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Capability of the Sentinel 2 mission for tropical coral reef mapping and coral bleaching detection

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... Traditional labor-intensive field surveys offer point and transect records that can only be applied to small areas [3]. While field-based methods can collect detailed information along coral reef transects, these data are often limited to very small areas and are inadequate for monitoring large areas [4]. However, satellite remote sensing technology, when combined with field survey data, provides a solution to repeatedly map and monitor coral reef benthic habitats over large geographic areas [5]. ...
... In NASA MODIS image analyses, a typical portion of the cloud-free satellite images over reef regions ranges from 20% to 30% [8,9]. Previous coral reef studies have been conducted using mid-spatial resolution satellite images (e.g., Landsat-8, Sentinel-2) or high-resolution images with low temporal frequency (e.g., IKONOS, Worldview) [3,4,[10][11][12][13]. Coral reef mapping could benefit from high temporal frequency satellite sensors (e.g., Planet Dove). ...
... Surface reflectance contains large portions of non-bottom contribution (water column backscatter light) in deep water (depth >10 m). Therefore, it is critical that bottom reflectance is derived to properly classify benthic compositions [2,4]. For example, sandy bottom located at greater depths (>10 m) has a similar surface reflectance in the Dove's bands when compared with deep coral patch (Table 3, Figure 6). ...
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High spatial resolution benthic habitat information is essential for coral reef protection and coastal environmental management. Satellite-based shallow benthic composition mapping offers a more efficient approach than traditional field measurements, especially given the advancements in high spatial and temporal resolution satellite imagery. The Planet Dove satellite constellation now has more than 150 instruments in orbit that offer daily coverage at high spatial resolution (3.7 m). The Dove constellation provides regularly updated imagery that can minimize cloud in tropical oceans where dense cloud cover persists. Daily image acquisition also provides an opportunity to detect time-sensitive changes in shallow benthic habitats following coral bleaching events, storms, and other disturbances. We developed an object-based coral reef habitat mapping approach for Dove and similar multispectral satellites that provides bathymetry estimation, bottom reflectance retrieval, and object-based classification to identify different benthic compositions in shallow coastal environments. We tested our approach in three study sites in the Dominican Republic using 18 Dove images. Benthic composition classification results were validated by field measurements (overall accuracy = 82%). Bathymetry and bottom reflectance significantly contributed to identifying benthic habitat classes with similar surface reflectance. This new object-based approach can be effectively applied to map and manage coral reef habitats.
... Thus far, although some studies have documented the improved ability of Sentinel-2 for reef benthic classification and coral bleaching detection (Hedley et al., 2012(Hedley et al., , 2018, this data has not been effectively used for bleaching detection. Moreover, studies that performed bleaching detection using one image have considerable uncertainty, and the results are not satisfactorily accurate because of the similar and indistinguishable spectral signals of different reef substrates (Andréfouët et al., 2002;Philipson and Lindell, 2003;Clark et al., 2010). ...
... In previous studies, Sentinel-2 was verified to have improved ability in terms of its spatial resolution, instrument noise, usable acquisition rate, large coverage, etc. (Hedley et al., 2012(Hedley et al., , 2018. Therefore, the first part of this will follow-up on these analyses based on spectral and image simulation using a semianalytical (SA) model (Lee et al., 1998(Lee et al., , 1999 and the Sentinel-2 band relative spectral response (RSR) functions. ...
... Although previous studies verified that Sentinel 2 images can detect bleaching in shallow waters (Hedley et al., 2012), the applicable water depth range was limited. The ratios of reflectance at different depths to the reflectance without water absorption effect are calculated. ...
Article
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Sentinel-2 mission has been shown to have promising applications in coral reef remote sensing because of its superior properties. It has a 5-day revisit time, spatial resolution of 10 m, free data, etc. In this study, Sentinel-2 imagery was investigated for bleaching detection through simulations and a case study over the Lizard Island, Australia. The spectral and image simulations based on the semianalytical (SA) model and the sensor spectral response function, respectively, confirmed that coral bleaching cannot be detected only using one image, and the change analysis was proposed for detection because there will be a featured change signal for bleached corals. Band 2 of Sentinel-2 is superior to its other bands for the overall consideration of signal attenuation and spatial resolution. However, the detection capability of Sentinel-2 is still limited by the water depth. With rapid signal attenuation due to the water absorption effect, the applicable water depth for bleaching detection was recommended to be less than 10 m. The change analysis was conducted using two methods: one radiometric normalization with pseudo invariant features (PIFs) and the other with multi-temporal depth invariant indices (DII). The former performed better than the latter in terms of classification. The bleached corals maps obtained using the PIFs and DII approaches had an overall accuracy of 88.9 and 57.1%, respectively. Compared with the change analysis based on two dated images, the use of a third image that recorded the spectral signals of recovered corals or corals overgrown by algae after bleaching significantly improved the detection accuracy. All the preliminary results of this article will aid in the future studies on coral bleaching detection based on remote sensing.
... In addition to bathymetry, seafloor reflectance and water IOPs, which can be used to infer substrate and water quality respectively, can be simultaneously retrieved, and per-pixel uncertainties of all these parameters, including water depth, can also be determined. While originally developed for and tested on airborne hyperspectral imagery, physics-based methods for SDB have also been demonstrated for multispectral satellite sensors [11][12][13][14]. Physics-based methods can be implemented using either look-up tables (LUTs) [15,16] or semi-analytical optimization methods [17,18]. ...
... The NE∆Rrs can be used to assess the suitability of a satellite imagery for aquatic remote sensing applications. For example, it has been used to determine the suitability of the Compact Airborne Spectrographic Imager (CASI) for benthic mapping [11]. Therefore, following AC, we estimated the NE∆Rrs (sr −1 ) [39] by calculating the band-wise standard deviation of Rrs from a 33 × 33-pixel window over a homogeneous optically deep area using Equation (2) [40]. ...
... While Lee's inversion model uses the albedo of only one key benthic substrate (sand), our model includes a parameterization to set the seafloor reflectance as a linear mix of the two bottom types (i.e., sand and algae; [46]). To forward model the Rrs as a function of water depth, water quality parameters, and the seafloor reflectance, the adaptive look-up table (ALUT) method [11,16] was implemented, which ensures efficient construction and search through the table. In this approach, an LUT consisting of the modeled Rrs values of L8 bands 1-4, seafloor reflectance (Figure 3), water optical properties (absorption and scattering characteristics of water), and water depths of the optically shallow zone of the area in question (Table 3) is constructed. ...
Article
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Physics-based radiative transfer model (RTM) inversion methods have been developed and implemented for satellite-derived bathymetry (SDB); however, precise atmospheric correction (AC) is required for robust bathymetry retrieval. In a previous study, we revealed that biases from AC may be related to imaging and environmental factors that are not considered sufficiently in all AC algorithms. Thus, the main aim of this study is to demonstrate how AC biases related to environmental factors can be minimized to improve SDB results. To achieve this, we first tested a physics-based inversion method to estimate bathymetry for a nearshore area in the Florida Keys, USA. Using a freely available water-based AC algorithm (ACOLITE), we used Landsat 8 (L8) images to derive per-pixel remote sensing reflectances, from which bathymetry was subsequently estimated. Then, we quantified known biases in the AC using a linear regression that estimated bias as a function of imaging and environmental factors and applied a correction to produce a new set of remote sensing reflectances. This correction improved bathymetry estimates for eight of the nine scenes we tested, with the resulting changes in bathymetry RMSE ranging from +0.09 m (worse) to −0.48 m (better) for a 1 to 25 m depth range, and from + 0.07 m (worse) to −0.46 m (better) for an approximately 1 to 16 m depth range. In addition, we showed that an ensemble approach based on multiple images, with acquisitions ranging from optimal to sub-optimal conditions, can be used to estimate bathymetry with a result that is similar to what can be obtained from the best individual scene. This approach can reduce time spent on the pre-screening and filtering of scenes. The correction method implemented in this study is not a complete solution to the challenge of AC for satellite-derived bathymetry, but it can eliminate the effects of biases inherent to individual AC algorithms and thus improve bathymetry retrieval. It may also be beneficial for use with other AC algorithms and for the estimation of seafloor habitat and water quality products, although further validation in different nearshore waters is required.
... However, whereas broadband multispectral data of lower spectral and medium spatial resolution such as the Landsat series have become popular in landscape mapping, they could mask out specific spectral features of functional flowering groups, resulting in very low mapping accuracy. The newly launched relatively improved spectral and/or spatial resolution sensors such as WorldView-2, RapidEye, Spot-6 and Sentinel-2 offer great potential in detecting different colours of functional flowering groups [40][41][42]. Such sensors are specifically designed to capture spectral properties at additional wavebands such as red-edge and yellow spectrum that mimic over 90% of plant biophysiological information [43][44][45]. ...
... Sentinel-2 is multispectral sensor that was launched in 2015. It is characterised by 13 bands in the spectral ranges of visible/near infrared (VNIR) and shortwave infrared (SWIR) ( Table 4), with spatial resolution ranging from 10 to 60 metres [40,62,63]. Sentinel-2 data are freely available. ...
Article
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Pollination services and honeybee health in general are important in the African savannahs particularly to farmers who often rely on honeybee products as a supplementary source of income. Therefore, it is imperative to understand the floral cycle, abundance and spatial distribution of melliferous plants in the African savannah landscapes. Furthermore, placement of apiaries in the landscapes could benefit from information on spatiotemporal patterns of flowering plants, by optimising honeybees' foraging behaviours, which could improve apiary productivity. This study sought to assess the suitability of simulated multispectral data for mapping melliferous (flowering) plants in the African savannahs. Bi-temporal AISA Eagle hyperspectral images, resampled to four sensors (i.e. WorldView-2, RapidEye, Spot-6 and Sentinel-2) spatial and spectral resolutions, and a 10-cm ultra-high spatial resolution aerial imagery coinciding with onset and peak flowering periods were used in this study. Ground reference data was collected at the time of imagery capture. The advanced machine learning random forest (RF) classifier was used to map the flowering plants at a landscape scale and a classification accuracy validated using 30% independent test samples. The results showed that 93.33%, 69.43%, 67.52% and 82.18% accuracies could be achieved using WorldView-2, RapidEye, Spot-6 and Sentinel-2 data sets respectively, at the peak flowering period. Our study provides a basis for the development of operational and cost-effective approaches for mapping flowering plants in an African semiarid agroecological landscape. Specifically, such mapping approaches are valuable in providing timely and reliable advisory tools for guiding the implementation of beekeeping systems at a landscape scale.
... The visible bands allow Sentinel-2A to detect underwater objects with their water penetration ability and can be used to perform benthic habitat mapping of optically shallow coastal water [1,2]. Hedley et al. [3] reported simulation of Sentinel-2A on its capability to map coral reefs and detecting coral bleaching. For seagrass mapping, Topouzelis et al. [2] showed that Sentinel-2A obtained a very high accuracy in mapping seagrass habitat. ...
... As simulated in [3], Sentinel-2A performance for benthic habitat mapping is excellent. Benthic habitat map consists of three or four classes can be obtained with very good accuracy (>90%). ...
