Spectral discrimination of coral reef benthic communities

Effective identification and mapping of coral reef benthic communities using high-spatial and -spectral resolution digital imaging spectrometry requires that the different communities are distinguishable by their spectral reflectance characteristics. In Kaneohe Bay, Oahu, Hawaii, USA, we collected in situ a total of 247 spectral reflectances of three coral species (Montipora capitata, Porites compressa, Porites lobata), five algal species (Dictyosphaeria cavernosa, Gracilaria salicornia, Halimeda sp., Porolithon sp., Sargassum echinocarpum) and three sand benthic communities (fine-grained carbonate sand, sand mixed with coral rubble, coral rubble). Major reflectance features were identified by peaks in fourth derivative reflectance spectra of coral (at 573, 604, 652, 675 nm), algae (at 556, 601, 649 nm) and sand (at 416, 448, 585, 652, 696 nm). Stepwise wavelength selection and linear discriminant function analysis revealed that spectral separation of the communities is possible with as few as four non-contiguous wavebands. These linear discriminant functions were applied to an airborne hyperspectral image of a patch reef in Kaneohe Bay. The results demonstrate the ability of spectral reflectance characteristics, determined in situ, to discriminate the three basic benthic community types: coral, algae and sand. Accepted: 12 January 2000     

Fluorescence-Based Classification of Caribbean Coral Reef Organisms and Substrates

A diverse group of coral reef organisms, representing several phyla, possess fluorescent pigments. We investigated the potential of using the characteristic fluorescence emission spectra of these pigments to enable unsupervised, optical classification of coral reef habitats. We compiled a library of characteristic fluorescence spectra through in situ and laboratory measurements from a variety of specimens throughout the Caribbean. Because fluorescent pigments are not species-specific, the spectral library is organized in terms of 15 functional groups. We investigated the spectral separability of the functional groups in terms of the number of wavebands required to distinguish between them, using the similarity measures Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), SID-SAM mixed measure, and Mahalanobis distance. This set of measures represents geometric, stochastic, joint geometric-stochastic, and statistical approaches to classifying spectra. Our hyperspectral fluorescence data were used to generate sets of 4-, 6-, and 8-waveband spectra, including random variations in relative signal amplitude, spectral peak shifts, and water-column attenuation. Each set consisted of 2 different band definitions: ‘optimally-picked’ and ‘evenly-spaced.’ The optimally-picked wavebands were chosen to coincide with as many peaks as possible in the functional group spectra. Reference libraries were formed from half of the spectra in each set and used for training purposes. Average classification accuracies ranged from 76.3% for SAM with 4 evenly-spaced wavebands to 93.8% for Mahalanobis distance with 8 evenly-spaced wavebands. The Mahalanobis distance consistently outperformed the other measures. In a second test, empirically-measured spectra were classified using the same reference libraries and the Mahalanobis distance for just the 8 evenly-spaced waveband case. Average classification accuracies were 84% and 87%, corresponding to the extremes in modeled water-column attenuation. The classification results from both tests indicate that a high degree of separability among the 15 fluorescent-spectra functional groups is possible using only a modest number of spectral bands.     

不同生长状态珊瑚光谱特征

珊瑚礁生态系统迅速退化是目前重要的生态环境问题之一,应用遥感技术监测大范围珊瑚礁的结构组成和变迁有很大的潜力。珊瑚光谱响应特征受珊瑚生态习性影响,在光学上相似而容易造成混淆误判。采集了西沙群岛大量石珊瑚样品的光谱,对其光谱特征进行分析及成因探讨。通过导数光谱、主成分分析研究了不同生长状态珊瑚的光谱差异,并建立珊瑚生长状态高光谱遥感判别准则。结果表明,珊瑚的光谱特性及其变化均较为复杂,受珊瑚种类和生长环境影响,光谱形状主要由共生藻色素吸收决定的。结合520—530 nm、564—574 nm和600—605 nm的导数光谱可以区分健康珊瑚、白化珊瑚和藻类覆盖的死珊瑚。总体判定准确度优于80%,误判的主要来源是种内珊瑚反射率差异。研究表明珊瑚礁环境高光谱遥感可以定量评估珊瑚状态的变化。     

Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation

Canopy chlorophyll content (CCC) is an essential ecophysiological variable for photosynthetic functioning. Remote sensing of CCC is vital for a wide range of ecological and agricultural applications. The objectives of this study were to explore simple and robust algorithms for spectral assessment of CCC. Hyperspectral datasets for six vegetation types (rice, wheat, corn, soybean, sugar beet and natural grass) acquired in four locations (Japan, France, Italy and USA) were analysed. To explore the best predictive model, spectral index approaches using the entire wavebands and multivariable regression approaches were employed. The comprehensive analysis elucidated the accuracy, linearity, sensitivity and applicability of various spectral models. Multivariable regression models using many wavebands proved inferior in applicability to different datasets. A simple model using the ratio spectral index (RSI; R815, R704) with the reflectance at 815 and 704 nm showed the highest accuracy and applicability. Simulation analysis using a physically based reflectance model suggested the biophysical soundness of the results. The model would work as a robust algorithm for canopy‐chlorophyll‐metre and/or remote sensing of CCC in ecosystem and regional scales. The predictive‐ability maps using hyperspectral data allow not only evaluation of the relative significance of wavebands in various sensors but also selection of the optimal wavelengths and effective bandwidths.     

Bathymetry,water optical properties,and benthic classification of coral reefs using hyperspectral remote sensing imagery

The complexity and heterogeneity of shallow coastal waters over small spatial scales provides a challenging environment for mapping and monitoring benthic habitats using remote sensing imagery. Additionally, changes in coral reef community structure are occurring on unprecedented temporal scales that require large-scale synoptic coverage and monitoring of coral reefs. A variety of sensors and analyses have been employed for monitoring coral reefs: this study applied a spectrum-matching and look-up-table methodology to the analysis of hyperspectral imagery of a shallow coral reef in the Bahamas. In unconstrained retrievals the retrieved bathymetry was on average within 5% of that measured acoustically, and 92% of pixels had retrieved depths within 25% of the acoustic depth. Retrieved absorption coefficients had less than 20% errors observed at blue wavelengths. The reef scale benthic classification derived by analysis of the imagery was consistent with the percent cover of specific coral reef habitat classes obtained by conventional line transects over the reef, and the inversions were robust as the results were similar when the benthic classification retrieval was constrained by measurements of bathymetry or water column optical properties. These results support the use of calibrated hyperspectral imagery for the rapid determination of bathymetry, water optical properties, and the classification of important habitat classes common to coral reefs.     

Ningaloo Reef: Shallow Marine Habitats Mapped Using a Hyperspectral Sensor

Research, monitoring and management of large marine protected areas require detailed and up-to-date habitat maps. Ningaloo Marine Park (including the Muiron Islands) in north-western Australia (stretching across three degrees of latitude) was mapped to 20 m depth using HyMap airborne hyperspectral imagery (125 bands) at 3.5 m resolution across the 762 km² of reef environment between the shoreline and reef slope. The imagery was corrected for atmospheric, air-water interface and water column influences to retrieve bottom reflectance and bathymetry using the physics-based Modular Inversion and Processing System. Using field-validated, image-derived spectra from a representative range of cover types, the classification combined a semi-automated, pixel-based approach with fuzzy logic and derivative techniques. Five thematic classification levels for benthic cover (with probability maps) were generated with varying degrees of detail, ranging from a basic one with three classes (biotic, abiotic and mixed) to the most detailed with 46 classes. The latter consisted of all abiotic and biotic seabed components and hard coral growth forms in dominant or mixed states. The overall accuracy of mapping for the most detailed maps was 70% for the highest classification level. Macro-algal communities formed most of the benthic cover, while hard and soft corals represented only about 7% of the mapped area (58.6 km²). Dense tabulate coral was the largest coral mosaic type (37% of all corals) and the rest of the corals were a mix of tabulate, digitate, massive and soft corals. Our results show that for this shallow, fringing reef environment situated in the arid tropics, hyperspectral remote sensing techniques can offer an efficient and cost-effective approach to mapping and monitoring reef habitats over large, remote and inaccessible areas.     

