3D Coral Models from Repurposed Historical Image Datasets

3D Coral Models from Repurposed Historical Image Datasets | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article 3D Coral Models from Repurposed Historical Image Datasets Laurice Janette Dagum, Dorothy Joyce Marquez, Wilfredo Licuanan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8852997/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Time series structure-from-motion (SFM) three-dimensional (3D) models of underwater reef images were created from repurposed historical image datasets (RHID) collected during regular monitoring in Lian, western Luzon Island, Philippines from 2009 to 2025. The method’s novelty is its successful repurposing of data to create 3D coral models and to extract surface rugosity as a 3D structure component. These datasets were primarily captured using the photo quadrat method with different cameras at variable water conditions and a very limited number of images per data collection, yet structure component indices were validated in situ with an accuracy of 0.97. Results showed a similar trend between the rugosity and coral cover reflecting a high potential for RHID 3D structure components to proxy coral cover as a measure of reef recovery or decline over time. rugosity coral cover structure-from-motion disturbance 3D reconstruction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1. Introduction This work recreates the past physical 3D structure of a reef. It introduces a method that creates 3D coral models from repurposed historical image datasets (RHID) of time-series monitoring data from as early as 2009 using a structure-from-motion (SFM) algorithm. As sessile organisms that require space for settlement and can live for hundreds of years, corals can reveal information about past events, such as disturbances and their subsequent recovery [ 1 – 5 ]. Coral reefs worldwide face significant degradation due to repeated disturbances, including bleaching, sedimentation, typhoons, outbreaks of the crown-of-thorns starfish, and destructive human activities at various spatial and temporal scales and frequencies [ 6 – 14 ]. The apparent impact of these hazardous disturbances on reefs requires assessment and documentation to quantify the degree of degradation, recovery, and other changes over time. To quantify change, baseline information is fundamental, but references and standards also change through time due to updates in research methods and approaches [ 15 ]. Still, historical quantitative data, such as photo transects dating back to the 1960s, integrated with descriptive information, have revealed long-term trends and can still be used in present-day decision-making for conservation schemes. Examples include changes in colony morphology, which were found to be one of the best predictors of coral assemblage responses to disturbances, such as bleaching [ 16 , 17 ]. Coral cover metrics have become synonymous with reef recovery, as reflected in many works on reef recovery after disturbance over the past few decades [ 2 , 18 – 22 ]. The most recent study used artificial intelligence to automate estimation of the proportional cover of benthic components from 2012–2018 comprised of geo-referenced, high-resolution photo-quadrats [ 23 ]. However, the increasing frequency of disturbances can inhibit coral settlement and growth of some coral types, complicating the use of coral cover as a measure of recovery. There are certain coral types, particularly branching corals that serve as keystone structures to determine how a reef recovers [ 24 ]. Rugosity has also been recognized as a driver of recovery and reassembly of reef communities [ 6 – 8 , 10 – 14 , 24 – 26 ]. The Rugosity Index is measured as RI = linear/surface , where linear is the distance covered when the chain or tape is pulled tight, and surface is the linear distance between the start and end of the chain or tape when it is draped over the substrate’s contours. A coral-to-algal phase-shifted state with high rugosity can recover and transform back into a coral-dominated state, compared to those with degraded corals that have become algal-dominated [ 27 ]. Coral cover is a two-dimensional feature of a reef, while rugosity reveals a three-dimensional characteristic of the physical structure of the reef. Components of the reef’s physical three-dimensional (3D) structure should be able to show more about the degree of recovery of a disturbed reef, even if coral cover does not change much. The physical 3D structure of the reef, also known as structural complexity, involves the shapes and features of living organisms, such as corals, seagrass, and macroalgae, as well as non-living objects like rocks and sand [ 26 , 28 ]. Quantifying the 3D Structure of the reef involves measuring components such as surface rugosity, shelter capacity, viewshed area, trough volume and area, morphological forms, among others [ 25 , 29 – 32 ]. The earliest recorded and most common measure for quantifying the reef’s 3D physical structure is the rugosity index using the chain-and-tape method [ 33 ]. Rugosity at various spatial scales can be quantified using the fractal dimension D = 1 – S and the Point-Intercept Contour (PIC) among others [ 34 – 36 ]. D is the change in rugosity with changing scale of measurement, where S is the slope of rugosity values on a log-log plot with rugosity on the y-axis and spatial scale on the x-axis. Because they were measured manually during dives, data collection for these early metrics was subject to time constraints and safety risks, limiting the development of more statistically robust datasets. The advent of machine vision algorithms and breakthroughs in imaging instrumentation introduced safer and faster ways to evaluate the reef’s 3D structure. Among these technologies are the Light Detection and Ranging (LiDAR), Benthic Terrain Modeler, Structure-from-motion (SFM) photogrammetry, and others [ 37 – 41 ]. 3D models now enable the examination of water flow around the coral canopy and individual colonies [ 42 ]. Structure-from-motion photogrammetry is an algorithm that creates 3D coral models from overlapping image captures of a reef [ 7 , 25 – 26 , 16 – 30 ]. Rugosity indices can be directly measured from the 3D coral model, eliminating the need for on-site measurements [ 25 ]. Components other than the rugosity index have also been quantified from the SFM 3D coral models of reefs such as the presence of crevices and holes in a reef, viewshed, grazing area, verticality, elevation, surface angle, and shelter volume [ 30 , 31 , 32 , 43 , 44 ]. Of these components, rugosity was validated the most, by comparing real with virtual measurements, showing a linear relationship with slope almost equal to one. Most recent SFM tests even show that the 3D coral models can be accurate to the millimeter. This means that components can now be quantified from high-speed 3D habitat reconstructions at scales ranging from small corals to entire reef landscapes [ 7 , 45 ]. Rugosity indices of cold-water coral reefs can even be measured through SFM photogrammetry at almost 1000-meter depths [ 46 ]. Surface rugosity, defined as the ratio of the surface area to the planar area, has become the standard rugosity index in place of the chain-and-tape method [ 30 , 31 , 47 ]. SFM 3D coral models have enabled temporal comparisons of bleaching extent using underwater stereo imagery and coral growth rates [ 7 , 48 ]. Their framework yieled accurate 3D habitat reconstructions from image datasets, spanning scales from small corals to entire reef landscapes. SFM photogrammetry has also been utilized in conjunction with monocular-derived photogrammetry to simulate existing video/photo surveys to date [ 49 ]. However, no published work on historical time-series data sets obtained from monitoring protocols such as the photo-quadrat or photo-transect method was found. This work presents a novel and successful recreation of reefs from actual time series images collected in Lian, western Luzon Island, Philippines, as early as 2009, using SFM and successfully quantifying rugosity from the resulting 3D coral models. Rugosity values were then compared with percent coral cover to determine whether rugosity from 3D coral models can serve as a proxy for percent coral cover in quantifying reef recovery or decline over time. 2. Material and Methods 2.1. Study Region and Sampling Data collection for this study was conducted in 4m x 4m fixed plots within the reefs of Talim Bay, Lian, in western Luzon Island, Philippines, at regular monitoring sites distributed approximately every two km along the coast of Talim Bay (Fig. 1 ). The dataset was collected in Site 2, one of the regular monitoring sites in the bay. Site 2 has two stations, 2A and 2B, and each station has three (3) 4m x 4m fixed plots at a reef flat of 2 m to 6 m depth. These plots were imaged using the photo-quadrat method to quantify coral cover and composition biannually from 2009 to 2024. The photo quadrat method involves the use of a tetrapod as shown in Fig. 2 with an underwater camera one (1) meter in altitude facing the seafloor. The images of the plots were taken in a lawnmower pattern, with each image overlapping approximately 50% of the previous image. 