Chlorophyll-a Predicting Model Based on Dynamic Neural Network

Algal blooms are one of the most prevalent global problems. Studying the Chlorophyll-a (Chl-a) predicting model helps to control algal blooms. Predicting the behavior of algae is difficult because of the complex physical, chemical, and biological processes involved. Artificial neural network (ANN) models have been determined to be useful and efficient, especially for such problems for which the characteristics of the processes are difficult to describe using numerical models. An indoor simulated environment is designed for algal cultivation to analyze the temporal change in the algae biomass of Taihu Lake during summer. A Chl-a prediction model based on a nonlinear autoregressive neural network with exogenous inputs (NARX) that can detect and consider within the time dependency is proposed. The NARX model is compared to a static neural network and a dynamic neural network: feedforward neural network (FNN) and Elman recurrent neural network (ERNN). The performance of the proposed NARX model was examined with experimental data collected over 3 months in 2010. The results showed that the NARX model outperformed the other ANN models and significantly enhance the accuracy of Chl-a prediction.     

Passive optical remote sensing of cyanobacteria and other intense phytoplankton blooms in coastal and inland waters

Increased frequency and extent of potentially harmful blooms in coastal and inland waters world-wide require the development of methods for operative and reliable monitoring of the blooms over vast coastal areas and a large number of lakes. Remote sensing could provide the tool. An overview of the literature in this field suggests that operative monitoring of the extent of some types of blooms (i.e. cyanobacteria) is relatively straightforward. Operative monitoring of inland waters is currently limited to larger lakes or using airborne and hand-held remote sensing instruments as there are no satellite sensors with sufficient spatial resolution to provide daily coverage. Extremely high spatial and vertical variability in biomass during blooms of some phytoplankton species and the strong effects of this on the remote sensing signal suggest that water sampling techniques and strategies have to be redesigned for highly stratified bloom conditions, especially if the samples are collected for algorithm development and validation of remote sensing data. Comparing spectral signatures of different bloom-forming species with the spectral resolution available in most satellites and taking into account variability in optical properties of different water bodies suggests that developing global algorithms for recognizing and quantitative mapping of (harmful) algal blooms is questionable. On the other hand some authors cited in the present paper have found particular cases where satellites with coarse spectral and spatial resolution can be used to recognize phytoplankton blooms even at species level. Thus, the algorithms and methods to be used depend on the optical complexity of the water to which they will be applied. The aim of this paper is to summarize different methods and algorithms available in an attempt to assist in selecting the most appropriate method for a particular site and problem under investigation.     

赤潮卫星遥感监测与应用研究进展

赤潮监测是海洋遥感应用的一个重要组成部分。近30 a来,国内外学者发展了许多基于卫星平台的赤潮遥感监测算法。通过回顾赤潮卫星遥感监测的发展历史,总结了各种传感器的赤潮卫星遥感监测算法,分析了各种算法的性能与适用范围。最后,对目前赤潮卫星遥感监测面临的问题进行了讨论,并对未来赤潮遥感监测的发展提出展望。     

Remote sensing chlorophyll a of optically complex waters (rias Baixas, NW Spain): Application of a regionally specific chlorophyll a algorithm for MERIS full resolution data during an upwelling cycle

This study takes advantage of a regionally specific algorithm and the characteristics of Medium Resolution Imaging Spectrometer (MERIS) in order to deliver more accurate, detailed chlorophyll a (chla) maps of optically complex coastal waters during an upwelling cycle. MERIS full resolution chla concentrations and in situ data were obtained on the Galician (NW Spain) shelf and in three adjacent rias (embayments), sites of extensive mussel culture that experience frequent harmful algal events. Regionally focused algorithms (Regional neural network for rias Baixas or NNRB) for the retrieval of chla in the Galician rias optically complex waters were tested in comparison to sea-truth data. The one that showed the best performance was applied to a series of six MERIS (FR) images during a summer upwelling cycle to test its performance. The best performance parameters were given for the NN trained with high-quality data using the most abundant cluster found in the rias after the application of fuzzy c-mean clustering techniques (FCM). July 2008 was characterized by three periods of different meteorological and oceanographic states. The main changes in chla concentration and distribution were clearly captured in the images. After a period of strong upwelling favorable winds a high biomass algal event was recorded in the study area. However, MERIS missed the high chlorophyll upwelled water that was detected below surface in the ria de Vigo by the chla profiles, proving the necessity of in situ observations. Relatively high biomass “patches” were mapped in detail inside the rias. There was a significant variation in the timing and the extent of the maximum chla areas. The maps confirmed that the complex spatial structure of the phytoplankton distribution in the rias Baixas is affected by the surface currents and winds on the adjacent continental shelf. This study showed that a regionally specific algorithm for an ocean color sensor with the characteristics of MERIS in combination with in situ data can be of great help in chla monitoring, detection and study of high biomass algal events in an area affected by coastal upwelling such as the rias Baixas.     