Estimation of leaf nitrogen content from spectral characteristics of rice canopy

Ground-based remotely sensed reflectance spectra of hyperspectral resolution were monitored during the growing period of rice under various nitrogen application rates. It was found that reflectance spectrum of rice canopy changed in both wavelength and reflectance as the plants developed. Fifteen characteristic wavebands were identified from the apparent peaks and valleys of spectral reflectance curves, in accordance with the results of the first-order differentiation, measured over the growing season of rice. The bandwidths and center wavelengths of these characteristic wavebands were different among nitrogen treatments. The simplified features by connecting these 15 characteristic wavelengths may be considered as spectral signatures of rice canopy, but spectral signatures varied with developmental age and nitrogen application rates. Among these characteristic wavebands, the changes of the wavelength in band 11 showed a positive linear relationship with application rates of nitrogen fertilizer, while it was a negative linear relationship in band 5. Mean reflectance of wavelengths in bands 1, 2, 3, 5, 11, and 15 was significantly correlated with application rates. Reflectance of these six wavelengths changed nonlinearly after transplanting and could be used in combination to distinguish rice plants subjected to different nitrogen application rates. From the correlation analyses, there are a variety of correlation coefficients for spectral reflectance to leaf nitrogen content in the range of 350-2400 nm. Reflectance of most wavelengths exhibited an inverse correlation with leaf nitrogen content, with the largest negative value (r = -0.581) located at about 1376 nm. Changes in reflectance at 1376 nm to leaf nitrogen content during the growing period were closely related and were best fitted to a nonlinear function. This relationship may be used to estimate and to monitor nitrogen content of rice leaves during rice growth. Reflectance of red light minimum and near-infrared peak and leaf nitrogen content were correlated nonlinearly.     

Best hyperspectral indices for assessing leaf chlorophyll content in a degraded temperate vegetation

Extensive studies have focused on assessing leaf chlorophyll content through spectral indices; however, the accuracy is weakened by limited wavebands and coarse resolution. With hundreds of wavebands, hyperspectral data can substantially capture the essential absorption features of leaf chlorophyll; however, few such studies have been conducted on same species in various degraded vegetations. In this investigation, complete combinations of either original reflectance or first‐order derivative spectra we conducted a complete combination on either original reflectance or its first‐order derivative value from 350 to 1000 nm to quantify leaf total chlorophyll (Chll), chlorophyll‐a (Chla), and chlorophyll‐b (Chlb) contents. This was performed using three hyperspectral datasets collected in situ from lightly, moderately, and severely degraded vegetations in temperate Helin County, China. Suitable combinations were selected by comparing the numbers of significant correlation coefficients with leaf Chll, Chla, and Chlb contents. The combinations of reflectance difference (D_ij), normalized differences (ND), first‐order derivative (FD), and first‐order derivative difference (FD(D)) were found to be the most effective. These sensitive band‐based combinations were further optimized by means of a stepwise linear regression analysis and were compared with 43 empirical spectral indices, frequently used in the literature. These sensitive band‐based combinations on hyperspectral data proved to be the most effective indices for quantifying leaf chlorophyll content (R² > 0.7, p < 0.01), demonstrating great potential for the use of hyperspectral data in monitoring degraded vegetation at a fine scale.     