Article
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Sentinel‐2A accuracy for benthic habitat composition mapping was tested and compared to ALOS AVNIR‐2. Aerial image acquired using custom‐made unmanned aerial vehicle was used to train and validate the model. The mapping was conducted regardless of the benthic class and at individual benthic class. Benthic habitat class spatial distribution was obtained using the combination of image segmentation and classification tree analysis. The aerial image was interpreted based on the percentage of the constructed and non‐constructed classes. The constructed class includes coral reefs, dead coral, seagrass, and macroalgae, while non‐constructed class covers carbonate sand, rock, and rubble. Sentinel‐2A produced higher accuracy (92%) than ALOS AVNIR‐2 (78%) for benthic habitat spatial distribution mapping. However, in the empirical modelling of benthic habitat composition, ALOS AVNIR‐2 (SE 23–24%) produced slightly better accuracy than Sentinel‐2A (SE 23–27%). Several factors affected the low accuracy, which include the sub‐pixel mixing of benthic habitat and constructed class, the delay between dates of acquisition, and radiometric quality of the images. Since the fundamental relationship between reflectance value and the percentage of the constructed class has been justified and consistent, given more experiments it has the potential to predict benthic habitat composition with higher accuracy in the future.
... This flexibility allows for the easy monitoring of reef structures, such as patch reefs in lagoons, far from the coast. Of course, UAVs cannot be compared to a satellite in terms of spatial cover: satellite imagery analysis can be used to map an entire reef system [22,[85][86][87]. However, even if the spatial and temporal resolutions of the satellite sensors have improved in the last decade (Sentinel 2 offers 10 m/pix in visible bands and a revisit time of 5 days), they are not comparable to the centimetric resolution obtained from UAV surveys. ...
... The classification maps produced from the processing of orthomosaics with OBIA had good overall accuracy, 79%, which confirmed the validity of the classification process ( Table 2). The workflow adopted allows for the classification of benthic substrate types (sand, coral rubble and hard coral) with higher accuracy than maps realized from free satellite images (Sentinel 2) with the same methodology [65,86]. The maps represent the composition of the substrate that characterizes the surrounding reef rim a few months after the 2016 coral bleaching event and more than two years later. ...
Article
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Coral reefs are declining worldwide as a result of the effects of multiple natural and 15 anthropogenic stressors, including regional-scale temperature-induced coral bleaching. Such events 16 have caused significant coral mortality, leading to an evident structural collapse of reefs and shifts 17 in associated benthic communities. In this scenario, reasonable mapping techniques and best 18 practices are critical to improving data collection to describe spatial and temporal patterns of coral 19 reefs after a significant bleaching impact. Our study employed the potential of a consumer-grade 20 drone, coupled with Structure from Motion and Object-Based Image Analysis, to investigate for the 21 first time a tool to monitor changes in substrate composition and the associated deterioration in reef 22 environments in Maldivian shallow-water coral reef. Three key substrate types (Hard Coral, Coral 23 Rubble and Sand) were detected with high accuracy on high-resolution orthomosaics collected from 24 four sub-areas. Multi-temporal acquisition of UAV data allowed us to compare the classified maps 25 over time (February 2017, November 2018) and obtain evidence of the relevant deterioration in 26 structural complexity of flat reef environments that occurred after the 2016 mass bleaching event. 27 We believe that our proposed methodology offers a cost-effective procedure that is well suited to 28 generate maps for the long-term monitoring of changes in substrate type and reef complexity in 29 shallow water. 30
... In shallow waters, the estimation of each of these parameters is easier using hyperspectral than multispectral images [91]. The higher number of spectral bands as well as the increased spectral resolution reduce the confounding effects between optically active parameters [26,80,84,98,135]. In coastal environments, several studies have demonstrated the ability of hyperspectral tool for characterizing and mapping the coastal substrates and benthic communities. ...
... These models approximate the radiative transfer equation and generally simulate the reflectance of shallow waters as a function of illumination/observation geometry, depth, bottom albedo and optical properties inherent to the water column (i.e., its absorption and scattering properties). Various cost functions can be used to assess the goodness-of-fit between the observation and the model, including Euclidean distance, spectral angle mapper distance or maximum likelihood when using LUTs [81,80,115] or iterative optimization to invert the model [3,47,74,89,96,105,103]. ...
... Two complementary studies (Hedley et al., 2012b, Botha et al., 2013 offered a framework to evaluate the sensor and environmental limits to mapping accuracy as a function of the spectral characteristics of the substratum, the depth and composition of the water column, and on the sensor spectral and radiometric resolutions. Sub-pixel spectral mixing is also a primary limiting factor for benthic mapping (Hedley et al., 2012a) raising the need for spatial resolution of~10 m or less (Hedley et al., 2012a, Hedley et al., 2018aGiardino et al., 2019). ...
... Two complementary studies (Hedley et al., 2012b, Botha et al., 2013 offered a framework to evaluate the sensor and environmental limits to mapping accuracy as a function of the spectral characteristics of the substratum, the depth and composition of the water column, and on the sensor spectral and radiometric resolutions. Sub-pixel spectral mixing is also a primary limiting factor for benthic mapping (Hedley et al., 2012a) raising the need for spatial resolution of~10 m or less (Hedley et al., 2012a, Hedley et al., 2018aGiardino et al., 2019). ...
Article
Technical advancements have widened the limits of remote sensing in mapping shallow water benthic habitats and bathymetry over the last decades. On the other hand, the needs of shallow water remote sensing have pushed instrument development. In this manuscript we provide 50-year retrospective of the developments in the field in terms of both instrumentation and methods. We also show that spectral features characteristic of the main benthic groups in shallow water are consistent from the tropics to sub-arctic regions and from salty to freshwaters. The fundamental limiting factor in both benthic mapping and bathymetry is absorption of light by water molecules. However, spectral absorption by water molecules is the key to bathymetry derivation. Variable backscattering by particles and absorption by dissolved organic matter is a confounding factor for all objectives. The combination of using the spectral and textural characteristics of bottom features and our knowledge about these features have now resulted in the ability to map habitats over large coastal systems. This manuscript has shown that optically shallow water remote sensing has reached levels where the satellite derived bathymetry and habitat maps are accepted by different end users (including the International Maritime Organisation) and are routinely used in ecological studies, monitoring and management of coastal environments.
... In shallow waters, the estimation of each of these parameters is easier using hyperspectral than multispectral images [91]. The higher number of spectral bands as well as the increased spectral resolution reduce the confounding effects between optically active parameters [26,80,84,98,135]. In coastal environments, several studies have demonstrated the ability of hyperspectral tool for characterizing and mapping the coastal substrates and benthic communities. ...
... These models approximate the radiative transfer equation and generally simulate the reflectance of shallow waters as a function of illumination/observation geometry, depth, bottom albedo and optical properties inherent to the water column (i.e., its absorption and scattering properties). Various cost functions can be used to assess the goodness-of-fit between the observation and the model, including Euclidean distance, spectral angle mapper distance or maximum likelihood when using LUTs [81,80,115] or iterative optimization to invert the model [3,47,74,89,96,105,103]. ...
... The visible bands allow Sentinel-2A to detect underwater objects with their water penetration ability and can be used to perform benthic habitat mapping of optically shallow coastal water [1,2]. Hedley et al. [3] reported simulation of Sentinel-2A on its capability to map coral reefs and detecting coral bleaching. For seagrass mapping, Topouzelis et al. [2] showed that Sentinel-2A obtained a very high accuracy in mapping seagrass habitat. ...
... As simulated in [3], Sentinel-2A performance for benthic habitat mapping is excellent. Benthic habitat map consists of three or four classes can be obtained with very good accuracy (>90%). ...
Article
Biodiversity of benthic habitats is among the highest of all ecological communities. This study was conducted to model benthic habitat biodiversity indices using a remote sensing approach in optically shallow waters in Karimunjawa Islands-Indonesia. These islands have a wide variety of benthic environments. Two multispectral imagers, namely Sentinel-2A and Landsat 8 OLI, were used. A series of statistical tests were applied in the empirical modeling using the pixel values of both images with in situ Shannon index (H), Simpson index (D), and Shannon’s Equitability (EH) calculations. The modeling inputs were sunglint-corrected bands, water column-corrected bands, PCA-transformed bands, MNF bands, and occurrence texture bands. The results indicate that multispectral remote sensing images can be used to map benthic habitat biodiversity indices. However, the difference between the concepts of H, D, and EH calculations and the reflectance value recorded by the sensor remove the possibility of obtaining higher accuracy. H, D, and EH maps derived from Sentinel-2A had varying levels of accuracy, namely 46.8%, 59.1%, and 54.5%, respectively, while Landsat 8 OLI produced these three maps with 45.81%, 57.34%, and 53.81% accuracy.
... The free availability of their data significantly advances the applications of remote sensing with medium spatial resolutions (Roy et al., 2014;Wulder et al., 2015;Zhang et al., 2018). Thanks to the improvement of their spectral, radiometric, and temporal resolutions, they can expand the range of their applications to several natural resources and environmental domains for monitoring, assessing, and investigating (Hedley et al., 2012a, b). Moreover, the orbits of these four satellites' constellation (Sentinel 2A and 2B and Landsat 8 and 9) are designed to ensure a revisiting interval time of less than 2 d Li and Chen, 2020), thereby substantially increasing the monitoring capabilities of the Earth's surface and ecosystems (Drusch et al., 2012). ...
... Moreover, Wood (2012) demonstrated the potential of the synergy between the field spectra and hyperspectral data for seagrass sensing and mapping in Redfish Bay, Texas, in the US. Exploiting modeled and simulated data, Hedley et al. (2012a) demonstrated that Sentinel-MSI has an improved capability to detect and discriminate the marine environment compared to Satellite pour l'Observation de la Terre (SPOT)-4 and Landsat-ETM+. Furthermore, Fyfe (2003) reported that the spectral signatures measured on harvested wet leaves (out of water) of different seagrass species were spectrally distinct. ...