Seasonal and spatial distribution of chlorophyll-a concentrations and water conditions in the Gulf of Tonkin, South China Sea

The Gulf of Tonkin is a semi-closed gulf northwest of the South China Sea, experiencing reversal seasonal monsoon. Previous studies of water conditions have been conducted in the western waters of the gulf, but very few studies of the Chlorophyll-a (Chl-a) distribution have been carried out for the entire gulf. The present study investigates seasonal and spatial distributions of Chl-a and water conditions in the Gulf of Tonkin by analyzing Sea-viewing Wide Field-of-View Scanner (SeaWiFS) derived Chlorophyll-a (Chl-a), in situ measurements, sea surface temperatures (SST), and other oceanographic data obtained in 1999 and 2000. The results show seasonality of Chl-a and SST variations in the Gulf of Tonkin, and reveal phytoplankton blooming events in the center part of the gulf during the northeast monsoon season. In summer, Chl-a concentrations were relatively low (<0.3 mg m⁻³) and distributed uniformly throughout most of the area, with a belt of higher Chl-a concentrations along the coast, particularly the coast of Qiongzhou Peninsula; in winter, Chl-a concentration increased (0.5 mg m⁻³) in the entire gulf, and phytoplankton blooms offshore-ward from the northeast coast to the center of the gulf, while Chl-a concentrations reached high levels (0.8-1 mg m⁻³) in the center of the blooms. One peak of Chl-a concentrations was observed during the northeast monsoon season in the year. SST were high (27-29 °C) and distributed uniformly in summer, but lower with a large gradient from northeast (17 °C) to southwest (25 °C) in winter, while strong northeast winds (8-10 m/s) were parallel to the east coast of the gulf. Comparison of Chl-a values shows that SeaWiFS derived Chl-a concentrations match well with in situ measurements in most parts of the gulf in May 1999, but SeaWiFS derived Chl-a are higher than in situ data in river mouth waters. The seasonal variation of Chl-a concentrations and SST distribution were associated with the seasonally reversing monsoon; the winter phytoplankton blooms were related to vertical mixing and upwelling nutrients drawn by the northeast wind.     

基于CBERS-2卫星数据的艾比湖浮游植物生物量的反演研究

以艾比湖湖区为研究对象,利用CBERS\|2卫星影像的光谱信息分析了遥感影像提取的各因子与浮游植物实测生物量之间的相关关系,建立了相关性显著的遥感因子与浮游植物生物量的线性和非线性回归模型。通过对比分析和残差分析得到最优模型,对艾比湖进行了浮游植物生物量的遥感反演,分析湖区浮游植物生物量的分布特征并估算湖体浮游植物生物总量。艾比湖浮游植物生物量的最优估测模型是二元线性回归模型:Y=3.819-0.027(G-B)-0.04(G-R),其拟合度为0.832,平均残差系数为6.9%,艾比湖湖体浮游植物的总生物量为9.95×10⁵ kg。利用遥感方法研究艾比湖浮游植物的生物量对于艾比湖水域生物估产及其生物量消长规律,以及艾比湖生态系统具有重要意义。研究分析艾比湖生物量的空间分布特征,为艾比湖水体大范围\,快速\,长期的动态监测和获取浮游生物信息和水质参数提供了有力依据。     

The ABDMAP (Algal Bloom Detection,Monitoring and Prediction) Concerted Action

This paper describes the ABDMAP (Algal Bloom Detection, Monitoring And Prediction) project, which was a Concerted Action funded by the European Commission and carried out from April 1997 to March 1999. In the course of the project a number of workshops were held in various European countries. These workshops were devoted to various scientific aspects of algal blooms and, in particular, to the use of Earth observation (remote sensing) data for the study of such blooms. Great attention was given to the needs of users or potential users from the academic and research communities, from organizations with operational responsibilities for the introduction of legislation or for the monitoring of algal blooms. A state-of-the-art review of scientific knowledge and of applications of this knowledge was compiled and a number of actions were undertaken to widen the appreciation of the potential use of Earth observation (remote sensing) data for the study of algal blooms, including toxic algal blooms.     

