Centre National d’Etudes Spatiales
Cyanobacteria in Baltic sea, 30-June-2003, MODIS.
State of the Art Remote Sensing for Health applications N° Ref.: 044-R755-v1.1-a 15 July 2010
Centre National d’Etudes Spatiales Page: 1/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL Centre National d’Etudes Spatiales Page: 2/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL
Control document table
Nom et Fonction Date Signature Prepared by Jérôme Bruniquel 15/07/2010 Chloé Vincent 15/07/2010
Reviewed by Antoine Mangin 15/07/2010
Authorised Odile Fanton d’Andon 15/07/2010 by
Change log
Version Date Description Modification
1.0 15/07/2010 Final report on the state of the art – Remote Translation of the Sensing applied to Health French version
Centre National d’Etudes Spatiales Page: 3/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL Centre National d’Etudes Spatiales Page: 4/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL Table of content
1 INTRODUCTION ...... 7 2 INFECTIOUS DISEASES TRANSMITTED BY AGENTS OR BY WATER ...... 8 2.1 CONTEXT AND MAIN RESEARCH AXIS ...... 8 2.2 PRESENTATION OF SOME KEY WORKS ...... 8 2.3 ASSESSMENT OF CURRENT RESEARCH STATUS...... 13 2.4 SUMMARY OF WORKS DEALING WITH THIS THEMATIC FIELD...... 14 3 REMOTE SENSING OF (HARMFUL) ALGAL BLOOMS (H)ABS ...... 16 3.1 CONTEXT AND MAIN SYNDROMES CAUSED BY HARMFUL ALGAE ...... 16 3.2 PRESENTATION OF SOME KEY WORKS ...... 18 3.3 ASSESSMENT OF THE CURRENT RESEARCH STATE-OF-THE-ART ...... 24 3.4 SUMMARY OF WORKS DEALING WITH THIS THEMATIC FIELD...... 25 4 DANGEROUS SPECIES...... 27 5 EUROPEAN WATER REGULATIONS ...... 28 5.1 WATER FRAMEWORK DIRECTIVE (WFD) 2000/60/CE ...... 28 5.2 BATHING WATER QUALITY...... 30 5.3 MARINE STRATEGY FRAMEWORK DIRECTIVE ...... 33 6 REFERENCES ...... 34 Table of Abbreviations ...... 39 ANNEX 1: SCIENTIFIC COMMUNITY ON CYANOBACTERIA AND HABS...... 41 ANNEX 2: HISTORIC AND PROJECTED OCEAN COLOUR MISSIONS – PERFORMANCES OF THEIR SENSORS...... 43
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Figure 1 (left) : Oct. 2008 forecast of RVF in Africa, source GEIS...... 12 Figure 2 (right): Rainfall anomalies forecast for Malaria risk areas for the period 11-20 sept.2009, source ADDS...... 12 Figure 3 : Absorption Spectra for different algae species, from Ruddick-HABWatch, 2003. 18 Figure 4 : Diagram of similarity between different algae species, from Millie et al., 2002.... 19 Figure 5 : Relation of low chl-bbp ratio in the case of a Karenia brevis bloom in situ (left) and modelled (right)...... 20 Figure 6 : Measured Chl-a VS Fluorescence Line Height MODIS for Karenia brevis blooms identification...... 20 Figure 7 : “True colour” MODIS image of Nodularia cyanobacteria bloom in Baltic sea on 30 June 2003...... 22 Figure 8 : Extract of a HAB forecast bulletin for Florida – 14 Sept 2009, source HAB-FS NOAA...... 23 Figure 9 : Example of forecast of chlorophyll concentration...... 24 Figure 10 : 3 days forecasts (26/09/2009) of the presence of jellyfishes in the Chesapeake bay ...... 27
List of Tables
Table 1 : Required bathing water quality for the directive 1976/160/CEE...... 31 Table 2 : Required Quality for bathing waters for the directive 2006/60/EC...... 32 Table 3: Caracteristics of Regional and Global Ocean Colour Missions’ (Sources IOCCG, ACRI-ST)...... 43
Centre National d’Etudes Spatiales Page: 6/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL 1 Introduction
This document presents the state-of-the-art of remote sensing techniques applied to health and aims at being representative of the use of satellite data to characterise water quality, either in marine environment or for inland waters sufficiently extended.
This report is organised around main families of organisms whose presence which trigger a risk for health, can be evaluated by satellite acquisitions. Hence, the development or the transmission of infectious disease, the presence of toxic or dangerous species having an impact on the water quality are developed in this state-of-the-art. A specific section is dedicated to the regulations in force at European level.
For each organism, various modes of detection can be studied. We have grouped them together in 3 categories: - Direct remote sensing by observing water colour, as for instance in the case of algal bloom, which can have certain limitations, - Statistical indirect remote sensing computing the likelihood of presence of certain vectors of risk or pathogens from the environmental parameters observed by satellite. This technique is widely used to quantify the risk of development of epidemics like cholera, - Deterministic indirect remote sensing where the environmental characteristics are observed by satellite, and then integrated in a development model. This approach is based on a situation initially observed which can be confirmed by a study on ground and allows forecasting its evolution by using environmental and/or bio-geochemical models.