Article
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This paper assesses the reflectance difference values between the respective spectral bands in the visible and near-infrared (VNIR) of Sentinel 2A/2B Multi-Spectral Instrument (MSI) and Landsat 8/9 Operational Land Imager (OLI) sensors for seagrass, algae, and mixed species discrimination and monitoring in a shallow marine environment southeast of Bahrain Island in the Arabian Gulf. To achieve these, a field survey was conducted to collect samples of seawater, underwater sediments, seagrass (Halodule uninervis and Halophila stipulacea), and algae (green and brown). In addition, an experimental mode was established in a goniometric laboratory to simulate the marine environment, and spectral measurements were performed using an Analytical Spectral Devices (ASD) spectroradiometer. Measured spectra and their transformation using the continuum-removed reflectance spectral (CRRS) approach were analyzed to assess spectral separability among separate or mixed species at varying coverage rates. Afterward, the spectra were resampled and convolved in the solar-reflective spectral bands of MSI and OLI sensors and converted into water vegetation indices (WVIs) to investigate the potential of red, green, and blue bands for seagrass and algae species discrimination. The results of spectral and CRRS analyses highlighted the importance of the blue, green, and near-infrared (NIR) wavelengths for seagrass and algae detection and likely discrimination based on hyperspectral measurements. However, when resampled and convolved in MSI and OLI bands, spectral information loses the specific and unique absorption features and becomes more generalized and less precise. Therefore, relying on the multispectral bandwidth of MSI and OLI sensors, it is difficult or even impossible to differentiate or to map seagrass and algae individually at the species level. Instead of the red band, the integration of the blue or the green band in WVI increases their power to discriminate submerged aquatic vegetation (SAV), particularly the water adjusted vegetation index (WAVI), water enhanced vegetation index (WEVI), and water transformed difference vegetation index (WTDVI). These results corroborate the spectral and the CRRS analyses. However, despite the power of blue wavelength to penetrate deeper into the water, it also leads to a relative overestimation of dense SAV coverage due to more scattering in this part of the spectrum. Furthermore, statistical fits (p<0.05) between the reflectance in the respective VNIR bands of MSI and OLI revealed excellent linear relationships (R2 of 0.999) with insignificant root mean square difference (RMSD) (≤ 0.0015). Important agreement (0.63 ≤ R2 ≤ 0.96) was also obtained between respective WVI regardless of the integrated spectral bands (i.e., red, green, and blue), yielding insignificant RMSD (≤ 0.01). Accordingly, these results pointed out that MSI and OLI sensors are spectrally similar, and their data can be used jointly to monitor accurately the spatial distribution of SAV and its dynamic in time and space in shallow marine environments, provided that rigorous data pre-processing issues are addressed.
... In shallow waters, the estimation of each of these parameters is easier using hyperspectral than multispectral images [91]. The higher number of spectral bands as well as the increased spectral resolution reduce the confounding effects between optically active parameters [26,80,84,98,135]. In coastal environments, several studies have demonstrated the ability of hyperspectral tool for characterizing and mapping the coastal substrates and benthic communities. ...
... These models approximate the radiative transfer equation and generally simulate the reflectance of shallow waters as a function of illumination/observation geometry, depth, bottom albedo and optical properties inherent to the water column (i.e., its absorption and scattering properties). Various cost functions can be used to assess the goodness-of-fit between the observation and the model, including Euclidean distance, spectral angle mapper distance or maximum likelihood when using LUTs [81,80,115] or iterative optimization to invert the model [3,47,74,89,96,105,103]. ...
Chapter
This chapter gives an overview on the use of hyperspectral imagery in remote sensing. Specifically, four thematic applications dealing with the characterization of natural landscapes are presented, giving the reader a glimpse of the role that hyperspectral imaging technologies play in environmental monitoring. Namely, applications related to planetary sciences, coastal areas, cryosphere, and vegetation are reported. For each remote sensing application considered in this chapter, some context is recalled allowing then to introduce a real case study and finally to present some open challenges.
... Spatial studies using other satellite products include that of Hamylton, Hedley, and Beaman (2015) for two sites on the Great Barrier Reef using WorldView-2 images, where models based on Stumpf's (using blue and green bands) and optimization methods such as the adaptive look-up table (ALUT) model inversion method (Hedley, Roelfsema, and Phinn 2009;Hedley et al. 2012) were assessed. An extension to a spatial error model was constructed which significantly improved model performance, reporting R 2 values of 0.95. ...
Article
Optical satellite data is an efficient and complementary method to hydrographic surveys for deriving bathymetry in shallow coastal waters. Empirical approaches (in particular, the models of Stumpf and Lyzenga) provide a practical methodology to derive bathymetric information from remote sensing. Recent studies, however, have focused on enhancing the performance of such empirical approaches by extending them via spatial information. In this study, the relationship between multibeam depth and Sentinel-2 image bands was analyzed in an optically complex environment using the spatial predictor of kriging with an external drift (KED), where its external drift component was estimated: a) by a ratio of log-transformed bands based on Stumpf’s model (KED_S) and b) by a log-linear transform based on Lyzenga’s model (KED_L). Through the calibration of KED models, the study objectives were: 1) to better understand the empirical relationship between Sentinel-2 multispectral satellite reflectance and depth, 2) to test the robustness of KED to derive bathymetry in a multitemporal series of Sentinel-2 images and multibeam data, and 3) to compare the performance of KED against the existing non-spatial models described by Stumpf et al. and Lyzenga. Results showed that KED could improve prediction accuracy with a decrease in RMSE of 89% and 88%, and an increase in R² of 27% and 14%, over the Stumpf and Lyzenga models, respectively. The decrease in RMSE provides a worthwhile improvement in accuracy, where results showed effective prediction of depth up to 6 m. However, the presence of higher concentrations of suspended materials, especially river plumes, can reduce this threshold to 4 m. As would be expected, prediction accuracy could be improved through the removal of outliers, which were mainly located in the channel of the river, areas influenced by the river plume, abrupt topography, but also very shallow areas close to the shoreline. These areas have been identified as conflictive zones where satellite-derived bathymetry can be compromised.
... Most OSMs involving multispectral data first set the water properties to a constant state throughout the mapping area to reduce the unknown parameters and then estimate the water depth [18][19][20]. In fact, bathymetry results can also be estimated without setting the water IOPs to a constant state using efficient searching algorithms or optimization algorithms, such as the adaptive LUT and "active set" algorithms [8][9][10]34]. The uncertainties of all estimated parameters are not equal within the constrained ranges and form of the model of optically shallow water [8]. ...
Article
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Optimization-based semi-analytical methods (OSMs) and empirical methods (EMs) have been developed to derive bathymetry maps from satellite-based multispectral data of coral reefs, allowing for the management, monitoring, and protection of coral reefs. However, OSMs are often criticized due to the time-consuming requirements of iterative computations, yet they are praised for working without the need for in situ bathymetry data. EMs are praised for their time-saving characteristics and criticized for their need for in situ measurements. To estimate the water depth from multispectral data quickly without in situ bathymetry data, we provide a new EM that combines our previously developed OSM called the unmixing-based multispectral optimization process exemplar method (UMOPE) and an EM called Stumpf's ratio method (SRM). In the new method, reflectance values from a small number of sampled pixels and the corresponding water depths estimated by UMOPE are used to determine the regression parameters for SRM. Thus, SRM determines the upper limit of accuracy for the new method, and UMOPE determines the possibility of reaching the upper limit. The new method was evaluated using three types of imagery of Xisha Islands, namely, WorldView-2 imagery with three traditional visible bands (WV-2a), Landsat 8 imagery with four visible bands, and WV-2 imagery with six visible bands (WV-2b). The results show that the new method can perform as well as SRM for Landsat 8 data and WV-2b data with similar root mean square error values at different depths. The lack of a coastal band in WV-2a imagery may cause large errors for the new method in deep water regions, especially when the water-leaving reflectance is noise perturbed. We found that even though the depths estimated by UMOPE are not error free at different ranges of water depth, if the regression line between the depths estimated by UMOPE and the measured depths is near the 1:1 line, the new method can perform as well as SRM. The new method may facilitate the rapid estimation of bathymetry from free Landsat 8 data of optically shallow waters around the world without in situ bathymetry data.
... A key advantage is that they allow the simultaneous retrieval of water inherent optical properties (IOPs), water depth and seafloor features (Dekker et al. 2011;Brando et al. 2009), which are also essential for monitoring and management of nearshore environments. Although radiative transfer models (RTMs) were initially developed for airborne hyperspectral, recent studies suggest that they can even be applied to multi-spectral satellite data with acceptable results (Hedley, 2012). However, because these RTMs rely on estimates of absolute radiometry they are sensitive to errors caused by imperfect sensor calibration and atmospheric correction. ...
... The combination of high spatial resolution of 10-20-60 m, novel spectral capabilities, a swath width of 290 km, a global coverage of land surfaces from 56 • S to 84 • N and frequent revisit times (5-15 days) provides unprecedented views of Earth. Sentinel mission not only offers continuity of services for the moderate resolution multispectral Spot XS and Landsat Thematic Mapper series sensors, but it also has several technical improvements that may lead to enhanced capability in coral reef mapping applications [22][23][24]. ...
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Sustainable and ecosystem-based marine spatial planning is a priority of Pacific Island countries basing their economy on marine resources. The urgency of management coral reef systems and associated coastal environments, threatened by the effects of climate change, require a detailed habitat mapping of the present status and a future monitoring of changes over time. Here, we present a remote sensing study using free available Sentinel-2 imagery for mapping at large scale the most sensible and high value habitats (corals, seagrasses, mangroves) of Palau Republic (Micronesia, Pacific Ocean), carried out without any sea truth validation. Remote sensing ‘supervised’ and ‘unsupervised’ classification methods applied to 2017 Sentinel-2 imagery with 10 m resolution together with comparisons with free ancillary data on web platform and available scientific literature were used to map mangrove, coral, and seagrass communities in the Palau Archipelago. This paper addresses the challenge of multispectral benthic mapping estimation using commercial software for preprocessing steps (ERDAS ATCOR) and for benthic classification (ENVI) on the base of satellite image analysis. The accuracy of the methods was tested comparing results with reference NOAA (National Oceanic and Atmospheric Administration, Silver Spring, MD, USA) habitat maps achieved through Ikonos and Quickbird imagery interpretation and sea-truth validations. Results showed how the proposed approach allowed an overall good classification of marine habitats, namely a good concordance of mangroves cover around Palau Archipelago with previous literature and a good identification of coastal habitats in two sites (barrier reef and coastal reef) with an accuracy of 39.8–56.8%, suitable for survey and monitoring of most sensible habitats in tropical remote islands.
... Practically, a combination of sensor characteristics (e.g., multi-spectral or hyper-spectral resolutions), water column properties (e.g., turbid or clear waters) and substrate types (e.g., clear sand, green, yellow or brown macro-algae; seagrasses, corals) determine the possibilities to obtain accurate estimates (e.g. , Hedley et al., 2012 for all parameters. clearly demonstrated that by going from multispectral to hyperspectral sensing greater depth penetration and improved benthic classification is achieved. ...
... However, whereas broadband multispectral data of medium spatial resolution such as the Landsat series have become popular in landscape mapping, they could mask out specific spectral features of flowering plant species, resulting in very low mapping accuracy. The relatively newly launched higher spectral and/or spatial resolution sensors such as WorldView-2, RapidEye, Spot-6 and Sentinel-2 offer great potential in detecting flowering 55 plants (20)(21)(22). Such sensors are specifically designed to capture spectral properties at additional wavebands such as red-edge and yellow spectrum that mimic over 90% of plant biophysiological information (23)(24)(25). ...