2.2. 3D Reconstruction and Rugosity Validation Validation of the SFM algorithm to create 3D coral models was conducted by quantifying rugosity using the chain-and-tape method, both in-situ and from the 3D coral models. The in-situ chain-and-tape method involved draping a 6-m length of stainless-steel chain over the reef contour within the reef plot boundary. Each link of the chain was approximately 1.5 cm x 4 cm, allowing it to be visible to the diver taking photos from an altitude of 1 m. The distance from the start and end point of the chain was then measured using a transect tape. Rugosity is the ratio between this distance value and 6 m. The same 4m x 4m plot where the chain was draped over the reef contour was captured in photos using the lawnmower pattern in the photo-quadrat method. The images were then processed using the Agisoft Metashape software with the parameters listed in Table 1 [ 50 ]. Agisoft Metashape incorporates the SFM algorithm, which utilizes common features in images to enable initial estimates of camera positions and object coordinates, refined iteratively through non-linear least-squares minimization [ 50 , 51 ]. The outputs were a 3D representation, 3D point cloud, or 3D coral model of the reef, a collection of points, each containing spatial coordinates x, y, z, proportional to its actual position in the reef. Information contained from the point cloud was then used to create an ortho-projection, orthomosaic, or an aerial view of the reef. The length of the chain and the distance between the start and end of the chain in the 3D model were measured using Agisoft Metashape’s draw polyline and measure functions. The draw polyline traces the chain in the 3D model, then once tracing is done, the measure function shows the coordinates of each point in the polyline. The total length of the polyline is the sum of the distance between all the points. Rugosity is the ratio of the chain polyline length and the polyline length between the start and end of the chain. Rugosity values from the 3D coral models were then compared with the actual values. 2.3. RHID 3D Coral Models RHID that were obtained using the photo quadrat method from 4m x 4m reef plots employed different camera models through time. Some cameras used during imaging, especially the earlier models, captured a fish-eye view of the reef and also included the base of the tetrapod in the images. This was rectified using a camera calibration and image correction function in MATLAB, and subsequently cropped to remove the base of the tetrapod that is visible in the image [ 52 , 53 , 54 ]. The cropped RHID was processed in Agisoft Metashape using the same parameters in Table 1 . The use of various cameras produced images of different sizes and qualities, such that the 3D models formed are not of the same size. To correct this, they were rescaled and aligned in CloudCompare, an open-source 3D software, using the alignment feature with at least three (3) common points identified between two 3D coral models [ 55 ]. Objects of known sizes, such as coral settlement tiles, dive weights, and transect tapes, were used as reference during rescaling. Trimming was then performed in MATLAB to ensure that all models are of the same size. 2.4. Percent Coral Cover Massive, columnar, branching, tabular, and other morphological forms of corals in the orthomosaic were outlined using the Fiji plugin in ImageJ, and their respective total areas were calculated [ 56 ]. These values correspond to the coral cover in the reef plot. The time series percent coral cover data from the plots were compared with the monitoring percent coral cover data collected using the photo-transect method of van Woesik et al. (2009) [ 57 – 59 ]. Unlike the photo-quadrat method that was used on fixed plots, the photo-transect method incorporates five randomly deployed 50m transects within a 75 m by 25 m area in which the fixed plot stations were located. At least fifty images of a 1 m 2 area along the 50m transect were captured, which were then analyzed using Coral Point Count with Excel extensions (CPCe), where ten random points in the image were scored according to benthic categories [ 59 ]. The percent coral cover was extracted from the categories that belong to the hard coral taxa. 2.5. Rugosity as a 3D Structure Component The rugosity index was obtained from the ratio of the plot’s area and its surface area. The surface area was calculated from the 3D mesh formed by connecting the point clouds into triangles derived using the Delaunay triangulation method [ 60 ]. The surface area is the sum of the area of each triangle. The ratio of the actual areato this value, translatedinto an actual surface area based on scale markers during image capture, gives the rugosity index of the plot. 3. Results and Discussion 3D coral models were successfully created from the RHID in Talim Bay from February 2009 to March 2024, as summarized in Table 2 . The shaded cells indicate the successfully created 3D coral models of the 4m x 4m plots, while the unshaded cells represent those with limited or no available data. Coral morphology from the 4m x 4m plots was compared to transect data from the same monitoring stations. In situ values obtained with the chain-and-tape method were also compared with the values from the corresponding 3D coral models. Surface rugosity, as a 3D structure component, was extracted from the 3D coral models. 3.1. In Situ vs 3D Coral Model Measurements The chain-and-tape method for rugosity was done both on site and on the 3D coral models to validate measurements. All six monitoring plots were measured for the ratio of the tape length from both ends of the chain to the length of the chain draped over the surface of the corals as shown in Fig. 3. Images from the first three plots were captured on the first dive day, where visibility was better than on the second day. Rugosity in the 3D coral model was successfully measured using polylines that traced the chain and its two endpoints, as shown in Fig. 4. Comparing the rugosity values reveals a linear relationship between the actual and the 3D coral model measurements, with a slope of 1 and a mean percent error of 3%, as determined by the equation, 100* (actual value – calculated value)/actual value . The error is likely due to measurements taken with the transect tape being affected by water movement, which shifts the transect tape and creates parallax issues during reading, or low visibility during the reading process. Another source of error was that a few parts of the chain were not included in the final 3D coral model because they were embedded deep within the reef crevice that the camera was unable to capture. 3.2. Image Processing Image rectification of the original image in Fig. 5a through camera calibration, image correction, and trimming resulted in a correction of the fish-eye effect and removal of the tetrapod base as shown in Figs. 5b and 4c, respectively. The corrected images in Fig. 6 still contain overlaps, which are required for the SFM algorithm to extract common features in images to estimate depth to form 3D coral models. 3.3. 