This PDF file contains the front matter associated with SPIE Proceedings Volume 12268, including the Title Page, Copyright information, Table of Contents, and Conference Committee Page.

This conference presentation was prepared for the Earth Resources and Environmental Remote Sensing/GIS Applications XIII conference at SPIE Remote Sensing 2022.

Satellite remote sensing is key for large scale monitoring of urban and suburban areas with high revisit time and spatial resolution. However, in some applications involving targets characterized by limited extension and unknown spectral response and spatial distribution, data acquired from space could be insufficient. This is the case of micro-dumps mapping, which represent a highly diffuse environmental hazard in Southern Italy and, in general, in many suburban areas all around the world. Their intrinsic heterogeneity hampers the usage of descriptors based on the spectral response of the targets. The limited extension makes it imaged in few pixels, even using very high-resolution data. In such cases, the exploitation of proximal sensing becomes fundamental. This paper presents a new architecture for real-time characterization of illegal micro-dumps. It is composed, at hardware level, by a customized remotely piloted aerial platform equipped with an innovative payload able to deliver the necessary data for online three-dimensional processing. In particular, a depth camera coupled with a tracking sensor and the drone autopilot (connected with a GPS receiver) is exploited to retrieve a sparse point-cloud, which is geo-localized thanks to positioning information provided by a sensor fusion algorithm. The software component is designed to synchronize, align, segment and extract a volume estimate from the available point cloud with a real time processing. Experimental results show that the developed system is able to deliver accurate volume estimates compared to manual measurements and values obtained via classic offline structure from motion techniques.

Satellite-based information for mapping flood extent is not always available. Sometimes due to satellite orbits and revisit time or due to clouds in the case of optical satellites. The intention of this research was to develop a two-step ML-based automatic workflow that would first detect the flood from radar (Sentinel 1) or multispectral (Sentinel 2) satellite. Then in the second step to model the possible flood extent (under the clouds or between the revisit time) from the detected flood extent with the help of the Random Forest algorithm and the variables extracted from the Digital Elevation Model. Preliminary results confirm the potential to detect the extent of the existing flood under the clouds or to predict the possible maximum extent of the downstream flood in the upcoming days. These results can greatly shorten the time for flood/water management-related actions, especially in situations when every minute can mean the difference between life and death.

Treetop detection and tree crown delineation are common tasks in forest-related studies since they are necessary steps for data analysis on an individual tree level. In recent years, related studies have concentrated on Machine Learning approaches for which training data are needed. The results of the methodology presented in this paper and applied to a freely available data basis can be used to train such Machine Learning algorithms.

The National Ecological Observatory Network (NEON) provides data products from 81 American field sites. This study evaluates the suitability of the NEON dataset – especially the Canopy Height Model (CHM) – for the automatic treetop detection and tree crown delineation in natural mixed forests that provide difficult real-world conditions. Both tasks are conducted exemplarily on two NEON field sites, BART and HARV. For comparison, data from a study area located in Meppen, Germany, is used.

The general workflow consists of three steps. First, the data is pre-processed by masking irrelevant pixels. Then, the treetops are detected with a method based on the Top-Hat by Reconstruction operation. Finally, the tree crowns are delineated with a region-growing segmentation method proposed by Dalponte and Coomes (2016). Both methods rely on tree height information to locate the treetops and extract the tree crown boundaries. Achieved results reveal that the NEON CHM is suitable for treetop detection. However, the CHM’s spatial resolution is too coarse for tree crown delineation, i.e. further data has to be considered for an accurate outcome.

In recent years, many reports have shown increasing pollution of the world's oceans with plastic waste, even the formation of plastic islands on the sea surface. Considering that marine currents are the major factor of the plastic waste movement into the marine environment, an approach for determining of sea surface flows using zonal and meridional components of ocean currents velocity data of “Physics analysis and forecast” products delivered by Copernicus Marine Service. By numerical modeling and simulations in MATLAB environment are derived sea surface flows which are considered to be the main transport routes of plastic waste on the sea surface. A georeferenced map, as a result of study focused on the West Black Sea aquatory is presented.

The preservation of cultural heritage and landscapes, which are constantly endangered by man-made hazards and natural disasters (earthquakes, landslides, floods, fires, urbanization, etc.) is a strategic priority for humanity. Nowadays, remote sensing stands out as an important, innovative and cost-effective tool with great potentialities for the protection and monitoring of cultural heritage as well as the prevention of monuments and sites, due to non-invasiveness in such a vulnerable research objective. In this context, remote sensing data, acquired by Unmanned Aerial Vehicles (UAV) and Terrestrial Laser Scanning (TLS) surveys were utilized for the precise mapping and monitoring of rockfalls occurred over an eleventh-century BC. Holy Monastery. The monastery is built on a rocky slope, which hangs over the bank of the ancient river Triton, and it is located a few kilometers away of the village of Andritsena within the Region of Western Greece. Significant damages to the dome of the monastery were detected after the occurrence of rockfalls in late January 2021. Remote sensing data were collected over the monastery area and were processed appropriately. UAV and TLS point cloud were generated and change detection methods were applied for the precise mapping of the rockfalls. Moreover, UAV and TLS point clouds were integrated towards a more comprehensive mapping.

