## 4.2 BNN Results

The BNN, trained with the hyperspectral features and the positive and negative labels, generated a mineral prospectivity map with associated uncertainty estimates. The map highlighted areas with high mineral potential, guiding future exploration efforts. In Figure 9 and 10, the warmer colors near orange and red represent the areas with the highest prospectivity, where the prospectivity values are close to one. The resulting mineral prospectivity maps can be interpreted as follows: areas with values greater than 0.5 are the most prospective. These are the areas where mineralization is most likely to be found. In this case, the mineralization of interest is the Cu-Co-Ni mineral association.

<figure><img src="image_264.png" /><figcaption>Figure 9: BNN prospectivity mapping results.</figcaption></figure>

The results in Figure 9 and 10 also reveal an interesting pattern: many of the prospective areas are aligned with important structural features. These include sections of the E-W Aramo fault and spatial associations with lines representing the Aramo thrust

front. This association between prospective areas and structural features corroborates the geological understanding of the area, which suggests structural control over mineralization. However, due to the complex structural nature of the area, it is challenging to identify this association based solely on geological observation. These results can help guide future field efforts to validate these findings and improve our understanding of the factors controlling mineralization in the area. The uncertainty estimates provided a measure of confidence in the predictions, allowing for more informed decision-making.

<figure><img src="image_265.png" /><figcaption>Figure 10: Zoom-in to BNN prospectivity mapping results.</figcaption></figure>

## 5 DISCUSSION

Our research highlights the feasibility and advantages of integrating hyperspectral data from multiple sources with deep learning techniques for mineral prospectivity mapping. This approach surpasses traditional methods by offering a non-invasive, high-resolution, cost-effective, and highly accurate alternative for identifying potential mineral deposits. By leveraging remote sensing and machine learning, it minimizes environmental impact, reduces exploration costs, and enhances predictive reliability, making it particularly suitable for early-stage exploration and challenging terrains.

A key innovation in our methodology is the introduction of data-driven negative sampling, a critical step in training BNN models for mineral prospectivity mapping. Negative sampling is a well-known challenge in machine learning applications, as incorrectly labeled negative samples can significantly degrade model performance. Traditional methods often rely on random sampling or expert-defined barren areas, which may not adequately capture the true background variability. To overcome this, we
employed a systematic data-driven approach using Self-Organizing Maps (SOM) to generate reliable negative labels. By co-registering SOM clusters with known mineral occurrences (positive labels), we identified regions highly likely to be barren (negative labels). This ensured that the training data more accurately reflected the real geological variability of the study area, improving model robustness and reducing bias in mineral prospectivity predictions.

Another major contribution of our study is the use of Bayesian Neural Networks (BNNs) for predictive modeling and uncertainty quantification. Unlike conventional artificial neural networks (ANNs), which provide only point estimates, BNNs estimate a probability distribution over model parameters, allowing them to quantify the uncertainty in their predictions. This is particularly valuable in mineral exploration, where decision-making is inherently uncertain and high-risk. The Bayesian framework enables the estimation of uncertainty in model outputs (Figure 11), offering a confidence measure for each prospectivity prediction. This allows for more strategic resource allocation, as exploration efforts can be prioritized in areas with high predictive confidence while regions with high uncertainty can be flagged for further data acquisition. By integrating uncertainty quantification directly into the model, our approach provides a more transparent and interpretable decision-support system, reducing the risk of false positives and missed discoveries.

<figure><img src="image_266.png" /><figcaption>Figure 11: BNN Uncertainty associated to prospectivity mapping results.</figcaption></figure>

Beyond its theoretical advantages, our approach offers several practical benefits over traditional methods. First, its non-invasive nature minimizes environmental impact by reducing reliance on intrusive ground surveys such as drilling and trenching. This is particularly important in ecologically sensitive or remote areas where physical access is limited. Second, the high-resolution spectral information from hyperspectral imaging, combined with the BNN and SOM, allows for the identification of subtle features, leading to more accurate and efficient exploration efforts. Third, the cost-effectiveness of our methodology is significant; by automating feature extraction, we reduce the need for extensive manual interpretation from the hyperspectral data, and the high prospective areas can guide a more targeted oriented surface exploration, cutting exploration costs substantially.

It is also important to highlight the significance of the quantity and quality of training samples, particularly the positive samples. Although the methodology presented here offers an improved solution as a method for data-driven negative sampling selection, the importance of positive samples cannot be overstated. The positive samples are the single input data that will most significantly affect the results of the BNN models. In real-world scenarios, the quantity and quality of samples are not always optimal, as exemplified by the Aramo study case presented in this publication. The very challenging climatic conditions characteristic of this area in Spain resulted in several flight lines of the planned airborne hyperspectral acquisition being not possible to fly (this is the major data gap observed in Figure 2, 7, 8 and 9). Unfortunately, this area coincides with the location exhibiting the clearest surface mineralization and where the majority of the samples intended for training points were located. As shown in Figure 8 and 9, most of the training data coincides with this gap. Furthermore, a significant percentage of the remaining acquired data was obscured by clouds, rendering the spectral information unusable and necessitating the development of an automatic algorithm to mask cloud-covered areas, further reducing the available data. Therefore, a dataset with greater spatial coverage and a larger number of training samples in areas with available data would greatly enhance the quality of the results.

In the context of the growing global demand for critical raw materials, this research contributes to the integration of multi-scale hyperspectral remote sensing with BNN and SOM presenting an innovative workflow for data fusion and prospectivity mapping
with uncertainty quantification aiming to improve mineral exploration, offering a scalable and data-driven solution. As hyperspectral imaging technology advances and more high-quality datasets become available, the accuracy and effectiveness of this method will continue to improve. Additionally, ongoing developments in deep learning architectures, Bayesian inference, and self-supervised learning will further enhance predictive capabilities and uncertainty quantification.

Our findings emphasize the importance of data-driven approaches in addressing key challenges in training data selection and model interpretability. The combination of SOM-based negative label generation and BNN-driven uncertainty estimation provides a novel framework for improving the reliability and confidence of mineral prospectivity predictions. This methodology not only enhances the accuracy of the models but also offers a structured approach to handling uncertainty, making it a powerful tool for risk-aware decision-making in exploration projects.

# 6 CONCLUSIONS

Our innovative workflow for mineral prospectivity mapping supports the objectives of the EU's S34I project by providing high-value, unbiased datasets and improving the perception of mining through the application of cutting-edge, sustainable exploration technologies.

In conclusion, this study represents an advancement in mineral exploration by providing a scientifically rigorous, scalable, and environmentally responsible approach to identifying potential mineral deposits. The integration of hyperspectral data, SOM-driven negative sampling, and Bayesian Neural Networks has proven to improve exploration strategies, supporting a sustainable and efficient pathway to securing critical raw materials for a green energy future.

In the Aramo study case, the mineral prospectivity maps reveal an interesting pattern, showing many prospective areas aligned along important structural features, including sections of the E-W Aramo fault and the Aramo thrust front. This alignment corroborates the area's geological understanding, which suggests that mineralization is in some degree structurally controlled. These results can guide future fieldwork to validate these findings and enhance our understanding of the factors controlling mineralization in the area.

The integration of advanced deep learning and remote sensing data not only accelerates the

exploration process but also significantly reduces costs and environmental impact. This approach has the potential to transform mineral exploration, supporting the sustainable and responsible sourcing of critical raw materials for the EU's green energy transition.