Preprint
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Pollination services and honeybee health in general are important in the African savannahs particularly to farmers who often rely on honeybee products as a supplementary source of income. Therefore, it is imperative to understand the floral cycle, abundance and spatial distribution of melliferous plants in the African savannah landscapes. Furthermore, placement of apiaries in the landscapes could benefit from information on spatiotemporal patterns of flowering plants, by optimising honeybees' foraging behaviours, which could improve apiary productivity. This study sought to assess the suitability of simulated multispectral data for mapping melliferous (flowering) plants in the African savannahs. Bi-temporal AISA Eagle hyperspectral images, resampled to four sensors (i.e. WorldView-2, RapidEye, Spot-6 and Sentinel-2) spatial and spectral resolutions, and a 10-cm ultra-high spatial resolution aerial imagery coinciding with onset and peak flowering periods were used in this study. Ground reference data was collected at the time of imagery capture. The advanced machine learning random forest (RF) classifier was used to map the flowering plants at a landscape scale and a classification accuracy validated using 30% independent test samples. The results showed that 93.33%, 69.43%, 67.52% and 82.18% accuracies could be achieved using WorldView-2, RapidEye, Spot-6 and Sentinel-2 data sets respectively, at the peak flowering period. Our study provides a basis for the development of operational and cost-effective approaches for mapping flowering plants in an African semiarid agroecological landscape. Specifically, such mapping approaches are valuable in providing timely and reliable advisory tools for guiding the implementation of beekeeping systems at a landscape scale.
... An important aspect that needs to be considered in both tuning approaches, empirical and optimization, is the atmospheric influence. Especially for model inversion methods, the correction of atmospheric effects is considered a critical step for obtaining accurate bathymetry data (Goodman et al., 2008;Hedley et al., 2012;Eugenio et al., 2017). At satellite altitude, up to 90% of the sensor-measured signal in blue wavelengths can be due to atmospheric and surface reflectance (Gordon and Morel, 1983). ...
Article
This study presents an assessment of a model inversion approach to derive shallow water bathymetry in optically complex waters, with the aim of both understanding localised capability and contributing to the global evaluation of Sentinel-2 for coastal monitoring. A dataset of 12 Sentinel-2 MSI images in three different study areas along the Irish coast has been analysed. Before the application of the bathymetric model two atmospheric correction procedures were tested: Deep Water Correction (DWC) and Case 2 Regional Coastal Color (C2RCC) processor. DWC clearly outperformed C2RCC in the majority of the satellite images showing more consistent results. Using DWC for atmospheric correction before the application of the bathymetric model, the lowest average RMSE was found in Dublin Bay (RMSE = 1.60, bias = −0.51), following by Mulroy Bay (RMSE = 1.66, bias = 1.30) while Brandon Bay showed the highest average error (RMSE = 2.43, bias = 1.86). However, when the optimal imagery selection was considered, depth estimations with a bias less than 0.1 m and a spread of ±1.40 m were achieved up to 10 m. These results were comparable to those achieved by empirical tuning methods, despite not relying on any in-situ depth data. This conclusion is of particular relevance as model inversion approaches might allow future modifications in crucial parts of the processing chain leading to improved results. Atmospheric correction, the selection of optimal images (e.g. low turbidity), the definition of suitably limited ranges for the per-pixel occurrence of optical constituents (phytoplankton, CDOM, backscatter) and seabed reflectances, in combination with the understanding of the specifics characteristics at each particular site, were critical steps in the derivation of satellite bathymetry.
... The free availability of their data significantly advances the virtual constellation paradigm for mid-resolution land imaging (Roy et al. 2014;Wulder et al. 2015a, b;Zhang et al. 2018). Thanks to the improvement of their spectral, radiometric, and temporal resolutions, they can expand the range of their applications to several natural resources and environmental domains for monitoring, assessing, and investigating (Hedley et al. 2012). The orbits of this satellite constellation are designed to ensure a revisiting interval time, less than 5 days , thereby substantially increasing monitoring capabilities of the Earth's surface and ecosystems (Drusch et al. 2012). ...
Chapter
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The free-available data acquired with multispectral instruments (MSI) onboard Sentinel-2 satellites and Operational Land Imager (OLI) installed on Landsat 8 satellite significantly advances the virtual constellation paradigm for Earth observing and monitoring with medium spatial resolutions ensuring a revisiting interval time less than 5 days. Although these instruments are designed to be similar, they have different spectral response functions and different spectral and spatial resolutions, and, therefore, their data probably cannot be reliably used together. In this chapter, we analyzed exclusively the impact of dissimilarities caused by spectral response functions between these two sensors for high temporal frequency for soil salinity dynamic monitoring in an arid landscape. Knowing that the shortwave infrared (SWIR) spectral bands are the most appropriate for soil salinity discrimination, modeling, and monitoring, only the land surface reflectances in the SWIR spectral bands are considered and converted to the Soil Salinity and Sodicity Index (SSSI) and to the semiempirical predictive model (SEPM) for soil salinity mapping. These three products were compared, and the impact of the sensors’ (OLI and MSI) spectral response function differences was quantified. To achieve these, analysis was performed on two pairs of images acquired in July 2015 and August 2017 with 1-day difference between each other over the same study area, which is characterized by several soil salinity classes (i.e., extreme, very high, high, moderate, low, and nonsaline). These images were not cloudy, without shadow, and not contaminated by cirrus. They were radiometrically and atmospherically corrected, and bi-directional reflectance difference factors (BRDF) were normalized. To generate data for analysis, similarly to Landsat-OLI, Sentinel-MSI images were resampled in 30 m pixel size considering UTM projection and WGS84 datum. The comparisons of the derived products were undertaken using regression analysis (p ≤ 0.05) and root mean square difference (RMSD). In addition to the visual analysis, kappa coefficient was also used to measure the degree of similarity between the derived salinity maps using SEPM. The results obtained demonstrate that the two used pair’s dataset, acquired during 2 different years over a wide range of soil salinity degrees (2.6 ≤ EC-Lab ≤ 600 dS m⁻¹), had very significant fits (R² of 0.99 for the SWIR land surface reflectances and R² ≥ 0.95 for SSSI and SEPM). Moreover, excellent agreement was observed between the two sensor products, yielding RMSD values less than 0.012 (reflectance units) for the SWIR bands and less than 0.006 for SSSI. For the SEPM, the calculated RMSD vary between 0.12 and 2.65 dS m⁻¹, respectively, for nonsaline and extreme salinity classes, reflecting relative errors varying between 0.046 and 0.005 for the considered soil salinity classes. Statistical similarity between the derived salinity maps based on SEPM using kappa coefficient revealed an excellent agreement (0.94). Therefore, MSI and OLI sensors can be used jointly to characterize and to monitor accurately the soil salinity and its dynamic in time and space in arid landscape, provided that rigorous preprocessing issues (sensor calibration, atmospheric corrections, and BRDF normalization) must be addressed before.
... The free-availability of their data significantly advances the virtual constellation paradigm for mid-resolutions land imaging [19]- [21]. Thanks to the improvement of their spectral, radiometric, and temporal resolutions, they can expand the range of their applications to several natural resources and environmental domains for monitoring, assessing, and investigating [22]. The orbits of the both satellites are designed to ensure a revisiting interval time of approximately less than 5 days [23], thereby substantially increasing monitoring capabilities of the Earth's surface and ecosystems [24]. ...
Article
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This paper analyse and compare the difference between the reflectance in the homologous spectral bands of MSI and OLI sensors, VNIR and SWIR, for high temporal frequency monitoring of soil salinity dynamic in an arid landscapes. In addition, their conversion in term of Soil Salinity and Sodicity Index and in term of Semi-Empirical Predictive Model for soil salinity mapping were compared, and their sensor differences were quantified. To achieve these, analyses were performed on simulated data and on two pairs of images acquired over the same area in July 2015 and August 2017 with one day difference between each pair. For simulated data, a field survey was organized and 160 soil samples were collected with various degrees of soil salinity classes. The bidirectional reflectance factor was measured above each soil sample in a Goniometric-Laboratory using an ASD spectroradiometer. These measurements were resampled and convolved in the solar-reflective bands of SMI and OLI using the CAM5S RTC and the relative spectral response profiles characterizing the filters of these instruments. Furthermore, the used pairs of images were not cloudy, or cirrus contaminated, and without shadow effects. They were radiometrically and atmospherically corrected, and the differences related to BRDF were normalized. Therefore, obtained results show that we can conclude that the MSI and OLI sensors can be used jointly to characterize and to monitor accurately the soil salinity and it's dynamic in time and space in arid landscape, provided that rigorous preprocessing issues must be addressed before.
... The free availability of their data significantly advances the virtual constellation paradigm for mid-resolution land imaging (Roy et al. 2014;Wulder et al. 2015a, b;Zhang et al. 2018). Thanks to the improvement of their spectral, radiometric, and temporal resolutions, they can expand the range of their applications to several natural resources and environmental domains for monitoring, assessing, and investigating (Hedley et al. 2012). The orbits of this satellite constellation are designed to ensure a revisiting interval time, less than 5 days (Li and Roy 2017), thereby substantially increasing monitoring capabilities of the Earth's surface and ecosystems (Drusch et al. 2012). ...
Chapter
Soil salinity significantly affects agricultural production and environmental quality. A salinity indicator is that symptom which suggests the impacts of soil salinity. The visual characters serve as diagnostic criteria in identifying the salt-affected soils. The physical indicators of salt-affected soils include flocculation, dispersion of clays and surface salt crusts; and conventional chemical indicators of soil salinity include electrical conductivity (EC), pH, total dissolved solids (TDS), exchangeable sodium percentage (ESP), electrochemical stability index (ESI) and sodium adsorption ratio (SAR). Plant species that serve as indicator species can be commonly used in combination with physical and chemical indicators to determine soil salinity. The relationship between soil electrical conductivity and sodium adsorption ratio serves as an important baseline, with modifications such as soil texture, clay type, leaching fraction and rainfall, for a better site-specific understanding of how plants will be affected by salts and, in particular, sodium. Many plant species, found only grown in highly saline soil (true halophytes) or in tidal zone (mangroves), are the indicators of salinity. So is true with many microbes found in mud flats associated with high salinity in mangal formations. The variation of environmental conditions may influence the behaviours of bioindicators including plants and microbes. Halophyte plant species and halotolerant or halophilic bacteria also serve as viable indicators of salinity as they are adaptive to stress through different mechanisms. This chapter discusses various physical, chemical and biological indicators of soil salinity as well as sodicity, their measurements and impacts.
... These higher resolution public good sensors have still been extensively used for coastal and shallow water applications, and shown to have capabilities including monitoring water constituents (Braga et al., 2016;Pahlevan et al., 2019), mapping benthic habitats and deriving bathymetry (Hedley et al., 2012) and mapping coastlines and intertidal regions (Murray et al., 2012;Sagar et al., 2017). In the following sections we introduce a selection of these applications and highlight some of the particular issues that must be considered when dealing with complex coastal and shallow waters. ...
Technical Report
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Report to the National Environmental Science Program, Marine Biodiversity Hub - Project D2 - Standard operating procedures for survey design, condition assessment and trend detection
... In Jay et al. (2018), the semi-analytical model developed by Lee et al. (1998) is used to express the water remote-sensing reflectance as a function of water clarity parameters (for optically deep waters), depth, and bottom brightness (for optically shallow waters). Similarly, as in Hedley et al. (2012) and Jay et al. (2017), the deviations between measured and modeled reflectances are described using an additive noise term, which is assumed to be Gaussiandistributed with zero mean and spectral covariance matrix Γ s describing the environmental noise (Brando and Dekker 2003). Γ s is usually estimated over optically deep water, and includes all the sources of above-water reflectance variability that are not accounted for by the physical model (e.g., sensor noise and sea surface roughness). ...
