches in continuous monitoring of transport infrastructure.

Monitoring the actual conditions of transport infrastructures is a priority for asset owners and administrators to ensure structural stability, operational safety and to prevent damages and deterioration. Currently, most protocols for assessing roads pavement conditions are based on visual on-site inspection conducted by specialized operators and, rarely, the local application of ground-based technologies and sensors such as terrestrial laser scanners (TLSs). However, the high costs of maintenance operations and on-site surveys still limit the application of these advanced procedures at the network level. Accordingly, the definition of innovative methodologies and procedures for continuous monitoring operations, especially for road pavements monitoring purposes, is still an open challenge. This research aims at investigating the viability of an automatic methodology for the detection and classification of pavement distresses based on Machine Learning (ML) models. More specifically, the methodology is based on the latest generation of Deep Neural Networks (DNN) algorithms, among which “YOLO v5”, and “Faster R-CNN”. To this purpose, an experimental evaluation is conducted by the acquisition and the processing of a publicly open-source dataset made available within the context of the “IEEE Global Road Damage Detection Challenge” (GRDDC2020). The implementation of the presented approach provides a technologically enhanced and reliable methodology for the provision of the identification and localization of roads damages to be more rapidly processed and conclusively actioned by asset owners and management agencies giving crucial information that could be implemented for the prioritisation of maintenance activities within Pavements Management Systems (PMSs). The outcomes of this study demonstrate that ML approaches and DNN algorithms, can be applied to complement Non- Destructive Remote Sensing technologies (e.g., ground-penetrating radars, Laser Scanners, satellite radar interferometry), localizing automatically the pavement damages, thereby paving the way for integrated approaches in the smart monitoring of infrastructure assets.

Third-party interference is widely documented as being a major cause of damage to buried pipelines. In addition to routine surveillance, maintaining a minimum depth of cover is recognized as a key means of mitigation against thirdparty interference. We know that the depth of cover over pipelines can change with time. Changes in depth of cover can also be an indication of thermal upheaval, frost heave or ground movement. Current techniques available for measuring depth of cover on buried pipes require significant effort to produce a high-resolution survey for an entire pipeline. A UK Innovation project completed with National Grid Gas Transmission in 2017 successfully demonstrated a methodology for estimating pipeline depth of cover by combining ground elevation data with high-resolution inertial measurement unit (IMU) data collected during in-line inspection. Since the end of the project, ROSEN has completed a further eight pipeline depth of cover inspections, with a total length exceeding 400 kilometers and including a range of pipe diameters.

This paper proposes a methodology based on Artificial Neural Networks (ANNs) for integrating products derived by three on-ground Non-Destructive high-performance Techniques (NDTs) to estimate the International Roughness Index (IRI) of flexible road pavements. About that, IRI of 93 two-lane road sections has been detected by a Laser Profiler (LaP) and considered as output target to be predicted. Structural and geometrical road pavement parameters recognized by Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR) have been considered as input features, along with climate and rainfall information. Accordingly, different ANNs architectures have been trained (by using 70% of samples), validated (15% of samples), and tested (15% of samples) by the Levenberg-Marquardt (LM) backpropagation learning algorithm. We chose the LM since it allows handling limited data and avoiding overfitting issues. Outcomes reveal that a Deep Neural Network (DNN) recognizes hidden patterns between different road surveys and made the integration of NDTs possible and reliable. Specifically, a DNN architecture composed of two hidden layers containing 23 and 12 artificial neurons, respectively, shows a Determination Coefficient (R2) of 0.813 for the training phase, 0.761 for the validation phase, and 0.741 for the test phase. Also, the residual distribution is Gaussian with zero mean. Supported by these findings, we have deployed the DNN including all road sections, obtaining an R2 parameter of 0.762 and a Root Mean Square Error (RMSE) of 0.450 mm/km. Road authorities can consider ANNs and the LM backpropagation learning algorithm for appropriately predicting IRI and efficiently integrating NDT-based outcomes.

In this work, a novel signal processing framework for polarimetric GPR measurements is presented for inspection of tree trunks’ decay. The framework combines a polarimetric noise filter and an arc-shaped diffraction imaging algorithm. The polarimetric noise filter can increase the signal-to-noise ratio (SNR) of B-scans caused by the bark and the high-loss propriety of the tree trunk based on a 3D Pauli feature vector of the Bragg scattering theory. The arc-shaped diffraction stacking and an imaging aperture are then designed to suppress the effects of the irregular shape of the tree trunk on the signal. The proposed detection scheme is successfully validated with real tree trunk measurements. The viability of the proposed processing framework is demonstrated by the high consistency between the results and the real-truth trunk cross-sections.