冠层水平互花米草叶片光合色素含量的高光谱遥感估算模型

以闽江河口鳝鱼滩湿地互花米草(Spartina alterniflora)的实测冠层高光谱反射率和叶片光合色素含量(LPPC)为数据源,在分析LPPC与原始光谱反射率、一阶导数光谱反射率、22种已报道光谱指数和14种新构建的植被指数相关性的基础上,利用直线回归、指数回归、对数回归以及乘幂回归方法,系统地比较了36种植被指数在估算互花米草LPPC中的表现。研究表明:(1)一阶导数光谱反射率组合的植被指数用于估算互花米草的LPPC优于原始光谱反射率;(2)红边区域一阶导数光谱是估测互花米草LPPC的最佳波段;(3)对于单一色素含量的估算,叶绿素a(Chla)的最佳估算指数为FDNDVI[723,703];叶绿素b(Chlb)的最佳估算指数为FDRVI[723,525];类胡萝卜素(Cars)的最佳估算指数为FDNDVI[723,703];(4)对于使用统一参量同时估算Chla、Chlb、Cars,由FDRVI[723,703]建立的对数估算模型效果最佳。研究成果可为湿地植物生化参量反演提供参考,也可为闽江河口湿地入侵种互花米草的动态监测和生态评估管理提供有力的科学依据。     

利用高光谱参数反演水稻叶片类胡萝卜素含量

为了探讨快速、准确预测水稻(Oryza sativa)叶片类胡萝卜素(Car)含量的敏感光谱波段和光谱指数, 通过实施涉及不同年份、不同生态点、不同施氮水平和不同品种类型的4个田间试验, 于主要生育期同步测定了水稻顶部4张叶片的光谱反射率及Car含量, 系统分析了350-2 500 nm范围内任意两波段组合而成的比值(SR (λ1, λ2))、归一化(ND (λ1, λ2))及已报道的对Car敏感的光谱指数与水稻叶片Car含量间的定量关系。结果表明, 不同Car含量水平下水稻叶片光谱反射率存在着明显变化, 以绿光及红边波段对水稻叶片Car含量变化最为敏感。723 nm附近的波段与近红外波段的比值组合以及713 nm附近的波段与近红外波段的归一化组合可以较好地预测水稻叶片Car含量, 以SR (723, 770)和ND (770, 713)表现最好, 线性拟合R²分别达到0.897和0.898。基于独立的试验资料的检验表明, 预测值和实测值的拟合R²分别为0.856和0.858, 均方根差RMSE均为0.072, 平均相对误差RE分别为11.9%和12.0%, 表明SR (723,770)和ND (770, 713)可有效地估算水稻上部叶片的Car含量。     

Ground-level spectroscopy analyses and classification of coral reefs using a hyperspectral camera

With the general aim of classification and mapping of coral reefs, remote sensing has traditionally been more difficult to implement in comparison with terrestrial equivalents. Images used for the marine environment suffer from environmental limitation (water absorption, scattering, and glint); sensor-related limitations (spectral and spatial resolution); and habitat limitation (substrate spectral similarity). Presented here is an advanced approach for ground-level surveying of a coral reef using a hyperspectral camera (400–1,000 nm) that is able to address all of these limitations. Used from the surface, the image includes a white reference plate that offers a solution for correcting the water column effect. The imaging system produces millimeter size pixels and 80 relevant bands. The data collected have the advantages of both a field point spectrometer (hyperspectral resolution) and a digital camera (spatial resolution). Finally, the availability of pure pixel imagery significantly improves the potential for substrate recognition in comparison with traditionally used remote sensing mixed pixels. In this study, an image of a coral reef table in the Gulf of Aqaba, Red Sea, was classified, demonstrating the benefits of this technology for the first time. Preprocessing includes testing of two normalization approaches, three spectral resolutions, and two spectral ranges. Trained classification was performed using support vector machine that was manually trained and tested against a digital image that provided empirical verification. For the classification of 5 core classes, the best results were achieved using a combination of a 450–660 nm spectral range, 5 nm wide bands, and the employment of red-band normalization. Overall classification accuracy was improved from 86 % for the original image to 99 % for the normalized image. Spectral resolution and spectral ranges seemed to have a limited effect on the classification accuracy. The proposed methodology and the use of automatic classification procedures can be successfully applied for reef survey and monitoring and even upscaled for a large survey.     

