Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira
Abstract
:1. Introduction
- Microkarst and the dissolution of the limestone substrate: The paintings are applied to a limestone surface, which is highly susceptible to dissolution by water containing dissolved CO₂. The acidic nature of this water gradually erodes the limestone, weakening the adhesion of pigments and leading to their detachment from the rock surface.
- Pigment erosion and transport: Water infiltrating through the cave’s network of cracks and fissures plays a significant role in the erosion and transport of pigments. This is particularly problematic in the Polychrome Hall, where the Ceiling is directly exposed to infiltrating waters, resulting in the gradual dissolution and loss of pigments.
- Carbonate precipitation: Infiltration water, under certain environmental conditions, precipitates carbonates onto the painted surfaces, forming crusts that hide the Palaeolithic painting and engravings. If unchecked, these carbonate deposits can cover the paintings, making them difficult to study and appreciate art.
- The peeling and flaking of painted surfaces: The Polychrome Hall is characterized by high relative humidity and persistent water presence on much of the rock surface. Fluctuations in these environmental parameters can lead to the flaking of painted surfaces, as variations in humidity reduce the adhesion and cohesion of the pigments, causing them to peel away.
- Microbial colonization: The accumulation of microorganisms on the painted surfaces poses a significant risk to the preservation of the artworks. These microorganisms can lead to the transformation of pigments or the underlying rock through chemical reactions driven by their metabolic processes. Microbial biofilms can create microenvironments that promote further degradation of the paint or the supporting rock.
- Fragmentation and spalling: The detachment of rock fragments, including the calcite layers supporting the paintings, is another significant threat. This fragmentation can lead to the partial or complete destruction of the artworks, particularly in areas already weakened by environmental factors.
2. Materials and Methods
2.1. Creation of Reference Frame for Micro-Photogrammetry
- GNSS static and real-time kinematic (RTK) positioning: Techniques, such as static positioning or RTK, are applied outside the cave to establish a global reference frame [37,38]. At Altamira, a network of points was created using three TOPCON Hyper II (Topcon Corporation, Tokyo, Japan) receivers [39], which formed the basis of the geodetic reference system. The European Terrestrial Reference System 1989 (ETRS89) was used to create this reference frame. The reference frame had a mean accuracy of 1.7 cm, providing the foundation to integrate geospatial data from inside the cave with regional or global cartographic systems.
- Microgeodetic network with total station (TTS): Since GNSS signals cannot penetrate the cave’s interior, a microgeodetic network was established using a TOPCON GPT-7503 (Topcon Corporation, Tokyo, Japan) Total Station [40]. This network linked several high-precision targets within the cave, distributed across 16 traverse stations and adjusted to compensate for angular and linear closure errors. This setup ensured high accuracy, with the adjusted traverse having an angular error of 0.0218 g and a linear error in X, Y, and Z of less than 0.005 m.
- The placement of high-precision targets: A total of 66 reference targets were distributed throughout the cave to ensure a comprehensive coverage of significant structural areas, including those featuring Palaeolithic rock art. These checkerboard targets served as control points, measured precisely using the Total Station. They formed the basis for georeferencing the 3D scans obtained by 3D Terrestrial Laser Scanning (3DTLS).
- 3DTLS: For the surface mapping of the cave’s structure, a FARO FOCUS X-130 (Faro Technologies Inc., Lake Mary, FL, USA) laser scanner [41] was used to create a highly detailed point cloud. Around 300 scans were performed, capturing the cave’s surfaces with an accuracy of 2.7 mm for 95% of the points. These scans generated a comprehensive model of the cave, revealing essential structural features and rock art details.
- The co-registration of point clouds: The point clouds generated by 3DTLS were co-registered [30], meaning they were aligned to the microgeodetic control points (the installed targets). This step made sure the scans from different locations within the cave were spatially consistent with each other, resulting in a unified, georeferenced point cloud that accurately captured the cave’s entire geometry.
- The integration of GNSS, TTS, and 3DTLS data: The final step in the georeferencing process was to align the cave’s interior 3D point cloud with the external GNSS-based reference frame [31]. This integration was important for merging the internal cave model with external geographic information, such as terrain models generated by TOPCON Intel Falcon 8+ (Topcon Corporation, Tokyo, Japan) Drone surveys. The result was a complete, georeferenced 3D model of the Cave of Altamira, supporting both structural analysis and rock art preservation
- Ground control points (GCPs) extraction: From the co-registration of point clouds, two datasets of GCPs were extracted, derived from the point cloud with a mean Ground Sampling Distance (GSD) of 1–2 mm. This high-resolution point cloud allowed for the precise extraction of GCPs without the need for physical targets, which are not allowed on the delicate Polychrome Ceiling due to conservation restrictions. Also, this method significantly improved accuracy compared to traditional Total Station methods, which typically achieve a precision of 5–7 mm. One dataset was used for calibrating the 3D model, making sure all scanned data aligned with the microgeodetic reference framework, while the second dataset was used for validation. These GCP datasets were essential to the change detection process, helping with the accurate monitoring of structural changes over time.