Urbanisation often leads to the destruction of green areas in the urban environment. To this extent, urban trees help mitigating its detrimental effects, as well as offering a variety of socioeconomic and environmental benefits. The presence of root systems in built environments usually results in structural damage, as for example shrinkage of expansive soils due to water suction by roots, resulting in subsidence and fissures in foundations, or roots obstructing pipes and sewers and damaging roads and pavements. Ground Penetrating Radar (GPR) has been extensively used in various areas of civil and environmental engineering. Research has focused on implementing 3D algorithms and investigating root density, and a recent experimental research examined the feasibility of a novel tree root assessing methodology, that processes GPR data both in time and frequency domains. The aim of this research is to improve upon the above-mentioned data processing algorithm, investigating the variation of the frequency spectrum of the GPR signal in urban tree root systems’ surveys by means of a Short-Time Fourier Transform (STFT). Results proved the viability of the methodology and paved the way to further developments for the investigation of urban trees’ root systems using GPR.

This paper combines such powerful non-destructive technique as ground penetrating radar (GPR) with intelligent data analysis in order to acquire new knowledge on mapping/monitoring/verification systems aimed at initially facilitating the evaluation of the health of the buried assets of water distribution system (WDS) infrastructures in urban areas. Preprocessing techniques based on a multi-agent approach coupled with a suitable analysis of the properties of the obtained groups of objects found, as well as a classification supported by machine learning techniques, are presented in this work. The work is based on GPR image studies conducted under controlled laboratory conditions using a commercial antenna. The buried objects correspond to pipes of various materials commonly used in WDSs. Furthermore, GPR images of various pipes either empty or full of water, as well as leaking water, are also included in the dataset of the study. The dataset is divided into two subsets that pursue: 1) the development of the methodology; i.e. capturing objects in GPR images to favour the feature extraction process, and 2) the evaluation of the feasibility of implementation of the proposed classification, as well as the response of the methodology to various environmental interactions recorded in the images. The results of this work are promising in the sense of promoting the inclusion of powerful tools such as GPR towards the provision of smarter tools that adequately support technical management in WDSs, and, eventually, that of other surrounding infrastructure(s).

Coastal environments are under successive physical and morphodynamical pressure. Nowadays, it is crucial to have a reliable and simple tool in order to measure the changes occurred diachronically. Among the research community there have been developed and applied many relative statistical models which are quite reliable. In this study we try to compare in statistical terms the results of the most famous free extension, suited for commercial software for shoreline monitoring: The Digital Shoreline Analysis System (DSAS) and the Analyzing Moving Boundaries Using R (AMBUR). Both software are free open-source tools for shoreline analysis in Geographic Information System environment. The test site is located in the northwestern Peloponnese, Greece, between two littoral villages named Rogitika and Kaminia situated in the Gulf of Patras. The shoreline length is more than 6 kilometers. High-resolution images (air-photos mosaic and very high-resolution satellite data) for the years 1987, 1996, 2008, and 2018 were used. The images have been orthorectified and georeferenced to Hellenic Geodetic Reference System of 1987 (Greek Grid) using Leica Photogrammetry Suite (LPS). The data spatial resolution ranges from 0.25 m to 1.00 m. Transects every 50 meters were created and used for the measurements. We digitized the relative shorelines and we computed and compared the End Point Rate (EPR) rates calculated from both tools for three periods such as 1987-2018, 1996-2018, and 2008-2018 respectively. EPR calculates the annual rate of erosion or accretion computed in a specific transect. The rates between -0.10 and + 0.10 m were considered that correspond to a “stable” area. The tide height rates were considered negligible (0.00 m ± 10.00 cm) and so did not affect the computations. Moreover, a Linear Regression statistical process applied for correlation of EPR calculated rates from AMBUR and DSAS seaward and landward models. Finally, the results are presented and discussed in the current paper.