3D Coral Models from RHID The rectified RHID of a 4m x 4m reef plot in Fig. 7a, obtained in April 2014, provides a successful 3D representation of the reef in Fig. 7b and its corresponding orthomosaic in Fig. 7c. The RHID summarized in Table 2 shows the number of images per set. To cover a 4m x 4m plot at an altitude of 1 m with 50% overlap, at least eight images must be captured to cover one side of the plot. This means that at least 64 images must be obtained to secure a successful 3D reconstruction of the plot. Nevertheless, sets with only a little more than 50 images still successfully formed into 3D coral models. Figure 8 displays the aerial view of the 3D coral models from one of the plots in Lian. Dark areas that appear like holes are the overlooked areas during data capture. The use of various cameras produces different image sizes resulted in varied 3D coral model sizes as well, hence the need for alignment, rescaling, and trimming of the 3D coral models. Figure 9 displays an aerial view of the aligned, rescaled, and trimmed 3D coral models, where each 3D coral model has a plane cross-sectional area of 16 m 2 . The 3D coral models are shown as elevation matrices showing dark areas as dips and bright areas as crests. 3.4. 3D Coral Models and Coral Cover There were global bleaching events in 2010, 2016–2017, and 2020, which may have affected the reefs locally [ 61 , 62 ]. Coral reefs in Lian are subjected to multiple disturbances each year, such as runoff during the southwest monsoon season and typhoons, fishing pressure, and in the recent years (or in the last 15 years), thermal stress. The aerial view or orthomosaic of the 3D coral models were analyzed for coral cover by morphology as shown in Fig. 10 using the Labkit plugin of the Fiji toolkit by ImageJ [ 56 ]. A visual comparison of two successive orthomosaics, from February 2009 to May 2010, revealed an apparent change in morphological composition within a year. The percentage of the total morphological composition in the orthomosaic is the percent coral cover. This shift can be attributed to one or a combination of the disturbances affecting Lian. Transect data showed a sudden decline in Family Acroporidae (mostly branching) coral cover leaving members of Family Poritidae (mostly boulder-shaped) the lone dominant hard coral in the stations. Comparing the percent coral cover of the orthomosaics from the photo quadrat data with the photo transect data in Fig. 11 from the regular monitoring shows a very high correlation, indicating a consistent result despite the varied methods. It can be observed that the impact of a massive disturbance towards the end of 2009 is reflected in the decline of the percent coral cover. Since then, the trajectory has been relatively increasing, indicative of a recovering reef, despite the bleaching events that follow. 3.5. Rugosity as a 3D Structure Component The surface rugosity index, RI , used in this paper is: RI = 1 - P/S , (2) where P is the planar area and S is the surface area. A flat surface gives a rugosity index of 0 while a highly rugose surface will have values that are approaching 1. Figure 9 shows elevation matrices of a plot over time. From the time series elevation matrices of the six monitoring plots, the surface rugosity trajectory follows an increasing pattern from a mean rugosity value of 0.55 to 0.65 with a mean standard deviation of 0.14 as shown in Fig. 12 . 3.6. Rugosity and Coral Cover Rugosity follows an increasing trajectory similar to the percent coral cover from 2009 to 2024 as shown consistently in all six plots in Fig. 13 . Rugosity increased from 0.56 to 0.67 in plot A1, 0.55 to 0.69 in plot A2, 0.49 to 0.58 in plot A3, 0.50 to 0.61 in plot B1, 0.55 to 0.61 in plot B2, and 0.60 to 0.71 in plot B3. All plots display an increase in rugosity by 11% to 26%. On a scale of 0 to 1, 0 being the reef completely flat and 1 being the reef surface at optimal rugosity, this increase is already significant, representing a range of 6 to 16 m 2 increase in surface area, given the plot area is only 16 m 2 . Combining the values from all plots, as shown in Fig. 14 , indicates that the trends for both rugosity and coral cover are increasing. The increase in surface area, which is part of the rugosity Eq. (2), is attributed to increased coral cover. This implies that rugosity measurements can serve as a proxy for changes in reef cover over time. This allows the method to be replicated in other locations if the RHID of photo-quadrats is available, or to be used moving forward for monitoring efforts. Higher measurement accuracy can also be achieved because 3D coral models have already been created, allowing us to quantify rugosity with greater precision. Processing for a single 4m x 4m plot takes approximately two hours on a personal computer. Thus, an RHID of 10 plots can already be processed within one day to display rugosity values and project changes through time. In comparison, manual CPCe scoring of coral cover by experts typically takes 1.5 to 2 days per station. This suggests that changes over time can be quantified using RHID and rugosity measurements from 3D coral models, with less demand for expert diagnosis and reduced user bias for long-term series analyses. Declarations Funding Sources of funds for data collection are the De La Salle University’s Br. Alfred Shields FSC Ocean Research (SHORE) Center, the Commission on Higher Education (CHED) DARE TO Coral Project, and the Department of Science and Technology (DOST) funded National Assessment of Coral Reef Environment (NACRE) Program, Capacity Building on Reef Assessment and Coral Taxonomy (C-BRACT), and Reef Imaging and Monitoring (RIM) Project. Funds for LJD’s dissertation are from the DOST Accelerated Science and Technology Human Resource Development Program (DOST-ASTHRDP) and the Gerry Roxas Foundation USAID INSPIRE Student Research Grant 2024. Conflict of interest/Competing interests The authors have no conflicts of interest to declare that are relevant to the content of this article. Clinical trial number Not applicable Ethics approval and consent to participate Not applicable Consent for publication Not applicable Data availability The datasets generated during and/or analyzed during the current study are not publicly available due to national security reasons but can be made available on reasonable request. Materials availability Not applicable Code availability Not applicable Author Contribution LJD, WYL, and LTD wrote the main manuscript text, conceptualized the study and collected, processed, and/or analyzed data. DJM wrote, conceptualized, processed, and/or analyzed data related to the photo-transect method and coral cover. All authors reviewed the manuscript Acknowledgements The coral reef images used in this study are from the monitoring data of the De La Salle University’s Br. 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J., Miller, J., Larkin, E. M., Naar, D. F., Donahue, B. T., Anderson, S. M., & Battista, T. (2006). A benthic terrain classification scheme for American Samoa. Marine Geodesy, 29(2), 89–111. https://doi.org/10.1080/01490410600738021 Zawada, D. G., & Brock, J. C. (2009). A multiscale analysis of coral reef topographic complexity using lidar-derived bathymetry. Journal of Coastal Research , (10053), 6–15. https://doi.org/10.2112/SI53-002.1 Burns, J., Delparte, D., Gates, R., & Takabayashi, M. (2015). Integrating structure-from-motion photogrammetry with geospatial software as a novel technique for quantifying 3D ecological characteristics of coral reefs. PeerJ, 3, e1077. https://doi.org/10.7717/peerj.1077 Loke, L. H., & Lee, J. L. (2016). Structure-from-motion photogrammetry for three-dimensional reconstruction and quantitative assessment of intertidal habitats. Limnology and Oceanography: Methods, 14(12), 856–868. https://doi.org/10.1002/lom3.10134 Verma, S., & Bourke, M. C. (2018). High-resolution coastal topography: A comparison of Structure-from-Motion photogrammetry and the traversing micro-erosion meter for measuring erosion on shore platforms. Earth Surface Dynamics, 6(4), 1023–1039. https://doi.org/10.5194/esurf-6-1023-2018 Davis, K. A., Pawlak, G., & Monismith, S. G. (2021). Turbulence and coral reefs. Annual review of marine science, 13(1), 343–373. https://doi.org/10.1146/annurev-marine-042120-071823 De Floriani, L., & Magillo, P. (1999). Intervisibility on terrains. Geographic Information Systems: Principles, Techniques, Managament and Applications , 543–556. https://www.geos.ed.ac.uk/~gisteac/gis_book_abridged/files/ch38.pdf Tebbett, S. B., Streit, R. P., & Bellwood, D. R. (2020). A 3D perspective