基于高光谱的苹果花磷素含量监测模型

在室内条件下,利用ASD FieldSpec 3地物光谱仪,测定了盛花期苹果花的高光谱反射率,在分析苹果花原始光谱反射率及其一阶导数特征的基础上,分析了高光谱反射率与磷素含量间的相关关系,确定其敏感波段,构建了特征光谱参数,并建立了苹果花磷素含量的监测模型.结果表明：苹果花磷素含量与350～370 nm、670～1385 nm、1620～1760 nm波段的原始光谱反射率以及波段500～520 nm光谱反射率的一阶导数呈极显著正相关,与波段670～730 nm一阶导数呈极显著负相关;光谱参数DVI(936,676)、DVI(977,676)、NDVI(936,676)、NDVI(977,676)与苹果花磷素含量的相关性较好,其相关系数均达到0.77以上,由此建立了以4种光谱参数为自变量的磷素含量监测模型,其中,以NDVI(936,676)为自变量构建的监测模型具有最大的决定系数（R²=0.9385）、最小的均方根误差（RMSE=0.6883）和最小的相对误差（RE=7.6%）,预测精度达到92.4%,为最佳监测模型.     

Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy

Aims

This study aimed to compare stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and support vector machine regression (SVMR) for estimating soil total nitrogen (TN) contents with laboratory visible/near-infrared reflectance (Vis/NIR) of selected coarse and heterogeneous soils. Moreover, the effects of the first (1st) vs. second (2nd) derivative of spectral reflectance and the importance wavelengths were explored.

Methods

The TN contents and the Vis/NIR were measured in the laboratory. Several methods were employed for Vis/NIR data pre-processing. The SMLR, PLSR and SVMR models were calibrated and validated using independent datasets.

Results

Results showed that the SVMR and the PLSR models had similar performances, and better performances than the SMLR. The spectral bands near 1450, 1850, 2250, 2330 and 2430 nm in the PLSR model were important wavelengths. In addition, the 1st derivative was more appropriate than the 2nd derivative for spectral data pre-processing.

Conclusions

PLSR was the most suitable method for estimating TN contents in this study. SVMR may be a promising technique, and its potential needs to be further explored. Moreover, the future studies using outdoor and airborne/satellite hyperspectral data for estimating TN content are necessary for testing the findings.     

