Spectral reflectance of coral reef bottom-types worldwide and implications for coral reef remote sensing

Coral reef benthic communities are mosaics of individual bottom-types that are distinguished by their taxonomic composition and functional roles in the ecosystem. Knowledge of community structure is essential to understanding many reef processes. To develop techniques for identification and mapping of reef bottom-types using remote sensing, we measured 13,100 in situ optical reflectance spectra (400-700 nm, 1-nm intervals) of 12 basic reef bottom-types in the Atlantic, Pacific, and Indian Oceans: fleshy (1) brown, (2) green, and (3) red algae; non-fleshy (4) encrusting calcareous and (5) turf algae; (6) bleached, (7) blue, and (8) brown hermatypic coral; (9) soft/gorgonian coral; (10) seagrass; (11) terrigenous mud; and (12) carbonate sand. Each bottom-type exhibits characteristic spectral reflectance features that are conservative across biogeographic regions. Most notable are the brightness of carbonate sand and local extrema near 570 nm in blue (minimum) and brown (maximum) corals. Classification function analyses for the 12 bottom-types achieve mean accuracies of 83%, 76%, and 71% for full-spectrum data (301-wavelength), 52-wavelength, and 14-wavelength subsets, respectively. The distinguishing spectral features for the 12 bottom-types exist in well-defined, narrow (10-20 nm) wavelength ranges and are ubiquitous throughout the world. We reason that spectral reflectance features arise primarily as a result of spectral absorption processes. Radiative transfer modeling shows that in typically clear coral reef waters, dark substrates such as corals have a depth-of-detection limit on the order of 10-20 m. Our results provide the foundation for design of a sensor with the purpose of assessing the global status of coral reefs.     

A Bayesian Belief Network to assess rate of changes in coral reef ecosystems

It is crucial to identify sources of impacts and degradation to maintain functions and services that the physical structure of coral reef provides. Here, a Bayesian Network approach is used to evaluate effects that anthropogenic and climate change disturbances have on coral reef structure. The network was constructed on knowledge derived from the literature and elicited from experts, and parameterised on independent data.Evaluation of the model was conducted through sensitivity analyses and data integration was fundamental to obtain a balanced dataset. Scenario analyses, conducted to assess the effects of stressors on the reef framework state, suggested that calcifying organisms and carbonate production, rather than bioerosion, had the largest influence on the reef carbonate budgetary state. Despite the overall budget remaining positive, anthropogenic pressures, particularly deterioration of water quality, affected reef carbonate production, representing a warning signal for potential changes in the reef state.     

Mass-consistent models for wind fields over complex terrain: The state of the art

Among the diagnostic models for wind field simulation, mass-consistent models play an important role, thanks to the simplicity of the physics involved and their capacity to accept several measurements of wind at different points of the domain. The general procedure and mathematical supports for this kind of simulation, with particular reference to the approximation that characterize the different models developed, are analyzed. Evidently, a large number of simulations is required if one needs to know the average wind over a region, with a consequent long calculation time. Some methods reducing this time, without losing fundamental information, are described.     

Spectral reflectance of coral reef benthos and substrate assemblages on Heron Reef,Australia

Studies investigating the spectral reflectance of coral reef benthos and substrates have focused on the measurement of pure endmembers, where the entire field of view (FOV) of a spectrometer is focused on a single benthos or substrate type. At the spatial scales of the current satellite sensors, the heterogeneity of coral reefs even at a sub-metre scale means that many individual image pixels will be made up of a mixture of benthos and substrate types. If pure endmember spectra are used as training data for image classification, there is a spatial discrepancy, because many pixels will have a mixed endmember spectral reflectance signature. This study investigated the spectral reflectance of coral reef benthos and substrates at a spatial scale directly linked to the pixel size of high spatial resolution imaging systems, by incorporating multiple benthos and substrate types into the spectrometer FOV in situ. A total of 334 spectral reflectance signatures were measured of 19 assemblages of the coral reef benthos and substrate types. The spectra were analysed for separability using first derivative values, and a discrimination decision tree was designed to identify the assemblages. Using the decision tree, it was possible to identify 15 assemblages with a mean overall classification accuracy of 62.6%.     

