Search Results (33)

The clumping index (CI) describes the level of foliage grouping relative to the random distribution within the canopy. It plays a vital role in the derivation of other important parameters (e.g., the leaf area index, (LAI)) that are usually employed in hydrological, ecological and climatological modeling. In recent years, several satellite-based CI products have been developed using multi-angle reflectance data. However, these products have been validated through the use of a “point-to-point” comparison, which rarely involves a quantitative analysis of spatial representativeness for field-measured CIs in most cases. In this study, we developed a methodological framework to validate the MODIS CI at three different data scales on the basis of intense field measurements, high-resolution unmanned aerial vehicle (UAV) observations and Landsat 8 data. This framework was used to understand the impacts of the scale issue and subpixel variance of the CI in the validation of the MODIS CI for a case study of 12 gridded 500 m pixels in Saihanba National Forest Park, Hebei, China. The results revealed that the MODIS CIs in the study area were in good agreement with the upscaled field CIs (R = 0.75, RMSE = 0.05, bias = 0.02) and UAV CIs. Through a comparison of the observed CIs along the 30 m transects with the 500 m MODIS CIs, we gained insight into the uncertainty caused by the direct “point-to-pixel” evaluation method, which ranged from −0.21~+0.27 for the 10th and 90th percentiles of the observed-MODIS CI error distribution for the twelve pixels. Moreover, semivariogram analysis revealed that the representativeness assessments based on high-resolution albedo and CI maps could reflect the spatial heterogeneity within pixels, whereas the CI map provided more information on the variation in vegetation structures. The average observational footprint needed for a spatially representative sample is approximately 209 m according to an analysis of the high-resolution CI map. The uncertainty of mismatched MODIS land cover types can lead to a deviation of 0.33 in CI estimates, and compared with the CLX method, the scaled-up CI method based on simple arithmetic averages tends to overestimate CIs. In summary, various validation efforts in this case study reveal that the accuracy of the MODIS CIs is generally reliable and in good agreement with that of the upscaled field CIs and UAV CIs; however, with the development of surface process modeling and remote sensing technology, substantial measurements of field CIs in conjunction with high-resolution remotely sensed CI maps derived from single-angle advanced methods are urgently needed for further validation and potential applications. Certainly, such a validation effort will help to improve the understanding of MODIS CI products, which, in turn, will further support the methods and applications of global geospatial information. Full article

Multi-angle remote sensing observations play an important role in the remote sensing of solar radiation absorbed by land surfaces. Currently, the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) teams have successively applied the Ross–Li kernel-driven bidirectional reflectance distribution function (BRDF) model to integrate multi-angle observations to produce long time series BRDF model parameter products (MCD43 and VNP43), which can be used for the inversion of various surface parameters and the angle correction of remote sensing data. Even though the MODIS and VIIRS BRDF products originate from sensors and algorithms with similar designs, the consistency between BRDF parameters for different sensors is still unknown, and this likely affects the consistency and accuracy of various downstream parameter inversions. In this study, we applied BRDF model parameter time-series data from the overlapping period of the MODIS and VIIRS services to systematically analyze the temporal and spatial differences between the BRDF parameters and derived indices of the two sensors from the site scale to the region scale in the red band and NIR band, respectively. Then, we analyzed the sensitivity of the BRDF parameters to variations in Normalized Difference Hotspot–Darkspot (NDHD) and examined the spatiotemporal distribution of zero-valued pixels in the BRDF parameter products generated by the constraint method in the Ross–Li model from both sensors, assessing their potential impact on NDHD derivation. The results confirm that among the three BRDF parameters, the isotropic scattering parameters of MODIS and VIIRS are more consistent, whereas the volumetric and geometric-optical scattering parameters are more sensitive and variable; this performance is more pronounced in the red band. The indices derived from the MODIS and VIIRS BRDF parameters were compared, revealing increasing discrepancies between the albedo and typical directional reflectance and the NDHD. The isotropic scattering parameter and the volumetric scattering parameter show responses that are very sensitive to increases in the equal interval of the NDHD, indicating that the differences between the MODIS and VIIRS products may strongly influence the consistency of NDHD estimation. In addition, both MODIS and VIIRS have a large proportion of zero-valued pixels (volumetric and geometric-optical parameter layers), whereas the spatiotemporal distribution of zero-valued pixels in VIIRS is more widespread. While the zero-valued pixels have a minor influence on reflectance and albedo estimation, such pixels should be considered with attention to the estimation accuracy of the vegetation angular index, which relies heavily on anisotropic characteristics, e.g., the NDHD. This study reveals the need in optimizing the Clumping Index (CI)-NDHD algorithm to produce VIIRS CI product and highlights the importance of considering BRDF product quality flags for users in their specific applications. The method used in this study also helps improve the theoretical framework for cross-sensor product consistency assessment and clarify the uncertainty in high-precision ecological monitoring and various remote sensing applications. Full article