- Polychrome Ceiling photogrammetry: In 2014, a dedicated photogrammetry campaign documented the Polychrome Hall ceiling with sub-millimetre resolution [42]. This effort complemented the laser scanning and produced a 3D digital model of the Ceiling with about 200 million polygons. The photogrammetric support for this campaign involved detecting homologous points, extracted from the 3D laser scanner’s point clouds, which served as GCPs for model calibration and validation. Specifically, 80 control points were strategically placed throughout the Hall, half of which were used to validate the model. These high-resolution images were collectively adjusted, resulting in a point cloud of approximately 11 billion points, which was filtered down to 3.5 billion points to generate a detailed 3D model.
2.2. Change Detection Workflow
2.2.1. Micro-Orthoimage Generation
- Image acquisition: High-resolution images are captured using a Sony (Sony Corporation, Tokyo, Japan) A7R Mark II camera with a 90 mm lens, making sure detailed features of the cave’s surface are recorded. The lighting system is important for image clarity, especially in the humid environment of the cave where moisture on the rock surfaces can cause unwanted reflections. To address this, an F & V ( F & V Europe B.V., Helmond, The Netherlands) HDR-300 LED ring light equipped with a 45–135° polarizing filter is used, while a circular polarizer is fitted to the camera lens. This setup allows for cross-polarization, a technique in which the polarizers on the light source and the camera are oriented at 90 degrees to each other. Cross-polarization effectively eliminates surface reflections or “glints” caused by water or moisture on the cave walls, as only light waves vibrating in a specific direction pass through both filters. This significantly enhances the visibility of the underlying textures and features of the rock surface by suppressing specular reflections and glare, which can obscure details. A milk diffuser is also applied to the light source to ensure even illumination, further reducing harsh shadows or hotspots that could interfere with the image quality and subsequent analysis.
- Control area orthoimage: The images captured during each campaign are processed to generate a detailed 3D model of the control zone’s surface. This process has evolved significantly from the initial campaigns. Currently, images are taken in RAW format and then developed using a SpiderCheckr colour calibration chart [44], which ensures accurate colour management. This is essential for consistent colour reproduction, allowing for precise colour comparison between different campaigns. Such comparisons are important for detecting subtle changes in the surface, such as pigment alterations or the appearance of new biological growth.
2.2.2. Change Detection
- Data alignment: Once the orthoimages are created, they are co-registered to a common reference frame to make sure any detected changes between campaigns are the result of actual surface alterations rather than misalignments during data collection. This step is critical for accurate comparison, enabling the identification of both subtle and significant changes on the control areas. The co-registration process aligns the newly acquired datasets with reference models from earlier campaigns, ensuring consistency and reliability in the measurements.
- Classification using the IsoData algorithm: After data alignment, the next step involves applying the IsoData classification algorithm [51] to the generated orthoimages. This unsupervised classification technique automatically groups pixels into clusters based on their spectral properties, effectively categorizing different surface materials. The output classes are then assigned specific labels such as different types of pigment (categorized by colour), fungal growth, bacterial colonies, rock without pigment, and areas with glints (marked as no data).
- Initial classification: The algorithm iteratively adjusts the cluster centroids and reassigns pixels to different classes based on their spectral distance until optimal cluster separation is achieved. This step is crucial for creating distinct categories that accurately reflect the surface materials and biological elements present on the cave walls.
- Class assignment and naming: Once the clustering process is complete, each class is manually reviewed and assigned a name according to its characteristics. For example, classes may be named “Red Pigment”, “Black Pigment”, “Fungal Growth”, “Bacterial Growth”, “Exposed Rock”, and “Glints” (no data). This manual assignment makes sure the automated classification aligns with the actual surface conditions observed in the cave.
- Separability analysis: To ensure the classification’s accuracy, a separability analysis is conducted. This analysis measures the statistical distance between classes, determining the degree of overlap and making sure each class is sufficiently distinct from the others. A high separability score indicates that the classes are well-defined and that the producer accuracy is robust. This step is essential for validating the classification results and ensuring reliable data for subsequent analysis.
- 2D change detection: After the classification is complete, a pixel-by-pixel change detection analysis is performed to identify shifts between classes across different campaigns (Figure 5). This analysis compares each pixel’s class assignment from one campaign to the next, calculating the frequency and nature of changes, such as pigment loss, microbial growth, or alterations in surface composition. This method provides detailed information on how surface materials have evolved over time, offering insights into the dynamics of deterioration processes.