HBIM (Heritage Building Information Modelling) is used for the documentation of the Church of Panayia Karmiotissa, near Limassol, Cyprus. The HBIM model is based on the geometric information obtained by digital documentation of the monument. Using laser scanning and Structure for Motion photogrammetric techniques, exterior images were collected using a drone with a 20MP camera and the interior images were taken using an SLR 20 MP camera. Ground control points were used both externally and internally to geo-reference the images using SfM photogrammetry. A Z+F latest technology 3D laser scanner was utilized to scan both the exterior and interior of the church. The images were used in order to create a 3D reconstruction of the Church and used photogrammetry to generate a point cloud of the church and a 3D surface texture model with ortho-images. Following, the data generated by the laser scanner and photogrammetry was processed to create a 3D model to document the church using Heritage Building Information Modeling (HBIM). The main objective of this case study was to create an HBIM model of the Church of Panayia Karmiotissa to digitally register and holistically to document the monument. HBIM is an important tool for the multidisciplinary area of conservators, architects and in general engineer activities, as it provides virtual details that are useful for monitoring, preservation and restoration.

A digital twin model can be defined as a connection between a virtual model and its real-life counterpart, by providing an interactive virtual environment that represents the real world. The use of digital twin models in cultural heritage enables better decision-making about preventive maintenance, heritage management, and interpretation, as well as accurate digital documentation. In cultural heritage, digital twin models are important in order to convey the real-life experience of the space through an accurate and detailed virtual representation. The use of a realistic virtual environment depends on the theory that people perceive and respond to virtual environments in the same manner as the real world. Therefore, a very realistic virtual environment is expected to create the emotion in the person as they would have achieved in the physical site. In this study, a digital twin model was created using Structure for Motion photogrammetry and a depth camera mapping system and compared to a model generated using laser scanning. The use of in-depth camera mapping utilizes panoramic image walkthroughs by facilitating panoramic image views by rendering an overview of the space. The use of digital twin models in cultural heritage provides a 4th dimension in space by providing additional semantic and geometric information to the user.

The monitoring and preservation of natural resources are vital in present times. With regards to urban trees, their benefits to the environment and the community are widely recognised. Nevertheless, the coexistence between street trees and the built environment is based on a delicate balance, as safeguarding the natural asset may conflict with the damaging mechanisms that tree roots cause to roads, building foundations and underground utilities.

Ground penetrating radar (GPR) is becoming popular as a reliable non-destructive testing (NDT) method for assessing and mapping tree roots. In street tree management, there is an increasing need for dedicated investigation methods, mainly related to accessibility constraints. Recent studies have investigated the feasibility of novel survey and processing methodologies for fast tree root evaluation based on time-frequency analysis of GPR data.

The purpose of this research is to present an analysis combining GPR with geostatistics, a branch of spatial statistics focused on the analyses and modelling of spatial data. The complex spatial patterns of tree roots represent a challenging spatial field to be investigated both from the geophysical as well as from the spatial statistical perspective. Twodimensional GPR outputs from a real-life case study were analysed to assess the spatial correlation of radar data and evaluate the best interpolation approaches, allowing for more reliable detection and mapping of tree roots. The interpretation of the results confirmed the feasibility of the proposed approach, paving the way for novel and faster survey methodologies for urban trees.

Advances in data processing and the availability of larger SAR datasets from high-resolution (X-Band) satellite missions have consolidated the use of the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technique in the near-real-time assessment of bridges and the health monitoring of transport infrastructures. This research aims to investigate the viability of a novel non-destructive health-monitoring approach based on satellite remote sensing and ground-based techniques for structural assessment of bridges and the prevention of damages related to structural subsidence. To this purpose, high-resolution X-Band SAR products were acquired and processed by MT-InSAR technique. Analyses were developed to identify and monitor the structural displacements of the historical “Ponte Sisto” masonry bridge located in Rome, Italy, crossing the Tiber River. To this extent, the historical time-series of deformations were processed by Persistent Scatterers (PSs) relevant to critical structural elements of the bridge (i.e., bridge piers and arcs). On the other hand, an on-site Ground Penetrating Radar investigation was directly conducted over the masonry bridge. More specifically, several frequencies were implemented for this purpose, with the aim to investigate the condition of the layers of the superstructures at different propagation lengths. A novel data interpretation approach is proposed based on the selection of several PS data-points with coherent deformation trends and location on the bridge, and the analysis of the B-Scan obtained by GPR investigations. The outcomes of this study demonstrate how multitemporal InSAR remote sensing techniques can be applied to complement non-destructive ground-based analyses (e.g., ground-penetrating radars), paving the way for future integrated approaches for monitoring of infrastructure assets.