The clumping index (CI) is a key structural parameter that quantifies the nonrandomness of the spatial distribution of vegetation canopy leaves. Investigating seasonal variations in the CI is crucial, especially for estimating the leaf area index (LAI) and studying global carbon and water cycles. However, accurate estimations of the seasonal CI have substantial challenges, e.g., from the need for accurate hot spot measurements, i.e., the typical feature of the bidirectional reflectance distribution function (BRDF) shape in the current CI algorithm framework. Therefore, deriving a phenologically simplified stable CI product from a high-frequency CI product (e.g., 8 days) to reduce the uncertainty of CI seasonality and simplify CI applications remains important. In this study, we applied the discrete Fourier transform and an improved dynamic threshold method to estimate the start of season (SOS) and end of season (EOS) from the CI time series and indicated that the CI exhibits significant seasonal variation characteristics that are generally consistent with the MODIS land surface phenology (LSP) product (MCD12Q2), although seasonal differences between them probably exist. Second, we divided the vegetation cycle into two phenological stages based on the MODIS LSP product, ignoring the differences mentioned above, i.e., the leaf-on season (LOS, from greenup to dormancy) and the leaf-off season (LFS, after dormancy and before greenup of the next vegetation cycle), and developed the phenologically simplified two-stage CI product for the years 2001–2020 using the MODIS 8-day CI product suite. Finally, we assessed the accuracy of this CI product (RMSE = 0.06, bias = 0.01) via 95 datasets from 14 field-measured sites globally. This study revealed that the CI exhibited an approximately inverse trend in terms of phenological variation compared with the NDVI. Globally, based on the phenologically simplified two-stage CI product, the CI_LOS is smaller than the CI_LFS across all land cover types. Compared with the LFS stage, the quality for this CI product is better in the LOS stage, where the QA is basically identified as 0 and 1, accounting for more than ~90% of the total quality flag, which is significantly higher than that in the LFS stage (~60%). This study provides relatively reliable CI datasets that capture the general trend of seasonal CI variations and simplify potential applications in modeling ecological, meteorological, and other surface processes at both global and regional scales. Therefore, this study provides both new perspectives and datasets for future research in relation to CI and other biophysical parameters, e.g., the LAI. Full article

The Panchthar–Ilam–Taplejung Corridor in Eastern Nepal, managed through community forestry, is a crucial habitat for the Himalayan red panda, an endangered carnivore threatened by forest degradation and illegal trade. We deployed the altitude line intercept and ten-tree plotless methods to evaluate the distribution of Himalayan red pandas and the environmental factors affecting them within four community forests, namely Singhadevi, Chitre-Hile, Chhipchhipe, and Kalikhop-Dadehli, of the corridor. We established a total of 23 transects and 92 plots, identifying 41 plots with evidence of the Himalayan red panda’s presence. The sign occurrence revealed a clumped distribution of the species across all four community forests. The Himalayan red panda signs were observed between 2200 m and 2700 m above sea level (asl) and the majority of them were from habitats with a moderate slope within elevations of 2400 m to 2500 m asl. The primary sites for the defecation were large horizontal tree branches (78.12%), followed by forest ground (15.62%) and rocks (6.25%). The dominant tree species in their habitats included Lithocarpus pachyphylla (Importance value index, IVI = 45.05), Symplocus theifolia (IVI = 37.19), Symplocos pyrifolia (IVI = 20.99), Quercus lamellosa (IVI = 19.25), and Magnolia campbellii (IVI = 17.25). Among the thirteen environmental variables examined, proximity to water, distance to road, bamboo density, and Normalized Difference Vegetation Index were identified as the major factors influencing the Himalayan red panda’s distribution. This research provides crucial insights to develop site-specific habitat management plans for community forestry. Full article