Remote sensing of phytoplankton pigment distribution in the United States northeast coast

Phytoplankton pigments constitute many more compounds than chlorophyll a that can be applied to study phytoplankton diversity, populations, and primary production. In this study, field measurements were applied to develop ocean color satellite algorithms of phytoplankton pigments from in-water radiometry measurements. The match-up comparisons showed that the satellite-derived pigments from our algorithms agree reasonably well (e.g. 30-55% of uncertainty for SeaWiFS and 37-50% for MODIS-Aqua) to field data, with better agreement (e.g. 30-38% of uncertainty for SeaWiFS and 39-44% for MODIS-Aqua) for pigments abundant in diatoms. The seasonal and spatial variations of satellite-derived phytoplankton biomarker pigments, such as fucoxanthin, which is abundant in diatoms, peridinin, which is found only in peridinin-containing dinoflagellates, and zeaxanthin, which is primarily from cyanobacteria in coastal waters, revealed that higher densities of diatoms are more likely to occur on the inner shelf and during winter-spring and obscure other abundant phytoplankton groups. However, relatively higher densities of other phytoplankton, such as dinoflagellates and cyanobacteria, are likely to occur on the mid- to outer-continental shelf and during summer. Seasonal variation of riverine discharge may play an important role in stimulating algal blooms, in particular diatoms, while higher abundances of cyanobacteria coincide with warmer water temperatures and lower nutrient concentrations.     

The satellite-measured sea surface temperature change in the Gulf of Finland

Climate change in Baltic region and in the Gulf of Finland is an accomplished fact in human brains and in science. The purpose of this research is to retrieve quantitative level of changes for sea surface temperature (SST) of the Gulf of Finland. Two space systems National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) provided satellite data about temperature of the sea surface. SST data covers period 1981–2014 and includes 444 monthly data scenes with spatial resolution about 10 km. Data quality analysis displays high reliability of NOAA/AVHRR and Aqua/MODIS satellite information. The Gulf of Finland’s average annual SST has changed from 6.8°C in 1982 up to 8.2°C in 2014. Its mean speed of warming is about 0.04°C year^–1. The growth of the temperature was irregular, in the middle of 80th year, the temperature dropped down to 5.0°C, and then sharply increased up to 7.3°C in 1989. SST growth in the Gulf of Finland coincides with air temperature and sea temperature growth. The climate change in the Gulf of Finland has special significance due to the fragility of the northern ecosystems and high anthropogenic load.     

Two-component modeling of the optical properties of a diatom bloom in the Southern Ocean

Diatom cells have distinctive optical characteristics, originating from their relatively large cell size, fucoxanthin content and silica cell wall. It has been proposed that diatom-dominated phytoplankton blooms can be identified by optical remote sensing and that specifically tuned chlorophyll and primary production algorithms should be applied in regions where these blooms are present. However there have been few studies on how the optical properties of diatom blooms change as they progress from active growth to senescence, and it is unlikely that measurements on laboratory cultures encompass the full range of physiological states found in natural waters. We have therefore examined the inherent optical properties (IOPs) of the waters around the island of South Georgia at the end of the spring diatom bloom. Considerable variability was found in the relationships between the inherent optical properties and analytically determined chlorophyll a concentrations even in the surface layer, which meant that the usual bio-optical assumptions for Case 1 waters did not apply. To account for this variability, phytoplankton absorption and scattering were modeled as a two-component mixture, with the components representing actively growing and senescent material. The specific inherent optical properties of the two components were derived by linear regression of total IOPs against chlorophyll concentration and a fraction of the suspended mineral concentration. These specific IOPs were used to develop radiative transfer models of diatom blooms in varying stages of growth and senescence. Remote sensing reflectances calculated using this technique confirmed the tendency of the standard algorithms employed in SeaWiFS, MODIS and MERIS data processing to under-estimate near-surface chlorophyll concentrations in diatom blooms. However the inclusion of increasing proportions of senescent material had a significant effect on algorithm performance only at chlorophyll concentrations below 10 mg m^− 3. Optical depths predicted by the model around South Georgia were 9 +/− 2 m at 512 nm, indicating that a large fraction of the phytoplankton biomass was located below the depth from which the remote sensing signals originated.     