Technical Report
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This report on "Uncertainties in Ocean Colour Remote Sensing" summarizes the state of the knowledge on uncertainties related to ocean colour (OC) products and identifies ideas and recommendations to achieve significant progress on how uncertainties are quantified and distributed. The report starts with a presentation of terminology and concepts (Chapter 2). For a proper use of OC data, it is necessary to be aware of the potential problems and limitations associated with OC remote sensing products, and to identify the sources contributing to their uncertainties, from top-of-atmosphere (TOA) data to gridded products. This report makes a review of these factors (Chapter 3). Even though up to now very few OC products have been distributed with uncertainty estimates, a number of approaches to quantify OC product uncertainties have been proposed in recent years; providing a review of these methods and discussing their respective advantages appear particularly timely (Chapter 4). It is also necessary to discuss how information on uncertainty could be conveyed to user communities (Chapter 5) and to describe example requirements from these communities (Chapter 6). General recommendations are provided in the final chapter (Chapter 7).
... Therefore, it can be freely obtained and likely used in various applications (ESA 2015). Due to the limitations of high-resolution satellites data (WorldView-2, IKONOS, SPOT, such as high cost, low temporal resolution, and small swath size) and Landsat 8 imagery (medium spatial and temporal resolution), the potential of Sentinel-2 can be used for shallow-water habitat study (Hedley et al 2012;Fauzan et al 2017;Immordino et al 2019). However, mapping in Nusa Lembongan has not been performed using data from Sentinel-2. ...
Article
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The mapping of shallow-water benthic habitats using remote sensing techniques is critical for understanding responses to spatial and temporal environmental changes. However, the ongoing challenges in remote sensing are distribution assessments on the small island with high complexity ecosystem composition. The recently released and freely accessible Sentinel-2B (S2B) imagery presents a new opportunity for these challenges. Therefore, this study aimed to examine the ability of the S2B sensor in mapping shallow-water using two methods. These include a true color composite and images with water column correction using the Lyzenga algorithmwith or without water column correction. The results from S2B were compared with the maps from Landsat 8 data, and the error matrix was applied to test classification accuracy. The results show that the output maps had average overall accuracies of 65% for benthic coverage maps. In conclusion, S2B and Landsat 8 were good enough for mapping shallow-water benthic habitats, but water column correction on both sensors increased the accuracy.
... In [16], the authors demonstrated through an Earth observation image inversion experiment over coral reefs that the pixel-to-pixel variability in the water column optical properties was derived to be sufficiently significant when compared to assuming homo-geneity over a 5 * 5 25-pixel area. In [22], a modeling study was undertaken for Earth observation-based coral reef assessments. In their model, the non-pure water fraction of the absorption coefficient at 440 nm ranged from 0 to 0.3 m −1 , and the particulate backscattering coefficient, b bp (440), ranged from 0 to 0.006 m −1 . ...
Article
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The bio-optical properties of coral reef waters were examined across coral reef ecosystems not influenced by land-derived run-off, in the Great Barrier Reef lagoon (Heron Island) and the Coral Sea (the Coringa-Herald and Lihou Reefs). The aim was to determine whether the absorption properties, the concentration-specific absorption properties, and the phytoplankton and non-algal pigmented particle (NAP) absorption concentrations varied from the ocean waters flushing onto the reef at high tide to those waters on the reef or flushing off the reef at low tide. The optical and biogeochemical properties of on-reef waters systematically differed from the surrounding ocean waters. The chl a concentration values varied up to 7-fold and the NAP concentrations up to 29-fold; for the reef samples, the chl a values were on average 2 to 3 times lower than for the oceans whilst the NAP values were slightly higher on the reefs. The spectral absorption values of the chl a, NAP, and colored dissolved organic matter (CDOM) varied up to 6-fold for reef waters and up to 15-fold for ocean waters. The spectral absorption for chl a was up to 3-fold lower on the reef waters, the absorption by the CDOM was up to 2-fold higher and the NAP absorption was 1.6-fold higher on the reef waters. The concentration-specific absorption coefficients for chl a and NAP varied up to 9-fold in reef waters and up to 30-fold in ocean waters. In the case of Heron Island and Coringa-Herald cays, this concentration-specific absorption was on average 1.3 to 1.7-fold higher for chl a and up to 2-fold lower for NAP on the reefs. The Lihou Reef measurements were more ambiguous between the reef waters and ocean waters due to the complex nature and size of this reef. Based on our results, the assumption that the optical properties of on-reef waters and the adjacent ocean waters are the same was shown to be invalid. Ocean waters flowing on to the reef are higher in phytoplankton, whilst waters on the reef or flowing off the reefs are higher in CDOM and NAP. We found differences in the pico,- nano-, and microplankton distributions as well as in the ratios of photosynthetic to photoprotective pigments. The variability in the bio-optical properties between the reef waters and adjacent ocean waters has implications for the estimations of sunlight absorption along the water column, the UV penetration depth, the temperature distributions, and the nutrient and carbon fluxes in coral reef ecosystems. As Earth observation algorithms require proper parameterization for the water column effects when estimating benthic cover, the actual optical properties need to be used. These results will improve the use of Earth observation to systematically map the differences in the water quality between reefs and the adjacent ocean.
... Considering our constraints, we aimed for deriving Satellite-Derived Bathymetry (SDB) using preferentially the free wide-swath Sentinel-2 multispectral images. With a five-day revisit time, a 10 m medium-scale native resolution and its suitable spectral resolution, the European Space Agency sensor have high potential for marine and coastal applications [30][31][32][33][34], and its use for bathymetry retrievals is increasing [21][22][23][24]. ...
Article
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To achieve high accuracy bathymetry retrieval using remote sensing images with robust performance in a 0 to 25 m-deep lagoon with sharp bottom depth variations, a new Iterative Multiple Band Ratio (IMBR) algorithm is tested against known Multiple Band Ratio (MBR) and Single Band Ratio (SBR) algorithms. The test was conducted using the five multispectral bands, at 10 to 60 m resolution, of a Sentinel-2 image of the 25 km2 Poe lagoon, a UNESCO World Heritage Area. The IMBR approach requires training datasets for the definitions of depth threshold at which optimal band ratios vary. IMBR achieved accuracy, quantified with an original block cross-validation procedure across the entire depth range reached a mean absolute error of 46.0 cm. It compares very favorably against MBR (78.3 cm) and the various SBR results (188–254 cm). The method is suitable for generalization to other sites pending a minimal ground-truth dataset crossing all the depth range being available. We stress that different users may need different precisions and can use MBR or SBR algorithms for their applications. For the hydrodynamic modelling applications that are developing in New Caledonia, the IMBR solutions applied to Sentinel imagery are optimal.
... Images with low incidence of clouds and high rate of return are required to detect bleaching. Previous research points to the potential of Sentinel-2 images for bleaching detection (Hedley et al., 2012(Hedley et al., , 2018Collin et al., 2016;Wouthuyzen et al., 2019). ...
Article
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Thermal stress is now considered the major recent cause of coral reef degradation; yet few studies have been conducted describing those effects on Southwestern Atlantic (SWA) reefs. The SWA represents a coral endemism hotspot with low-functional redundancy and therefore high extinction risk. Recent research has suggested a “thermal refuge” potential for SWA; however, evidence could suggest a different trend. We report herein an unprecedented coral mortality on the largest coastal Brazilian Marine Protected Area (MPA) following the worst thermal stress event since 1985. Degree Heating Week (DHW) values over 4.0 were observed for 107 days, averaging 8.70 for the period, with a maximum of 12.1. Average live coral cover was reduced by 18.1% while average turf algae cover increase by 19.3%. Mortality was highest for three coral species, with a mean mortality of 50.8% per transect for Millepora braziliensis, 32.6% for Mussismilia harttii and 16.6% for Millepora alcicornis. Our unique data for SWA indicates that the populations of two Brazilian endemic species (Millepora braziliensis and Mussismilia harttii) are under severe threat from global warming and that overall coral cover has been dramatically reduced. Hence, the idea of a possible “thermal” refugia within the SWA must be taken with caution for this coral endemism hotspot.
... These methods vary in their spatial extent and accuracy of bleaching detection. Although satellite imagery may revolutionize broad-scale monitoring, especially for remote reefs (Xu et al., 2020), such data have several limitations, including being informative only for the shallowest parts of the reef and providing little, if any, taxonomic resolution (Hedley et al., 2012). Therefore, there is a need to integrate coral-bleaching datasets at multiple biological, spatial, and temporal scales ( Figure 2) and match those data with appropriate environmental predictors to make survey results scalable across large geographic regions. ...
Article
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The global impacts of climate change are evident in every marine ecosystem. On coral reefs, mass coral bleaching and mortality have emerged as ubiquitous responses to ocean warming, yet one of the greatest challenges of this epiphenomenon is linking information across scientific disciplines and spatial and temporal scales. Here we review some of the seminal and recent coral-bleaching discoveries from an ecological, physiological, and molecular perspective. We also evaluate which data and processes can improve predictive models and provide a conceptual framework that integrates measurements across biological scales. Taking an integrative approach across biological and spatial scales, using for example hierarchical models to estimate major coral-reef processes, will not only rapidly advance coral-reef science but will also provide necessary information to guide decision-making and conservation efforts. To conserve reefs, we encourage implementing mesoscale sanctuaries (thousands of km2 ) that transcend national boundaries. Such networks of protected reefs will provide reef connectivity, through larval dispersal that transverse thermal environments, and genotypic repositories that may become essential units of selection for environmentally diverse locations. Together, multinational networks may be the best chance corals have to persist through climate change, while humanity struggles to reduce emissions of greenhouse gases to net zero.
... Ayrıca, jeolojik çalışmaların dışından Sentinel-2A uydu verileri, çok çeşitli uygulama alanlarını kapsamaktadır [5]. Bu çalışma alanları; yaprak alan indeksi [6], [7]; bitkilerdeki klorofil ve azot oranı [8]; su kalitesi belirleme [9]; göl kıyısındaki habitatların belirlenmesi [10]; bitki ve ağaç türlerinin sınıflandırılması [11]; su kalitesinin değerlendirilmesi [12] ve mercan resiflerinin haritalanması [13] şeklinde özetlenebilir [14]. ...
... The processed spectral signatures were extracted from a Sentinel-2 multispectral image. Such high-resolution sensors were already used to map water quality [19], turbidity [20] or bathymetry and coral reefs in tropical environments similar to the one investigated in this study [21,22]. In the present study, the processing steps used to cluster the seabed spectral reflectance at the investigated area are described and the strengths and shortcomings of the approach are discussed. ...