Wetland ecosystems in arid and semi-arid regions are under increasing stress due to demands for water resources, encroachment of urban and agricultural land use, and changes in climate and hydrologic regimes. An integrated geospatial and remote sensing approach is required to understand the current state and expected range of variability in these systems. Here we describe a research tool called the Wetland Analysis Toolbar (WetBar) and its application in a multi-agency restoration effort for the threatened Lahontan cutthroat trout in the Great Basin region of the United States. A significant amount of research for restoring this species has been performed at the field scale, but remote sensing and GIS methods are required to scale these efforts to the species’ historic range. This will include methods for riparian mapping, determining an appropriate spatial scale for schemes for disaggregating the historic range, and developing metrics for choosing which regions should be targeted for habitat protection or restoration activities. The WetBar software integrates Google Earth Engine cloud computing directly into the ESRI ArcMap environment, enabling decades-long time series of multispectral satellite and climate data to be used within a querying environment. WetBar disaggregates the network of wetland environments to appropriate spatial units based on elevation and geometric operators and automatically tags these subunits with land use attributes, environmental data, and statistics that capture multi-decadal trends observed by satellite remote sensing. This information will then be used in objective functions to allocate the limited resources for restoration in a coordinated and effective manner.

Plastics in the marine environment constitute a significant problem globally. It is estimated that almost 8 million tonnes of plastic enter the oceanic ecosystem every year. A high concentration of plastics is found within the Mediterranean Sea, which is approximately 22,000 tonnes. Indeed, the South-East Mediterranean, where Cyprus is located, faces a significant problem with plastic debris. Aquaculture fisheries can contribute to marine debris, especially as a result of storm damage or accidents, as their plastic rings float in the ocean and end up in the coastline. Remote sensing techniques can be used to monitor fisheries and plastic debris in marine settings. More recently, research has focused on the ability to detect plastic litter in the water using remote sensing techniques. This paper examines how temporal series Sentinel-2 satellite images can be used to detect the plastic rings from aquaculture fisheries in the Vassiliko area in the south coast of Cyprus. This detection methodology can be used to manage and monitor fisheries using Sentinel-2 images.

Wildfires play a key role on forest composition and structure in the Mediterranean biomes. Hence, Mediterranean species are adapted to fire, developing ecological strategies to naturally recover. Nevertheless, climate change impacts and land use changes are expected to increase the frequency and intensity of extreme wildfire events, endangering forest resilience to fire. Combining LiDAR and Landsat data provides a valuable opportunity to temporally extend detailed information on the forest structure. This study attempts to evaluate the feasibility of extrapolating LiDAR-derived canopy cover variables, as indicators of vegetation recovery, to Landsat time-series using Support Vector Regression (SVR) in a large forest fire. Canopy Cover (CC) and Canopy Cover above 2 m (CC2m) were derived from LiDAR data acquired in 2009 and 2016 from the National Plan for Aerial Orthophotography of Spain (PNOA) and time-series of annual Landsat composites for the period 1990-2020 were generated through the Google Earth Engine platform. We calibrated a SVR model from a stratified random sample using a 60% of the sample from 2016 for calibrating and the remaining 40% from both 2016 and 2009 for spatial and temporal validation, respectively. The two canopy cover variables yielded highly acceptable accuracy, with an R² of 0.78 (CC) and 0.64 (CC2m), and an RMSE around 12.5-15% for the spatial validation, and with an R² of 0.74 (CC) and 0.51 (CC2m), and an RMSE around 14-16.5% for the temporal validation. These results ensure the applicability of the extrapolation of the LiDAR-derived canopy cover variables to Landsat timeseries..