Detection limits of coral reef bleaching by satellite remote sensing: Simulation and data analysis

Monitoring of coral reef bleaching has hitherto been based on regional-scale, in situ data. Larger-scale trends, however, must be determined using satellite-based observations. Using both a radiative transfer simulation and an analysis of multitemporal Landsat TM images, the ability of satellite remote sensing to detect and monitor coral reef bleaching is examined. The radiative transfer simulation indicates that the blue and green bands of Landsat TM can detect bleaching if at least 23% of the coral surface in a pixel has been bleached, assuming a Landsat TM pixel with a resolution of 30×30 m on shallow (less than 3 m deep) reef flats at Ishigaki Island, Japan. Assuming an area with an initial coral coverage of 100% and in which all corals became completely bleached, the bleaching could be detected at a depth of up to 17 m. The difference in reflectance of shallow sand and corals is compared by examining multitemporal Landsat TM images at Ishigaki Island, after normalizing for variations in atmospheric conditions, incident light, water depth, and the sensor's reaction to the radiance received. After the normalization, a severe bleaching event when 25-55% of coral coverage was bleached was detected, but a slight bleaching event when 15% of coral coverage was bleached was not detected. The simulation and data analysis agreed well with each other, and identified reliable limits for satellite remote sensing for detecting coral reef bleaching. Sensitivity analysis on solar zenith angle, aerosol (visibility) and water quality (Chl a concentration) quantified the effect of these factors on bleaching detection, and thus served as general guidelines for detecting coral reef bleaching. Spatial misregistration resulted in a high degree of uncertainty in the detection of changes at the edges of coral patches mainly because of the low (∼30 m) spatial resolution of Landsat TM, indicating that detection of coral reef bleaching by Landsat TM is limited to extremely severe cases on a large homogeneous coral patch and shallow water depths. Satellite remote sensing of coral reef bleaching should be encouraged, however, because the development and deployment of advanced satellite sensors with high spatial resolution continue to progress.     

The effects of ecologically determined spatial complexity on the classification accuracy of simulated coral reef images

Numerous studies have been conducted to compare the classification accuracy of coral reef maps produced from satellite and aerial imagery with different sensor characteristics such as spatial or spectral resolution, or under different environmental conditions. However, in additional to these physical environment and sensor design factors, the ecologically determined spatial complexity of the reef itself presents significant challenges for remote sensing objectives. While previous studies have considered the spatial resolution of the sensors, none have directly drawn the link from sensor spatial resolution to the scale and patterns in the heterogeneity of reef benthos. In this paper, we will study how the accuracy of a commonly used maximum likelihood classification (MLC) algorithm is affected by spatial elements typical of a Caribbean atoll system present in high spectral and spatial resolution imagery.The results indicate that the degree to which ecologically determined spatial factors influence accuracy is dependent on both the amount of coral cover on the reef and the spatial resolution of the images being classified, and may be a contributing factor to the differences in the accuracies obtained for mapping reefs in different geographical locations. Differences in accuracy are also obtained due to the methods of pixel selection for training the maximum likelihood classification algorithm. With respect to estimation of live coral cover, a method which randomly selects training samples from all samples in each class provides better estimates for lower resolution images while a method biased to select the pixels with the highest substrate purity gave better estimations for higher resolution images.     

Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems

Large areas of the world's coastal marine environments remain poorly characterized because they have not been mapped with sufficient accuracy and at spatial resolutions high enough to support a wide range of societal needs. Expediting the rate of seafloor mapping requires the collection of multi-use datasets that concurrently address hydrographic charting needs and support decision-making in ecosystem-based management. While active optical and acoustic sensors have previously been compared for the purpose of hydrographic charting, few studies have evaluated the performance and cost effectiveness of these systems for providing benthic habitat maps. Bathymetric and intensity data were collected in shallow water (< 50 m depth) coral reef ecosystems using two conventional remote sensing technologies: (1) airborne Light Detection and Ranging (LiDAR), and (2) ship-based multibeam (MBES) Sound Navigation and Ranging (SoNAR). A comparative assessment using a suite of twelve metrics demonstrated that LiDAR and MBES were equally capable of discriminating seafloor topography (r = > 0.9), although LiDAR depths were found to be consistently shallower than MBES depths. The intensity datasets were not significantly correlated at a broad 4 × 5 km spatial scale (r = − 0.11), but were moderately correlated in flat areas at a fine 4 × 500 m spatial scale (r = 0.51), indicating that the LiDAR intensity algorithm needs to be improved before LiDAR intensity surfaces can be used for habitat mapping. LiDAR cost 6.6% less than MBES and required 40 fewer hours to map the same study area. MBES provided more detail about the seafloor by fully ensonifying high-relief features, by differentiating between fine and coarse sediments and by collecting data with higher spatial resolutions. Surface fractal dimensions and fast Fourier transformations emerged as useful methods for detecting artifacts in the datasets. Overall, LiDAR provided a more cost effective alternative to MBES for mapping and monitoring shallow water coral reef ecosystems (< 50 m depth), although the unique advantages of MBES may make it a more appropriate choice for answering certain ecological or geological questions requiring very high resolution data.     

Airborne lidar sensing of massive stony coral colonies on patch reefs in the northern Florida reef tract

In this study we examined the ability of the NASA Experimental Advanced Airborne Research Lidar (EAARL) to discriminate cluster zones of massive stony coral colonies on northern Florida reef tract (NFRT) patch reefs based on their topographic complexity (rugosity). Spatially dense EAARL laser submarine topographic soundings acquired in August 2002 were used to create a 1-m resolution digital rugosity map for adjacent NFRT study areas characterized by patch reefs (Region A) and diverse substratums (Region B). In both regions, sites with lidar-sensed rugosities above 1.2 were imaged by an along-track underwater videography system that incorporated the acquisition of instantaneous GPS positions. Subsequent manual interpretation of videotape segments was performed to identify substratum types that caused elevated lidar-sensed rugosity. Our study determined that massive coral colony formation, modified by subsequent physical and biological processes that breakdown patch reef framework, was the primary source of topographic complexity sensed by the EAARL in the NFRT. Sites recognized by lidar scanning to be topographically complex preferentially occurred around the margins of patch reefs, constituted a minor fraction of the reef system, and usually reflected the presence of massive coral colonies in cluster zones, or their derivatives created by mortality, bioerosion, and physical breakdown.     

Detecting end-member structural and biological elements of a coral reef using a single-beam acoustic ground discrimination system

A thematic map of benthic habitat was produced for a coral reef in the Republic of Palau, utilizing hydroacoustic data acquired with a BioSonics DT-X echosounder and a single-beam 418 kHz digital transducer. This article describes and assesses a supervised classification scheme that used a series of three discriminant analyses (DAs) to refine training samples into end-member structural and biological elements utilizing E1′ (leading edge of first echo), E1 (trailing edge of first echo), E2 (complete second echo), fractal dimension (first echo shape) and depth as predictor variables. Hydroacoustic training samples were assigned to one of six predefined groups based on the plurality of benthic elements (sand, sparse submerged aquatic vegetation (SAV)) rubble, pavement, rugose hardbottom, branching coral) that were visually estimated from spatially co-located ground-truthing videos. Records that classified incorrectly or failed to exceed a minimum probability of group membership were removed from the training data set until only ‘pure’ end-member records remained. This refinement of ‘mixed’ training samples circumvented the dilemma typically imposed by the benthic heterogeneity of coral reefs, that is either train the acoustic ground discrimination system (AGDS) on homogeneous benthos and leave the heterogeneous benthos unclassified, or attempt to capture the many ‘mixed’ classes and overwhelm the discriminatory capability of the AGDS. It was made possible by a conjunction of narrow beam width (6.4°) and shallow depth (1.2 to 17.5 m), which produced a sonar footprint small enough to resolve the microscale features used to define benthic groups. Survey data classified from the third-pass training DA were found to: (i) conform to visually apparent contours of satellite imagery, (ii) agree with the structural and biological delineations of a benthic habitat map (BHM) created from visual interpretation of IKONOS imagery and (iii) yield values of benthic cover that agreed closely with independent, contemporaneous video transects. The methodology was proven on a coral reef environment for which high-quality satellite imagery existed, as an example of the potential for single-beam systems to thematically map coral reefs in deep or turbid settings where optical methods are not applicable.     