Natural regeneration is deemed essential for maintaining biodiversity and ecosystem stability. Previous studies, however, have primarily concentrated on regions exhibiting limited environmental and climatic variability, overlooking the classification of natural regeneration based on age and source. Research conducted at the mesoscale, characterized by increased environmental variability and the incorporation of neighborhood competition and understory-associated vegetation, enhances our comprehension of the multifaceted influences on natural regeneration. To comprehend this issue, this study implemented 60 plots, each measuring 20 m × 20 m, across five distinct areas of Chongqing, China. Twenty explanatory variables were chosen from five diverse categories: understory vegetation, neighborhood competition, stand structure, climatic factors, and environmental factors. And the naturally regenerated species were classified into seedlings and saplings, as well as endogenous and exogenous species, based on their age and origin. We examined the response of the different categories of natural regeneration to various factors and constructed a structural equation model (SEM) for significant factors to investigate their direct and indirect effects on natural regeneration. A total of 61 regenerated tree species belonging to 29 families and 42 genera were found in the study area, and the naturally regenerating species with high importance values were Quercus fabri, Robinia pseudoacacia, Alangium chinense, Cunninghamia lanceolata, and Ligustrum lucidum. It was found that neighborhood competition and understory-associated vegetation explained the largest proportion (more than 50%) of the variation in the different categories of natural regeneration, and forests with clumped distribution (W), a high mingling index (M) and strong competition (H) had a reduced natural regeneration capacity. Understory-associated herbs significantly reduced natural regeneration and the crowdedness index (C) significantly inhibited the understory-associated herbs, thus indirectly promoting natural regeneration. The shrub cover is significantly and positively correlated with the number of naturally regenerated plants and can be used as an indicator of a forest community’s regeneration potential. Understanding the differences in the importance of various factors at the mesoscale, as well as their direct and indirect impacts, can help us further comprehend the mechanisms of natural regeneration and provide a foundation for the sustainable development of forests. Full article

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m⁻², NRMSD = 64–84%, bias = −0.17–0.89 g m⁻²). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m⁻², NRMSD = 52–73%, bias = 0.03–0.42 g m⁻²) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation. Full article

The utilization of radiative transfer models for interpreting remotely sensed data to evaluate forest disturbances is a cost-effective approach. However, the current radiative transfer modeling approaches are either too abstract (e.g., 1D models) or too complex (detailed 3D models). This study introduces a novel multilayer heterogeneous 3D radiative transfer framework with medium complexity, termed MART3D, for characterizing forest disturbances. MART3D generates 3D canopy structures accounting for the within-crown clumping by clustering leaves, which is modeled as a turbid medium, around branches, applicable for forests of medium complexity, such as temperate forests. It then automatically generates a multilayer forest with grass, shrub and several layers of trees using statistical parameters, such as the leaf area index and fraction of canopy cover. By employing the ray-tracing module within the well-established LargE-Scale remote sensing data and image Simulation model (LESS) as the computation backend, MART3D achieves a high accuracy (RMSE = 0.0022 and 0.018 for red and Near-Infrared bands) in terms of the bidirectional reflectance factor (BRF) over two RAMI forest scenes, even though the individual structures of MART3D are generated solely from statistical parameters. Furthermore, we demonstrated the versatility and user-friendliness of MART3D by evaluating the band selection strategy for computing the normalized burn ratio (NBR) to assess the composite burn index over a forest fire scene. The proposed MART3D is a flexible and easy-to-use tool for studying the remote sensing response under varying vegetation conditions. Full article