on sediment accumulation in algal turfs: Implications of coral reef flattening. Journal of Ecology, 108(1), 70–80. https://doi.org/10.1111/1365-2745.13235 Figueira, W., Ferrari, R., Weatherby, E., Porter, A., Hawes, S., & Byrne, M. (2015). Accuracy and precision of habitat structural complexity metrics derived from underwater photogrammetry. Remote Sensing, 7(12), 16883–16900. https://doi.org/10.3390/rs71215859 Price, D. M., Robert, K., Callaway, A., Lo Lacono, C., Hall, R. A., & Huvenne, V. A. (2019). Using 3D photogrammetry from ROV video to quantify cold-water coral reef structural complexity and investigate its influence on biodiversity and community assemblage. Coral Reefs , 38 (5), 1007–1021. https://doi.org/10.1007/s00338-019-01827-3 Agudo-Adriani, E. A., Cappelletto, J., Cavada-Blanco, F., & Cróquer, A. (2019). Structural complexity and benthic cover explain reef-scale variability of fish assemblages in Los Roques National Park, Venezuela. Frontiers in Marine Science , 6 , 690. https://doi.org/10.3389/fmars.2019.00690 Lange, I. D., & Perry, C. T. (2020). A quick, easy and non-invasive method to quantify coral growth rates using photogrammetry and 3D model comparisons. Methods in Ecology and Evolution, 11(6), 714–726. https://doi.org/10.1111/2041-210X.13388 Ferrari, R., McKinnon, D., He, H., Smith, R. N., Corke, P., González-Rivero, M., ... & Upcroft, B. (2016). Quantifying multiscale habitat structural complexity: a cost-effective framework for underwater 3D modelling. Remote Sensing, 8(2), 113. https://doi.org/10.3390/rs8020113 Agisoft Metashape Professional (Version 2.2.1). (2025). http://www.agisoft.com/ Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012). ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300–314. https://doi.org/10.1016/j.geomorph.2012.08.021 The MathWorks Inc. (2025). MATLAB version: 9.8.0 (R2020a), Natick, Massachusetts: The MathWorks Inc. https://www.mathworks.com Heikkila, J., & Silven, O. (1997). A four-step camera calibration procedure with implicit image correction. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1106–1112). IEEE Comput. Soc. https://doi.org/10.1109/CVPR.1997.609468 Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330–1334. https://doi.org/10.1109/34.888718 CloudCompare (version 2.11.1) [GPL software]. (2025). http://www.cloudcompare.org/ Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., … Cardona, A. (2012). Fiji: an open-source platform for biological-image analysis. Nature methods, 9(7), 676–682. Licuanan, A. M., Reyes, M. Z., Luzon, K. S., Chan, M. A. A., & Licuanan, W. Y. (2017). Initial findings of the nationwide assessment of Philippine coral reefs. Philippine Journal of Science, 146(2), 177–185. https://philjournalsci.dost.gov.ph/initial-findings-of-the-nationwide-assessment-of-philippine-coral-reefs/ Van Woesik, R., Gilner, J., & Hooten, A. J. (2009). Standard operating procedures for repeated measures of process and state variables of coral reef environments. coral reef targeted research and capacity building for management program. Melbourne: The University of Queensland. (34p). Kohler, K. E., & Gill, S. M. (2006). Coral Point Count with Excel extensions (CPCe): A Visual Basic program for the determination of coral and substrate coverage using random point count methodology. Computers & geosciences, 32(9), 1259–1269. Delaunay, B., & Sur, L. S. V. (1934). Izvestia Akademii Nauk SSSR. Otdelenie Matematicheskikh i Estestvennykh Nauk, 7, 793–800. Licuanan, W. Y., & Mordeno, P. Z. B. (2021). Citizen science reveals the prevalence of the 2020 mass coral bleaching in one town. Philippine Journal of Science, 150(3), 945–949. https://doi.org/10.22288/pjs.v150i3.1537 Feliciano, G. N. R., Rollon, R. N., & Licuanan, W. Y. (2023). Coral community structure of Philippine fringing reefs is shaped by broad-scale hydrologic regimes and local environmental conditions. Coral Reefs, 42(4), 873–890. https://doi.org/10.1007/s00338-023-02403-5 Tables Tables 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8852997","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597074992,"identity":"058c470b-2c2d-476d-8d64-016c05807e52","order_by":0,"name":"Laurice Janette Dagum","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACPoYEEGXDwMAMotmA+ACUjQuwQbSkka7lMJxLhBb25MeveXPOR8u3M7BJF5QxyPHdSGB7XIBPC88zM2vebbdzNxwGaplxjsFY8kYCu/EMfFokEsyMwVqYgVp42xgSNwBtkebBqyX9G1DLudz5zRAt9URoyTF+zLvtQG7DYYiWBAOCWnjelDHO3ZYM9Atjs/WMcxKGM888bDfGp4WfPX3zh7fb7HLn9x8+eLugzEae73jyscf4tIDdBqEZG4DRAWIztuHXAIy3D3AWzBBCWkbBKBgFo2BkAQCcJ0Vc+I4LBwAAAABJRU5ErkJggg==","orcid":"","institution":"University of the Philippines Diliman","correspondingAuthor":true,"prefix":"","firstName":"Laurice","middleName":"Janette","lastName":"Dagum","suffix":""},{"id":597074993,"identity":"a669370f-3ffa-4411-85c0-91770c4db5df","order_by":1,"name":"Dorothy Joyce Marquez","email":"","orcid":"","institution":"De La Salle University","correspondingAuthor":false,"prefix":"","firstName":"Dorothy","middleName":"Joyce","lastName":"Marquez","suffix":""},{"id":597074994,"identity":"78ca4ede-45f8-42ab-9d87-eb0a8db0bdf3","order_by":2,"name":"Wilfredo Licuanan","email":"","orcid":"","institution":"De La Salle University","correspondingAuthor":false,"prefix":"","firstName":"Wilfredo","middleName":"","lastName":"Licuanan","suffix":""},{"id":597074995,"identity":"fd8008f3-8867-42d1-b16e-65f1655e76a3","order_by":3,"name":"Laura David","email":"","orcid":"","institution":"University of the Philippines Diliman","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"David","suffix":""}],"badges":[],"createdAt":"2026-02-11 14:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8852997/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8852997/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104168209,"identity":"16294922-780c-4a00-ad42-74719815c8f2","added_by":"auto","created_at":"2026-03-08 14:29:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32225,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the monitoring locations in Talim Bay, Lian, western Luzon Island, Philippines. Source: Bing Maps\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/8e16258e42a682fe9750d57b.jpg"},{"id":104168211,"identity":"589f21eb-c883-4bad-8345-0c15c0f3426c","added_by":"auto","created_at":"2026-03-08 14:29:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63430,"visible":true,"origin":"","legend":"\u003cp\u003e(a) A tetrapod used to capture a 1 sqm portion of the reef plot and (b) lawnmower pattern followed by the diver during image data collection on a 4m x 4m plot.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/c298605f8c41348449c80918.jpg"},{"id":104403835,"identity":"90343aee-e3c7-4882-8e4a-1fbf4d8348ba","added_by":"auto","created_at":"2026-03-11 12:19:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":161344,"visible":true,"origin":"","legend":"\u003cp\u003e(a) In situ chain-and-tape method and (b) the corresponding 3D coral model.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/8210a235501798686f82a276.jpg"},{"id":104168214,"identity":"3043b267-6e3c-46a4-a314-19d254935607","added_by":"auto","created_at":"2026-03-08 14:29:52","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":126803,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Polylines tracing the chain in the 3D coral model and (b) rugosity values of the chain-and-tape method from the 3D coral models versus the actual on-site measurements.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/66d1e7edbaea499c118b4c3b.jpg"},{"id":104404486,"identity":"342def58-27d3-4a52-a0db-fc4fc28fcb09","added_by":"auto","created_at":"2026-03-11 12:20:22","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":155130,"visible":true,"origin":"","legend":"\u003cp\u003eSample image with (a) fish-eye effect, then (b) calibrated and corrected, and (c) trimmed.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/66c84d5ecb0e06ceda48a5d3.jpg"},{"id":104403941,"identity":"e0af9281-000b-42b3-ba7f-b18df629e1e3","added_by":"auto","created_at":"2026-03-11 12:19:26","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":332616,"visible":true,"origin":"","legend":"\u003cp\u003eSample dataset of trimmed images for 3D processing\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/eae87550bd6fb11a0b8a3935.jpg"},{"id":104168215,"identity":"1c1d23d7-3f4e-4675-ba9c-6c3d92c96acc","added_by":"auto","created_at":"2026-03-08 14:29:52","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":128088,"visible":true,"origin":"","legend":"\u003cp\u003eData captured in April 2014 showing its (a) rectified and trimmed images, (b) 3D point cloud, and (c) aerial view or orthomosaic.