# ACKNOWLEDGEMENTS

This research has been done within the framework of the project S34I—Secure and sustainable supply of raw materials for EU industry—, coordinated by Ana C. Teodoro. This project has received funding from the European Union’s HORIZON Research and Innovation. Grant Agreement No. 101091616 (https://doi.org/10.3030/101091616). Portuguese National Funds also support this work through the FCT – Fundação para a Ciência e a Tecnologia, I.P. (Portugal), projects UIDB/04683/2020 (https://doi.org/10.54499/UIDB/04683/2020) and UIDP/04683/2020 (https://doi.org/10.54499/UIDP/04683/2020).

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SHORT PAPERS
<figure><img src="image_267.png" /></figure>
# Application of UAV, GNSS and InSAR Techniques in the Raw Material Supply Chain

Tanja Grabrijan¹, Krištof Oštir¹, Klemen Kozmus Trajkovski¹, Dejan Grigillo¹,
Veronika Grabrovec Horvat¹, Polona Pavlovčič Prešeren¹, Veton Hamza¹, Antonio Pepe²,
Fabiana Calò², Francesco Falabella², Enoc Sanz-Ablanedo³, Mihaela Gheorghe⁴, Teodora Selea⁴
and Ana Cláudia Teodoro⁵

¹University of Ljubljana, Faculty of Civil and Geodetic Engineering, Jamova cesta 2, Ljubljana, Slovenia

²Italian National Research Council, Institute for Electromagnetic Sensing of the Environment (IREA),
328, Diocleziano, Napoli, Italy

³Grupo de Investigación en Geomática e Ingeniería Cartográfica (GEOINCA), Universidad de León, León, Spain

⁴GMV Innovating Solutions SRL, Bucharest, Romania

⁵University of Porto, Department of Geosciences, Environment and Spatial Planning, and Institute of Earth Sciences (ICT),
Rua do Campo Alegre 687, Porto, Portugal

{tanja.grabrijan, kristof.ostir, klemen.kozmus-trajkovski, dejan.grigillo, veronika.grabrovec-horvat,
polona.pavlovcic-preseren, veton.hamza}@fgg.uni-lj.si, {pepe.a, calo.f, falabella.f}@irea.cnr.it, esana@unileon.es,
{mgheorghe, teodora.selea}@gmv.com, amteodor@fc.up.pt

**Keywords:** Mining, UAV, Tri Stereo, SfM Photogrammetry, Low-Cost GNSS, InSAR, Super-Resolution.

**Abstract:** The marble quarry, located in southern Austria produces high-quality marble, both in open-pit and underground extraction sites. Extraction, transportation and accumulation of material require close monitoring to maintain the stability of the whole area and to observe changes in waste dumps. In this study, we have shown how different methodologies can be used to support the monitoring of the entire raw material supply chain. Several unmanned aerial vehicle (UAV) surveys were performed to compute high-resolution digital elevation models (DEM) to serve as a reference for comparing stereo and tri-stereo DEMs calculated from satellite imagery. A low-cost global navigation satellite system (GNSS)-based monitoring system was set up to estimate horizontal and vertical displacements. In addition, the state-of-the-art technique of Interferometric synthetic aperture radar (InSAR) provided displacement time series for a broader area.

## 1 INTRODUCTION

The Secure and Sustainable Supply of Raw Materials for EU Industry (S34I) project aims to increase European autonomy over raw materials resources through research and development of new data-driven methods for analysing Earth observation (EO) data. It will improve the systematic exploration of minerals and ensure continuous monitoring of all mining activities, i.e., extraction, closure, and post-closure.

This paper presents the preliminary results using various techniques and methods, including satellite data, airborne systems, unmanned aerial vehicles (UAVs), ground-based measurements, and traditional in-situ methods. Improved volume maps of mining

waste deposits using a multispectral sensor mounted on a UAV have been used at the extraction site. Ground instability maps are obtained through new InSAR methods for the Sentinel-1 radar data to compute long-term, i.e. 2014-2023 ground displacement time series. Mineral stockpile volume is calculated through satellite photogrammetry from optical data. Displacement time series are measured and analysed using low-cost global navigation satellite system (GNSS) receivers. The results presented are preliminary and will require further validation to ensure their accuracy and reliability for long-term monitoring applications.

a https://orcid.org/0009-0009-7374-9251
## 2 STUDY AREA

The extraction site is located in southern Austria and produces high-quality marble, both in open-pit and underground extraction sites. This marble formed during the Devonian period, around 350-400 million years ago, and has a colour ranging from light grey to white. This material is used to produce mineral fillers and pigments. In this study, only the open pit mine is considered. The slopes are represented by hard rock, which requires high-precision measurements to assess their stability. The area of interest is about 1.2 km² and includes a waste dump and an active quarry (Figure 1).

<figure><img src="image_268.png" /><figcaption>Figure 1: The area of interest: waste dump and active quarry.</figcaption></figure>

## 3 InSAR DISPLACEMENT MAPPING

Multi-frequency and multi-orbit SAR satellite data were used for Interferometric SAR (InSAR) displacement mapping purposes. C-Band Sentinel-1 A/B ascending and descending complex SAR images gathered from October 2014 to March 2023 were used. Complementary to medium-resolution Sentinel-1 images, high-resolution X-Band COSMO-SkyMed SAR images acquired along the descending orbit were also used.

Sentinel-1 images were independently processed by following the standard steps of SAR image registration, topography removal using an external one arc-second Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), orbit compensation via precise Sentinel-1 supplementary

information, additional fine co-registration procedure on the images, and finally, interferograms generation. The interferograms were then unwrapped using the multigrid algorithm (Falabella et al., 2022) to obtain the high-resolution unwrapped phases. Line-of-sight (LOS)-projected ground displacement time series were obtained by inverting the unwrapped single-look interferograms via Small BAseline Subset (SBAS) algorithm (Berardino et al., 2002).

The same processing operations, up to the unwrapping stage, were also performed on COSMO-SkyMed datasets. Subsequently computed Sentinel-1 vertical (up-down) and horizontal (east-west) ground displacement time-series were used in feedback to the multigrid unwrapping operation for flattening the COSMO-SkyMed interferograms. This auxiliary step was incorporated into the processing flow to facilitate phase-unwrapping by reducing low-frequency displacement contributions, thereby minimizing ambiguity numbers during the unwrapping process. Once they were unwrapped, the COSMO-SkyMed LOS displacements were obtained, as well as the enhanced combined Sentinel-1 (resampled onto the spatial resolution grid of COSMO-SkyMed) and COSMO-SkyMed vertical (up-down) and horizontal (east-west) displacement time-series.

The multisensor data combination aims to enhance the temporal sampling of measurements rather than perform cross-calibration. Independent ground displacement time-series are computed and geocoded before solving a constrained linear problem to derive 2-D ground displacement time-series, following Pepe et al. (2016).

Results are shown in Figure 2 and Figure 3. Figure 2 shows the 2-D InSAR products obtained using the detailed multigrid InSAR methodology, while Figure 3 shows displacement time-series related to the SAR measure point indicated in Figure 2.

## 4 DEEP LEARNING FOR SAR IMAGE ENHANCEMENT

We investigated several deep learning models using Sentinel-1, Sentinel-2 and COSMO-SkyMed (CSK) data to improve the resolution of Synthetic Aperture Radar (SAR) images. The dataset used in this study comprises 135,013 patches extracted these images. The CSK data preprocessing involved using a half-band filter to generate the corresponding low-resolution (LR) images for training purposes. This filter applies downsampling while preserving phase information, which is essential for SAR applications
IREA-CNR (Multigrid InSAR Processing)
2-D (Vertical and Horizontal) InSAR Ground Displacement Profiles

<figure><img src="image_269.png" /><figcaption>Figure 2: Deformation velocity maps generated through advanced InSAR processing technique show vertical (left) and horizontal (right) components of displacements.</figcaption></figure>

<figure><img src="image_270.png" /><figcaption>Figure 3: Displacement time-series related for points indicated in the upper map. From the top: first two time series show the subsidence (Up-Down) and Horizontal (E-W) deformation trend. At the bottom: time series related to stable measurement points. Also, the velocity of deformation is reported in the graphs.</figcaption></figure>

such as interferometry. Each SAR CSK patch consists of real and imaginary components, and the filter was applied independently to each channel. Sentinel-1 patches from the Sen1-2 dataset, and its optical Sentinel-2 equivalents acted as supplementary inputs in several models.