costeLAB is a pre-operative thematic cloud-type platform characterized by and specialized in the sector of geospatial services for coastal environment management. The platform integrates a large set of processors and algorithms and exploits multi-mission and multi-sensor Earth observation data, from the European and Italian Space Agencies’ missions, such as ESA Sentinels and Italian Space Agency (ASI)’s COSMO-SkyMed and (in future) PRISMA missions. It covers a broad range of application domains from emergency management in the event of sea storms, incidents of occasional pollution or instability of coastal landslides, to monitoring and protection of the territory. This platform, developed entirely with Open Source technologies, aims to provide various types of users, from both public and private organizations, a homogeneous environment equipped with a set of software tools to visualize, analyse and process data from multiple sources (Ground Truth, multi-mission Earth Observation, etc.) and with various dimensions (single observations, time series, etc.). Furthermore, costeLAB allows the generation and visualization of new products, providing scientific users with a collaborative environment for the development and testing of innovative algorithms in order to build and test new processing chains. The approach of the proposed architecture keeps computing resources close to data, i.e. by exploiting the access and processing capabilities typical of the cloud, costeLAB avoids the transfer of large amounts of data to the user. The platform is addressed to institutional, scientific and industrial users and allows the study, experimenting and developing new downstream pre-operational services for the monitoring of the coastal area environment. The platform has been designed and developed to run in a pre-operational context in the framework of "Progetto Premiale Rischi Naturali Indotti dalle Attività Umana - COSTE", n. 2017-I-E.0, funded by the Italian Ministry of University and Research (MUR), and promoted and coordinated by ASI. The platform has been developed by e-GEOS S.p.A. and Planetek Italia with the participation of the National Research Council of Italy (CNR), Meteorological Environmental Earth Observation (MEEO) and Geophysical Applications Processing (G.A.P.) s.r.l. as subcontractors.

This methodologic paper arises from the necessity to gather Land Surface Temperature (LST) data over a relatively large period and territory: 2000-2018, Portugal. The computational power required to complete this task was found to be a major barrier. However, platforms such as Google Earth Engine (GEE) offer a vast data archive freely accessible through a web interactive development environment or an application programming interface, namely, Python’s API. Additionally, the computation using GEE is hosted in Google’s servers, drastically reducing the processing times. However, computing LST through Landsat-7 satellite imagery resulted on a difference of -8ºC±6ºC compared to the values from meteorological ground stations. As such, this paper aims to further calibrate computed LST through meteorological stations and make the methodology and corresponding code available, thus encouraging cooperation on the development and integration of local calibration methods. A sensitivity analysis of the representativeness of each station was performed using three methods of temperature extraction: station coordinate’s pixel, buffers around the station, and surrounding soil occupation (identifying the area with the same soil occupation as the station’s location). Pearson’s correlation coefficient was on average significant at 0.81 in the raw data and increased to 0.89 after clearing data from outliers . The best representativeness method for meteorologic stations was the one based on soil occupation, which resulted on a Pearson’s r of 0.91. As a result, we advise researchers to complement their remote sensing work with ground data whenever possible through the usage of a method like the one here described.

The performance of supervised classification depends on the size and quality of the training data. Multiple studies have used reference datasets to extract training data automatically in an efficient way. However, automatic extraction might be inappropriate for some classes. Furthermore, classes can have distinct spectral characteristics across large areas. Thus, dividing the study area into subregions can be beneficial. This study proposes to assess the impact of the introduction of spatial stratification and manually collected training data on classification performance. Two classifications were conducted with the Random Forest classifier and multi-temporal Sentinel-2 data. The classifications’ performance was evaluated by accuracy metrics and visual inspection of the maps. The results indicate that introducing spatial stratification and manual training yielded a higher overall accuracy (66.7%) when compared to the accuracy of a benchmark classification (60.2%) conducted without stratification and with training data collected exclusively by automatic methods. Visual inspection of the maps also revealed some advantages of the novel approach, namely constraining some land cover classes to be present only within specific strata, which avoids commission errors of the class to spread freely across the map. Most of the classification improvements were observed in subregions with specific landscapes and spectral patterns, although these strata represent a small fraction of the study area, which might have contributed to the small increase in accuracy.

Due to an increased occurrence frequency of drought events and pest infestations, large amounts of deadwood are a current issue in temperate forests. Accurate monitoring of deadwood and analysis of its spatial and temporal distribution is, therefore, more important than ever, as it facilitates faster response to pest outbreaks or increased risk of forest fires. As highlighted by previous studies, state-of-the-art remote sensing platforms, such as UAVs, provide great synergies for deadwood monitoring in combination with machine- and deep-learning approaches of computer vision. Key challenges that remain are the acquisition of sufficient amounts of labeled data for model training and identifying deadwood on the single-tree level, which is required to estimate the deadwood volume in an area. The presented work demonstrates how it is possible to obtain very accurate instance segmentation in the combined RGB and elevation domain and with limited training data. A high-performance Mask R-CNN model was trained to map standing and lying deadwood instances in German forests, achieving outstanding results with an overall accuracy of 92.4% and a mean average precision of 43.4%. To compensate for the possibly insufficient amount of annotated images, we performed experiments with a semi-supervised active learning pipeline. Here, each time after the model predicted a batch of new data, only the instances that achieved a high prediction score were added to the pool of training data to re-compute the model for the next iteration step. Even though the application of the fully supervised approach led to superior results, overall, this study proves that the proposed method can reliably map individual deadwood objects. The approach not only represents an end-to-end framework for image annotation, model acquisition, and large-scale mapping of deadwood, but also is adoptable with reasonable effort to solve similar problems in the future.