An evaluation of remote sensing techniques for enhanced detection of the toxic dinoflagellate, Karenia brevis

Optical techniques were investigated to enhance current bloom detection capabilities in support of an operational system for forecasting harmful Karenia brevis blooms along the west coast of Florida, within the Gulf of Mexico. Algorithms pertaining to backscatter and changes in spectral shape of remote-sensing reflectance were applied to SeaWiFS and MODIS imagery during known K. brevis and non-K. brevis events. A method to remove resuspended chlorophyll in Texas showed limited use when applied to several scenes following tropical storms off the west Florida coast. This analysis suggests that an ensemble image approach, wherein a combination of a chlorophyll anomaly, spectral shape at 490 nm and a backscatter ratio product would provide an improvement in satellite detection of K. brevis blooms. For southwest Florida, the combination of these methods through an ensemble approach may lead to an increase in user accuracy by 30-50%, as a result of correctly identifying non-K. brevis features. Where available, MODIS FLH scenes were analyzed to determine their use in K. brevis detection. However, insufficient imagery was available to make a fair assessment. Similar approaches could be applied to bloom tracking and monitoring in other regions.     

One-dimensional ecosystem model tests in the Po Prodelta area (Northern Adriatic Sea)

The application of a one-dimensional ecosystem model to a water column in front of the Po Prodelta area in the Northern Adriatic Sea is illustrated here. Validation was carried out for pelagic nutrients and phytoplankton biomasses by comparing simulations with historical data. Calibration was limited to the sediment parameters and to the suspended inorganic matter data from recent PRISMA-I (Programma di RIcerca e Sperimentazione per il Mare Adriatico) data sets. Primary production and nutrient abundance is found to be in overall agreement with climatological observations at the seasonal time scales. Model-data discrepancies are interpreted in the light of model assumptions. Main conclusions concern the importance of the inorganic suspended matter concentrations in determining the seasonal cycle of primary producers hinting to a strong light limitation in algal growth in this river dominated area. The need for further improvements in the pelagic dynamical processes of the silica cycle is also discussed.     

SeaWiFS discrimination of harmful algal bloom evolution

The discrimination of harmful algal blooms (HABs) from space would benefit both the capability of early warning systems and the study of environmental factors affecting the initiation of blooms. Unfortunately, there are no published techniques using global monitoring satellite sensors to distinguish the resulting subtle changes in ocean colour, so in situ sampling is needed to identify the species in any observed bloom. This paper investigates multivariate classification as an objective means to discriminate harmful and harmless algae and monitor their dynamics using ocean colour data derived from satellite sensors. The classifier is trained and tested using Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) data, though the method is designed to be generic for other sensors. Time‐series results are presented using the new HAB likelihood index and suggest that SeaWiFS has some capability for observing the dynamic evolution of harmful blooms of Karenia mikimotoi, Chattonella verruculosa and cyanobacteria. Further, a multi‐band spatial subtraction algorithm is described to automate the identification of bloom regions and improve the accuracy in discriminating HABs.     