We evaluated the following deep learning architectures for SAR super-resolution:

1. Residual-in-Residual Dense Block (RRDB) (Wang et al., 2018): A convolutional neural network that incorporates multiple residual

blocks to enhance feature extraction and avoid information loss.

2. Super-Resolution U-Net (SRUN) (Ronneberger et al., 2015): A modified U-Net architecture optimized for SAR data, integrating downsampling and upsampling layers for effective feature recovery.

3. Optical-Guided Super-Resolution Network (OGSRN) (Li et al., 2015): A dual-network system utilizing both SAR and optical images to enhance SAR resolution.
4. DC2SCN (Deep CNN with Skip Connection and Network in Network): A model designed specifically for SAR super-resolution, handling the real and imaginary parts of SAR data separately to preserve phase integrity.

Each model was trained using a combination of Sentinel-1, COSMO-SkyMed, and Sen1-2 dataset patches. The training process involved Z-score normalization, where pixel intensities were standardized to ensure optimal network convergence. The models were assessed using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). As a baseline, we compared the deep learning models against bicubic interpolation, a commonly used upscaling method in image processing.

The experimental results, evaluated at a global scale using the Sen1-2 dataset, indicate significant improvements in SAR image resolution using deep learning techniques. The RRDB model outperformed the other architectures, achieving the highest PSNR and SSIM scores. The OGSRN model, which incorporates Sentinel-2 optical data, performed worse than RRDB despite its additional guidance input. This could be explained by variations in domain between SAR and optical images, which might cause feature extraction conflicts.

Overall, the results show that deep learning-based super-resolution can enhance SAR imagery quality significantly. Compared to bicubic interpolation, the best-performing model demonstrated an 18% PSNR increase (e.g., ~25 dB to ~29.5 dB) and a 2% SSIM improvement (e.g., 0.85 to 0.87), with RRDB outperforming OGSRN by 2-3 dB, highlighting SAR-only models' strengths. The visual result presented in Figure 4 (e.g., original vs. filtered COSMO-SkyMed patches) reinforced these findings, showing sharper edges and textures. This approach enhances SAR imagery for ground instability mapping in Gummern, aligning with S34I goals for extraction and closure phases of mining.

These findings underscore the potential of deep learning for SAR image enhancement and highlight future directions for improving model architecture and dataset quality.

<figure><img src="image_271.png" /><figcaption>Figure 4: Original patch (first row) and filtered patch (second row) of COSMO-SkyMed sample.</figcaption></figure>

# 5 UAV SURVEY

UAV Photogrammetry was used to perform several UAV surveys at the quarry with Phantom 4 Real Time Kinematic (RTK) + RGB camera. Because of variable terrain heights, terrain following method, i.e. flying at the same height above ground, proved to be the most efficient. The steepest part of the quarry was additionally surveyed with so-called angled flight mode. Ground Control Points (GCPs) were established for each survey and measured with RTK GNSS equipment to use later in image processing, ensuring better product accuracy. The latter are dense point clouds, 3D models, digital surface models (DSM), digital terrain models (DTM), and orthophotos. The flight height above ground was 100 m, resulting in a Ground Sampling Distance (GSD) of 3 cm.

Since multiple surveys were conducted, performing change detection for the same product across different dates is possible. One of the possibilities is monitoring deposited material in waste dumps by comparing two or more DEMs, which is especially useful in the northern part of the quarry at waste dumps. Figure 5 represents a calculated difference between DEMs, created from two different surveys.

High-resolution UAV data (especially DEM) served as a reference for less detailed remote sensing methods for terrain mapping, such as tri-stereo mapping, which is discussed in the Chapter 6.
<figure><img src="image_272.png" /><figcaption>Figure 5: Differences between two DEMs produced from UAV.</figcaption></figure>

# 6 TRI-STEREO MAPPING

Pléiades Neo tri-stereo consists of images that were collected during the same pass from different sensor-viewing angles (along-track stereo): forward (F), close to nadir (N) and backward (B). In the study, only panchromatic bands are used. Table 1 summarises the main acquisition parameters and characteristics of the images used.

<table><caption>Table 1: Acquisition parameters of the used Pléiades Neo tri-stereo images: incident angles (across-track, along-track and overall), GS), area, the computed [5] stereo intersection angles between two Pléiades scenes and their base-to-height ratio (B/H).</caption><thead><tr><th>View</th><th>F</th><th>N</th><th>B</th></tr></thead><tbody><tr><td>Across-track (°)</td><td>6.34</td><td>4.56</td><td>2.76</td></tr><tr><td>Along-track (°)</td><td>-7.67</td><td>-0.29</td><td>7.10</td></tr><tr><td>Overall (°)</td><td>9.90</td><td>4.57</td><td>7.61</td></tr><tr><td>GSD (m)</td><td>0.31 x 0.31</td><td>0.30 x 0.30</td><td>0.30 x 0.30</td></tr><tr><td>Area (km²)</td><td>130.89</td><td>130.84</td><td>130.31</td></tr><tr><td>Convergence (°)</td><td>7.55 (FN)</td><td>7.60 (NB)</td><td>15.14 (FB)</td></tr><tr><td>B/H Ratio</td><td>0.13 (FN)</td><td>0.13 (NB)</td><td>0.27 (FB)</td></tr></tbody></table>

The initial orientation of the images was set using the Rational Function Model (RFM) containing 80 Rational Polynomial Coefficients (RPCs). RPCs allow the transformation between object and image space. Additionally, 25 tie points were automatically measured to improve the relative orientation of the images. GCPs, which were evenly distributed across the area, avoiding hilly and overgrown areas, were then measured on all tri-stereo images to improve

their georeferencing. Multiple rounds of bundle block adjustment were performed using different GCPs as checkpoints. The maximum difference in the individual coordinate components of the checkpoints was 31 cm. In the final block adjustment, we used all GCPs surveyed in the field. Final Root Mean Square Error (RMSE) was 0.35 on image level (given in pixels) and 0.21 m on GCPs (0.14 m in Easting, 0.15 m in Northing and 0.03 m in height). The dense image matching algorithms were applied to oriented images to extract surface points, from which the DEM with a spatial resolution of 0.5 m was interpolated. Figure 6 shows the terrain shaded relief of DEM produced from Pléiades Neo tri-stereo panchromatic images.

<figure><img src="image_273.png" /><figcaption>Figure 6: Terrain shaded relief of digital elevation model produced from tri-stereo imagery.</figcaption></figure>

The accuracy of the produced DEM was estimated by comparing it to those produced with UAV surveys that were performed before and after the tri-stereo acquisition. 190 control points were extracted from UAV DEMs in the areas where no changes were detected between two consecutive surveys. Additionally, the satellite DEM was registered to those un-changed areas using Iterative Closest Point (ICP). The high accuracy of DEM, produced from satellite images was evaluated using the methodology proposed by EuroSDR (Höhle & Potuckova, 2011). We have achieved a mean value of 4.9 cm, RMSE of 30.1 cm, and standard deviation of 29.7 cm. Figure 7 shows the absolute distances between Pléiades Neo DEM and UAV DEM. The most extensive distances occur at the steep terraces in the quarry. The RMSE size is in the range of spatial resolution of the images and comparable to the results obtained by other authors (e.g. Loghin et al., 2020).