The validation of low-resolution remote sensing products using high-resolution data may be affected by different error factors that would eventually imply unrealistic accuracy estimations. The usual validation methodologies were designed for high or medium resolution but may be not very adequate for coarse resolution, particularly when trying to separate those errors associated to classification from those related to the actual pixel size. The Pareto Boundary methodology can be a good alternative to discriminate between those two sources of errors. We tested its application to a recently released global burned area product based on AVHRR data. This product was developed within the Fire_cci project of the European Space Agency (ESA). The product, named FireCCITL11, has the coarsest resolution (0.05°) and the longest time series (1982-2018) compared to all other global BA products. Furthermore, FireCCILT11 is the only global BA product without a dichotomy classification which detects BA proportions. The accuracy of the FireCCILT11 was validated by Pareto Boundary and an independent reference dataset of Landsat at 0.05°. FireCCILT11 was usually close to boundary curve or below it, which indicates suitable performance. Commission errors (Ce) were usually lower than Omission errors (Oe) in the time series, like other BA products such as those based on MODIS sensor. Both types of accuracy errors present low values, although there were unbalanced years. Year 2014 showed the lowest errors for the entire time series with balanced errors (Ce = 0.12 and Oe = 0.14).

Being able to detect and identify offshore oil slicks and in particular oil pollutions is important to reduce the detrimental effect of pollution on ecosystems. For decades, SAR images have been used to detect pollutions. In some cases, SAR polarimetric derived parameters are used to improve detection or discrimination. However, the acquisition of multi-polarization channels is at the expense of the extent of the monitored area. Also, some papers question the effectiveness of polarization derived parameters for detection or identification. SAR single polarization is quite efficient to detect hydrocarbons but there are many look-alikes and in general SAR is not much sensitive to oil thickness which is a very important element for pollution remediation. Optical data on the other side have been shown to be sensitive to oil thickness and to some extent to the type of oil and the physical state of the oil: emulsion vs plain film. The main drawback of optical data is that they will not show the ocean surface when clouds are presents or during the night, which is a big constrain in tropical areas, Northern areas and winter high latitude spots. But when clouds are not hiding the area of interest, optical data offer good insight into the nature of the sea surface. Lab measurements helps us to find means to detect, identify, and qualitatively quantify oil on water. But in real life other features will impact the detection and identification: surface ripples, clouds, algae, smoke, shadows, glint. In this paper we will use airborne hyperspectral proprietary data and satellite multispectral data, to understand the spectral signature of oil and non-oil slicks, the impact of oil thickness and a combination of spectral indices to discriminate oil from look-alike on different case studies. When a big pollution occurs, planes are mobilized carrying optical, radar, thermal as well as UV systems which together will discriminate oil slicks from look-alikes. But in most cases only open-source satellite data will be available and it will be necessary to use them at best to identify and characterize the floating slicks. VV single pol SAR data remain the best images to detect hydrocarbons but with some potential false alarms. On the optical side, the hydrocarbon index and Area1700 index are good at detecting thick hydrocarbons but cannot be used with multispectral data. The Fluorescence index can discriminate hydrocarbons but rejection of clouds, and infrastructure require additional indices or processing. Some Algae are potentially the most difficult features to discriminate from hydrocarbon. A new index is proposed to discard them. The objective is to detect hydrocarbons with the minimum of in-situ knowledge.