This study explores the quantitative relationship between canopy structure and the canopy escape ratio (f_esc), measured as the ratio of near-infrared reflectance of vegetation (NIRv) to the fraction of absorbed photosynthetically active radiation (fAPAR). We analyzed the correlation between f_esc and key indicators of canopy structure—specifically, leaf area index (LAI) and clumping index (CI)—utilizing both Sentinel-2 satellite data and in situ observations. Our analysis revealed a moderate correlation between f_esc and LAI, evidenced by an R² value of 0.37 for satellite-derived LAI, which contrasts with the lower correlation (R² of 0.15) observed with field-measured LAI. Conversely, the relationship between f_esc and CI proved to be significantly weaker (R² < 0.1), indicating minimal interaction between foliage distribution and light escape at the canopy level. This disparity in correlation strength was further evidenced in time series analysis, which showed little phenological variation in f_esc compared to LAI. Our findings elucidate the complexities of estimating f_esc based on the NIRv to fAPAR ratio and underscore the need for advanced methodologies in future research to enhance the accuracy of canopy escape models. Full article

The invasion of alien and invasive plants into the threatened Amathole Forest in Hogsback, Eastern Cape Province (South Africa) is an emerging priority conservation issue. The objective of this pilot study was to document and compare the foraging visits of two chacma baboon (Papio ursinus) troops in their natural and human habitats and their foraging behavioural activities to understand their potential to disperse ingested alien seeds in Hogsback. We also estimated the number of seeds per faecal sample collected from the foraging trails of the two troops of baboons, and determined potential dispersal distances using allometric equations. Since the focal troops used preferred sleeping and foraging sites, we predicted that these sites would have a high concentration of propagules. We applied the normalised difference vegetation index (NDVI) to discern possible vegetation cover changes. Overall, the two chacma baboon troops showed a similar number of daily foraging visits, although they preferred to forage more in human-modified than natural habitats. Their feeding and moving activities were significantly greater than other activities recorded during the study. There were significant differences in the numbers of seeds of six different fruiting plant species: 82.2 ± 13.3% (n = 284) for Acacia mearnsii; 78.9 ± 12.1% (n = 231) for Pinus patula, and 64.0 ± 20.0% (n = 108) for Solanum mauritianum. The two baboon troops could transport about 445 536 seeds from the six focal fruiting plant species considered in this study. Baboons’ seed dispersal distances were long at > 5 km per daily foraging activity. The NVDI vegetation cover analysis (i.e., 1978–2023) shows that the dense vegetation cover expanded by 80.9 ha, while the moderate and sparse vegetation cover collectively decreased by 10.3 ha. Although the seed dispersal pattern was neither clumped nor displayed any recognisable pattern, against our prediction, the number of faecal samples containing alien seeds and the observed foraging movement patterns suggest that chacma baboons disperse alien plant seeds that may establish and facilitate the deterioration of the natural forest. Further quantitative studies investigating the diversity of the plant species dispersed, their germination rates after ingestion by baboons, and their seasonal patterns are required to understand the baboon seed dispersal systems in the Amathole forests of Hogsback. Full article