Review of coral reef ecosystem remote sensing

Coral reef communities face unprecedented pressures at local, regional and global scales as a consequence of climate change and anthropogenic disturbance. Remote sensing, from satellites or aircraft, is possibly the only means to measure the effects of such stresses at appropriately large spatial scales. In the past 30 years, remote sensing of coral reefs has made rapid progress. However, the current technology is still not mature enough to monitor complicated coral reef ecosystems. Compared with foreign research in this field, our work lags far behind. There are still deficiencies in many aspects, such as basic data collection, theoretical research and platform construction. In our nation, it is even unclear how coral reefs disperse and where they may be unhealthy. In this paper, general characteristics of coral reef ecosystems and spectral features of different reef benthos have been summarized, based initially on a review of relevant literature in recent years. Based on the spectral separability of different reef types or benthos, remote sensing can be used to monitor two aspects of coral reefs: (1) Measurement of the ecological properties of reefs. (2) Health assessment of the coral reef ecosystem. In the first part, optical remote sensing methods are widely used to map reef geomorphology and habitats or biotopes. The investigation of geomorphologic zonation has proven to be one of the most successful applications, as different geomorphologic zones are associated with characteristic benthic community structures and occur at spatial scales of tens to hundreds of meters, they are amenable to remote detection by moderate to high resolution sensors. With more and more attention on the ecological problems of coral reefs, a number of studies have used high resolution sensors to map reef communities. The number of classes distinguishable depends on many factors, including the platforms, resolution (spectral, spatial and temporal resolution) and environmental conditions (water depth, water clarity, surface roughness, etc.). Compared with deep water color remote sensing, or terrestrial remote sensing, three techniques for the measurement of reef ecological properties are examined in this paper: (1) Coral reef classification system using remote sensing. (2) Techniques of sea surface correction and water column correction. (3) Techniques of coral reef information extraction from images. In terms of the complexity of coral reef ecosystems, the current techniques still need further improvement or optimization. In the health assessment of coral reef ecosystems, there are two ways to carry out the monitoring using remote sensing: (1) Monitoring the pigment or symbiotic zooxanthellae contents in corals. (2) Measuring the environmental properties of reefs. The first way is theoretically feasible, but difficult to achieve in practice. Currently, most reef health assessments are carried out by measuring environmental parameters, including sea surface temperature, solar radiation, ultraviolet radiation, water color, wind speed and direction, rainfall, ocean acidification, sea level, etc., of which sea surface temperature has been routinely measured by NOAA to monitor coral bleaching. In addition to the contents above, this article puts forward five main prospects for development in the future: (1) Establishment of a coral reef classification system using remote sensing. (2) Satellite launch for monitoring coral reefs. (3) Theoretical and methodological development. (4) Establishment of a spectral database for different reef benthos. (5) Integrated application of multi-source remote sensing data. It is hoped that the information provided here will be a reference for subsequent similar studies.     

水稻上部叶片叶绿素含量的高光谱估算模型

叶片叶绿素 (Chl) 状况是评价植株光合效率和营养胁迫的重要指标,实时无损监测Chl状况对作物生长诊断及氮素管理具有重要意义.以不同生态点、不同年份、不同施氮水平、不同类型水稻品种的4个田间试验为基础,于主要生育期同步测定了水稻主茎顶部4张叶片的高光谱反射率及Chl含量,并计算了350～2500 nm范围内任意两波段组合而成的比值(SR[λ1,λ2])和归一化(ND[λ1,λ2])光谱指数以及已报道的对Chl敏感的光谱指数,进一步系统分析了叶片Chl含量与上述光谱指数之间的定量关系.结果表明,红边波段的比值和归一化光谱指数可以较好地预测水稻上部4叶的Chl含量(R~2>0.9),但对于不同Chl指标其最佳组合波段有所差异.估算叶绿素a (Chla)、叶绿素总量(Chla+b)和叶绿素b (Chlb)的最佳比值光谱指数分别为SR(724,709)、SR(728,709)和SR(749,745),方程拟合决定系数R~2分别是0.947、0.946、0.905;最佳归一化光谱指数分别为ND(780,709)、ND(780,712)和ND(749,745),R~2分别是0.944、0.943、0.905.引入445 nm波段反射率对上述光谱指数进行修正,可以降低叶片表面反射差异的影响,提高模型的应用范围.利用不同年份独立的试验资料对所建模型进行了检验,结果表明,修正型比值光谱指数 mSR(724,709)、mSR(728,709) 和 mSR(749,745),以及修正型归一化光谱指数mND(780,709)、mND(780,712) 和 mND(749,745) 预测 Chla、Chla+b 和 Chlb 的效果更好,其测试的RMSE分别为 0.169、0.192、0.052、0.159、0.176、0.052,RE分别为8.18%、7.74%、13.01%、8.26%、7.59%、12.96%,均较修正前降低,说明修正后的光谱指数普适性更好.     