Ground Penetrating Radar (GPR) systems allow the acquisition of images displaying the contents of the underground. Hence, GPR is used everywhere, where structures underneath the visible surface have to be investigated. Consequently, typical application fields are archeology and civil engineering, especially the detection of cables, pipes or other manmade objects.

GPR sensors can consist of one channel or of multiple channels, placed side by side. In the latter case, it is possible to acquire a two-dimensional image for each measurement, where the number of channels represents the number of columns in the image matrix.

Since a typical track of measurements often contains multiple of thousands GPR images, a visual analysis with focus on the detection of buried objects might be uneconomically. Moreover, due to its noisy characteristic in relation to the specific underground, it is often not easy to interpret GPR images immediately.

In this study, an unsupervised approach is presented, that provides both help for the visual analysis of GPR images and for the detection of potential buried objects. Therefore, it is usable to quickly generate or enlarge training datasets for machine learning approaches aiming at the analysis of GPR data.

As test data, several measuring tracks acquired by the multi-channel Stream C system at the site of Frankfurt University (GER) are available.

The workflow consists of two central processing steps: Change detection and data augmentation.

Monitoring the actual conditions of critical infrastructure assets is a priority for administrators to guarantee high-standard in terms of structural stability, and operational safety and to prevent damages and deterioration. Nowadays, most protocols for assessing road pavement conditions, including viaducts and bridges, are based on visual inspections conducted by specialized operators and, rarely, the local application of ground-based technologies such as IoT Wireless Sensors, laser scanners, Falling Weight Deflectometers, and accelerometers. However, the high costs of maintenance operations and on-site surveys, which involves the partial or total temporary closure of the infrastructure for the duration of the tests, still limit the implementation of these procedures at the network-scale level. Accordingly, the definition of innovative, automatic, and low-cost procedures for continuous monitoring operations, especially for road pavements and concrete bridges, is still an open challenge. In this context, recent developments in Artificial Neural Networks (ANNs) have opened new possibilities in the automatic recognition and geo-localization of damages affecting critical transportation assets, such as bridges and viaducts. This research presents an experimental application for the automatic detection of deteriorated bridge-deck expansion joints and the classification of pavement distresses based on Artificial Neural Networks models. More specifically, the methodology is founded on the latest generation of ANNs algorithms, among which “YOLO v5”. For this purpose, an experimental evaluation is conducted by the acquisition of a large dataset of imageries of road damages and bridge-deck joints, collected by open-source datasets, and the processing by ANNs. Furthermore, a field experiment was conducted in order to collect several videos of bridges, selected as case-studies, by means of an instrumented vehicle, through a High-Definition resolution camera. The acquired images were post-processed and implemented for the training phases of the ANNs and the verification phases of the developed model. The application of the presented approach provides a reliable, affordable, and promising methodology for the automatic identification and localization of pavement distresses and bridge-deck joints damages, to be rapidly managed and decisively actioned by administrative authorities and asset owners, giving crucial information that could be implemented for the prioritisation of maintenance activities within Asset Management Systems. The findings of this study demonstrate that ANNs approaches and Deep Learning algorithms, can be applied to complement Non-Destructive Remote Sensing technologies (e.g., satellite radar interferometry, Laser Scanners), localizing automatically the pavement and infrastructure damages, paving the way for integrated approaches in the smart monitoring of infrastructure assets.

The monitoring of the structural integrity of road and transport infrastructures, and in particular of bridges and viaducts, has now become a priority issue, emphasized by the progressive decay of the characteristics of building materials, and deterioration of the works, which can cause damage and collapses with dramatic consequences. Following recent bridge collapses, including the "Polcevera Bridge” in Genoa, Italy, the Ministry of Infrastructures and Sustainable Mobility (MIMS) issued in 2020 specific Guidelines on the classification and risk management, safety assessment and monitoring of existing bridges. In this context, the creation of information catalogues for the mapping of structures and the implementation of innovative procedures for monitoring, appear to be a crucial step for public bodies and managing bodies end of assessing the state of the civil structures such as bridges and identifying the priorities maintenance actions. To date, monitoring operations of bridges are typically conducted through visual inspections, coring and punctual measurements carried out by specialized operators and, more rarely, through the use of Non-Destructive Technologies (NDT). On the other hand, in recent years, satellite radar remote sensing techniques have always been applied much more effectively at monitoring infrastructures, civil works, bridges and viaducts. Among the modern techniques, Multi-Temporal Satellite Radar Interferometry (MT-InSAR) is currently established as a reliable tool for control and monitoring of deformation phenomena of the soil and large-scale infrastructures, capable of returning information with millimetre accuracy. Moreover, the knowledge about structural materials, structural elements and defect state of the bridge collected by onsite inspection, is crucial to verify the stability to actual traffic and dynamic loads, not expected in the design phases. This work reports a methodology useful for evaluating the viaducts and bridges stability at the network-scale level, proposing a multi-level monitoring protocol from the integration of information from different sources and technologies, including ground-based, structural and remote sensing information. The activities are carried out as a part of the “MLAZIO” project, approved and supported by the Lazio Region. The first phase of this project involves the creation of a vector database for bridges mapping and localization, and the development of an informatic catalogue for the management of works, also including a view of the geo-eGovernment of the infrastructures. Such a system is developed on a platform interoperable in a Geographical Information System (GIS), already prepared for the expected integrations in the later stages of the project, which includes on-site inspections, NDTs ground-based and satellite remote sensing investigations. This project aims to create a reliable and effective methodology for the risk assessment and management of existent bridges. This study paves the way for optimizing the planning procedures of the interventions of ordinary and extraordinary maintenance activities by developing a prototype for the development of a Bridge Management System (BMS) at a regional level, increasing the resilience of the investigated infrastructures and bridges.