Some key studies led in each category are presented and, in each domain, a table summarises all the studies analysed in the frame of the state-of-the-art.
The annex 1 of this document is devoted to the organisation of the community working on algae and cyanobacteria bloom. A second annex provides a table which summarises past, current and future ocean colour space missions.
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2.1 Context and main research axis
Satellite data give access to the environmental situation with a large coverage and with a regular frequency. In this section, we will focus more particularly on epidemic diseases whose evolution can be monitored by remote sensing of parameters linked to the marine environment.
Various pathogens can be responsible for these diseases: bacteria, viruses or parasites. Depending on the species, two modes of development linked to the presence of water are possible for these diseases: vector-borne or water-borne diseases.
The infectious agents of vector-borne diseases survive in a host organism which, during its activities, transmits the pathogen to other organisms. The mosquito is a very efficient carrier, especially for diseases such as malaria. It grows in wet areas which can be monitored with high resolution satellite data.
In case of water-borne diseases, the pathogen can survive alone in aqueous environment. These diseases spread very quickly in countries where sanitary networks are inefficient or inexistent. Pollution is thus carried by run-off or by infiltration in fresh water sources, then infecting drinkable water and food. Its dissemination inside animal or human population is as much faster as they are in contact with the contaminating sources. Cholera is a well-known example of water-borne disease.
In each of these cases, remote sensing technique can provide environmental conditions and thus help to evaluate the risk. It is often used to quantify the vulnerability of populations against these contaminations. The LANDSAT satellite allows for instance to obtain land cover maps to evaluate the proximity between the populations and potentially infected areas.
Several studies have been conducted about the impact of inland waters but few have been published on coastal waters (Lleo et al., 2008). Since the optical device onboard the satellite does not allow the direct detection of the infectious agents or their hosts, the scientists endeavoured to the correlation between the evolution of the environment and the development of pathogens.
A state of the art gathering studies about these infectious diseases linked to water is presented in the next section, with a focus on some studies representative of achieved progresses and current status of researches.
2.2 Presentation of some key works
Cholera is recurrently addressed in papers combining remote sensing and water-related diseases. This is why we decided to detail here the key studies dealing with this topic.
Cholera is a diarrhoeal epidemic disease, strictly human, due to a bacterium: the Vibrio cholera (Institut Pasteur, 2009). This disease rages on most continents, except South and Centre National d’Etudes Spatiales Page: 8/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL Central America, and grows up mainly in wet areas. In fresh water, the bacteria can survive 4 days (Ferdous, 2009) but its lifetime can be of tens days in sea water. (Hood & Ness, 1982 ; Ferdous, 2009). The studies led on this disease rely on a good knowledge of both the environment and the declared diseases which allow deriving the optimal survival conditions of the pathogen.
One of the first studies dealing with this topic was from Lobitz et al. in 2000. The objective was to observe the environmental conditions (SST over 1989-1995 ; SSH over 1992-1995) and the cholera epidemics in Bangladesh during the same periods. Interesting relationships have been highlighted between the Sea Surface Temperature (SST), the Sea Surface Height (SSH) and the number of cholera cases, but no correlation study has been done to evaluate these relationships more precisely. The link with the elevation of the free surface (SSH) in sea is probably due the topography of the country which favours contact between people and seawater in case of high sea level rise. The authors mention as a perspective the potential use of other environmental parameters such as chlorophyll concentrations and the salinity.
Later, Mendelsohn & Dawson have conducted in 2008 a second study about a cholera epidemic disease over 13 months (2000-2001) in South Africa.
Data which have been analysed are the sea surface temperature, the chlorophyll, the pluviometry and the SSH in answer to the study made by Lobitz mentioned above. The sea surface temperature (r²= 0.749) and the rain ahead of 2 months (r²= 0.744) have strong correlation with the epidemic diseases of 2000-2001. The same goes for the presence of chlorophyll which is well correlated (r²=0.656) with cholera cases occurring 6 months later. This link can be explained by the fact that the vibrio cholera is attached to zooplankton, the 6 months might correspond to the delay between the phytoplankton and the zooplankton peaks but this assumption remains controversial. The elevation of the free surface has no obvious link with cholera in this case (r²=0.326). To continue this analysis, the authors had envisaged to focus on a longer period.
Constantin de Magny et al. (2008) have presented their work done with a 9-year time series on cholera over two sites in Bangladesh along with information about anomalies of chlorophyll, rain and sea surface temperature. The following model to forecast the likelihood of occurrence of a cholera epidemic disease is derived from these data.
where y represents the number of observed cases, η the adjusted model, Po the Poisson’s distribution, β i the model parameters, I MAM ,I JJA , I SON the quarter average values of cholera cases in March-April-May, June-July-August and September-October-November respectively.