Pléiades Neo-derived DEM was additionally compared to DEM, created from WorldView-2 data. The latter captures images of the same area from different angles (stereo images). The slight displacement between the stereo pairs allows for depth perception and DEMs creation. The volume changes in the areas where waste dumping occurred are summarized in Figure 8.
<figure><img src="image_274.png" /><figcaption>Figure 7: The absolute distances between Pléiades Neo DEM and UAV DEM after the registration.</figcaption></figure>

<figure><img src="image_275.png" /><figcaption>Figure 8: Volumes of waste dumps between Pléiades Neo-derived DEM and from WorldView-2 DEM.</figcaption></figure>

<figure><img src="image_276.png" /><figcaption>Figure 9: LGMS installed in Gummern.</figcaption></figure>

Geodetic monitoring of displacements started on 03.04.2023 and is still ongoing, with the LGMS operating continuously for almost two years. The open-source software RTKLIB (demo33_g) was used to post-process GNSS observations, with further details on the processing strategy provided in Table 2.

<table><caption>Table 2: Processing parameters used in RTKLIB.</caption><tr><td>Positioning mode</td><td>Static</td></tr><tr><td>Frequencies</td><td>L1+L2/E5b</td></tr><tr><td>Filter type</td><td>Forward</td></tr><tr><td>Cutt-off angle</td><td>10°</td></tr><tr><td>Sampling rate</td><td>10s</td></tr><tr><td>Ionosphere/Troposphere correction</td><td>Broadcast/Sastamoinen</td></tr><tr><td>Satellite ephemeris</td><td>Broadcast</td></tr><tr><td>Satellite systems</td><td>GPS+GLO+Galileo</td></tr><tr><td>Ambiguity</td><td>Continuous (Lambda)</td></tr><tr><td>GNSS Antenna</td><td>AS-ANT2BCAL(igs14)</td></tr></table>

The results indicated that in ST1 horizontal and vertical displacements of 20 and 40 mm occurred, respectively, while larger displacements of 40 mm horizontally and 70 mm vertically were noticed in ST2.

# 7 GNSS MONITORING

Low-Cost GNSS Monitoring Station (LGMS) was developed using low-cost GNSS receivers and antennas to continuously monitor displacement with high precision and decreased costs (Hamza et al., 2021, 2023). The main components of the LGMS are the ZED-F9P GNSS module and survey-calibrated antenna (AS-ANT2BCAL). The LGMS can work in remote areas without electricity connection since it uses solar panels to charge the battery from which the systems get the electricity (Figure 9). It acquires and stores GNSS observations continuously, which are later post-processed to estimate the daily displacement of the monitoring locations.

Four LGMS stations were deployed in Gummern, with two stations, F1 and F2 (Figure 10), installed on stable ground to be used as reference stations. These reference stations were used to estimate displacements at ST1 and ST2 (Figure 11 and Figure 12), which were positioned on the waste dump where displacements were expected.
<figure><img src="image_277.png" /><figcaption>Figure 10: Locations of GNSS measuring stations.</figcaption></figure>

<figure><img src="image_278.png" /><figcaption>Figure 11: Horizontal and vertical displacement in ST1.</figcaption></figure>

<figure><img src="image_279.png" /><figcaption>Figure 12: Horizontal and vertical displacement in ST2.</figcaption></figure>

## 8 CONCLUSIONS

This work presents preliminary results of several EO methodologies designed to address specific needs in monitoring mining activities. We produced DEMs using photogrammetric data and satellite imagery, specifically from stereo and tri-stereo images. The results demonstrate that the quality of the DEM generated from satellite data is comparable to that of UAV-derived DEMs. Additionally, we found that both InSAR and low-cost GNSS data capture the same ground motion trends. While GNSS provides daily displacement measurements, which InSAR does not, InSAR offers a much denser result with broader spatial coverage. Furthermore, we showed that SAR imagery can enhance spatial resolution when processed with higher-resolution images.

By evaluating these methods, we provide decision makers with important information on the trade-offs between accuracy, spatial coverage and cost-effectiveness. The results emphasize the importance of a multi-sensor approach for comprehensive monitoring. However, further work is needed to validate these results. We are focusing on defining pipelines that will utilize the most effective methods and develop them into tools and services. This includes estimating the cost and value that the products will have for users so that operational services can be offered commercially.

## ACKNOWLEDGEMENTS

This study is funded by the European Union under grant agreement no. 101091616 (https://doi.org/10.3030/101091616), project S34I - SECURE AND SUSTAINABLE SUPPLY OF RAW MATERIALS FOR EU INDUSTRY.

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* Falabella, F., Serio, C., Masiello, G., Zhao, Q., & Pepe, A. (2022). A Multigrid InSAR Technique for Joint Analyses at Single-Look and Multi-Look Scales. *IEEE Geoscience and Remote Sensing Letters, 19*. https://doi.org/10.1109/LGRS.2021.3086271

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* Höhle, J. & Potuckova, M. (2011). Assessment of the Quality of Digital Terrain Models. European Spatial Data Research. https://www.eurosdr.net/sites/default/files/uploaded_files/eurosdr_publication_ndeg_60.pdf

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Pepe, A., Solaro, G., Calò, F., & Dema, C. (2016). A minimum acceleration approach for the retrieval of multiplatform InSAR deformation time series. *IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing*. 9(8), 3883–3898. https://doi.org/10.1109/JSTARS.2016.2577878

Ronneberger, O., Fischer, P., & Brox, T. (2015). *U-Net: Convolutional Networks for Biomedical Image Segmentation*. https://doi.org/10.48550/arXiv.1505.04597

Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Loy, C. C., Qiao, Y., & Tang, X. (2018). *ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks*. http://arxiv.org/abs/1809.00219
# Exploring and Mapping Marine Placers in Vigo Estuary Shoreline Using GIS Cartographic Tools

Wai L. Ng-Cutipa<fnref n="1" />,<fnref n="4" /> Ⓓa, Ana Lobato<fnref n="1" /> Ⓓb, Francisco J. González<fnref n="1" /> Ⓓc, Georgios P. Georgalas<fnref n="2" /> Ⓓd, Irene Zananiri<fnref n="2" /> Ⓓe, Joana Cardoso-Fernandes<fnref n="3" /> Ⓓf and Ana C. Teodoro<fnref n="3" /> Ⓓg

**Keywords:** Marine Placer, Integration, GIS, Heavy Minerals, Cartography.

**Abstract:** Marine placer deposits are accumulations of heavy minerals in coastal areas, both on beaches and in shallow water, usually consisting of ilmenite, rutile and zircon, and less commonly rare earth minerals (REEs) such as monazite and xenotime. This study investigates marine placer deposits in the Vigo Estuary (NW Spain), focusing on integrating diverse on and offshore cartographic data (geology, mineral resources, drainage, bathymetry, tides, Earth observation and others) for exploration. Six hundred two information points of marine placers have been analysed, 379 of them from shallow water, where Thiessen polygons have been spatially calculated. Our results, integrating regional cartographies in a Geographic Information System (GIS), shown great potential of placer minerals on the Santa Marta and Vao beaches (with presence of garnet, ilmenite, zircon, monazite and, locally, xenotime). This work 1) highlights the importance of collecting and analysing different previous information for marine placer exploration in integrated digital cartographies, 2) allows to program new activities to investigate local areas, 3) remark the remote sensing applications (cheap, easy and non-invasive tool), better applied to inaccessible areas, and 4) contribute to allow a sustainable resource exploration and coastal management.