Alepu marsh is a protected area in the category of natural landmarks, part of the Ropotamo Ramsar site and sand dunes Alepu. It is situated on the Bulgarian Black Sea coast, within Burgas Province, south of the resort town of Sozopol. It is also situated within the territory of the protected area of the European ecological network Natura 2000 under the Birds directive – Ropotamo Compex. Alepu marsh is covered with reeds and other swamp vegetation. The area is habitat for many rare animals and plant species. The main problem of this area is the overgrowing with reeds and the gradual swamping that leads to reduction of the open water areas in the protected area. This leads to the loss of valuable habitats, and respectively their inhabiting animal and plant species. In the study paper assessment of the dynamics of the marsh for a period of eight years (2013 – 2020) was done. Data from Landsat 8 and Sentinel 2 were used. Classification of NDVI was made for this study period. Sentinel 2 data were also used to apply an orthogonal transformation model that classifies and analyzes the processes associated with the dynamics of change affecting the main components of the earth's surface: soil, water and vegetation. The NDGI model was also used, which evaluates the dynamics of the vegetation in the marsh. The results obtained show a monitoring of the wetland for a sufficiently long period of time, which gives an idea of its condition and the need to take the necessary conservation measures for its protection.

Landslide occurrences are result of natural or human activities, but regardless of the origin they change the landscape, destroy infrastructure and in some cases even leads to loss of human lives. In order to assess the hazard of this phenomenon remotely sensed data from aerial and satellite instruments are widely used to monitor the ground motions at regular intervals. Those methods are less expensive and less time consuming than terrain inspections and measurements and the other hand the size of the studied areas is larger. This was the rationale to initiate a study on the surface deformations in the area of “Fish-Fish” landslide located on the north part of the Black Sea coast of Bulgaria. Two sources of data were used to create a map of recent surface displacements in the said area – photogrammetric surveys with UAV and remotely sensed images from synthetic aperture radar from satellite instruments. The area of the landslide was investigated by photogrammetry in years 2019 and 2020 and as result created were two digital elevation models. The accuracy allowed registration of the surface motions at centimeter scale using ground control points located inside and outside the perimeter of the landslide. The satellite SAR data are provided at no cost by ESA originating from the twin constellation of Sentinel-1 mission. The authors downloaded SAR data for the same periods when the UAV surveys were made. Due to peculiarities of the local terrain it was possible only SAR images from descending orbit to be used. The processing of those data was done by verified interferometric processing method implemented in the SNAP software. Finally the results from control points for both sources were compared and good correlation between them was established. A map of the landslide area depicting the registered ground displacements was produced.

The present research introduces a satellite-based plant senescence reflectance index (PSRI2) involving in its calculation two red edge bands (0.705 μm and 0.783 μm) and the green peak band (0.560 μm) of Sentinel-2 sensors. The index takes the advantages of the effectiveness of the red edge and green peak bands in monitoring alterations of cellular structures and anthocyanins content induced by drought stress. Environmental stresses are associated with accelerated cellular leaf senescence and browning of plant tissues. As browning progresses, the spectral reflectance in the whole range of the spectrum decreases. The decrease in reflectance is most pronounced in the green peak and in the range between 0.750 and 0.800 μm, and is much smaller in the red and the blue regions, which are employed in the calculation of the original PSRI. Ranging PSRI2 values, representative for various land cover classes and ecosystems, developing under various environmental and climatic conditions were calculated. The studied land cover classes include broad-leaved, coniferous and mixed forest, transitional woodlands/shrubs, and natural grassland and pastures. Satellite images, acquired in different growing seasons, distinguished with different environmental conditions were used. A comparative analysis between the newly presented index and widely recognized indices for assessment of water content and moisture stress was made. The obtained results could be used in various studies related to monitoring of drought impact and climate change on ecosystems, assessment of ecosystem degradation processes, natural resource management, sustainability and planning.

Paleoseismological studies are related to the analysis of past earthquakes. One of the most common methods in paleoseismological studies is the excavation of trenches across the faults, or parallel to them in the case of strike-slip. Accurate stratigraphic correlation of sedimentary layers cropping out in different trenches is key to detect and quantify fault movements with precision. Very often, this correlation is a difficult task, plagued with uncertainties owing to the homogeneous appearance and composition of the deposits. Laboratory and field reflectance spectroscopy are applied to 11 samples corresponding to two paleochannels in two parallel trenches excavated in the left-lateral Carboneras fault (Tostana site, Almeria, southeastern Iberian Peninsula). Each sample was sieved into four fractions (<4mm, 4-2 mm, 2-1 mm and <1mm) and together with the total sample, the spectral response was measured with an ASD FieldSpec4 Spectroradiometer. The results show: a) the highest similarity values usually appear between samples of the same fraction size; b) the “total fraction” analysis shows the lowest similarity values between samples; c) the results also confirm the previous characterization of the paleochannels based on field characteristics, with the highest similarity values between samples of the same paleochannel in each trench, and the lowest similarity values between samples of different paleochannels; d) a similar mineralogical composition (presence of dolomite) in all the samples suggests the same source area for the alluvial fan sediments. These results allow to assess the plausibility of correlations based on macroscopic observation in the field, and to help reduce uncertainties in the future quantification of the lateral slip rate for this segment of the Carboneras fault.