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/3dc48a8334a858505c414472.jpg"},{"id":104168219,"identity":"3b08fa7a-b10c-458a-b006-035dd548fc80","added_by":"auto","created_at":"2026-03-08 14:29:52","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":247497,"visible":true,"origin":"","legend":"\u003cp\u003eAerial photos of the 3D coral models of a 4m x 4m plot from 2010 to 2023.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/7806c847d6fc9e61fac2f2f1.jpg"},{"id":104168221,"identity":"c6c09937-190b-4636-8448-e67bb32b4f0f","added_by":"auto","created_at":"2026-03-08 14:29:52","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":252001,"visible":true,"origin":"","legend":"\u003cp\u003eRescaled and trimmed aerial view of the 3D models elevation matrices from the 2010 data to 2023 showing dark areas as dips and bright areas as crests.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/49a8fa2795e747a490c91917.jpg"},{"id":104168220,"identity":"2da6df0e-5930-4c11-a853-19d5d9ad5e40","added_by":"auto","created_at":"2026-03-08 14:29:52","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":348432,"visible":true,"origin":"","legend":"\u003cp\u003eMorphological composition of a sample reef plot from (a) February 2009 and (b) May 2010.\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/a7b6567a0bab2b0c4a5e3652.jpg"},{"id":104168217,"identity":"b283a15d-59d2-49f2-9c59-51ec52725c78","added_by":"auto","created_at":"2026-03-08 14:29:52","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":72879,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of percent hard cover estimates from the line transects and the photo transects.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/b7a8ddff6079de4cdffdd953.jpg"},{"id":104404481,"identity":"2a44c009-caae-4acf-bf73-b461ae313d86","added_by":"auto","created_at":"2026-03-11 12:20:21","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":63180,"visible":true,"origin":"","legend":"\u003cp\u003eSurface rugosity of all six plots over time\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/9d20dbc9df169db6d74059a9.jpg"},{"id":104404602,"identity":"b0038c94-e9a5-4182-a7f9-579e7a742907","added_by":"auto","created_at":"2026-03-11 12:20:36","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":138132,"visible":true,"origin":"","legend":"\u003cp\u003eRugosity and coral cover in plots A1, A2, A3, B1, B2, and B3 from 2009 to 2024.\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/b06c95a535cfb3e148dbb762.jpg"},{"id":104168223,"identity":"eee0a979-8efd-4cc4-aac3-b056c58e7342","added_by":"auto","created_at":"2026-03-08 14:29:53","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":85208,"visible":true,"origin":"","legend":"\u003cp\u003eRugosity and percent coral cover of all the successfully created 3D coral models from Lian Station 2A and 2B, 2009 to 2024 showing increasing an increasing trend.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/5c493956dabe1dfad2bff24f.jpg"},{"id":105133950,"identity":"8cff279a-55b5-4c73-b4c7-f058518b3c05","added_by":"auto","created_at":"2026-03-22 06:09:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2831859,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/51fe409d-5e13-48eb-a245-8c727426be2e.pdf"},{"id":104168210,"identity":"335b3706-79d9-4785-8fb6-12ccac0a5aa6","added_by":"auto","created_at":"2026-03-08 14:29:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20230,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8852997/v1/a35858ecee47183a22ef51b9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"3D Coral Models from Repurposed Historical Image Datasets","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThis work recreates the past physical 3D structure of a reef. It introduces a method that creates 3D coral models from repurposed historical image datasets (RHID) of time-series monitoring data from as early as 2009 using a structure-from-motion (SFM) algorithm. As sessile organisms that require space for settlement and can live for hundreds of years, corals can reveal information about past events, such as disturbances and their subsequent recovery [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCoral reefs worldwide face significant degradation due to repeated disturbances, including bleaching, sedimentation, typhoons, outbreaks of the crown-of-thorns starfish, and destructive human activities at various spatial and temporal scales and frequencies [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12 CR13\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The apparent impact of these hazardous disturbances on reefs requires assessment and documentation to quantify the degree of degradation, recovery, and other changes over time. To quantify change, baseline information is fundamental, but references and standards also change through time due to updates in research methods and approaches [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Still, historical quantitative data, such as photo transects dating back to the 1960s, integrated with descriptive information, have revealed long-term trends and can still be used in present-day decision-making for conservation schemes. Examples include changes in colony morphology, which were found to be one of the best predictors of coral assemblage responses to disturbances, such as bleaching [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCoral cover metrics have become synonymous with reef recovery, as reflected in many works on reef recovery after disturbance over the past few decades [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The most recent study used artificial intelligence to automate estimation of the proportional cover of benthic components from 2012\u0026ndash;2018 comprised of geo-referenced, high-resolution photo-quadrats [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the increasing frequency of disturbances can inhibit coral settlement and growth of some coral types, complicating the use of coral cover as a measure of recovery.\u003c/p\u003e \u003cp\u003eThere are certain coral types, particularly branching corals that serve as keystone structures to determine how a reef recovers [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Rugosity has also been recognized as a driver of recovery and reassembly of reef communities [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The Rugosity Index is measured as \u003cem\u003eRI\u0026thinsp;=\u0026thinsp;linear/surface\u003c/em\u003e, where \u003cem\u003elinear\u003c/em\u003e is the distance covered when the chain or tape is pulled tight, and \u003cem\u003esurface\u003c/em\u003e is the linear distance between the start and end of the chain or tape when it is draped over the substrate\u0026rsquo;s contours. A coral-to-algal phase-shifted state with high rugosity can recover and transform back into a coral-dominated state, compared to those with degraded corals that have become algal-dominated [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCoral cover is a two-dimensional feature of a reef, while rugosity reveals a three-dimensional characteristic of the physical structure of the reef. Components of the reef\u0026rsquo;s physical three-dimensional (3D) structure should be able to show more about the degree of recovery of a disturbed reef, even if coral cover does not change much.