图像扩散去噪模型的分析与改进

总结与分析了已有图像扩散去噪模型的优缺点。在理论上明确解释了张量型扩散模型的物理意义,通过分析P-M扩散模型的局部扩散行为,提出一个新的扩散系数,进一步给出一个改进的张量型扩散模型。从主观与客观两个方面比较各种扩散去噪模型的效果都不容易,因为需要合适耦合各个模型的参数及数值离散方法等,为此给出了扩散模型统一的数值实现算法,可用来比较各个模型的去噪效果。数值模拟实验的结果表明,改进的扩散模型在有效去除噪声的同时,能很好地对图像中的边缘、角点、纹理等特征进行保护,去噪后的图像有较好的视觉效果。     

Simultaneous estimation of benthic fractional cover and shallow water bathymetry in coral reef areas from high-resolution satellite images

This article describes the development of a technique to estimate shallow water benthic cover and depth simultaneously from high-resolution satellite images of reef areas, specifically from the high-resolution sensor onboard IKONOS. The technique to derive the estimates of five bottom benthic cover types (sand, coral, seagrass, macroalgae and pavement) and depth from the four-band images uses a coupling of radiative transfer (RT) theory and spectral unmixing implemented in an iterative manner. To resolve the cover types for the unmixing, the method employed a combinatorial approach to select benthic cover composition. The estimation technique was applied to two reef areas around the coast of the Ishigaki in southern Ryukyus, namely, the Fukido River mouth area and the Shiraho Reef. The IKONOS images of Fukido River mouth area and Shiraho Reef were acquired in 2003 and 2002, respectively. The accuracy of the fractional cover and the depth estimates from the satellite images are then presented and compared with sea truth data and depth measurements. The results indicate good correspondence between estimated and measured depths, while the estimates for the benthic cover were at reasonable levels of accuracy.     

Characterization of coral bleaching environments and their variation along the Bahia state coast,Brazil

The relation between climate variability and coral bleaching in the Bahia reefs was investigated in an attempt to characterize the bleaching environments. The following 13-year time series were derived from the remote-sensing, analysis and reanalysis data: maximum summertime sea surface temperature (SST), maximum sea surface temperature (MaxSST) accumulated in 5 days (SSTAc5day), diffuse attenuation coefficient for downward irradiance at 490 nm (K ₄₉₀), rainfall and magnitude of surface wind fields, including the zonal (U) and meridional components. Principal component analysis, non-metric multidimensional scaling (MDS) and cluster and similarity analyses indicate the complex nature of the bleaching patterns and the influence of the strong 1997–1998 El Niño. A significant (global R-value?=?0.65; p < 0.01) compounding effect of the reef location and bleaching intensity on the differentiation of bleaching environments was detected. A combination of high SSTAc5day and low K ₄₉₀ may cause coral bleaching in the northernmost reefs. Evidence clearly points to a scenario where the influence of reef location, bleaching year and intensity may produce a compounded effect that determines the bleaching environments in Bahia.     

Unification of the models for types, classes and state machines

The static model, specified formally, and the dynamic model, represented by hierarchical state machines, are intimately related. By defining a mapping between the two, we are able to provide a definition of inheritance, multiple inheritance and behavioral subtyping for state machines based on that for formally specified types and classes, and provide a graphical representation for formal specifications in terms of state machines. The state machine notation is based on statecharts. It, however, supports both a declarative style, appropriate for types, and an imperative style, appropriate for classes. State machines may be parameterized and may be viewed from different perspectives, based on an arbitrary choice of state predicates. And states are interpreted not as an expression of concurrency, but result from a choice of independent state predicates.     