Article
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Monitoring chlorophyll-a concentration or turbidity is crucial for understanding and managing oligo- to mesotrophic coastal waters quality. However, mapping bio-optical components from space in such shallow settings remains challenging because of the strong interference of the complex bathymetry and various seabed colors. Correcting the total satellite reflectance signal from the seabed reflectance in ocean color with high resolution sensors is promising. This article shows how unsupervised clustering approaches can be applied to Sentinel-2 images to classify seabed colors in shallow waters of a tropical oligotrophic lagoon in New Caledonia. Data processing included Lyzenga correction for estimating the water column reflectance, optical spectra standardization for attenuating water absorption effects and clustering using the unsupervised k-means method. This methodological approach was applied on the 497, 560, 664 and 704 nm optical bands of the selected Sentinel-2 image. When applied on non-standardized data, our unsupervised classification retrieved three seafloor clusters, whereas five seafloor clusters could be retrieved using standardized data. For each of these two trials, the computed membership values explained more than 75% of the inertia in each Sentinel-2 wavelength band used for the clustering. However, the accuracy of the method was slightly improved when applied on standardized data. Confusion index mapping of the unsupervised clustering retrieved from these data emphasized the relevance and robustness of our methodological approach. Such an approach for seabed colors classification in optically complex shallow settings will be particularly helpful to improve remote sensing of biogeochemical indicators such as chlorophyll-a concentration and turbidity in fragile coastal environments.
... The latest generation of mid-resolution multispectral sensors, with free image availability, such as the Landsat-8 (L8) and Sentinel-2 (S2) satellites, offer advanced opportunities for synoptic view of the entire area of interest, fine-scale, and high-frequency monitoring [28]. These satellites were not specifically designed for water observation but are promising for a detailed analysis of water quality [15,29,30], thanks to their fine radiometric sensitivity [31,32]; 10 to 30-m spatial resolution; high revisit frequency (every 2-3 days combining L8 and S2 satellites) and the improved configuration of the spectral bands in the visible and near infrared range [33]. ...
Article
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In this paper, the authors use remote-sensing images to monitor the water quality of reservoirs located in the semiarid region of Northeast Brazil. Sentinel-2 MSI TOA Level 1C reflectance images were used to remotely estimate the concentration of chlorophyll-a (chl-a), the main indicator of the trophic state of aquatic environments, in five reservoirs in the state of Ceará, Brazil. A three-spectral band retrieval model was calibrated using 171 water samples, collected from November 2015 through July 2018 in 5 reservoirs. For validation, 71 additional samples, collected from August 2018 through December 2019, were used to ensure a robust accuracy assessment. The TOA Level 1C products performed very well, achieving a relative RMSE of 28% and r2 = 0.80. Data on wind direction and speed, solar radiation and reservoir volume were used to generate a conceptual model to analyze the behavior of chl-a in the surface waters of the Castanhão reservoir. During 2019, the reservoir water quality showed strong variation, with concentration fluctuating from 30 to 95 µg/L We showed that the end of the dry season is marked by strong eutrophic conditions corresponding to very low water inflows into the reservoir. During the rainy season there is a large decrease in the chl-a concentration following the increase of the lake water storage. During the following dry season, satellite data show a progressive improvement of the trophic state controlled by wind intensity that promotes a better mixing of the reservoir waters and inhibiting the development of most phytoplankton.
... However, those satellites images had limited by temporal and spatial resolutions. The recently launch Sentinel 2 instrument provide an m and 20 m in wavelength range 490-860 nm [24]. These improved satellite parameters open the door for more investigations of Sentinel-2 potential for estimating inland water quality where few previous studies have used it to developed same works in the lakes [25][26][27][28]. ...
Conference Paper
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The purpose of this article is to investigate the possibility of employing Multispectral water. CDOM can be defined as an essential indicator of water quality as it has a key role in biogeochemical processing. This study held in three important Jordanian dams. Mujib, Wadi Al Arab, and lastly King Talal Dam. From these three dams, sixty water samples were taken fairly. The prediction models were validated using the remaining samples after the models were calibrated using forty samples. The predictive models were tested using the remaining samples .The results show that the sentinel 2 blue to green band (B2/B3) has provided the best CDOM retrieval algorithm with an accuracy coefficient of determination R 2 = 0.8155, root-mean-squared error RMSE= 1.754 m-1 .The study showed that Sentinel 2 data might be used to determine CDOM in a variety of inland water body quality ranges, meaning that remote sensing can be a valuable tool for inland water quality monitoring. This study contains empirical data that can be utilized as a starting point for further research that includes more sites and circumstances.
... The bleaching response (increased reflectivity) was strongest in early September and significantly decreased in October, consistent with the analysis of the difference images. Given previous analysis of the Sentinel-2 endmember reflectance spectra [29], the reflectance may decrease obviously when its state changes. The reflectance results on 4 October obviously decreased, and the median reflectivity on 13 November was significantly lower than before, suggesting that there was a high probability of coral recovery, algal cover increase, and even coral death after the coral bleaching event. ...
Article
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In recent years, coral reef ecosystems have been affected by global climate change and human factors, resulting in frequent coral bleaching events. A severe coral bleaching event occurred in the northwest of Hainan Island, South China Sea, in 2020. In this study, we used the CoralTemp sea surface temperature (SST) and Sentinel-2B imagery to detect the coral bleaching event. From 31 May to 3 October, the average SST of the study area was 31.01 °C, which is higher than the local bleaching warning threshold value of 30.33 °C. In the difference images of 26 July and 4 September, a wide range of coral bleaching was found. According to the temporal variation in single band reflectance, the development process of bleaching is consistent with the changes in coral bleaching thermal alerts. The results show that the thermal stress level is an effective parameter for early warning of large-scale coral bleaching. High-resolution difference images can be used to detect the extent of coral bleaching. The combination of the two methods can provide better support for coral protection and research.
... Several research studies showed the capability of Sentinel-2A data for estimating biophysical and biochemical parameters such as leaf area index (Richter et al., 2009Atzberger and Richter, 2012), chlorophyll and nitrogen biophysical parameters (Verrelst et al., 2012;Frampton et al., 2013), the red edge position (Clevers and Gitelson, 2013;D'Odorico et al., 2013;Delegido et al., 2013), and mapping of coral reefs (Hedley et al., 2012) and water quality (Salama et al., 2014). However, studies using Sentinel-2A data for geological applications of the arid region are fewer. ...
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This study demonstrates the use of ASTER data for the mapping of gypsum deposits and associated geological formations that occurred in the Thumrait region of southern Oman. The measurement of spectra over samples of gyp-sum in the 1,300-2,500 nm wavelength using a PIMA spectrometer showed the presence of distinct absorptions at 1400-1600, 1750, 1940, 2,100, and 2,400 nm characteristics to O H stretching, H 2 O combinations, and S O bending overtones and stretching, respectively. Studying the unique spectral absorption characters of gypsum samples, we developed a false color composite (FCC) and an image by decorrelation stretch using the spectral bands 7, 3, and 2 of ASTER. The results FCC showed the regions of gypsum occurrences , and the decorrelated image discriminated the gypsum occurrences from other geological formations of the area. The study of surface mineralogy of the region using the VNIR-SWIR bands by the spectral angle mapper method showed the presence of sulfate, carbonate, and clay minerals of the geological formations in the study area. We compared the results of ASTER with the results obtained using spectral bands 12, 8, and 4 of Sentinel-2A processed by the same methods. The study showed that the spectral bands of ASTER can be used for mapping the gypsum and associated geological formations. K E Y W O R D S Sentinel-2A, gypsum, mapping, spectral absorption, image processing, Sultanate of Oman
... The Sentinel-2 satellite is a generational satellite designed by the European Space Agency (ESA) for earth observation with open-source data that are freely accessible. Sentinel 2 does have better ability for discrimination of reef benthic composition over SPOT-4 and Landsat ETM+ (Hedley et al. 2012). Hedley et al. (2018) confirmed that Sentinel-2 has excellent performance to meet several scientific and monitoring objectives for coral reefs. ...
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Kartikasari A, Pristianto T, Hanintyo R, Ampou EE, Wibawa TA, Borneo BB. 2021. Representative benthic habitat mapping on Lovina coral reefs in Northern Bali, Indonesia. Biodiversitas 22: 4766-4774. Satellite optical imagery datasets integrated with in situ measurements are widely used to derive the spatial distribution of various benthic habitats in coral reef ecosystems. In this study, an approach to estimate spatial coverage of those habitats based on observation derived from Sentinel-2 optical imagery and a field survey, is presented. This study focused on the Lovina coral reef ecosystem of Northern Bali, Indonesia to support deployment of artificial reefs within the Indonesian Coral Reef Garden (ICRG) programme. Three specific locations were explored: Temukus, Tukad Mungga, and Baktiseraga waters. Spatial benthic habitat coverages of these three waters was estimated based on supervised classification techniques using 10m bands of Sentinel-2 imagery and the medium scale approach (MSA) transect method of in situ measurement.The study indicates that total coverage of benthic habitat is 61.34 ha, 25.17 ha, and 27.88 ha for Temukus, Tukad Mungga, and Baktiseraga waters, respectively. The dominant benthic habitat of those three waters consists of sand, seagrass, coral, rubble, reef slope and intertidal zone. The coral reef coverage is 29.48 ha (48%) for Temukus covered by genus Acropora, Isopora, Porites, Montipora, Pocillopora. The coverage for Tukad Mungga is 8.69 ha (35%) covered by genus Acropora, Montipora, Favia, Psammocora, Porites, and the coverage for Baktiseraga is 11.37 ha (41%) covered by genus Montipora sp,
... The T. testudinum leaf reflectance and transmittance ( Figure 2F) were fixed as used in Hedley et al. (2016) but initial results using a sand reflectance spectrum previously used in bathymetry work (Hedley et al., 2018, and the ICESat-2 depth data. It was determined that the sand reflectance was too bright when used in the relatively simple LAI bottom reflectance model: in comparison, the previous bathymetry work used a multi-endmember linear mixing model (spectra in Hedley et al., 2012). Iteration on a few options indicated that a set of three sand reflectances being 40-70% scaled versions of the original reflectance ( Figure 2F) produced better results. ...
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... The mathematical description of the noise makes it possible to implement a statistical model, supporting the propagation of the measurement uncertainty up to the estimated parameters. Previous studies [2], [7] assumed an unbiased measurement with a Gaussian distribution on the remote-sensing reflectance products. Likewise, we model the measured subsurface reflectance as a vector y = [y(λ 1 ), y(λ 1 ), ...., y(λ N )] that follows a multivariate Gaussian distribution : ...
Conference Paper
We consider the study of performing likelihood-based inference in Lee's radiative transfer semi-analytical model. This model is widely used for the inversion of bathymetric products in coastal areas. We perform assessment of uncertainty on retrieved bathymetry in terms of hypothesis testing. Uncertainty results are compared to Monte Carlo simulations showing that inference based on the likelihood ratio statistic is much more reliable than the Wald statistic. Our results suggest that the Wald statistic based on Fisher observed information outperforms the Cramer-Rao lower bound in evaluating the estimation uncertainty. Confidence intervals derived from likelihood ratio statistic reveal the asymmetry of the maximum likelihood uncertainty arguing that variance-based uncertainties may be irrelevant for inferring uncertainty on bathymetric products.
... The gradient of the regression line is the quest of significance for given band i (bi). According to Hedley et al. [65,66], the algorithm was performed as follows: ...