Monitoring of critical civil engineering infrastructures, and especially viaducts and bridges, has become a priority. Specific guidelines on risk classification and management, safety assessment and monitoring of existing bridges have been issued in Italy. Several laws and regulations have been issued on the same topic, emphasizing the crucial role of BIM-based procedures for the design and management of civil infrastructures. This study aims at examining the potential of an interoperable and upgradeable BIM platform supplemented by non-destructive survey data, such as Mobile Laser Scanner (MLS), Ground Penetrating Radar (GPR) and satellite-based information. The main goal of the work is to implement an infrastructure management platform that aims at reducing the limits associated to the separate observation of these assessments and to provide a more efficient way to store data regarding the status of bridges and viaducts, to the advantage of an integrated analysis. This work aims to develop an informative BIM platform of the investigated bridges, interoperable within a Geographic Information System (GIS) database. As on-site surveys are carried out, a preliminary multi-source database of information is created. Preliminary results have shown promising viability of the data management model for supporting asset managers in the various management phases.

Telecommunications (TLC), Navigation (NAV) and Earth Observation (EO) represent the space sectors with the strongest growth, outstanding to the important spread of applications and services associated to them.

Space-borne data generate information with unique added value in the context of Earth’s surveillance, geohazards mapping and infrastructure monitoring. Furthermore, the integrated implementation of satellite-based services with other multi-source and multi-resolution technologies, including ground-based, aerial and non-destructive sensors, appears to become a crucial step for public and managing bodies in developing tools to assess actual state of civil engineering structures. Therefore, the Italian Space Agency has recently planned specific investments aimed at guaranteeing and improving real-time services, contributing to economic, scientific, technological and sustainable growth of our country. In this context, this work summarizes first outcomes of an ASI’s initiative focused on opportunities for the community operating in the sector of national infrastructures, offered by current supply chain of space systems, services and applications. Scope of this work is to present the main outputs and analyze them, to identify several potential R&D topics related to EO/NAV/TLC applications able to trigger a significant advance to support plan, design, construct and manage civil infrastructures at the network-scale level.

In the framework of ASI’s initiative, administrative authorities and national managers of transport infrastructures were called to present needs and requirements, as well as several on-going research activities and business existing services were considered. Status-of-the-art and gaps are useful to identify requirements of future space infrastructures and services, aimed to effectively support critical infrastructure assets management.

Climate change constitutes a serious global challenge with consequences that are directly affecting infrastructure. Thus, there is a great need to develop reliable cost-effective systems, which integrate remote sensing data, in situ measurements and advanced methods for infrastructure monitoring. In this framework, the European Union and the Hellenic government are financially supporting a R&D project, named “PROIΟΝ”. The purpose of the project is the development of a platform for the continuous monitoring of high-priority infrastructures (public infrastructure, dams, bridges, etc.) which are located in a particularly active area in terms of tectonics and seismicity. Monitoring is based on the combination of instrumental and remote sensing measurements along with fuzzy logic networks methods and machine learning algorithms in order to generate an innovative decision-making and decision-support tool. Specifically, measurements derived from three-axis accelerometers, Global Navigation Satellite System (GNSS) receivers and Persistent Scatterer Interferometry are imported into the platform. The measurements will be validated using high-accuracy reference representations arising from data acquired by Terrestrial Laser Scanning (TLS) surveys and Unmanned Aerial Vehicles (UAV) campaigns and subsequently, deformation maps will be generated. Intelligent data analysis methods will contribute to making decisions on the current as well as the future state of the infrastructure. At this initial stage of the project, the proposed monitoring system is described in detail.

Heat island effect is usually present in urban areas that experience higher temperatures than rural areas. Materials used for buildings, roads and other infrastructure absorb and re-emit the sun's heat more than green areas and water bodies. This effect results in discomfort to the residents of the affected areas and negatively impacts ecological and energetic variables. The most common way of detecting and analyzing Heat Island Effect using remote sensing is based on the use of evening and night surface satellite images.