\u003c/p\u003e \u003cp\u003eThe physical 3D structure of the reef, also known as structural complexity, involves the shapes and features of living organisms, such as corals, seagrass, and macroalgae, as well as non-living objects like rocks and sand [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Quantifying the 3D Structure of the reef involves measuring components such as surface rugosity, shelter capacity, viewshed area, trough volume and area, morphological forms, among others [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe earliest recorded and most common measure for quantifying the reef\u0026rsquo;s 3D physical structure is the rugosity index using the chain-and-tape method [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Rugosity at various spatial scales can be quantified using the fractal dimension \u003cem\u003eD\u0026thinsp;=\u0026thinsp;1 \u0026ndash; S\u003c/em\u003e and the Point-Intercept Contour (PIC) among others [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. \u003cem\u003eD\u003c/em\u003e is the change in rugosity with changing scale of measurement, where \u003cem\u003eS\u003c/em\u003e is the slope of rugosity values on a log-log plot with rugosity on the y-axis and spatial scale on the x-axis. Because they were measured manually during dives, data collection for these early metrics was subject to time constraints and safety risks, limiting the development of more statistically robust datasets.\u003c/p\u003e \u003cp\u003eThe advent of machine vision algorithms and breakthroughs in imaging instrumentation introduced safer and faster ways to evaluate the reef\u0026rsquo;s 3D structure. Among these technologies are the Light Detection and Ranging (LiDAR), Benthic Terrain Modeler, Structure-from-motion (SFM) photogrammetry, and others [\u003cspan additionalcitationids=\"CR38 CR39 CR40\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. 3D models now enable the examination of water flow around the coral canopy and individual colonies [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStructure-from-motion photogrammetry is an algorithm that creates 3D coral models from overlapping image captures of a reef [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Rugosity indices can be directly measured from the 3D coral model, eliminating the need for on-site measurements [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Components other than the rugosity index have also been quantified from the SFM 3D coral models of reefs such as the presence of crevices and holes in a reef, viewshed, grazing area, verticality, elevation, surface angle, and shelter volume [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Of these components, rugosity was validated the most, by comparing real with virtual measurements, showing a linear relationship with slope almost equal to one. Most recent SFM tests even show that the 3D coral models can be accurate to the millimeter. This means that components can now be quantified from high-speed 3D habitat reconstructions at scales ranging from small corals to entire reef landscapes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Rugosity indices of cold-water coral reefs can even be measured through SFM photogrammetry at almost 1000-meter depths [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Surface rugosity, defined as the ratio of the surface area to the planar area, has become the standard rugosity index in place of the chain-and-tape method [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSFM 3D coral models have enabled temporal comparisons of bleaching extent using underwater stereo imagery and coral growth rates [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Their framework yieled accurate 3D habitat reconstructions from image datasets, spanning scales from small corals to entire reef landscapes. SFM photogrammetry has also been utilized in conjunction with monocular-derived photogrammetry to simulate existing video/photo surveys to date [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, no published work on historical time-series data sets obtained from monitoring protocols such as the photo-quadrat or photo-transect method was found.\u003c/p\u003e \u003cp\u003eThis work presents a novel and successful recreation of reefs from actual time series images collected in Lian, western Luzon Island, Philippines, as early as 2009, using SFM and successfully quantifying rugosity from the resulting 3D coral models. Rugosity values were then compared with percent coral cover to determine whether rugosity from 3D coral models can serve as a proxy for percent coral cover in quantifying reef recovery or decline over time.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Region and Sampling\u003c/h2\u003e \u003cp\u003eData collection for this study was conducted in 4m x 4m fixed plots within the reefs of Talim Bay, Lian, in western Luzon Island, Philippines, at regular monitoring sites distributed approximately every two km along the coast of Talim Bay (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The dataset was collected in Site 2, one of the regular monitoring sites in the bay. Site 2 has two stations, 2A and 2B, and each station has three (3) 4m x 4m fixed plots at a reef flat of 2 m to 6 m depth. These plots were imaged using the photo-quadrat method to quantify coral cover and composition biannually from 2009 to 2024. The photo quadrat method involves the use of a tetrapod as shown in Fig.\u0026nbsp;2 with an underwater camera one (1) meter in altitude facing the seafloor. The images of the plots were taken in a lawnmower pattern, with each image overlapping approximately 50% of the previous image.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. 3D Reconstruction and Rugosity Validation\u003c/h2\u003e \u003cp\u003eValidation of the SFM algorithm to create 3D coral models was conducted by quantifying rugosity using the chain-and-tape method, both in-situ and from the 3D coral models. The in-situ chain-and-tape method involved draping a 6-m length of stainless-steel chain over the reef contour within the reef plot boundary. Each link of the chain was approximately 1.5 cm x 4 cm, allowing it to be visible to the diver taking photos from an altitude of 1 m. The distance from the start and end point of the chain was then measured using a transect tape. Rugosity is the ratio between this distance value and 6 m.\u003c/p\u003e \u003cp\u003eThe same 4m x 4m plot where the chain was draped over the reef contour was captured in photos using the lawnmower pattern in the photo-quadrat method. The images were then processed using the Agisoft Metashape software with the parameters listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAgisoft Metashape incorporates the SFM algorithm, which utilizes common features in images to enable initial estimates of camera positions and object coordinates, refined iteratively through non-linear least-squares minimization [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The outputs were a 3D representation, 3D point cloud, or 3D coral model of the reef, a collection of points, each containing spatial coordinates x, y, z, proportional to its actual position in the reef. Information contained from the point cloud was then used to create an ortho-projection, orthomosaic, or an aerial view of the reef.\u003c/p\u003e \u003cp\u003eThe length of the chain and the distance between the start and end of the chain in the 3D model were measured using Agisoft Metashape\u0026rsquo;s \u003cem\u003edraw polyline\u003c/em\u003e and \u003cem\u003emeasure\u003c/em\u003e functions. The \u003cem\u003edraw polyline\u003c/em\u003e traces the chain in the 3D model, then once tracing is done, the measure function shows the coordinates of each point in the polyline. The total length of the polyline is the sum of the distance between all the points. Rugosity is the ratio of the chain polyline length and the polyline length between the start and end of the chain. Rugosity values from the 3D coral models were then compared with the actual values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. RHID 3D Coral Models\u003c/h2\u003e \u003cp\u003eRHID that were obtained using the photo quadrat method from 4m x 4m reef plots employed different camera models through time. Some cameras used during imaging, especially the earlier models, captured a fish-eye view of the reef and also included the base of the tetrapod in the images. This was rectified using a camera calibration and image correction function in MATLAB, and subsequently cropped to remove the base of the tetrapod that is visible in the image [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe cropped RHID was processed in Agisoft Metashape using the same parameters in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The use of various cameras produced images of different sizes and qualities, such that the 3D models formed are not of the same size. To correct this, they were rescaled and aligned in CloudCompare, an open-source 3D software, using the alignment feature with at least three (3) common points identified between two 3D coral models [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Objects of known sizes, such as coral settlement tiles, dive weights, and transect tapes, were used as reference during rescaling. Trimming was then performed in MATLAB to ensure that all models are of the same size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Percent Coral Cover\u003c/h2\u003e \u003cp\u003eMassive, columnar, branching, tabular, and other morphological forms of corals in the orthomosaic were outlined using the Fiji plugin in ImageJ, and their respective total areas were calculated [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. These values correspond to the coral cover in the reef plot.