## 1 INTRODUCTION

Marine placer deposits are accumulations of heavy minerals found on beaches and shallow water near the coast (Rona, 2008; Van Gosen et al., 2014; Hou et al., 2017). Some minerals of economic interest include ilmenite and rutile (Ti), zircon (Zr), cassiterite (Sn), monazite and xenotime (Rare Earth Elements- REE), gold (Au), diamonds, and gems, which accumulate due to their higher density compared to other minerals. These deposits are distributed globally on most coasts (Rona, 2008). In particular, marine placer

deposits with REE content are located on the coasts of South Carolina and Florida (USA), Australia, Brazil, India, Sri Lanka, China, Thailand and Malaysia (Segupta and Van Gosen, 2016).

Marine placers contain several critical raw materials (CRMs) that can contribute to the supply of the European Union (EU) (Grohol and Veeh, 2023) and other countries, thereby supporting the ecological transition. Indeed, the European platforms EMODnet Geology (https://emodnet.ec.europa.eu/en/geology), GeoERA-MINDESEA and EGDI provide an overview of marine mineral resources in their reports

<footnote n="1">¹Geological Survey of Spain (IGME-CSIC), Rios Rosas 28, Madrid, Spain</footnote>

<footnote n="2">²Hellenic Survey of Geology & Mineral Exploration (HSGME), Spirou Loui 1, Athens, Greece</footnote>

<footnote n="3">³Institute of Earth Sciences, Faculty of Sciences, University of Porto, Rua Campo Alegre s/n, Porto, Portugal</footnote>

<footnote n="4">⁴Facultad de Ciencias Geológicas, Universidad Complutense de Madrid, José Antonio Novais 12, Madrid, Spain
wl.ngcutipa@igme.es, ab.lobato@igme.es, fj.gonzalez@igme.es, ggeorgalas@eagme.gr, izananiri@eagme.gr,
joana.fernandes@fc.up.pt, amteodor@fc.up.pt</footnote>

<footnote>a https://orcid.org/0000-0003-2431-0604</footnote>

<footnote>b https://orcid.org/0000-0002-8620-6506</footnote>

<footnote>c https://orcid.org/0000-0002-6311-1950</footnote>

<footnote>d https://orcid.org/0009-0003-1180-2519</footnote>

<footnote>e https://orcid.org/0000-0002-3440-4316</footnote>

<footnote>f https://orcid.org/0000-0001-8265-3897</footnote>

<footnote>g https://orcid.org/0000-0002-8043-6431</footnote>
and viewer, including marine placer deposits and occurrences (Zananiri et al., 2021; González et al., 2023; GSEU, 2024).

Multiple authors have described these deposits and proposed their exploration from different approaches, such as the commodity approach (e.g., focused on Ti according to Force (1991)), geological, geochemical and geophysical techniques (Van Gosen et al., 2014), and specialized mapping (GIS), aerial photography and topography, remote sensing techniques, geological methods, drilling, and 3D modelling (Hou et al., 2017). Recently, and along with advances in Earth observation (EO), remote sensing applications have been initiated for placer exploration (e.g., Rejith et al., 2020 and 2021; Cardoso-Fernandes et al., 2023; Ng-Cutipa et al., 2024). This work proposes a methodology for integrating land-sea information in a geographic information system (GIS), aimed at: 1) analysing existing information layers to select local pilot areas, and 2) applying field exploration and EO tools focused on the study of marine placers in NW Spain.

## 2 REGIONAL SETTING

The Vigo Estuary, one of the Rías Baixas estuaries, is located in NW Spain. The area's geology comprises intrusive and metamorphic rocks ranging in age from the Paleoproterozoic to the Jurassic (González and Vicente, 2004). The study area is situated within the MAGNA 50k map sheets of Cíes (0222) and Vigo (0223), where the estuary is located, with an approximate NE-SW orientation. In the outer part of the estuary, the Cíes Islands are located, which are oriented N-S and act as a natural geographic barrier that protects the inner area of the estuary (Figure 1). The main tributary river of the estuary is the Verdugo River; however, there are numerous smaller tributary drainages, with those located on each side of the central axis of the estuary being much shorter.

## 3 DATA AND GIS INTEGRATION

Previous studies have synthesized and grouped exploration techniques for marine placer deposits based on geological, geophysical, remote sensing conditions, etc. (e.g., Van Gosen et al., 2014; Hou et al., 2017). Within the S34i project, we have focused on geological information at regional and local scales, which can contribute to the successful search for marine placer occurrences in beach and shallow-

water sands. In general, we have grouped the available data by their geographic environment with respect to their coverage: on-shore, off-shore and intertidal zones. To this, we have added a fourth group, "EO data" (Figure 2), that, depending on the characteristics of the image, can be used to obtain information from the three zones described above. We have also integrated a fifth group called "restrictive areas", which are areas of natural protection, economic activities or other specifications.

<figure><img src="image_280.png" /><figcaption>Figure 1. Location of Vigo estuary.</figcaption></figure>

### 3.1 On-Shore Data

The analysed datasets correspond to:

- Topography from the National Geographic Institute (IGN) of Spain (https://www.ign.es/web/ign/portal/inicio), which constitutes the base layer with information on toponymy and hydrography. The sheets used are 0222 (Cíes) and 0223 (Vigo).

- Basins and drainages from MITECO (https://www.miteco.gob.es/es/cartografia-y-sig/ide/descargas/agua.html) at a scale of 1:25,000 (© Ministerio para la Transición Ecológica y el Reto Demográfico), which are located within the map sheets 0222 and 0223.

- Continuous digital geological cartography at a scale of 1:50,000 (GEODE survey, https://info.igme.es/cartografiadigital/geologica/geo-de.aspx) from the IGME-CSIC (© CN Instituto Geológico y Minero de España (IGME), with geological and structural information of the Z1200 Galicia Tras-Os-Montes area. Around the estuary, a clear predominance of intrusive and metamorphic rocks with an approximate N-S orientation is observed. Quartz dikes can be secondary sources of heavy minerals of economic interest, such as tourmaline, cassiterite and gold; however, at a scale
of 1:50,000, they are scarce around the estuary,
according to GEODE.

-Mineral resources database (BDMIN, http://info.igme.es/catalogo/resource.aspx?portal=1&catalog=3&ctt=1&lang=spa&dlang=eng&llt=drop down&master=infoigme&resource=23) from the IGME-CSIC (© CN Instituto Geológico y Minero de España (IGME)), is a database that integrates geological-mining information on evidence, deposits and exploitation of rocks and minerals in Spain.

<figure><img src="image_281.png" /><figcaption>Figure 2. Integration methodology for marine placer deposit exploration in the NW Spain.</figcaption></figure>

## 3.2 Off-Shore Data

Data related to:

-Bathymetry and seabed substrate data are from EMODNet Geology (https://emodnet.ec.europa.eu/en/geology), a European portal that provides harmonized data. These allow us to know the morphology and type of seabed. In the study area, the seabed substrate with Folk 7 classification is available.

-The mineral exploration studies of the continental shelf of the FOMAR project (IGME, 1976 and 1979) have samples of beach and shallow-water and semi-quantitative analyses of heavy mineral contents in Rias Baixas. The most abundant minerals are generally garnets, ilmenite, zircon and monazite.

-Thiessen polygons were calculated using ArcGIS from the samples collected on the shallow water in the FOMAR project, providing an approximate area

of similar heavy mineral content. Ng-Cutipa et al. (2023) classified the heavy mineral content into five classes: <1 g, 1-5 g, 5-50 g, >50 g and absence.