In this study, the Land-surface temperature (LST) products of the Copernicus Sentinel-3 satellite were used to provide surface temperature from night images and produce heat maps of the urban area of Limassol, Cyprus. The affected areas were examined based on their dependence on green space, building density and population density. The study examined time-series data for the summer period, which is the hottest month of the year in Cyprus. Such information is vital to assist decision and policymakers to adopt effective mitigation strategies and improve urban planning.

The current study investigates the shoreline forecasting results combining the IRS and SPOT medium resolution satellite imagery with the high-resolution air-photos. The littoral area of the bay of Katakolo situated in the Prefecture of Ilia’s, Greece, has been used as a test site.

Past shoreline positions have been demarcated, through on-screen digitizing method, for the years of 1945, 1960, 1965, 1968, 1975, 1987, and 1996, based on the respective high-resolution images. The shoreline evolution for the 1945-1996 period was estimated for each dataset via the Linear Regression Rate (LRR) using for the 1996 the shorelines derived from the High Resolution (HR), SPOT, and IRS datasets respectively. The results were cross-validated using the R-squared, the Root Mean Square Error (RMSE), and the Mean Annual Error (MAE) coefficient methods. In addition, future position of the shoreline (forecasted shoreline) was estimated for 10 years (2006) by alternating the reference shoreline of the 1996 with the relative shoreline derived from the IRS and SPOT-3 satellite images respectively. The integrated forecasting function of the fifth version of the Digital Shoreline Analysis System (DSAS) tool of the ArcGIS software was used for the computations. The End Point Rate (EPR) was calculated for the 1996 - 2006 period, using the 1996 actual and the 2006 forecasted shorelines based on the SPOT and the IRS data, and compared to the respective rates derived from the high-resolution actual shorelines. Moreover, the Shoreline Change Envelope (SCE) rates were computed in order to estimate the mean rate of distance between the 2006 actual and forecasted shoreline of each dataset.

The images have been orthorectified and georeferenced to Hellenic Geodetic Reference System of 1987 (Greek Grid) using Leica Photogrammetry Suite (LPS), while transects every 30 meters along the coastline were created and used for the measurements.

It was proved that although both datasets provide similar results, the SPOT (Pan) images seem to have an advantage, while the comparison based on the forecasting models should be avoided in sites where infrastructures have taken place as they distort the results.

Classification of landslide type is important in risk management, yet it is often missing in large inventories. Here we present a novel data-driven method that uses morphometric and geospatial input parameters to classify landslides type at a national scale in Italy by means of a shallow Artificial Neural Network. The overall True Positive Rate is 0.76 for a five-class classification of over 275000 landslides. The performances in the entire national territory are very good, with F-score higher than 0.9 in large areas. The method can be applied to any polygonal inventory, as those produced by automatic mapping from Earth Observation imagery.

Garhwal Himalayas is one of the landslide-prone regions of India, which is characterized by frequent landslide occurrences causing damage to property, natural resources, and lives. In this study, the historical landslide records were associated with natural factors (climate, physiography, soil, geology, and vegetation factors) and anthropogenic factors (population density, livestock population, distance from settlements, and distance from road) using the random forest model. On the basis of variable importance value, it was found that the distance to road, vegetation (NDVI mean), slope, distance to water, and distance to the settlement were the most important factors in the model development. It indicated that developmental activities were one of the major factors responsible for landslides in the region. The results showed that 4655.94 km2 was susceptible to landslide hazards, out of which 38.29% area was under high and very high susceptibility class. The results validation using AUC (0.97), kappa coefficient (0.8), and TSS (0.84) showed that the model performance was excellent in predicting the landslides. The results provide information about the sensitive areas which are prone to landslides in the region. The study highlights the significance of scientific development and plantation of deep-rooted trees and grasses in landslide-prone areas.

The vegetation phenology is defined as the seasonal timing of recurring biological events, the causes of their timing, their relationship to biotic and abiotic forces, and the interrelations among phases of the same or different species. Phenology is one of the most reliable indicators of vegetation dynamics. Geographical Information Systems (GIS) combined with freely available satellite imagery significantly contributes to the study of vegetation phenology. A GIS open-source application that estimates vegetation phenology metrics from time-series of Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) as proxies for phenology was already been developed. The application was based on products from MODIS sensor (MOD13 data) and considered two crops, vineyards and maize. However, this application is now obsolete in the recent version of QGIS software. This work aims to develop an improved version of the open-source application to automatically extract the phenological metrics from a set of Sentinel 2 input data. Sentinel 2 has the advantage of also being free, with higher spatial, spectral, and temporal resolution, being particularly suitable for “in-season crop forecasting”. Several limitations found in the previous version can be surpassed with the use of Sentinel 2 data. For instance, the small farms, due to the presence of multiple land cover types within a pixel, can now be monitored with much greater confidence. The application is free and open source, so it can be modified according to the user requirements and adopted to different crops and regions.