Recently, light use efficiency (LUE) models driven by remote sensing data have been widely employed to estimate the gross primary productivity (GPP) of different terrestrial ecosystems at global or regional scales. Furthermore, the two-leaf light use efficiency (TL-LUE) model has been reported to improve the accuracy of GPP estimation, relative to the big-leaf MOD17 model, by separating the entire canopy into sunlit and shaded leaves through the use of constant clumping index estimation (Ω). However, ignoring obvious seasonal changes in the vegetation clumping index (CI) most likely results in GPP estimation errors since the CI tends to present seasonal changes, especially with respect to the obvious presence or absence of leaves within the canopy of deciduous vegetation. Here, we propose a TL-CLUE model that considers the seasonal difference in the CI based on the TL-LUE model to characterize general changes in canopy seasonality. This method composites monthly CI values into two or three Ω values to capture the general seasonal changes in CI while attempting to reduce the potential uncertainty caused during CI inversion. In theory, CI seasonality plays an essential role in the distribution of photosynthetically active radiation absorbed by the canopy (APAR). Specifically, the seasonal difference in CI values mainly considers the state of leaf growth, which is determined by the MODIS land surface phenology (LSP) product (MCD12Q2). Therefore, the one-year cycle (OYC) of leaf life is divided into two (leaf-off and leaf-on) or three seasons (leaf-off, leaf-scattering, and leaf-gathering) according to this MODIS LSP product, and the mean CI of each corresponding season for each vegetation class is computed to smoothen the uncertainties within each seasonal section. With these two or three seasonal Ω values as inputs, the TL-CLUE model by which the seasonal differences in CI are incorporated into the TL-LUE model is run and evaluated based on observations from 84 eddy covariance (EC) tower sites across North America. The results of the analysis reveal that the TL-LUE model widely overestimates GPP for most vegetation types during the leaf-on season, particularly during the growth peak. Although the TL-LUE model shows that the temporal characteristics of GPP agree with the EC observations in terms of general trends, the TL-CLUE model further improves the accuracy of GPP estimation by considering the seasonal changes in the CI. The result of GPP estimation from the TL-CLUE model shows a lower error (RMSE = 2.46 g C m⁻² d⁻¹) than the TL-LUE model (RMSE = 2.75 g C m⁻² d⁻¹) and somewhat decreases the eight-day GPP overestimation in the TL-LUE model with a constant Ω by approximately 9.76 and 8.970% when adapting three and two Ωs from different seasons, respectively. The study demonstrates that the uncertainty of seasonal disturbance in the CI, quantified by a standard deviation of approximately 0.071 relative to the mean CI of 0.746, is diminished through simple averaging. The seasonal difference in CI should be considered in GPP estimation of terrestrial ecosystems, particularly for vegetation with obvious canopy changes, where leaves go through the complete physiological processes of germination, stretching, maturity, and falling within a year. This study demonstrates the potential of the MODIS CI application in developing ecosystem and hydrological models. Full article

Monitoring gross primary production (GPP) is necessary for quantifying the terrestrial carbon balance. The near-infrared reflectance of vegetation (NIRv) has been proven to be a good predictor of GPP. Given that radiation powers photosynthesis, we hypothesized that (i) the addition of photosynthetic photon flux density (PPFD) information to NIRv would improve estimates of GPP and that (ii) a further improvement would be obtained by incorporating the estimates of radiation distribution in the canopy provided by the foliar clumping index (CI). Thus, we used GPP data from FLUXNET sites to test these possible improvements by comparing the performance of a model based solely on NIRv with two other models, one combining NIRv and PPFD and the other combining NIRv, PPFD and the CI of each vegetation cover type. We tested the performance of these models for different types of vegetation cover, at various latitudes and over the different seasons. Our results demonstrate that the addition of daily radiation information and the clumping index for each vegetation cover type to the NIRv improves its ability to estimate GPP. The improvement was related to foliage organization, given that the foliar distribution in the canopy (CI) affects radiation distribution and use and that radiation drives productivity. Evergreen needleleaf forests are the vegetation cover type with the greatest improvement in GPP estimation after the addition of CI information, likely as a result of their greater radiation constraints. Vegetation type was more determinant of the sensitivity to PPFD changes than latitude or seasonality. We advocate for the incorporation of PPFD and CI into NIRv algorithms and GPP models to improve GPP estimates. Full article

The clumping index (CI) is a commonly used vegetation dispersion parameter used to characterize the spatial distribution of the clumping or random distribution of leaves in canopy environments, as well as to determine the radiation transfer of the canopy, the photosynthesis of the foliage, and hydrological processes. However, the method of CI estimation using the measurement instrument produces uncertain values in various forest types. Therefore, it is necessary to clarify the differences in CI estimation methods using field measurements with various segment lengths in different forest types. In this study, three 100 m × 100 m plots were set, and the CI and leaf area index (LAI) values were measured. The CI estimation results were compared. The results show that the accuracy of CI estimation was affected by different forest types, different stand densities, and various segment lengths. The segment length had a significant effect on CI estimation with various methods. The CI estimation accuracy of the LX and CLX methods increased alongside a decrease in the segment length. The CI evidently offered spatial heterogeneity among the different plots. Compared with the true CI, there were significant differences in the CI estimation values with the use of various methods. Moreover, the spatial distribution of the CI estimation values using the Ω_CMN method could more effectively describe the spatial heterogeneity of the CI. These results can provide a reference for CI estimation in field measurements with various segment lengths in different forest types. Full article