-EMODNet-Geology marine resources (https://emodnet.ec.europa.eu/en/geology) provide us with marine mineral occurrences in point and polygon format.

-Other publications, such as these articles and conferences, include various data related to marine placer occurrences( Manso, 2001; Ng-Cutipa et al., 2024).

## 3.3 Intertidal Zone Data

Low and high tide data from the Hydrographic Institute (IHM) of Spain provide a regional overview of the intertidal area, which can influence the surface occurrence of placer deposits. As mentioned before, the NW area of Spain has a mesotidal regime, with variations of 2 to 4 m in height in the intertidal zone.

## 3.4 EO Data

National Aerial Orthophotography Plan (PNOA) and Light Detection and Ranging (LiDAR) surveys from IGN (https://pnoa.ign.es/web/portal/inicio), consist of orthorectified visible and near-infrared imagery, and digital elevation information at a very high local resolution. The PNOA images (PNOA 2020 CC-BY 4.0 ign.es) have a spatial resolution of 15 cm per pixel in the visible and 25 cm per pixel in the near-infrared. For geological mapping, the most current images in the area (year 2020) have been used; the near-infrared has been used to highlight faults and lineaments (Ng-Cutipa et al., 2024). The most recent LIDAR flight (PNOA LiDAR 2020 CC-BY 4.0 ign.es) is from 2015, with a minimum point density of 0.5 - 2 points/m².

Additionally, images were collected through a UAV survey using a DJI Mini Pro drone for specific beach research. In March 2024, drone flights were carried out at 80 and 50 m altitudes, obtaining a spatial resolution of < 3 cm in the visible range. The images were taken on the Limens, Santa Marta, Vao, Fontaiña and Fechiño beaches. EO data provide the map beach morphology and better identify rock outcrops, dykes, faults and lineaments, and delineation of modern placer boundaries.

## 3.5 Restrictive Areas

This level of information includes the on- and off-shore areas with access restrictions, areas of local interest, economic activities, environmental
conservation, and those related to navigation and port activities of the Vigo Estuary (Figure 2).

# 4 RESULTS

## 4.1 Thiessen Polygons and Heavy Mineral Occurrences

In the Vigo Estuary, 602 information points from the FOMAR project (223 from the beach and 379 from the shallow water) have been identified, obtaining 379 Thiessen polygons on the shallow platform, maintaining the same semi-quantitative values of heavy mineral concentration (garnet, ilmenite, zircon, monazite and xenotime; Figure 3).

On the beach, occurrences of garnet of Class 4 (>50 g) are predominant compared to Class 3 (5-50 g, Figure 3). These are found in the middle-outer part of the estuary. Ilmenite (brown lines in Figure 3) are mainly associated with the same points where Class 4 (>50 g) garnets are located, except for the middle part of the estuary (E of Cangas), where there is an occurrence of ilmenite and zircon, both of Class 3 (5-50 g). In addition, zircon with concentrations of Class 4 (>50 g) is only related to Class 4 samples of garnets and ilmenite on the Santa Marta (W of Cangas) and Vao-Fuchiños (SW of Vigo) beaches.

<figure><img src="image_282.png" /><figcaption>Figure 3. Thiessen polygons in shallow water and heavy mineral content classes 4 and 3.</figcaption></figure>

To a lesser extent, there are also occurrences of REE minerals, such as monazite and xenotime. Monazite (green dots in Figure 3) is related to the Class 4 (>50 g) points described above, with abundant

garnets, ilmenite and zircon (Santa Marta and Vao-Fuchiños). Monazite is also found to the E of Cangas, as well as ilmenite and zircon, all Class 3. Xenotime is only identified with Class 1 (<1 g) in 3 samples from the same Cesantes Beach, in the inner part of the estuary (green X in Figure 3). Also, on the beaches around the Cíes Islands, garnets and ilmenite predominate.

On the shallow platform, there are no concentrations of class 4 or 3 in the estuary, indicating that there are no areas of preferential concentration in these classes.

## 4.2 Regional Integrated Map

The geological environment of the Vigo Estuary is dominated by granites, where outcrops of orthogneisses, schists and amphibolites mainly alternate, and to a lesser extent, quartzites and paragneisses. On the surrounding seabed, the rocky substrate and boulders continue from the middle to the outer part of the estuary, including the Cíes Islands. The interior of the estuary has a finer sediment (mud) that changes to mixed mainly. The other grain size transitions of the seabed appear more isolated towards the outside of the estuary. The faults and lineaments on land are generally NW-SE, NNW-SSE and NE-SW (Figure 4).

Mineral resources onshore are reported as indications of hematite (Fe, Mn, Ti) and feldspar (industrial mineral) in the inner part of the estuary around Redondela. There are several active and, to a lesser extent, abandoned exploitations of ornamental rocks (mainly granites). In addition, there are three zones with minerals hosted in rocks, pegmatites or dikes and one zone of marine placer occurrences. These last ones have identified garnets, ilmenite, zircon, monazite and xenotime, and other heavy minerals on the beach. As we have seen before in 4.1, these heavy minerals are less abundant on the shallow platform.

The areas of human activity and current restrictive areas have helped us to better plan our field trips and explore the occurrences of marine placers, previously identified in the 70s. Some areas of the estuary are no longer accessible due to the creation of new restricted areas.
<figure><img src="image_283.png" /><figcaption>Figure 4. Integrated cartography for placer deposit exploration in Vigo Estuary, NW Spain.</figcaption></figure>

## 4.3 Validation: Local Pilots' Exploration

With the information from 4.2 and the flowchart in Figure 2, we proceeded to schedule different local reconnaissance activities to validate the methodology, starting with an initial visit to confirm the presence of marine placers on beaches. Upon finding evidence of heavy minerals, we scheduled subsequent exploration activities to delve deeper into the study of marine placers, mainly on the Santa Marta and Vao beaches. For example, we carried out: detailed mapping using PNOA and UAV flights (for outcrops, beach extensions and areas with placers, Figure 5), identification of faults and dikes (in the field and with PNOA and UAV images), analysis of intertidal variation, pits to observe heavy minerals in depth, ground-penetrating radar exploration, among others.

<figure><img src="image_284.png" /><figcaption>Figure 5. Cartography from the UAV survey showing placer mineral (red polygons) in coastal areas of Santa Marta Beach (Ng-Cutipa et al., 2024).</figcaption></figure>

The campaigns in different seasons have allowed to identify the variation of heavy mineral concentrations and the variation of small streams that

contribute to the supply of these minerals from the onshore. The important role of tides in the redistribution and deposition of placers has also been observed. Likewise, in some areas, such as Vao, wind action plays an important role in accumulating heavy minerals at the base of the dune face where the wind has action (Figure 6).

<figure><img src="image_285.png" /><figcaption>Figure 6. Accumulation of reddish heavy minerals on the base of the coastal dune at Vao Beach, Vigo Estuary (November 2024).</figcaption></figure>

Sand samples have also been taken from different beaches and shallow water, with and without placers, to conduct chemical and mineralogical analyses, and reflectance measurements for their correlation and use in multi- and hyperspectral images.

# 5 DISCUSSIONS AND CONCLUSIONS

Thiessen polygons offer a primary spatial distribution of occurrence of heavy mineral in shallow-water, that were perfomed using semiquantitative data. Placer occurrence on beach show greater accumulation than shallow-water areas using the same semi-quantitative data.