\u003c/p\u003e \u003cp\u003eThe time series percent coral cover data from the plots were compared with the monitoring percent coral cover data collected using the photo-transect method of van Woesik et al. (2009) [\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Unlike the photo-quadrat method that was used on fixed plots, the photo-transect method incorporates five randomly deployed 50m transects within a 75 m by 25 m area in which the fixed plot stations were located. At least fifty images of a 1 m\u003csup\u003e2\u003c/sup\u003e area along the 50m transect were captured, which were then analyzed using Coral Point Count with Excel extensions (CPCe), where ten random points in the image were scored according to benthic categories [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The percent coral cover was extracted from the categories that belong to the hard coral taxa.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Rugosity as a 3D Structure Component\u003c/h2\u003e \u003cp\u003eThe rugosity index was obtained from the ratio of the plot\u0026rsquo;s area and its surface area. The surface area was calculated from the 3D mesh formed by connecting the point clouds into triangles derived using the Delaunay triangulation method [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The surface area is the sum of the area of each triangle. The ratio of the actual areato this value, translatedinto an actual surface area based on scale markers during image capture, gives the rugosity index of the plot.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003e3D coral models were successfully created from the RHID in Talim Bay from February 2009 to March 2024, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The shaded cells indicate the successfully created 3D coral models of the 4m x 4m plots, while the unshaded cells represent those with limited or no available data. Coral morphology from the 4m x 4m plots was compared to transect data from the same monitoring stations. In situ values obtained with the chain-and-tape method were also compared with the values from the corresponding 3D coral models. Surface rugosity, as a 3D structure component, was extracted from the 3D coral models.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. In Situ vs 3D Coral Model Measurements\u003c/h2\u003e \u003cp\u003eThe chain-and-tape method for rugosity was done both on site and on the 3D coral models to validate measurements. All six monitoring plots were measured for the ratio of the tape length from both ends of the chain to the length of the chain draped over the surface of the corals as shown in Fig.\u0026nbsp;3. Images from the first three plots were captured on the first dive day, where visibility was better than on the second day.\u003c/p\u003e \u003cp\u003eRugosity in the 3D coral model was successfully measured using polylines that traced the chain and its two endpoints, as shown in Fig.\u0026nbsp;4. Comparing the rugosity values reveals a linear relationship between the actual and the 3D coral model measurements, with a slope of 1 and a mean percent error of 3%, as determined by the equation, 100*\u003cem\u003e(actual value \u0026ndash; calculated value)/actual value\u003c/em\u003e. The error is likely due to measurements taken with the transect tape being affected by water movement, which shifts the transect tape and creates parallax issues during reading, or low visibility during the reading process. Another source of error was that a few parts of the chain were not included in the final 3D coral model because they were embedded deep within the reef crevice that the camera was unable to capture.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Image Processing\u003c/h2\u003e \u003cp\u003eImage rectification of the original image in Fig.\u0026nbsp;5a through camera calibration, image correction, and trimming resulted in a correction of the fish-eye effect and removal of the tetrapod base as shown in Figs.\u0026nbsp;5b and 4c, respectively. The corrected images in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003e still contain overlaps, which are required for the SFM algorithm to extract common features in images to estimate depth to form 3D coral models.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. 3D Coral Models from RHID\u003c/h2\u003e \u003cp\u003eThe rectified RHID of a 4m x 4m reef plot in Fig.\u0026nbsp;7a, obtained in April 2014, provides a successful 3D representation of the reef in Fig.\u0026nbsp;7b and its corresponding orthomosaic in Fig.\u0026nbsp;7c.\u003c/p\u003e \u003cp\u003eThe RHID summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the number of images per set. To cover a 4m x 4m plot at an altitude of 1 m with 50% overlap, at least eight images must be captured to cover one side of the plot. This means that at least 64 images must be obtained to secure a successful 3D reconstruction of the plot. Nevertheless, sets with only a little more than 50 images still successfully formed into 3D coral models. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003e displays the aerial view of the 3D coral models from one of the plots in Lian. Dark areas that appear like holes are the overlooked areas during data capture.\u003c/p\u003e \u003cp\u003eThe use of various cameras produces different image sizes resulted in varied 3D coral model sizes as well, hence the need for alignment, rescaling, and trimming of the 3D coral models. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e9\u003c/span\u003e displays an aerial view of the aligned, rescaled, and trimmed 3D coral models, where each 3D coral model has a plane cross-sectional area of 16 m\u003csup\u003e2\u003c/sup\u003e. The 3D coral models are shown as elevation matrices showing dark areas as dips and bright areas as crests.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. 3D Coral Models and Coral Cover\u003c/h2\u003e \u003cp\u003eThere were global bleaching events in 2010, 2016\u0026ndash;2017, and 2020, which may have affected the reefs locally [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Coral reefs in Lian are subjected to multiple disturbances each year, such as runoff during the southwest monsoon season and typhoons, fishing pressure, and in the recent years (or in the last 15 years), thermal stress. The aerial view or orthomosaic of the 3D coral models were analyzed for coral cover by morphology as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e10\u003c/span\u003e using the Labkit plugin of the Fiji toolkit by ImageJ [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. A visual comparison of two successive orthomosaics, from February 2009 to May 2010, revealed an apparent change in morphological composition within a year. The percentage of the total morphological composition in the orthomosaic is the percent coral cover. This shift can be attributed to one or a combination of the disturbances affecting Lian. Transect data showed a sudden decline in Family Acroporidae (mostly branching) coral cover leaving members of Family Poritidae (mostly boulder-shaped) the lone dominant hard coral in the stations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComparing the percent coral cover of the orthomosaics from the photo quadrat data with the photo transect data in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e11\u003c/span\u003e from the regular monitoring shows a very high correlation, indicating a consistent result despite the varied methods. It can be observed that the impact of a massive disturbance towards the end of 2009 is reflected in the decline of the percent coral cover. Since then, the trajectory has been relatively increasing, indicative of a recovering reef, despite the bleaching events that follow.