Many deep learning architectures exist for semantic segmentation. In this paper, their application to multi-modal remote sensing data is examined. Two well-known network architectures, U-Net and DeepLab V3+, developed originally for RGB image data, are modified to accept additional input channels, such as near infrared or depth information. In both networks, ResNet101 is used as the backbone, while data-preprocessing steps, including data augmentation, are identical. We compare both networks and experiment with different fusion techniques in U-Net and with hyper-parameters for weighting the input channels for fusion in DeepLab V3+. We also evaluate the effect of pre-training on RGB and non-RGB data. The results show a minimally better performance of the DeepLab V3+ model compared to U-Net, while for the certain classes, such as vehicles, U-Net yields a slightly superior accuracy.

Himachal Pradesh is a state located in the Indian Himalayan Region, one of the world's biodiversity hotspots, which assures its biodiversity richness and its ecological sensitivity. Due to the rapid rate of development, this Himalayan state has recently experienced many land use changes. To better understand the dynamics of land cover, the study's main objectives are to define the key LULC classes and compute and assess the size and rate of change for these classes during a 20-year period (2000 to 2020). For the purpose of this study, we processed and classified images using the Google Earth Engine Platform. The Landsat surface reflectance data products were selected for the years 2000, 2010 and 2020. Thereafter, the median of image collections was derived to get the final image to be classified for the study area. The Random Forest classifier was used to classify the images into seven landcover classes, i.e., agriculture, forest, water, glacier, built-up, grassland, and barren. The results revealed that the total area covered by agriculture has declined over time (0.26%), while forests (1.04%) and built-up areas (0.48%) in the state have expanded from the year 2000 to 2020. The main reason for the reduction in agriculture area is decrease in agricultural activities, while afforestation and continuous infrastructural development have resulted in an increase in forest cover and built-up areas, respectively. The result of this study gives a comprehensive picture of land cover changes in the state, which could further assist policymakers in strategizing for the state’s long-term socioeconomic development.

Wildfires are among the most common natural disasters, which occur mainly in extremely dry conditions (summer period) due to natural causes or human activities. They can potentially lead to permanent vegetation loss, soil erosion and consequently flooding and desertification, while they can also be particularly harmful to humans themselves. In recent years, a rapid increase in the number of fires has been observed, especially in areas such as Siberia, California, Australia as well as in regions with Mediterranean climate. In the current study, we mapped the burned areas in the northwestern Peloponnese for a ten-year period (2011-2021) using spaceborne remote sensing data. Specifically, we obtained Sentinel- 2 and Landsat data -provided free of charge to all users- covering the study area before and after wildfires. The identification and the subsequent mapping of the burnt areas was based on the calculation of the Normalized Difference Vegetation Index (NDVI) as well as on the estimation of the Normalized Burn Ratio (NBR) index. Burnt areas arising from both missions were integrated into an ArcGIS environment in order to be measured and compared. The assessment of the results was carried out using burnt areas, acquired by the European Forest Fire Information System (EFFIS).

GREENPEG is a H2020 project developing exploration toolsets for critical materials in pegmatites. Lineament mapping is a powerful tool to identify geological structures at different scales, which is commonly applied in mineral exploration. To extract lineament information, different types of data can be employed, namely: (i) optical remote sensing data, (ii) radar remote sensing data, and (iii) digital elevation models (DEMs). All these data have been used by GREENPEG, where image processing of Sentinel-1 and ALOS (DEM) datasets were evaluated to identify regional-scale tectonic structures, such as faults, that may have controlled pegmatite melt emplacement. Automated geomatics methods to extract lineament information and perform lineament mapping were employed and the mean direction of the extracted lineaments was evaluated through rose diagrams. In general, the results of the radar image processing were affected by noise in the coastal areas and by the topography of the region. ALOS data presented some advantages, due to less influence from topography and human structures (saving time in the visual inspection of the results). Moreover, even though higher thresholds for the minimum length to consider as a lineament were applied to Sentinel-1 images, ALOS-derived lineaments were longer. The main trend of the extracted lineaments is in agreement between the two datasets, although specific trends are clearer in the ALOS-derived lineaments because the shaded reliefs produced already filtered the azimuth of lineaments to be extracted according to light incidence angle. For all three project demonstration sites (Leinster, Ireland; Wolfsberg, Austria; and Tysfjord, Norway) it was possible to identify lineaments that may relate to structures that controlled pegmatite emplacement. This study demonstrates the usefulness of different satellite data in structural mapping and mineral exploration.