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Rabigh is a thriving coastal city located at the eastern bank of the Red Sea, Saudi Arabia. The city has suffered from shoreline destruction because of the invasive tidal action powered principally by the wind speed and direction over shallow waters. This study was carried out to calibrate the water column depth in the vicinity of Rabigh. Optical and microwave remote sensing data from the European Space Agency were collected over 2 years (2017–2018) along with the analog daily monitoring of tidal data collected from the marine station of Rabigh. Depth invariant index (DII) was implemented utilizing the optical data, while the Wind Field Estimation algorithm was implemented utilizing the microwave data. The findings of the current research emphasis on the oscillation behavior of the depth invariant mean values and the mean astronomical tides resulted in R 2 of 0.75 and 0.79, respectively. Robust linear regression was established between the astronomical tide and the mean values of the normalized DII (R 2 = 0.81). The findings also indicated that January had the strongest wind speed solidly correlated with the depth invariant values (R 2 = 0.92). Therefore, decision-makers can depend on remote sensing data as an efficient tool to monitor natural phenomena and also to regulate human activities in fragile ecosystems.
... Over the recent years, these ecosystems have been seriously threatened by various anthropogenic activities, ocean acidification, climate change, hydrographic changes, coastal development and tourism, and unsustainable finishing practices (Hoegh-Guldberg et al., 2007;Raitsos et al., 2017;Wilkinson, 1999). Recent studies have reported that many of these factors have caused a decline in the reef species diversity and an increase in the reef bleaching events (Hedley et al., 2012;Kemp et al., 2014;Prouty et al., 2017;Shuail et al., 2016;Silverstein et al., 2015). Numerous long-term and routine field monitoring programs have been established at the national and international levels to mitigate the increasing degradation of coral reef ecosystems and to maximize their biodiversity conservation and management (Tissot and Brosnan, 2000); for example, NOAA's National Coral Reef Monitoring Program (Levine et al., 2016), West Hawaii Aquarium Project (WHAP) (Tissot et al., 2009), International Coral Reef Initiative (ICRI) (Dight and Scherl, 1997;McManus, 1997;WOOD, 2018), and Great Barrier Reef Marine Monitoring Program (Kroon et al., 2016). ...
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Effective monitoring of coral reefs remains a major goal in marine ecology and conservation as these communities representing the most productive ecosystems and hosting approximately one third of all known marine species have been seriously threatened by various anthropogenic activities and climate change events. Space-borne reflectance data are commonly used to monitor coral reefs and improve their management practices, yet an effective use of such data remains a challenge due to a high level of spatial heterogeneity of coral reef communities and surrounding water environments. Thus, it is crucial to understand the optical properties coral reef environments and their influence on the spectral remote sensing reflectance. This study presents an optical model based on field and laboratory data that relates the inherent optical properties (IOPs, such as absorption and backscattering) of the water constituents (phytoplankton, suspended sediments, colored dissolved organic matter, water itself) and coral components (symbiotic + coralline algae) to remote sensing reflectance via the bidirectional quantity (f/Q) of the upwelling light field. The absorption and backscattering coefficients are parameterized as a function of the individual contributions of coral component (symbiotic and coralline algae) and water constituents and the bidirectionality factor f/Q is derived as a function of solar-sensor zenith angles and IOPs. The wider applicability of these parameterizations is tested in diverse water types (with chlorophyll-a concentration 0.07–1239 mg m⁻³, suspended sediment concentration 0.1–104 g m⁻³, absorption coefficient of colored dissolved organic matter 0.02 to 8.8 m⁻¹ at 412 nm). Modelled reflectance spectra were compared with in-situ data and Hydrolight radiative transfer simulations. For various coral reef species and water types, the modelled spectral reflectance and associated features across a spectral range of 400–750 nm showed good agreement with measured and simulated data within the error of 17%. The effect of strong attenuation of light by water which potentially affects and alters the spectral shapes and magnitudes of reflectance is further examined and discussed. This work will have significant implications for optical remote sensing of the coral reef ecosystems in diverse oceanic environments.
... This data streams are feeding an incredible and still growing number of applications like land cover mapping, agriculture, ocean, costal zones, change detection, before/after disaster, forests, lakes, snow, climate, etc... e.g. [12], [15], [14]. The open and free data policy fostered this wide use of data and triggered the development of new and innovative applications, allowing new players to enter the Earth Observation (EO) market. ...
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This paper assesses the reflectance difference values between the homologous visible and near-infrared (VNIR) spectral bands of Sentinel-MSI-2A/2B and Landsat-OLI-8/9 sensors for seagrass, algae, and mixed species discrimination and monitoring in a shallow marine environment southeastern of Bahrain in the Arabian Gulf. To achieve these, a field survey was conducted to collect samples of seawater, underwater sediments, seagrass (Halodule uninebell.netrvis and Halophila stipulacea) and algae (green and brown). As well, an experimental mode was established in a Goniometric-Laboratory to simulate the marine environment, and spectral measurements were performed using an ASD spectroradiometer over each separate and different case of seagrass and algae mixed species at different coverage rate (0, 10, 30, 75, and 100 %) considering the bottom sediments with clear and dark colors. All measured spectra were analyzed and transformed using continuum-removed reflectance spectral (CRRS) approach to assess spectral separability among separate or mixed species at varying coverage rates. Afterward, the spectra were resampled and convolved in the solar-reflective spectral bands of MSI and OLI sensors and converted into water vegetation indices (WVI) to investigate the potential of red, green, and blue bands for seagrass and algae species discrimination. For comparison and sensor differences quantification, statistical fits (p
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Knowledge about the optical features of benthic objects is essential for quantifying spectral signatures, remote sensing-based mapping, and ecological monitoring in coral reefs. However, the spectral identification of benthic species and the accurate measurement of the in situ reflectance spectra of relevant research objects remain underexplored. An underwater radiation measuring system suitable for coral reef environments was specifically designed to obtain in situ reflectance spectra and match benthic photographs of various substrate targets. This instrument has the advantages of obtaining hyperspectral, dual-channel simultaneous measurements, and automatically adjusting the integration time according to the light intensity. Based on in situ hyperspectral datasets, the linear discriminant analysis (LDA) was used for exploring and discriminating spectral characteristics from three taxonomic ranks, which include typical substrates of six community groups, nine coral families, and six Acroporidae species. In situ full-resolution (1-nm) spectra provided the best discrimination ability with mean accuracies of 97.5%, 90.9%, and 91.6% for typical substrates, coral families, and coral species, respectively. The spectral abilities of remote sensors were assessed by applying the spectral response functions of three multispectral sensors (Landsat 8 OLI, Sentinel-2A, and World View-2) to the full-resolution spectra. Discrimination analyses of the simulated spectra demonstrated that the spectral separations of typical substrates might be apparent, with overall classification accuracies of 89.6%, 88.2%, and 90.4% for the Landsat 8 OLI, Sentinel-2A, and World View-2 sensors, respectively. The spectral separation for different corals, however, may not be effective when using multispectral sensors. The discrimination analyses of families and species produced overall classification accuracies of 67.1% and 69.6%, respectively, for the Landsat 8 OLI, 56.0% and 56.0% for the Sentinel-2A sensor, and 64.5% and 61.8% for the World View-2 sensor. In summary, this method has the potential for identifing substrate targets in communities and taxonomic coral groups by applying in situ hyperspectral datasets. Furthermore, multispectral satellite sensors are currently inadequate for spectrally separate corals, while spectral discrimination is possible and practical for different substrate targets with visual spectral differences.
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Sustainable and ecosystem-based marine spatial planning is a priority of Pacific Island countries basing their economy on marine resources. The urgency of management coral reef systems and associated coastal environments, threatened by the e_ects of climate change, require a detailed habitat mapping of the present status and a future monitoring of changes over time. Here, we present a remote sensing study using free available Sentinel-2 imagery for mapping at large scale the most sensible and high value habitats (corals, seagrasses, mangroves) of Palau Republic (Micronesia, Pacific Ocean), carried out without any sea truth validation. Remote sensing ‘supervised’ and ‘unsupervised’ classification methods applied to 2017 Sentinel-2 imagery with 10 m resolution together with comparisons with free ancillary data on web platform and available scientific literature were used to map mangrove, coral, and seagrass communities in the Palau Archipelago. This paper addresses the challenge of multispectral benthic mapping estimation using commercial software for preprocessing steps (ERDAS ATCOR) and for benthic classification (ENVI) on the base of satellite image analysis. The accuracy of the methods was tested comparing results with reference NOAA (National Oceanic and Atmospheric Administration, Silver Spring, MD, USA) habitat maps achieved through Ikonos and Quickbird imagery interpretation and sea-truth validations. Results showed how the proposed approach allowed an overall good classification of marine habitats, namely a good concordance of mangroves cover around Palau Archipelago with previous literature and a good identification of coastal habitats in two sites (barrier reef and coastal reef) with an accuracy of 39.8–56.8%, suitable for survey and monitoring of most sensible habitats in tropical remote islands.
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The availability of frequently updated maps has always been the ultimate goal of remote sensing in benthic habitat data acquisition. Although the techniques offer cost and time effectiveness, the lack of consistency of inputted images can cause issues in successfully performing accurate multi-temporal mapping with minimum effort. This research aimed to assess the consistency of Sentinel-2 images for benthic habitat mapping in the waters of Labuan Bajo, East Nusa Tenggara, Indonesia. We employed two approaches: 1) assessing the consistency of Sentinel-2 image used to map four classes of benthic habitat from 2017 to 2019 and 2) an in-depth analysis of three images in May 2019 to assess the consistency of four-class benthic habitat mapping (coral reef, seagrass, macroalgae and bare substratum). This research incorporated atmospheric, sunglint and water column corrections and four classification algorithms: Isodata, Random Forest, Support Vector Machine, and Maximum Likelihood. The consistency of Sentinel-2 images was assessed using overall accuracy (OA), agreement percentage (AP) and total classification performance (TCP). Our results indicate that Sentinel-2 images are reliable enough for accurate benthic habitat mapping with OA >80% and consistent with agreement >80% in both approaches given the right image conditions, which are minimum cloud cover, haze, and sunglint. These factors strongly contribute to the consistency of the resulting benthic habitat classification from the Sentinel-2 images. Also, to maximize the rich image availability in the Sentinel-2 archive database, we suggest selecting images with minimal atmospheric and sunglint disturbances instead of performing image corrections that may introduce noise, leading to lower accuracy and consistency. Finally, we encourage the use of Sentinel-2 images in benthic habitat monitoring since the long-term plan of the Sentinel-2 mission guarantees the availability of these datasets.