Estimating the coverage of coral reef benthic communities from airborne hyperspectral remote sensing data: multiple discriminant function analysis and linear spectral unmixing

A staged approach for the application of linear spectral unmixing techniques to airborne hyperspectral remote sensing data of reef communities of the Al Wajh Barrier, Red Sea, is presented. Quantification of the percentage composition of four different reef components (live coral, dead coral, macroalgae and carbonate sand) contained within the ground sampling distance associated with an individual pixel is demonstrated. In the first stage, multiple discriminant function analysis is applied to spectra collected in situ to define an optimal subset combination of derivative and raw image wavebands for discriminating reef benthos. In the second phase, unmixing is applied to a similarly reduced subset of pre-processed image data to accurately determine the relative abundance of the reef benthos (R ² > 0.7 for all four components). The result of a phased approach is an increased signal-to-noise ratio for solution of the linear functions and reduction of processing burdens associated with image unmixing.     

Combining Landsat images with historic records to estimate the live coral cover of Luhuitou fringing reef in Northern South China Sea

As an efficient indicator of coral reef health, live coral cover (LCC) is regularly surveyed and recorded by many coral reef documents. However, there usually exist some blanks for the historic records, while current in-field surveys are impossible to fill the blanks. To overcome such difficulties, we focus on exploiting the potential of optical satellite images. The purpose is to fill the blanks of the records over the past and further estimate the LCC in future. As historic records were usually lack of accurate geographical locations to match to the satellite images, a spectral index was defined based on the mean of the subsurface remote sensing reflectance. The index was then used to link the LCC with the satellite images by a cubic polynomial function. Thereafter, the LCC and the coefficients of the polynomial function were finally estimated by simultaneously combining the mean subsurface remote sensing reflectance, the historic LCC records, and the constraints among LCC in adjacent years. Experiments on a series of Landsat images of Luhuitou fringing reef (1973 to 2018) demonstrated that the proposed method is effective and feasible, where the introduction of the satellite images can greatly improve the accuracy. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Relative Errors (MRE) of the LCC were able to reach 5.4%, 4.0%, and 15.9% respectively. This is regarded as the first test on LCC estimation by combining such a long-term LCC records with a series of satellite images.     

Live modeling in the context of state machine models and code generation

Software and Systems Modeling - Live modeling has been recognized as an important technique to edit behavioral models while being executed and helps in better understanding the impact of a design...     

A parallel DSDADI method for solution of the steady state diffusion equation

A parallel diagonally scaled dynamic alternating-direction-implicit (DSDADI) method is shown to be an effective algorithm for solving the 2D and 3D steady-state diffusion equation on large uniform Cartesian grids. Empirical evidence from the parallel solution of large gridsize problems suggests that the computational work done by DSDADI to converge over an N^d grid with continuous diffusivity is of lower order than O(N^d+α) for any fixed α > 0. This is in contrast to the method of diagonally scaled conjugate gradients (DSCG), for which the computational work necessary for convergence is O(N^d+1). Furthermore, the combination of diagonal scaling, spatial domain decomposition (SDD), and distributed tridiagonal system solution gives the DSDADI algorithm reasonable scalability on distributed-memory multiprocessors such as the CRAY T3D. Finally, an approximate parallel tridiagonal system solver with diminished interprocessor communication exhibits additional utility for DSDADI.     

On the numerical solution of space–time fractional diffusion models

A flexible numerical scheme for the discretization of the space–time fractional diffusion equation is presented. The model solution is discretized in time with a pseudo-spectral expansion of Mittag–Leffler functions. For the space discretization, the proposed scheme can accommodate either low-order finite-difference and finite-element discretizations or high-order pseudo-spectral discretizations. A number of examples of numerical solutions of the space–time fractional diffusion equation are presented with various combinations of the time and space derivatives. The proposed numerical scheme is shown to be both efficient and flexible.