The main purpose of this research is interoperability of data from different sources and creation of innovative models with high value data such as satellite information and Earth data and solutions for public administrations, business and citizens. Building base data to inform and train stakeholders and promote the adoption of good practices and innovations in environmental monitoring is also a leading goal. An assessment was made of several surface water bodies that have acquired personal types of permits for use and construction. The methodology contains a model of Open data processing steps, which are published in the Open Data Portal of the State Agency "E-Government" in Bulgaria, satellite data from Sentunel-1 and Sentune-2 and terrestrial data from many different monitoring devices. Different formats are integrated, and for this aim there must be transdisciplinary knowledge and a complex approach. Composite images of optical and SAR data, TCT and terrestrial data from Еnvironmental assessments and data from Basin Directorates in Bulgaria are combined. The model is further verified by the spectral characteristics of the objects, transformed images into dD (decibels) and statistical data. The interoperability of the data in this model will be a tool for restoring cooperation, coordination and communication between central and local administration, supply of services from the public sector, academia, business, NGOs and IT companies, development of solutions or information processing, in case of geospatial information and Environmental monitoring.

Currently, the problem of illegal mining is still acute. Such illegal use of natural resources harms the environment and leads to irrational use of mineral resources. Modern methods with the use of remote sensing technologies will effectively detect such law violations. In the current study, a method for automatically detection of non-metallic mineral extraction sites based on remote sensing data analysis has been developed. The study uses Sentinel-2 satellite images with spatial resolution 10 m and 20 m and considers four types of minerals: sand, clay, carbonate rocks and sand gravel mix. The spectral indices help to determine the specific quantitative characteristics of the mineral resources. The result is probability maps with mineral resourses characteristics in each pixel. In order to determine to which of known classes relates the point, you need to find the covariance matrices for all classes and take the class with the smallest Mahalanobis distance to the point. Based on the obtained probability maps, an analysis of the applicability of the selected spectral indices was performed, as well as a visual assessment of the quality of interpretation. For each spectral channel and index, two frequency histograms were created to determine how different the channels values and spectral indices on the entire scene and at the reference objects. Each object found by the program was checked for it presence on the studied territory. The developed system is a modern, secure, non-contact method for the rational land use monitoring and natural resources extracted by open-pit mining study.

The characterization of uranium-bearing minerals by different remote sensing technologies is a challenging task due to the implications for the exploration or the protection of uranium deposits. In nature, uranium-rich mineral ores are often associated with phosphates. Fossil bones may contain almost 1% of uranium as a result of interaction with ground water or other uranyl-bearing mineralizing fluids during fossil diagenesis. Sediments in the Lower Miocene fossil site of Córcoles (Guadalajara, Spain), in the Tajo Basin, host uranyl-vanadate metatyuyamunite associated with mammal fossils, constituting a remarkable example of the relationship between uranium ores and phosphates. The mineralogical composition and the spectral response of different phosphates (geological fluorapatite, fossil fluorapatite from Paracuellos III fossil site, and Recent hydroxyapatite) and uranyl-bearing minerals (metatorbernite, metautunite, metauranocircite and fossils from Córcoles Fm with metatyuyamunite) were studied through laboratory reflectance spectroscopy. Spectral features of the different samples were compared with XRD, XRF and FTIR-ATR to determine the relationships between reflectance spectral curves, mineralogy and chemical composition. It should be highlighted that reflectance signatures of uranyl micas and Ca-phosphates allow for the mineral identification. Fluorapatite samples show absorption bands at 586, 737, 750, 805 nm related to the phosphate anion, not always present in uranyl-phosphate micas and a characteristic chemical heterogeneity probably caused by REE content. Uranyl-bearing minerals show absorption features at 1100, 1330 and 1672 nm attributed to uranyl anions (UO₂ ²⁺). Although further research is needed this study highlights the use of laboratory reflectance spectroscopy in uranium detection.