Optical spectra and pigmentation of Caribbean reef corals and macroalgae

 Coastal reef degradation and widespread bleaching of corals, i.e. loss of pigments and/or symbiotic zooxanthellae, is increasing globally. Remote sensing from boats, aircraft or satellites has great potential for assessing the extent of reef change, but will require ground-verified spectral algorithims characteristic of healthy and degraded reef populations. We collected seven species of Caribbean reef corals and also representative macroalgae from reefs near Lee Stocking Island, Bahamas and quantified their pigments using high performance liquid chromatography. We also measured the fluorescence and reflectance spectra of corals and macroalgae using an in situ benthic spectrofluorometer. In visibly pigmented (unbleached) coral from 4 to 5 m depth, the mean (±SD) surface density of pigments (3.0±1.3 μg chlorophyll-a cm^-2 and 2.1±0.7 μg peridinin cm^-2) was similar between colonies of the same species, but differed among species. The mean quantity of pigment per zooxanthella (1.8±0.9 pg chl-a cell^-1 and 1.4±0.7 pg peridinin cell^-1) also differed among species and sometimes between colonies of the same species. Chl-a and peridinin densities per surface area of coral were positively correlated. When excited with blue light (480 nm), macroalgae and corals had typical chlorophyll fluorescence with a peak at 680 nm and a smaller shoulder peak at 730 to 740 nm. Most corals, unlike macroalgae, also had distinct fluorescence peaks between 500 and 530 nm. In visibly bleached corals 680 nm fluorescence was greatly reduced in amplitude. Pigmented coral, under natural lighting conditions, had a reflected light peak at about 570 nm. Reflectance increased over all wavelengths in bleached corals, with the greatest increase at the wavelengths where chlorophyll and accessory pigments absorb light, i.e. 670 and 450 to 550 nm. Both fluorescence and reflectance spectra appear promising to remotely differentiate between pigmented and bleached coral and between coral and macroalgae. Accepted: 15 March 1999     

Hyperspectral Sensing of Disease Stress in the Caribbean Reef-Building Coral,Orbicella faveolata - Perspectives for the Field of Coral Disease Monitoring

The effectiveness of management plans developed for responding to coral disease outbreaks is limited due to the lack of rapid methods of disease diagnosis. In order to fulfill current management guidelines for responding to coral disease outbreaks, alternative methods that significantly reduce response time must be developed. Hyperspectral sensing has been used by various groups to characterize the spectral signatures unique to asymptomatic and bleached corals. The 2010 combined bleaching and Caribbean yellow band disease outbreak in Puerto Rico provided a unique opportunity to investigate the spectral signatures associated with bleached and Caribbean yellow band-diseased colonies of Orbicella faveolata for the first time. Using derivative and cluster analyses of hyperspectral reflectance data, the present study demonstrates the proof of concept that spectral signatures can be used to differentiate between coral disease states. This method enhanced predominant visual methods of diagnosis by distinguishing between different asymptomatic conditions that are identical in field observations and photographic records. The ability to identify disease-affected tissue before lesions become visible could greatly reduce response times to coral disease outbreaks in monitoring efforts. Finally, spectral signatures associated with the poorly understood Caribbean yellow band disease are presented to guide future research on the role of pigments in the etiology.     

AUV-based bed roughness mapping over a tropical reef

Identifying fixed bed roughness scales of hydrodynamic relevance to waves and currents is challenging around coral reefs due to their highly inhomogeneous bathymetry. In order to characterize the spatial variability in reef roughness, a quantitative analysis of high-resolution sidescan sonar backscatter is performed for the identification of distinct substrates around a tropical reef and is related to echo sounder-based roughness measurements. Data were collected in the vicinity of the Kilo Nalu Observatory on the south shore of Oahu using sidescan sonar and a narrow beam echo sounder incorporated in a REMUS-100 (Remote Environmental Monitoring UnitS) autonomous underwater vehicle (AUV). With basic statistics and principal component analysis of variables derived from the backscatter data, it is possible to discriminate between areas of rough reef, bare reef, and rippled sand. Echo sounder-derived spectral analysis did not reveal dominant length scales. However, by combining the seabed classification obtained from sidescan measurements with echo sounder data, spectral root mean square (RMS) height values of approximately 3.3 cm and 7.3 cm are assigned to the bare reef and rough reef areas, respectively, for roughness with wavelengths between 0.2 and 6 m.     