A novel ocean color index to detect floating algae in the global oceans

Various types of floating algae have been reported in open oceans and coastal waters, yet accurate and timely detection of these relatively small surface features using traditional satellite data and algorithms has been difficult or even impossible due to lack of spatial resolution, coverage, revisit frequency, or due to inherent algorithm limitations. Here, a simple ocean color index, namely the Floating Algae Index (FAI), is developed and used to detect floating algae in open ocean environments using the medium-resolution (250- and 500-m) data from operational MODIS (Moderate Resolution Imaging Spectroradiometer) instruments. FAI is defined as the difference between reflectance at 859 nm (vegetation “red edge”) and a linear baseline between the red band (645 nm) and short-wave infrared band (1240 or 1640 nm). Through data comparison and model simulations, FAI has shown advantages over the traditional NDVI (Normalized Difference Vegetation Index) or EVI (Enhanced Vegetation Index) because FAI is less sensitive to changes in environmental and observing conditions (aerosol type and thickness, solar/viewing geometry, and sun glint) and can “see” through thin clouds. The baseline subtraction method provides a simple yet effective means for atmospheric correction, through which floating algae can be easily recognized and delineated in various ocean waters, including the North Atlantic Ocean, Gulf of Mexico, Yellow Sea, and East China Sea. Because similar spectral bands are available on many existing and planned satellite sensors such as Landsat TM/ETM+ and VIIRS (Visible Infrared Imager/Radiometer Suite), the FAI concept is extendable to establish a long-term record of these ecologically important ocean plants.     

Detection of algal blooms over optically complex waters of the Arabian Gulf and Sea of Oman using MODIS fluorescence data

The Arabian Gulf and the Sea of Oman are two of the most complex and turbid ecosystems in the world where algal blooms frequently occur. The conventional blue/green band ratio shows low performance to detect these algal batches in this region due to the effect of the non-algal parameters, shallow water depth, and atmospheric aerosols. Thus, an attempt to use MODIS (Moderate Resolution Imaging Spectroradiometer) fluorescence for the detection of algal blooms in this region have been undertaken using in situ measurements (Chlorophyll a: Chl-a, coloured dissolved organic matters: CDOM, Secchi disk depth: SDD, and radiometric) collected in 2006, 2013, and 2014, and MODIS satellite images. MODIS fluorescence line height (FLH in W m^?2 µm^?1 sr^?1) data showed low correlation (coefficient of determination: R² ~0.46) with near-concurrent in situ Chl-a (mg m^?3). This disparity is caused by the effect of the suspended sediments (SDD), CDOM (<2 mg m^?3 or >2 mg m^?3), and bottom reflectance (water depth: WD) parameters, where an increase of 1% in their magnitudes can cause a respective change of 13.4%, ?0.8% or 6%, and 1.4% in the FLH. In this work, the positions of the FLH bands have been relocated to include 645 nm to reduce the effect of these parameters on Chl-a, which has improved the performance to R² of 0.76. This modified FLH (MFLH) model was found to perform well in the Arabian Gulf where the estimated bias, root-mean-square error (RMSE), and coefficient of determination are, respectively, 0.03, 1.06, and 0.76. High values of MFLH are indicating the areas of the algal blooms, while no overestimation was observed in the mixed pixel coastal areas. This result is explained by less sensitivity of this model to the non-algal particles, shallow water, and aerosols.     

Solving the ocean color problem using a genetic programming approach

The ocean color problem consists in evaluating ocean components concentrations (phytoplankton, sediment and yellow substance) from sunlight reflectance or luminance values at selected wavelengths in the visible band. The interest of this application increases with the availability of new satellite sensors. Moreover, monitoring phytoplankton concentrations is a key point for a wide set of problems ranging from greenhouse effect to industrial fishing and signaling toxic algae blooms. To our knowledge, it is the first attempt at this regression problem with genetic programming (GP). We show that GP outperforms traditional polynomial fits and rivals artificial neural nets in the case of open ocean waters. We improve previous works by also solving a range of coastal waters types, providing detailed results on estimation errors. To our knowledge, we are the firsts to publish numerical results regarding coastal waters. Experiments were conducted with a dynamic fitness GP algorithm in order to speed up computing time through a process of progressive learning.     