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This dissertation is an evaluation of the generalization characteristics of machine learning classifiers as applied to the detection of coral reefs using remote sensing images. Three scientific studies have been conducted as part of this research: 1) Evaluation of Spatial Generalization Characteristics of a Robust Classifier as Applied to Coral Reef Habitats in Remote Islands of the Pacific Ocean 2) Coral Reef Change Detection in Remote Pacific Islands using Support Vector Machine Classifiers 3) A Generalized Machine Learning Classifier for Spatiotemporal Analysis of Coral Reefs in the Red Sea. The aim of this dissertation is to propose and evaluate a methodology for developing a robust machine learning classifier that can effectively be deployed to accurately detect coral reefs at scale. The hypothesis is that Landsat data can be used to train a classifier to detect coral reefs in remote sensing imagery and that this classifier can be trained to generalize across multiple sites. Another objective is to identify how well different classifiers perform under the generalized conditions and how unique the spectral signature of coral is as environmental conditions vary across observation sites. A methodology for validating the generalization performance of a classifier to unseen locations is proposed and implemented (Controlled Parameter Cross-Validation,). Analysis is performed using satellite imagery from nine different locations with known coral reefs (six Pacific Ocean sites and three Red Sea sites). Ground truth observations for four of the Pacific Ocean sites and two of the Red Sea sites were used to validate the proposed methodology. Within the Pacific Ocean sites, the consolidated classifier (trained on data from all sites) yielded an accuracy of 75.5% (0.778 AUC). Within the Red Sea sites, the consolidated classifier yielded an accuracy of 71.0% (0.7754 AUC). Finally, long-term change detection analysis is conducted for each of the sites evaluated. In total, over 16,700 km2 was analyzed for benthic cover type and cover change detection analysis. Within the Pacific Ocean sites, decreases in coral cover ranged from 25.3% reduction (Kingman Reef) to 42.7% reduction (Kiritimati Island). Within the Red Sea sites, decrease in coral cover ranged from 3.4% (Umluj) to 13.6% (Al Wajh).
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T he ESA Sentinels will be the first series of loperational satellites to meet the Earth observation needs of the European Union-ESA Global Monitoring for Environment and Security (GMES) programme. The pair of Sentinel-2 satellites will routinely provide high-resolution (10–20 m) optical images globally with frequent revisits tailored to the needs of GMES land and emergency services. Sentinel-2 aims at ensuring continuity of Spot-and Landsat-type data, with improvements to allow service evolution. The first launch is expected in 2012. What Users Need The pair of Sentinel-2 satellites will routinely generate valuable information for the European Union (EU) and its Member States as part of the Global Monitoring for Environment and Security (GMES) programme, in the areas of global climate change (Kyoto Protocol and ensuing regulations), sustainable development, European environmental policies (such as spatial planning for the Soil Thematic Strategy, Natura 2000, and the Water Framework Directive), risk management, the
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Remote sensing can monitor coral reef health, provided the benthic substrates are spectrally resolvable through the water column and surface. We studied the separability of eight substrate types (live coral, dead coral, soft coral, sand, brown algae, green algae, red algae, cyanobacteria) and the influence of the overlying water. A spectral library of coral reef benthic communities was collected from the Great Barrier Reef. Hydrolight 4.1 was used to simulate remote sensing reflectances. One multispectral and two hyperspectral sensors were simulated: the Advanced Land Imager (ALI, space borne), Hyperion (space borne), and HyMap (airborne, at 1.5 km altitude). Spectral radiance differences above different substrates were calculated to estimate what substrates can be separated and to what depth of waters this can be done. The dominant features in reflectance spectra of,coral reef substrates are in the wavelength range 550-700 nm. Distinguishing various substrates in this part of the spectrum is limited to water depths of 5-6 m due to attenuation of the water. Below 550 nm some substrates have spectral features that are detectable by hyperspectral instruments even in deeper waters. Broader band instruments (e.g., ALI, Landsat) can provide some information about the substrate type. Sensors with a broad bandwidth provide fewer possibilities for developing analytical remote sensing algorithms for resolving significant numbers of substrate types in waters with variable depth. Hyperspectral sensors increase our capability to detect narrow spectral features that can be used for resolving various benthic communities.
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Recent transitions from coral to macroalgal dominance on some tropical reefs have engendered debate about their causes and effects. A widely accepted view is that reef environments support stable, alternative coral or non-coral assemblages, despite the lack of evidence to support this hypothesis. Confusion in the literature stems from (1) misunderstanding theory; and (2) conflating a switch between alternative stable states with a shift in the phase portrait of a single equilibrial system caused by a persistent change, or trend, in the environment. In the present paper we outline the conceptual derivation of the hypothesis of alternative stable states, distinguish it from the phase-shift hypothesis, and discuss the evidence required to support each one. For cases in which firm conclusions can be drawn, data from fossil and modern reefs overwhelmingly support the phase-shift hypothesis rather than the hypothesis of alternative stable states. On tropical reefs, a given environment evidently supports at most a single stable community. Corals dominate environments that are disturbed primarily by natural events and have small anthropogenic impacts. In such environments, macroalgae dominate a stage during some successional trajectories to the stable, coral-dominated community. In anthropogenically perturbed environments, the resilience of the coral-dominated community is lost, precipitating phase shifts to communities dominated by macroalgae or other non-coral taxa. The implication for reef management and restoration is both substantial and optimistic. To the extent that the environments of degraded reefs are restored, either passively or actively, the communities should return to coral dominance.
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Remote sensing is being applied with increasing success in the evaluation and management of coral ecosystems. We demonstrate a successful application of hyperspectral image analysis of the benthic composition in Kaneohe Bay, Hawaii using data acquired from NASA's Airborne Visible Infrared Imaging Spectrometer. We employ a multi-level approach, combining a semi-analytical inversion model with linear spectral unmixing, to extract information on the coral, algae and sand composition of each pixel. The unmixing model is based on the spectral characteristics of the dominant species and substrate types in Kaneohe Bay, and uses an optimization routine to mathematically invert the relationship of how each component spectrally interacts and mixes. The functional result is the ability to quantitatively classify individual pixel composition according to the percent contribution from each of three main reef components. Output compares favorably with available field measurements and habitat information for Kaneohe Bay, and the overall analysis illustrates the capacity to simultaneously derive information on water properties, bathymetry and habitat composition from hyperspectral remote sensing data. Further, the resulting spatial analysis capacity contributes an improved capability for monitoring coral ecosystems and an important basis for resource management decisions.
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Specular reflection of solar radiation on non-flat water surfaces is a serious confounding factor for benthic remote sensing in shallow-water environments. This problem was recently overcome by Hochberg et al., who provided an effective method for the removal of 'sun glint' from remotely sensed images by utilization of the brightness in a near-infrared (NIR) band. Application of the technique was shown to give an increase in the accuracy of benthic habitat classification. However, as presented, the method is sensitive to outlier pixels, requires a time-consuming masking of land and cloud, and is not formulated in a manner leading to ease of implementation. We present a revised version of the method, which is more robust, does not require masking and can be implemented very simply. The practical approach described here will hopefully expedite the routine adoption of this effective and simple technique throughout the aquatic remote sensing community.
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Factors causing global degradation of coral reefs are examined briefly as a basis for predicting the likely consequences of increases in these factors. The earlier consensus was that widespread but localized damage from natural factors such as storms, and direct anthropogenic effects such as increased sedimentation, pollution and exploitation, posed the largest immediate threat to coral reefs. Now truly global factors associated with accelerating Global Climate Change are either damaging coral reefs or have the potential to inflict greater damage in the immediate future: e.g. increases in coral bleaching and mortality, and reductions in coral calcification due to changes in sea-water chemistry with increasing carbon dioxide concentrations. Rises in sea level will probably disrupt human communities and their cultures by making coral cays uninhabitable, whereas coral reefs will sustain minimal damage from the rise in sea level. The short-term (decades) prognosis is indeed grim, with major reductions almost certain in the extent and biodiversity of coral reefs, and severe disruptions to cultures and economies dependent on reef resources. The long-term (centuries to millennia) prognosis is more encouraging because coral reefs have remarkable resilience to severe disruption and will probably show this resilience in the future when climate changes either stabilize or reverse.
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The services of ecological systems and the natural capital stocksthat produce them are critical to the functioning of the Earth’s life-support system. They contribute to human welfare, both directly and indirectly, and therefore represent part of the total economic value of the planet.We have estimated the current economic value of 17 ecosystem services for 16 biomes, based on published studies and a few original calculations. For the entire biosphere, the value (most of which is outside the market) is estimated to be in the range of US$16–54 trillion (1012) per year, with an average of US$33trillion per year. Because of the nature of the uncertainties, thismust be considered a minimum estimate. Global gross national product total is around US$18 trillion per year.
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Trends in coral cover are widely used to indicate the health of coral reefs but are costly to obtain from field survey over large areas. In situ studies of reflected spectra at the coral surface show that living and recently dead colonies can be distinguished. Here, we investigate whether such spectral differences can be detected using an airborne remote sensing instrument. The Compact Airborne Spectrographic Imager (Itres Research Ltd, Canada) was flown in two configurations: 10 spectral bands with 1-m2 pixels and 6 spectral bands with 0.25-m2 pixels. First, we show that an instrument with 10 spectral bands possesses adequate spectral resolution to distinguish living Porites, living Pocillopora spp., partially dead Porites, recently dead Porites (total colony mortality within 6 months), old dead (>6 months) Porites, Halimeda spp., and coralline red algae when there is no water column to confuse spectra. All substrata were distinguished using fourth-order spectral derivatives around 538nm and 562nm. Then, at a shallow site (Tivaru) at Rangiroa Atoll, Tuamotu Archipelago (French Polynesia), we show that live and dead coral can be distinguished from the air to a depth of at least 4m using first- and fourth-order spectral derivatives between 562–580nm. However, partially dead and recently dead Porites colonies could not be distinguished from an airborne platform. Spectral differences among substrata are then exploited to predict the cover of reef substrata in ten 25-m2 plots at nearby Motu Nuhi (max depth 8m). The actual cover in these plots was determined in situ using quadrats with a 0.01-m2 grid. Considerable disparity occurred between field and image-based measures of substrate cover within individual 25-m2 quadrats. At this small scale, disparity, measured as the absolute difference in cover between field and remote-sensing methods, reached 25% in some substrata but was always less than 10% for living coral (99% of which consisted of Porites spp.). At the scale of the reef (all ten 25-m2 quadrats), however, disparities in percent cover between imagery and field data were less than 10% for all substrata and extremely low for some classes (e.g. <3% for living Porites, recently dead Porites and Halimeda). The least accurately estimated substrata were sand and coralline red algae, which were overestimated by absolute values 7.9% and 6.6%, respectively. The precision of sampling was similar for field and remote-sensing methods: field methods required 19 plots to detect a 10% difference in coral cover among three reefs with a statistical power of 95%. Remote-sensing methods required 21 plots. However, it took 1h to acquire imagery over 92,500 m2 of reef, which represents 3,700 plots of 25 m2 each, compared with 3 days to survey 10 such plots underwater. There were no significant differences in accuracy between 1-m2 and 0.25-m2 image resolutions, suggesting that the advantage of using smaller pixels is offset by reduced spectral information and an increase in noise (noise was observed to be 1.6–1.8 times greater in 0.25-m2 pixels). We show that airborne remote sensing can be used to monitor coral and algal cover over large areas, providing that water is shallow and clear, and that brown fleshy macroalgae are scarce, that depth is known independently (e.g. from sonar survey).
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