The clumping index (CI) quantifies the foliage grouping within distinct canopies relative to randomly distributed canopies, which plays an important role in the vegetation radiative regime. Among the vegetation structure parameters, the global CI map can be retrieved by using multiangle remote sensing observations. The bidirectional reflectance distribution function (BRDF)/Albedo product (MCD43) of the Moderate-Resolution Imaging Spectroradiometer (MODIS) is the crucial input data of the global CI product, which provides validated spatiotemporal continuous directional reflectance data. To determine the impacts of updating the MCD43 product on the consistency and performance of global CI products, CIs retrieved from different MCD43 versions (Collection V005/V006, C5/6) were compared on a global scale and validated with field-measured CI data. The results showed that the global and seasonal comparisons of C5 and C6 CI data are generally consistent. Compared to C5 CI data, C6 CI data have improved quality with more main algorithm retrievals and fewer case of missing data. The comparisons over the field measurements indicate that both versions of CI data agree with field-measured CI data in terms of values and seasonal variations, while C6 CI data (R² = 0.89, RMSE = 0.05, bias = 0.02) are closer to field CIs than C5 CI data (R² = 0.80, RMSE = 0.07, bias = 0.03), indicating a higher accuracy for C6 CI data. The monthly CI is recommended for characterizing the overall seasonal patterns of surface CIs. Full article

Canopy clumping index (CI) is a key structural parameter related to vegetation phenology and the absorption of radiation, and it is usually retrieved from remote sensing data based on an empirical relationship with the Normalized Difference between Hotspot and Darkspot (NDHD) index. A rapid production software was developed to implement the CI algorithm based on the Google Earth Engine (GEE) to update current CI products and promote the application of CI in different fields. Daily, monthly, and yearly global CI products are continuously generated and updated in real-time by the software. Users can directly download the product or work with CI without paying attention to data generation. For the application case study, a change detection algorithm, LandTrendr, was implemented on the GEE to examine the global CI trend from 2000 to 2020. The results indicate that the area of increase trend (28.7%, ΔCI > 0.02) is greater than that of the decrease trend (17.1%, ΔCI < −0.02). Our work contributes toward the retrieval, application, and validation of CI. Full article

In temperate coastal regions of Western Europe, the polychaete Sabellaria alveolata (Linné) builds large intertidal reefs of several hectares on soft-bottom substrates. These reefs are protected by the European Habitat Directive EEC/92/43 under the status of biogenic structures hosting a high biodiversity and providing ecological functions such as protection against coastal erosion. As an alternative to time-consuming field campaigns, a UAV-based Structure-from-Motion photogrammetric survey was carried out in October 2020 over Noirmoutier Island (France) where the second-largest known European reef is located in a tidal delta. A DJI Phantom 4 Multispectral UAV provided a topographic dataset at very high resolutions of 5 cm/pixel for the Digital Surface Model (DSM) and 2.63 cm/pixel for the multispectral orthomosaic images. The reef footprint was mapped using a combination of two topographic indices: the Topographic Openness Index and the Topographic Position Index. The reef structures covered an area of 8.15 ha, with 89% corresponding to the main reef composed of connected and continuous biogenic structures, 7.6% of large isolated structures (<60 m²), and 4.4% of small isolated reef clumps (<2 m²). To further describe the topographic complexity of the reef, the Geomorphon landform classification was used. The spatial distribution of tabular platforms considered as a healthy stage of the reef in contrast to a degraded stage was mapped with a proxy that consists in comparing the reef volume to a theoretical tabular-shaped reef volume. Epibionts colonizing the reef (macroalgae, mussels, and oysters) were also mapped by combining multispectral indices such as the Normalised Difference Vegetation Index and simple band ratios with topographic indices. A confusion matrix showed that macroalgae and mussels were satisfactorily identified but that oysters could not be detected by an automated procedure due to their spectral complexity. The topographic indices used in this work should now be further exploited to propose a health index for these large intertidal reefs. Full article