Regarding to regional integration map, the exploration of marine placer deposits, as evidenced by Van Gosen et al. (2014) and Hou et al. (2017), highlights the importance of collecting and analysing different previous information. These depend on the state of advancement of the region and on the knowledge desired to acquire about marine placer deposits. With this, it is possible to propose complementary studies at the regional level and others to deepen research at the local level of these placer occurrences. On the other hand, existing geological maps at a scale of 1:50,000 offer a great approximation to select areas of greater interest to explore these deposits. It is true that, in the case of
NW Spain, the occurrences of marine placers of FOMAR (IGME, 1976 and 1979) and the occurrences of heavy minerals previously identified in the MAGNA project carried out by IGME (https://info.igme.es/cartografiadigital/geologica/Magna50.aspx) have facilitated the visualization of the aforementioned areas of interest.

Due to the same difficulty of access and nature of each area, geological maps and information are more detailed and updated on-shore than off-shore.

In the local pilots, UAVs offer a high spatial resolution image where surface placers have been identified around the shoreline. This survey depends on the season and the stage of development of the ephemeral drainages. It is very likely that the large exposure of surface placers is mainly due to the large drainage combined with wave and tidal action.

This work highlights the importance of 1) integrating on- and off-shore information and the intertidal zone, and 2) knowing the characteristics of marine placer occurrences in NW Spain, providing very good results. They have also allowed for the increase of knowledge of the coastal "white band" (land-sea transition). Similarly, EO surveys offer a cheap, easy and non-invasive tool that can be better applied to inaccessible areas, allowing for sustainable resource exploration and coastal management. Finally, in these last 2 years, improvements have been developed in the knowledge of placers at the local level that include integrated and improved geological mapping, new geochemical and mineralogical data of marine placers, and EO applications and development of algorithms for images of different satellites (Sentinel 2, WorldView, EnMap, others).

## ACKNOWLEDGEMENTS

This study is funded by the European Union under grant agreement no. 101091616 (https://doi.org/10.3030/101091616), Project S34I - SECURE AND SUSTAINABLE SUPPLY OF RAW MATERIALS FOR EU INDUSTRY.

## REFERENCES

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<figure><img src="image_286.png" /></figure>
AUTHOR INDEX

<table>
  <tbody>
    <tr>
      <td>Šteković, K. Z.</td>
      <td>253</td>
    </tr>
    <tr>
      <td>Abdul Aziz, N. A.</td>
      <td>264</td>
    </tr>
    <tr>
      <td>Abudu, D.</td>
      <td>203</td>
    </tr>
    <tr>
      <td>Alagialoglou, L.</td>
      <td>236</td>
    </tr>
    <tr>
      <td>Aobpaet, A.</td>
      <td>179</td>
    </tr>
    <tr>
      <td>Araújo, B. L.</td>
      <td>297</td>
    </tr>
    <tr>
      <td>Araujo, R. Z.</td>
      <td>150</td>
    </tr>
    <tr>
      <td>Arregui, H.</td>
      <td>172</td>
    </tr>
    <tr>
      <td>Azzalini, A.</td>
      <td>297</td>
    </tr>
    <tr>
      <td>Béjar-Pizarro, M.</td>
      <td>128</td>
    </tr>
    <tr>
      <td>Barra, A.</td>
      <td>128</td>
    </tr>
    <tr>
      <td>Barrios, M. S.</td>
      <td>317</td>
    </tr>
    <tr>
      <td>Bastin, L.</td>
      <td>203</td>
    </tr>
    <tr>
      <td>Belcore, E.</td>
      <td>103</td>
    </tr>
    <tr>
      <td>Bianchini, S.</td>
      <td>128</td>
    </tr>
    <tr>
      <td>Borges, J. G.</td>
      <td>51</td>
    </tr>
    <tr>
      <td>Brovkina, O.</td>
      <td>236</td>
    </tr>
    <tr>
      <td>Cai, G.</td>
      <td>39</td>
    </tr>
    <tr>
      <td>Calò, F.</td>
      <td>277, 331</td>
    </tr>
    <tr>
      <td>Cardoso-Fernandes, J.</td>
      <td>277, 297, 317, 339</td>
    </tr>
    <tr>
      <td>Carvalho, M.</td>
      <td>297, 317</td>
    </tr>
    <tr>
      <td>Cejudo, I.</td>
      <td>172</td>
    </tr>
    <tr>
      <td>Chang, X.</td>
      <td>15</td>
    </tr>
    <tr>
      <td>Chen, X.</td>
      <td>15</td>
    </tr>
    <tr>
      <td>Chettri, N. B.</td>
      <td>165</td>
    </tr>
    <tr>
      <td>Choi, I. G.</td>
      <td>85</td>
    </tr>
    <tr>
      <td>Chong, K.</td>
      <td>203</td>
    </tr>
    <tr>
      <td>Claridades, A. R. C.</td>
      <td>73</td>
    </tr>
    <tr>
      <td>Costa, L. R.</td>
      <td>150</td>
    </tr>
    <tr>
      <td>Cuevas-González, M.</td>
      <td>128</td>
    </tr>
    <tr>
      <td>De La Rosa, R.</td>
      <td>277, 305, 317</td>
    </tr>
    <tr>
      <td>Delabary, H. S.</td>
      <td>150</td>
    </tr>
    <tr>
      <td>Delopoulos, A.</td>
      <td>236</td>
    </tr>
    <tr>
      <td>Di Pietra, V.</td>
      <td>103</td>
    </tr>
    <tr>
      <td>Divjak, A. K.</td>
      <td>253</td>
    </tr>
    <tr>
      <td>Duarte, L.</td>
      <td>136</td>
    </tr>
    <tr>
      <td>El Meouche, R.</td>
      <td>211</td>
    </tr>
    <tr>
      <td>Eslahi, M.</td>
      <td>211</td>
    </tr>
    <tr>
      <td>Ezquerro, P.</td>
      <td>128</td>
    </tr>
    <tr>
      <td>Falabella, F.</td>
      <td>277, 331</td>
    </tr>
    <tr>
      <td>Farahnakian, F.</td>
      <td>277, 286</td>
    </tr>
    <tr>
      <td>Farazdaghi, E.</td>
      <td>211</td>
    </tr>
    <tr>
      <td>Gadal, S.</td>
      <td>61</td>
    </tr>
    <tr>
      <td>Georgalas, G. P.</td>
      <td>277, 339</td>
    </tr>
    <tr>
      <td>Ghantous, J.</td>
      <td>103</td>
    </tr>
    <tr>
      <td>Gheorghe, M.</td>
      <td>277, 331</td>
    </tr>
    <tr>
      <td>Gloaguen, T.</td>
      <td>61</td>
    </tr>
  </tbody>
</table>