Hyperspectral (HS) data offers a fast, non-invasive and non-destructive way of obtaining information about the composition of rocks. In the Hyperspectral Lapland (HypeLAP) -project we are using HS imaging and point spectral data to map the mineralogical and mineral chemical variations of a total of 500 m of drill core data. These data, acquired in the visible-near infrared-short-wave infrared (VNIR-SWIR; 400-2,500 nm) and long-wave infrared (LWIR; 8,000-12,000 nm) wavelength regions, represent two gold prospects (Ruosselkä and Hirvilavanmaa) and one Cu-Zn volcanogenic massive sulphide (VMS) prospect (Pahtavuoma) in northern Finland. In this paper, we present preliminary results from the Hirvilavanmaa study area, where we mapped the mineralogy of a single drill core box using the self-organizing maps (SOM) multivariate data analysis technology. In the process, point spectral data, Mineral Liberation Analyzer (MLA) data and X-Ray Fluorescence (XRF) data were used to guide the analysis of the HS image data. The results suggest that HS data has the potential to offer valuable information for mineral exploration activities in the Hirvilavanmaa study area. In the next steps, we will focus on analyzing LWIR wavelength region data from the three study areas to obtain a holistic view of their mineralogy and mineral chemistry. The HypeLAP-project is financed through the European Regional Development Fund (Sustainable growth and jobs 2014-2020 -programme).

Coastal areas are attractive for human populations as they are important areas of business and socio-economic activities. The Prefecture of Western Greece is experiencing population movement from the urban to the coastal areas causing nuisance and changes to the marine environment. These changes need to be assessed systematically in order to build models that can predict the evolution in the future. In addition, except for the man-made infrastructures, natural phenomena such as undulation, wind and coastal currents affect the shape of the beach, the shoreline and the seabed.

Remote sensing is an efficient way to measure these changes repeatedly using high resolution satellite data, air photos and airborne Lidar in conjunction with Digital Surface Models (DSMs) and orthophoto maps. These methods have disadvantages as they are high cost, especially in cases where multitemporal data is deeded.

Unmanned aerial vehicles (UAV) photogrammetry, offers a reliable alternative solution to the acquisition of high accuracy spatial data along the littoral zone, representing a rapid low-cost tool for coastline monitoring.

In the present study, the littoral area of the bay of Arkoudi situated in the Prefecture of Ilia’s, Greece, has been used as test site. Repeated UAV campaigns were performed in 2020, 2021 and 2022 in synergy with extensive Global Navigation Satellite System (GNSS) measurements and bathymetric campaigns. RGB and multispectral UAV data were processed in order to extract the coastline, the topography and the inclination of the beach and to estimate the evolution of the littoral zone of the area.

Different remote sensing methods already applied have proven efficient in identifying pegmatites, but the high number of the false positives and the size of the study areas involved, make the location of new points of interest for exploration a difficult task. In order to develop and evaluate more autonomous tools for localization of new points of interest, this study aims to apply the Envi Spectral Hourglass Wizard (SHW) algorithm and spectral analysis, both applied on PRISMA hyperspectral images, to determine mineral distribution in St. Austell greisen deposit, a Li exploration target located at Cornwall, UK. The SHW finds endmembers within the dataset to map their location and sub-pixel abundance. This processing workflow is composed of several steps: (i) MNF (Minimum Noise Fraction) Transform; (ii) PPI (Pixel Purity Index); (iii) n-D space visualizer, allowing the extraction of the endmembers and; (iv) the SAM (Spectral Angle Mapper) classification algorithm, which classifies the image creating a class for each collected endmember. The classification results show the potential of the method to indicate the presence of Li minerals being able to identify Kaolinite and map the distribution and abundance of Topaz, Tourmaline and Biotite. This approach is highly valuable for the Li mining industry.

A plethora of risk-assessment methodologies have been developed for the detection of rockfall-prone areas. In this study, a freely available add-on for QGIS software named QPROTO, was chosen for hazard and susceptibility analyses of a well-studied rockfall in Platiana village of Ilia prefecture. QPROTO is based on the Cone Method and focuses on the identification of areas vulnerable to rockfall phenomena. The software takes into consideration the slope’s topography and various empirical parameters (e.g. dip direction θ, energy angle φp, lateral angle a). It calculates block velocity, kinetic energy, as well as the estimation of the time-independent relative rockfall hazard. The basic input parameters in QPROTO are a Digital Elevation Model (DEM), aspect, detachment propensity, boulder mass, energy line angle and lateral spreading angle. Slope characteristics (slope inclination, forest coverage) and block characteristics (block volume, block shape) are the main parameters that influence the evolution of a rockfall phenomenon. As DEMs extract details pertaining to the topography of the slope it is an essential factor for rockfall quantative computation via the aforementioned plugin. Eight free available DEMs have been evaluated in this work aiming to assess the most efficient ones. A DSM created from UAV data, a DSM from the Greek Cadastral, DSM from the Ministry of Agriculture, ALOS AW3D30 DEM, ASTER GDEM, SRTM30 DEM, SRTM90 DEM and TanDEM_X are used. Diverse analyses were performed and the results are presented. It is proved that the spatial resolution of the DEM is very crucial for the accuracy of the results.