Algal blooming patterns and anomalies in the Mediterranean Sea as derived from the SeaWiFS data set (1998–2003)

Optical remote sensing allows to monitor the space and time heterogeneity of phytoplankton growth in marginal and enclosed seas, a factor critical to understanding their ecosystem dynamics. SeaWiFS-derived (1998–2003) data were used to monitor algal blooming patterns and anomalies in the Mediterranean basin. Yearly and monthly means of chlorophyll-like pigment concentration (chl) were computed for the 6 years available, and climatological means derived. The data set statistical variability was assessed by computing yearly and monthly chl anomalies, as the difference between each individual year/month and the corresponding climatological year/month. The space and time patterns of the chl field appear to concur with the Mediterranean general oceanographic climate, while the chl anomalies describe trends and “hotspots” of algal blooming. The analysis shows a general decrease of chl values in the yearly and monthly means, an increasing negative trend of chl anomalies over the basin interior, and the anticipation of the north-western spring bloom, in comparison to what seen in historical CZCS (1979–1985) data. These have been interpreted as symptoms of an increased nutrient-limitation, resulting from reduced vertical mixing due to a more stable stratification of the basin, in line with the general warming trend of the Mediterranean Sea in the last 25 years. The patterns of high chl, coupled to a positive trend of chl anomalies, recurring at near-coastal hotspots, appear to be linked to continental runoff and to a growing “biological dynamism” at these sites, i.e. to the intensification of noxious or harmful algal blooms, in the north-west, and of coastal fisheries, in the south-east.     

Comparing MODIS and MERIS spectral shapes for cyanobacterial bloom detection

A spectral shape algorithm applied to Medium Resolution Imaging Spectrometer (MERIS) imagery has detected cyanobacterial blooms, with extensive examples in Lake Erie. The detection algorithm uses an approximation of the second derivative as a measure of spectral shape around the 681 nm band S _2d(681). With the end of the MERIS mission on 8 April 2012, an analogue was developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) to continue monitoring for these blooms. The MODIS analogue uses the standard ρ_s (Rayleigh-corrected reflectance) to determine S _2d(678), which is computationally equivalent to the negative of the MODIS fluorescent line height (FLH). A comparison was made of the two products from image pairs during a period of relatively severe blooms of cyanobacteria (2008–2011). When the MODIS bands do not saturate due to surface scums from high cyanobacteria biomass or conditions of glint or dense aerosols, the algorithms produce comparable results with a linear transform of the MODIS S _2d(678). The results indicate that MODIS can be used to monitor these blooms. Dense cyanobacteria blooms will produce negative FLH showing a limitation of FLH for bloom detection. The S _2d(678) offers a tool to support monitoring for dense algal blooms.     

Freshwater algal bloom prediction by extreme learning machine in Macau Storage Reservoirs

Understanding and predicting dynamic change of algae population in freshwater reservoirs is particularly important, as algae-releasing cyanotoxins are carcinogens that would affect the health of public. However, the high complex nonlinearity of water variables and their interactions makes it difficult in modeling its growth. Recently, extreme learning machine (ELM) was reported to have advantages of only requirement of a small amount of samples, high degree of prediction accuracy and long prediction period to solve the nonlinear problems. In this study, the ELM-based prediction and forecast models for phytoplankton abundance in Macau Storage Reservoir are proposed, in which the water parameters of pH, SiO₂, and some other water variables selected from the correlation analysis were included, with 8-year (2001–2008) data for training and the most recent 3 years (2009–2011) for testing. The modeling results showed that the prediction and forecast (based on data on the previous 1st, 2nd, 3rd and 12th months) powers were estimated as approximately 0.83 and 0.90, respectively, showing that the ELM is an effective new way that can be used for monitoring algal bloom in drinking water storage reservoir.     

一种蓝藻生物量长期原位实时监测仪的研制

近年来,各类水体蓝藻水华灾害频繁发生,导致水体生态系统功能退化和破坏,并严重危害到水产养殖和用水安全.对水体中蓝藻生物量进行长期实时监测是防治蓝藻水华灾害的一项必要措施.基于蓝藻中所含藻蓝蛋白的特征荧光效应,即采用620 nm波长的强光激发出645 nm波长的荧光并通过检测其强度来推算水中蓝藻生物量,最终研制出了一种能对水体中活体蓝藻生物量实现原位快速监测的仪器,辅以专门设计的自清洗功能使之能长期免于人工维护和校准.为满足对广阔水域的时空分布动态监测的需要,采用GPS技术获得各测点地理位置信息,还采用无线通信技术实现其与监测中心的远程实时在线数据通信,藉此扩展了其实用范围.实验结果表明,蓝藻生物量与检测值的线性相关系数可达0.98以上,可见检测值能精确地反映蓝藻浓度的变化.