<table>
  <tbody>
    <tr>
      <td>Gomes, A. A. T.</td>
      <td>142</td>
    </tr>
    <tr>
      <td>Gonçalves, A.</td>
      <td>157</td>
    </tr>
    <tr>
      <td>González, F. J.</td>
      <td>297, 339</td>
    </tr>
    <tr>
      <td>Grabrijan, T.</td>
      <td>331</td>
    </tr>
    <tr>
      <td>Grammatikopoulos, L.</td>
      <td>195</td>
    </tr>
    <tr>
      <td>Grasso, N.</td>
      <td>103</td>
    </tr>
    <tr>
      <td>Grigillo, D.</td>
      <td>331</td>
    </tr>
    <tr>
      <td>Hamza, V.</td>
      <td>331</td>
    </tr>
    <tr>
      <td>Hanelli, D.</td>
      <td>277, 305</td>
    </tr>
    <tr>
      <td>Hao, Y.</td>
      <td>39</td>
    </tr>
    <tr>
      <td>Horvat, V. G.</td>
      <td>331</td>
    </tr>
    <tr>
      <td>Ichim, L.</td>
      <td>95</td>
    </tr>
    <tr>
      <td>Irigoyen, E.</td>
      <td>172</td>
    </tr>
    <tr>
      <td>Kalisperakis, I.</td>
      <td>195</td>
    </tr>
    <tr>
      <td>Kanwar, M.</td>
      <td>113</td>
    </tr>
    <tr>
      <td>Kawka, M. S.</td>
      <td>195</td>
    </tr>
    <tr>
      <td>Keller, S.</td>
      <td>244</td>
    </tr>
    <tr>
      <td>Kim, D. J.</td>
      <td>85</td>
    </tr>
    <tr>
      <td>Kim, G. C.</td>
      <td>85</td>
    </tr>
    <tr>
      <td>Kim, Y. H.</td>
      <td>85</td>
    </tr>
    <tr>
      <td>Kistner, F.</td>
      <td>244</td>
    </tr>
    <tr>
      <td>Knobloch, A.</td>
      <td>277, 305, 317</td>
    </tr>
    <tr>
      <td>Krtalić, A.</td>
      <td>253</td>
    </tr>
    <tr>
      <td>Lehmann, A.</td>
      <td>7</td>
    </tr>
    <tr>
      <td>Leventi, F.-P.</td>
      <td>121</td>
    </tr>
    <tr>
      <td>Li, C.</td>
      <td>231</td>
    </tr>
    <tr>
      <td>Li, S.</td>
      <td>231</td>
    </tr>
    <tr>
      <td>Li, Y.</td>
      <td>15</td>
    </tr>
    <tr>
      <td>Lim, S.</td>
      <td>113</td>
    </tr>
    <tr>
      <td>Lima, A.</td>
      <td>297</td>
    </tr>
    <tr>
      <td>Liu, Z.</td>
      <td>231</td>
    </tr>
    <tr>
      <td>Lobato, A.</td>
      <td>339</td>
    </tr>
    <tr>
      <td>Mañago, C. A. R.</td>
      <td>73</td>
    </tr>
    <tr>
      <td>Machado, R. O.</td>
      <td>219</td>
    </tr>
    <tr>
      <td>Malheiros, J.</td>
      <td>187</td>
    </tr>
    <tr>
      <td>Manakos, I.</td>
      <td>236</td>
    </tr>
    <tr>
      <td>Marques, S.</td>
      <td>51</td>
    </tr>
    <tr>
      <td>Maulud, K. N. A.</td>
      <td>5</td>
    </tr>
    <tr>
      <td>Mohamed Hashim, S. A.</td>
      <td>264</td>
    </tr>
    <tr>
      <td>Monserrat, O.</td>
      <td>128</td>
    </tr>
    <tr>
      <td>Morgado, P.</td>
      <td>187</td>
    </tr>
    <tr>
      <td>Moullou, D.</td>
      <td>121</td>
    </tr>
    <tr>
      <td>Nasalita, M. G.</td>
      <td>73</td>
    </tr>
    <tr>
      <td>Navarro, J. A.</td>
      <td>128</td>
    </tr>
    <tr>
      <td>Ng-Cutipa, W. L.</td>
      <td>339</td>
    </tr>
    <tr>
      <td>Nita, M. D.</td>
      <td>9</td>
    </tr>
    <tr>
      <td>Novotný, J.</td>
      <td>236</td>
    </tr>
    <tr>
      <td>Nygren, P.</td>
      <td>317</td>
    </tr>
  </tbody>
</table>

<table>
  <tbody>
    <tr>
      <td>Oštir, K.</td>
      <td>277, 331</td>
    </tr>
    <tr>
      <td>Obnam, S.</td>
      <td>179</td>
    </tr>
    <tr>
      <td>Palamà, R.</td>
      <td>128</td>
    </tr>
    <tr>
      <td>Pan, M.</td>
      <td>231</td>
    </tr>
    <tr>
      <td>Pandit, D.</td>
      <td>165</td>
    </tr>
    <tr>
      <td>Pavani-Biju, B.</td>
      <td>51</td>
    </tr>
    <tr>
      <td>Pepe, A.</td>
      <td>277, 331</td>
    </tr>
    <tr>
      <td>Popescu, D.</td>
      <td>95</td>
    </tr>
    <tr>
      <td>Poudel, P.</td>
      <td>165</td>
    </tr>
    <tr>
      <td>Prešeren, P. P.</td>
      <td>331</td>
    </tr>
    <tr>
      <td>Röder, M.</td>
      <td>203</td>
    </tr>
    <tr>
      <td>Rabadán, L.</td>
      <td>172</td>
    </tr>
    <tr>
      <td>Ragia, L.</td>
      <td>121</td>
    </tr>
    <tr>
      <td>Rahimi, I.</td>
      <td>136</td>
    </tr>
    <tr>
      <td>Rathnayaka, P.</td>
      <td>85</td>
    </tr>
    <tr>
      <td>Rodrigues de Almeida, C.</td>
      <td>157</td>
    </tr>
    <tr>
      <td>Sánchez-Migallón, J. M.</td>
      <td>317</td>
    </tr>
    <tr>
      <td>Sah, N.</td>
      <td>165</td>
    </tr>
    <tr>
      <td>Sammuneh, M. A.</td>
      <td>211</td>
    </tr>
    <tr>
      <td>Sanz-Ablanedo, E.</td>
      <td>277, 331</td>
    </tr>
    <tr>
      <td>Saveron, C. V.</td>
      <td>73</td>
    </tr>
    <tr>
      <td>Selea, T.</td>
      <td>331</td>
    </tr>
    <tr>
      <td>Serras, J.</td>
      <td>187</td>
    </tr>
    <tr>
      <td>Souza, E. M. F. R.</td>
      <td>142</td>
    </tr>
    <tr>
      <td>Stan, A.-S.</td>
      <td>95</td>
    </tr>
    <tr>
      <td>Steffen, M.</td>
      <td>317</td>
    </tr>
    <tr>
      <td>Stentoumis, C.</td>
      <td>195</td>
    </tr>
    <tr>
      <td>Sterjo, E.</td>
      <td>305</td>
    </tr>
    <tr>
      <td>Storch, I.</td>
      <td>317</td>
    </tr>
    <tr>
      <td>Sunga, Y. A. D.</td>
      <td>73</td>
    </tr>
    <tr>
      <td>Tan, S.</td>
      <td>15</td>
    </tr>
    <tr>
      <td>Teodoro, A. C.</td>
      <td>51, 136, 157, 277, 286, 297, 317, 331, 339</td>
    </tr>
    <tr>
      <td>Thanomsin, I.</td>
      <td>179</td>
    </tr>
    <tr>
      <td>Trajkovski, K. K.</td>
      <td>331</td>
    </tr>
    <tr>
      <td>Tsutsui, N.</td>
      <td>27</td>
    </tr>
    <tr>
      <td>Viegas, V. S.</td>
      <td>142</td>
    </tr>
    <tr>
      <td>Wang, Z.</td>
      <td>231</td>
    </tr>
    <tr>
      <td>Williams, V.</td>
      <td>277, 317</td>
    </tr>
    <tr>
      <td>Yaban, M.</td>
      <td>264</td>
    </tr>
    <tr>
      <td>Yodya, C.</td>
      <td>179</td>
    </tr>
    <tr>
      <td>Yokoyama, S.</td>
      <td>27</td>
    </tr>
    <tr>
      <td>Yousefi, M.</td>
      <td>286</td>
    </tr>
    <tr>
      <td>Zakaria, M. Z.</td>
      <td>264</td>
    </tr>
    <tr>
      <td>Zaleckis, K.</td>
      <td>61</td>
    </tr>
    <tr>
      <td>Zananiri, I.</td>
      <td>339</td>
    </tr>
  </tbody>
</table>
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