Linking Demographic Processes of Juvenile Corals to Benthic Recovery Trajectories in Two Common Reef Habitats

Tropical reefs are dynamic ecosystems that host diverse coral assemblages with different life-history strategies. Here, we quantified how juvenile (<50 mm) coral demographics influenced benthic coral structure in reef flat and reef slope habitats on the southern Great Barrier Reef, Australia. Permanent plots and settlement tiles were monitored every six months for three years in each habitat. These environments exhibited profound differences: the reef slope was characterised by 95% less macroalgal cover, and twice the amount of available settlement substrata and rates of coral settlement than the reef flat. Consequently, post-settlement coral survival in the reef slope was substantially higher than that of the reef flat, and resulted in a rapid increase in coral cover from 7 to 31% in 2.5 years. In contrast, coral cover on the reef flat remained low (~10%), whereas macroalgal cover increased from 23 to 45%. A positive stock-recruitment relationship was found in brooding corals in both habitats; however, brooding corals were not directly responsible for the observed changes in coral cover. Rather, the rapid increase on the reef slope resulted from high abundances of broadcast spawning Acropora recruits. Incorporating our results into transition matrix models demonstrated that most corals escape mortality once they exceed 50 mm, but for smaller corals mortality in brooders was double those of spawners (i.e. acroporids and massive corals). For corals on the reef flat, sensitivity analysis demonstrated that growth and mortality of larger juveniles (21–50 mm) highly influenced population dynamics; whereas the recruitment, growth and mortality of smaller corals (<20 mm) had the highest influence on reef slope population dynamics. Our results provide insight into the population dynamics and recovery trajectories in disparate reef habitats, and highlight the importance of acroporid recruitment in driving rapid increases in coral cover following large-scale perturbation in reef slope environments.     

Non‐reef habitats in a tropical seascape affect density and biomass of fishes on coral reefs

Nonreef habitats such as mangroves, seagrass, and macroalgal beds are important for foraging, spawning, and as nursery habitat for some coral reef fishes. The spatial configuration of nonreef habitats adjacent to coral reefs can therefore have a substantial influence on the distribution and composition of reef fish. We investigate how different habitats in a tropical seascape in the Philippines influence the presence, density, and biomass of coral reef fishes to understand the relative importance of different habitats across various spatial scales. A detailed seascape map generated from satellite imagery was combined with field surveys of fish and benthic habitat on coral reefs. We then compared the relative importance of local reef (within coral reef) and adjacent habitat (habitats in the surrounding seascape) variables for coral reef fishes. Overall, adjacent habitat variables were as important as local reef variables in explaining reef fish density and biomass, despite being fewer in number in final models. For adult and juvenile wrasses (Labridae), and juveniles of some parrotfish taxa (Chlorurus), adjacent habitat was more important in explaining fish density and biomass. Notably, wrasses were positively influenced by the amount of sand and macroalgae in the adjacent seascape. Adjacent habitat metrics with the highest relative importance were sand (positive), macroalgae (positive), and mangrove habitats (negative), and fish responses to these metrics were consistent across fish groups evaluated. The 500‐m spatial scale was selected most often in models for seascape variables. Local coral reef variables with the greatest importance were percent cover of live coral (positive), sand (negative), and macroalgae (mixed). Incorporating spatial metrics that describe the surrounding seascape will capture more holistic patterns of fish–habitat relationships on reefs. This is important in regions where protection of reef fish habitat is an integral part of fisheries management but where protection of nonreef habitats is often overlooked.