- Statistical analysis: Once the change detection is completed, statistical analyses are conducted to quantify the changes in specific control zones. For example, the analysis may reveal the percentage of pixels that have shifted from “Red Pigment” to “Rock” indicating pigment erosion, or from “Rock” to “Fungal Growth”, suggesting biological colonization. These statistics provide both general trends and localized data, enabling a comprehensive understanding of the affected areas.
3. Results
3.1. Detection and Documentation of Deterioration Processes
3.2. Impact of Water Infiltration on Pigment Migration
3.3. Documentation of Bacterial Colonization and Reduction
3.4. Detailed Analysis of ALT1 Control Zone
3.4.1. ALT1_1 Subzone
3.4.2. ALT1_2 and ALT1_4 Subzones
3.5. Implications and Future Monitoring
4. Discussion
4.1. Preventive Conservation Measures
4.2. Shifts in Conservation Conditions
4.3. Accelerated Deterioration Post-Discovery
4.4. Future Research Directions and Strategic Conservation Efforts
- The acceleration of deterioration: The processes of flaking, disaggregation, and loss of pigment cohesion have persisted, affecting new areas beyond those previously identified (Figure 18).
- Predictive monitoring: The systematic monitoring of the cave, coupled with the detailed analysis of environmental conditions, has allowed us to predict areas of potential damage before they occur. This predictive capacity is important as it is currently allowing us to implement punctual preventive conservation actions so that water flows involved in pigment migration within ALT1 do not generate new and repeated damage to the pigment (Figure 19).
- High-resolution GPR studies: The evaluation of the state of the overlying layer from the dolomitic geological level, located barely one metre from the ‘Polychrome’ level where the rock art paintings are located on its basal surface. A reliable and non-destructive solution based on the use of a high-frequency GPR system will be used for this purpose. The objective will be the high-resolution survey in different control areas affected by pigment migration processes to know in detail the trajectory followed by the water from the aforementioned dolomitic layer to the Polychrome level, allowing the calculation of thicknesses, the existence of possible voids, areas of presence/absence of water, detachments, and fractures. This study should be re-applied at different times of the year to know the difference in the behaviour and movement of the water in this area of the roof of Polychromes.
- Microenvironmental dynamics: Investigate the microenvironmental dynamics related to CO₂ fluxes between the cave and the external environment. Understanding these dynamics is essential for developing strategies to manage CO₂ concentrations, which play a critical role in the chemical degradation of the limestone substrate.
- Geochemical analysis of infiltrating water: Perform in-depth geochemical analyses of the water reaching ALT1. Preliminary results suggest carbonate dissolution is occurring, which indicates that the water may not always be calcifying. Understanding the specific chemical composition of this water is vital for determining its potential to dissolve the rock substrate and contribute to pigment loss.
- Microbial influence on deterioration: Expand research on the role of microorganisms in the cave’s deterioration processes, particularly their influence on CO₂ concentrations and carbonate dissolution. Previous studies, such as the work [39], have shown that certain bacterial colonies can capture CO₂ within the cave environment, potentially exacerbating the dissolution of the limestone substrate and further destabilizing the pigments.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Campaign | Date | Nº Pixels | % to C0 | Surface mm2 |
---|---|---|---|---|
C0 | 9 July 2013 | 88,758 | 100.00% | 142.0120 |
C1 | 23 March 2015 | 83,186 | 93.72% | 133.0980 |
C3 | 29 December 2017 | 60,252 | 67.88% | 96.4035 |
C4 | 18 February 2019 | 61,220 | 68.97% | 97.9515 |
C5 | 3 December 2019 | 56,198 | 63.32% | 89.9163 |
C6 | 14 December 2020 | 54,053 | 60.90% | 86.4848 |
C6.5 | 6 April 2021 | 90,909 | 102.42% | 145.4540 |
C7 | 20 January 2022 | 129,467 | 145.87% | 207.1480 |
C7.3 | 31 March 2022 | 126,980 | 143.06% | 203.1680 |
C7.5 | 10 May 2022 | 125,025 | 140.86% | 200.0401 |
C7.7 | 20 July 2022 | 149,077 | 167.96% | 238.5226 |
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Prada, A.; Bayarri, V. Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira. Conservation 2024, 4, 703-730. https://doi.org/10.3390/conservation4040042
Prada A, Bayarri V. Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira. Conservation. 2024; 4(4):703-730. https://doi.org/10.3390/conservation4040042
Chicago/Turabian StylePrada, Alfredo, and Vicente Bayarri. 2024. "Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira" Conservation 4, no. 4: 703-730. https://doi.org/10.3390/conservation4040042
APA StylePrada, A., & Bayarri, V. (2024). Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira. Conservation, 4(4), 703-730. https://doi.org/10.3390/conservation4040042