The ecological environment of the Yellow River Basin continues to deteriorate due to the rapid population growth and urban expansion. Understanding the land use change is helpful to analyze the land use trends under the interference of human activities and changes in the natural environment. In this manuscript, the TM and OLI images in 2008 and 2017 were mainly used to analyze the spatiotemporal dynamic of the land use in Henan Section of Yellow River Basin. The driving factors of land use changes were also quantified. Results show that the land use area of construction land and woodland gradually increased, and the arable land decreased significantly during the past ten years. The transfer of land use types was mainly reflected in the conversion of arable land to construction land. The total land use dynamic degree has reached 4.80% in the past decade. Based on the results, it can be concluded that the terrain and the socio-economic activities are the key factors affecting land use change in the study area. The research results can provide data support for ecological environmental protection and high-quality development of the Henan section of the Yellow River Basin, and help local governments take corresponding measures to achieve coordinated and sustainable socioeconomic and environmental development.

The modern development of mobile communications applied in remote sensing techniques requires continuous improvement of methods for transmitting information and developing new types of signals in order to achieve better resistance to interference, energy and structural secrecy of radiation, increase the efficiency of radio systems. These features have broad spectrum signals, the main advantages of which are: ensuring confidential transmission of information, extracting the signal from below the noise level, ensuring the reliability of the received information, even with a relatively small signal-to-noise ratio (SNR). Traditionally, the expansion of the spectrum of signals is done by multiplying the information signal by a code sequence, which is a random sequence, most often of binary symbols, formed by a determined law, but having the properties of a random sequence. Different classes of code sequences are known and used, which meet a number of mandatory requirements. However, they all have one or another drawback. An important reason for this is the lack of a universal mathematical apparatus through which to form a random sequence with the necessary structural complexity and correlation properties to ensure confidentiality in the transmission of information. With the emergence of a new direction in the development of communication technologies, based on the theory of dynamic chaos, there are alternative opportunities to expand the spectrum of signals. These are discrete chaotic signals, which, like pseudo-random sequences, are characterized by a quasi-continuous spectrum with uniform spectral density, with a steeply decreasing autocorrelation function and orthogonality. In the present work a structural scheme of a communication system between sensors is proposed, in which the spectrum of the information signal is expanded by a chaotic sequence. The noise immunity of the system is assessed by determining the probability of bit error rate (BER). The dependence of BER on SNR is studied. The connection with remote sensing could be review in the field wireless network using nodes. The methodology is tested in a simulation and in in-situ experiments. Some of the nodes are coated by new material and put into extreme conditions. The obtained results are analyzed and discussed.

A significant problem in radio communications is the detection and differentiation of signals at an increased level of interference. The current electromagnetic environment is too dynamic and the compatibility of electronic communications must be adapted to the changing parameters of interference. This involves interference analysis and selection of the most appropriate signals in order to ensure good resistance of the communication system against interference. Correlation analysis is a powerful tool in detecting or recognizing signals in conditions of intense disturbance, and there are many such studies in the literature. In signal synthesis tasks, the most common goal is to obtain autocorrelation function. But the correlation between two variables can also be established through dependencies on mathematical statistics. The task of synthesizing signals resistant to the interference is statistical a priory because of the random nature of the interference.

In the present work, an algorithm for identifying the useful signal from interference by determining the Spearman rank coefficient is proposed.

For this purpose, the measured values of the signal and the interference are replaced by their rank numbers. Ranks are obtained by assigning a different degree of possession of the observed quality to a number from 1 to n.

In order for the signal and interference to be independent, the ranking factor must be equal to 0, or minimal. Based on this, after determining the Spearman's coefficient for the reported values of the signal and the interference, a rearrangement is performed in the reports of the signal which ensures a reduction in the correlation between them.

The authors plan to apply the algorithm in in-situ experiments using remote sensing wireless sensor network based on the experience in the Remote Sensing Systems Department at the Space Research and Technology Institute at the Bulgarian Academy of Sciences.

Drinkable water is very limited on Earth, and groundwater is considered as one of its main sources, especially in the arid and semi-arid countries where there is scarcity in surface water sources, and the distribution of rainfall is not consistent all seasons. Mostly, groundwater is impacted by human activities. Changes in Land Use and Land Cover (LULC) are commonly human-induced activities that affect the groundwater system. Continuous growth in population rate forces governments to expand urban areas, which leads to negative consequences for groundwater storage. This study examines this issue extensively in Jebel Ali - Dubai. Recently, Jebel Ali continuously growing region, it is considered one of the most significant urban development areas in Dubai. This paper aims to map groundwater potential areas by applying 6 hydrological and topological factors to Analytic Hierarchy Process (AHP). As well as creating Land Use and Land Cover (LULC) classification of 2002 and 2020 in order to detect the changes in urban classes of the city in the last two decades. Afterwards, Dubai expansion can be evaluated by calculating the given presence of a high groundwater potential zone in areas that have become urban. To achieve these objectives, there are two types of data were used, DEM data provided from SRTM with 30 meters spatial resolution and multispectral image delivered from Landsat 7 and 8 with 30 meters spatial resolution. The result was found to be 496.7 sq KM in Dubai has High Groundwater Potential (HGP), with only 0.7% located in Jebel Ali. Considering the changes that occurred in this area, only 0.028 sq KM of the HGP has become urban since 2002.