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Rugosity as a 3D Structure Component\u003c/h2\u003e \u003cp\u003eThe surface rugosity index, \u003cem\u003eRI\u003c/em\u003e, used in this paper is:\u003c/p\u003e \u003cp\u003e \u003cem\u003eRI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003e1 - P/S\u003c/em\u003e, (2)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eP\u003c/em\u003e is the planar area and \u003cem\u003eS\u003c/em\u003e is the surface area. A flat surface gives a rugosity index of 0 while a highly rugose surface will have values that are approaching 1. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows elevation matrices of a plot over time. From the time series elevation matrices of the six monitoring plots, the surface rugosity trajectory follows an increasing pattern from a mean rugosity value of 0.55 to 0.65 with a mean standard deviation of 0.14 as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Rugosity and Coral Cover\u003c/h2\u003e \u003cp\u003eRugosity follows an increasing trajectory similar to the percent coral cover from 2009 to 2024 as shown consistently in all six plots in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e13\u003c/span\u003e. Rugosity increased from 0.56 to 0.67 in plot A1, 0.55 to 0.69 in plot A2, 0.49 to 0.58 in plot A3, 0.50 to 0.61 in plot B1, 0.55 to 0.61 in plot B2, and 0.60 to 0.71 in plot B3. All plots display an increase in rugosity by 11% to 26%. On a scale of 0 to 1, 0 being the reef completely flat and 1 being the reef surface at optimal rugosity, this increase is already significant, representing a range of 6 to 16 m\u003csup\u003e2\u003c/sup\u003e increase in surface area, given the plot area is only 16 m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCombining the values from all plots, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e14\u003c/span\u003e, indicates that the trends for both rugosity and coral cover are increasing. The increase in surface area, which is part of the rugosity Eq.\u0026nbsp;(2), is attributed to increased coral cover. This implies that rugosity measurements can serve as a proxy for changes in reef cover over time. This allows the method to be replicated in other locations if the RHID of photo-quadrats is available, or to be used moving forward for monitoring efforts. Higher measurement accuracy can also be achieved because 3D coral models have already been created, allowing us to quantify rugosity with greater precision. Processing for a single 4m x 4m plot takes approximately two hours on a personal computer. Thus, an RHID of 10 plots can already be processed within one day to display rugosity values and project changes through time. In comparison, manual CPCe scoring of coral cover by experts typically takes 1.5 to 2 days per station. This suggests that changes over time can be quantified using RHID and rugosity measurements from 3D coral models, with less demand for expert diagnosis and reduced user bias for long-term series analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eSources of funds for data collection are the De La Salle University\u0026rsquo;s Br. Alfred Shields FSC Ocean Research (SHORE) Center, the Commission on Higher Education (CHED) DARE TO Coral Project, and the Department of Science and Technology (DOST) funded National Assessment of Coral Reef Environment (NACRE) Program, Capacity Building on Reef Assessment and Coral Taxonomy (C-BRACT), and Reef Imaging and Monitoring (RIM) Project.\u003c/p\u003e \u003cp\u003eFunds for LJD\u0026rsquo;s dissertation are from the DOST Accelerated Science and Technology Human Resource Development Program (DOST-ASTHRDP) and the Gerry Roxas Foundation USAID INSPIRE Student Research Grant 2024.\u003c/p\u003e \u003cp\u003eConflict of interest/Competing interests\u003c/p\u003e \u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e \u003cp\u003eClinical trial number\u003c/p\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003cp\u003eEthics approval and consent to participate\u003c/p\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003cp\u003eConsent for publication\u003c/p\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003cp\u003eData availability\u003c/p\u003e \u003cp\u003eThe datasets generated during and/or analyzed during the current study are not publicly available due to national security reasons but can be made available on reasonable request.\u003c/p\u003e \u003cp\u003eMaterials availability\u003c/p\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003cp\u003eCode availability\u003c/p\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLJD, WYL, and LTD wrote the main manuscript text, conceptualized the study and collected, processed, and/or analyzed data. DJM wrote, conceptualized, processed, and/or analyzed data related to the photo-transect method and coral cover. All authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe coral reef images used in this study are from the monitoring data of the De La Salle University\u0026rsquo;s Br. Alfred Shields FSC Ocean Research (SHORE) Center, with support from the Commission on Higher Education (CHED) DARE TO Coral Project, and the Department of Science and Technology (DOST) funded National Assessment of Coral Reef Environment (NACRE) Program, Capacity Building on Reef Assessment and Coral Taxonomy (C-BRACT), and the Reef Imaging and Monitoring (RIM) Project. LJD was also a recipient of the DOST Accelerated Science and Technology Human Resource Development Program (DOST-ASTHRDP) and the Gerry Roxas Foundation USAID INSPIRE Student Research Grant 2024.\u003c/p\u003e "},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePearson, R. G. (1981). Recovery and recolonization of coral reefs. Marine Ecology Progress Series, 105\u0026ndash;122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.jstor.org/stable/24812966\u003c/span\u003e\u003cspan address=\"http://www.jstor.org/stable/24812966\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHughes, T. P., Graham, N. A., Jackson, J. B., Mumby, P. J., \u0026amp; Steneck, R. S. (2010). Rising to the challenge of sustaining coral reef resilience. 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Coral Reefs, 42(4), 873\u0026ndash;890. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00338-023-02403-5\u003c/span\u003e\u003cspan address=\"10.1007/s00338-023-02403-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"rugosity, coral cover, structure-from-motion, disturbance, 3D reconstruction","lastPublishedDoi":"10.21203/rs.3.rs-8852997/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8852997/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTime series structure-from-motion (SFM) three-dimensional (3D) models of underwater reef images were created from repurposed historical image datasets (RHID) collected during regular monitoring in Lian, western Luzon Island, Philippines from 2009 to 2025. The method\u0026rsquo;s novelty is its successful repurposing of data to create 3D coral models and to extract surface rugosity as a 3D structure component. These datasets were primarily captured using the photo quadrat method with different cameras at variable water conditions and a very limited number of images per data collection, yet structure component indices were validated in situ with an accuracy of 0.97. Results showed a similar trend between the rugosity and coral cover reflecting a high potential for RHID 3D structure components to proxy coral cover as a measure of reef recovery or decline over time.\u003c/p\u003e","manuscriptTitle":"3D Coral Models from Repurposed Historical Image Datasets","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 14:29:41","doi":"10.21203/rs.3.rs-8852997/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"86d93fd8-5af4-48ec-91c3-fbd11532952a","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-22T06:08:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 14:29:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8852997","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8852997","identity":"rs-8852997","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}