The same year, Emch (2008) took an interest in three sites in Bangladesh and in Vietnam over which he got 10 years of data related to health and environment: sea surface temperature (SST), sea surface height (SSH), Chlorophyll-a, rain, air temperature, river flows and related water heights.
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In the first site in Bangladesh, the chlorophyll has a strong positive correlation with cholera cases, and this correlation remains when it is shifted by 2 months. The river flow is negatively correlated with extreme epidemic cases, which, according to the author, can be explained by a too important mixing and a transportation of pathogen agents preventing from a development of bacteria and transmission to human populations. The other parameters have no significant links with the cholera epidemic diseases.
In Vietnam, over the Hue site, the sea surface elevation (with or without a 2 months shift), the river water heights and the 2 months ahead pluviometry have a significant negative correlation. SST has, on the contrary, an important positive correlation. On the other hand, chlorophyll and air temperature have no specific signature.
Over the last Vietnamese site, at Nha Trang, rain, river flow and water height (with a 2 months shift) are highly correlated to cholera epidemics. SST, SSH and air temperature have no significant correlation.
The temporal shifts between significant climate changes and emergence of cholera cases are very interesting with the perspective of prediction of epidemic diseases. However, the relationships between environment and cholera are dissembled from one site to another. This raises the issue of the transfer of occurrence likelihood model from one endemic zone to another.
These studies make use of chlorophyll data acquired by SeaWiFS with a 1 x 1 km resolution, taken at closest locations from studied sites. Unfortunately, the quality of these data has not been evaluated in these studies. Another study has been achieved in 2007 by CLS in the frame of the VIBRIO project to try to establish relationship between choleric bacteria and chlorophyll. The results obtained with basic MERIS and MODIS products show that the quality and the frequency of satellite data is not sufficient. An interesting alternative could consist in testing semi-analytic algorithms like GSM (Maritorena et al., 2002) with the merged datasets of SeaWiFS, MERIS et MODIS data from the GlobColour project (http://www.globcolour.info/) funded by the European Space Agency.
The presence of other vibrio such as the parahaemolyticus vibrio can also be correlated with environmental parameters. Vibrio parahaemolyticus is present in marine environment (mainly in coastal and estuary waters) where it is isolated from waters whom temperature is over 10°C. Its proliferation is favoured by a temperature above 20°C and a moderate salinity (15 to 25 g per litre). It is halophilic. Vibrio parahaemolyticus is either free in the water or linked to zooplankton and marine vegetation. Shells (oyster, mussel, scallop, …) are being infected by water filtration and the germ is present in the bowel and the tissues. Vibrio parahaemolyticus colonizes also gastropod mollusc (winkles, elms, Clithon corona, Clithon retropictus, Heminerita japonica, Nerita albicilla, …), some shellfish (prawns, crab, spider crab, lobster, …), cephalopods (squids, calamari) and has been isolated in the bowels of numerous fishes (anchovy, sardines, eels, white-spotted spinefoot, flat-tail cod, mullets, tilapias, mackerels,...)1. Zimmerman et al. (2007) found significant correlations, over an in-situ site in Mississippi, between turbidity, salinity and the presence of this pathogen agent. Surface water temperature
1 See http://www.bacterio.cict.fr/bacdico/vv/parahaemolyticus.html (in French) Centre National d’Etudes Spatiales Page: 10/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL and chlorophyll concentration were not found to be correlated with the occurrence of the vibrio. However, for another study site in Alabama, his work did not establish any link between the presence of vibrio and environmental parameters. Other studies must be done to consolidate the possible link between the environment and the epidemiology linked to this bacterium. For the time being, no study using remote sensing techniques has been led on this subject but this application based on remote sensing data could lead to interesting results if we can define an appropriate likelihood model of presence.
The presence of Escherichia coli or of other faecal coliforms can also cause enteric diseases. Different works showed that, depending on the study zone, there may be particular relationships between the presence of pathogen agents and environmental markers, such as turbidity (Busse et al., 2007) or other parameters (Clark et al., 2000; Affian et al., 2002). These results are very dependant on the localisation of the area of interest and anthropogenic supplies often explain the differences that are observed. The use of direct remote sensing to set up a forecast model of presence of faecal coliforms has been considered as well. Such a study has been presented by from Vincent et al. (2005) during the 16th W.T. Pecora Memorial Symposium “Global Priorities in Land Remote Sensing”, seems to have been dropped and the results were eventually not published.
Another research field consists in measuring in real-time the sources of pollution and modelling the development and the transport of bacteria from available environmental information. This approach has been chosen by DHI (Danish Hydraulic Institute) for their bathing water quality forecasting system and is used in particular in Copenhagen (http://bathingwater.dhigroup.com/earlywarningsystem.html) and in Gentofte (in Danish: http://www.waterforecast.com/Gentofte/badevand.htm). This tool provides customised information for each type of users. In the future, remote sensing data assimilation by these models may prove its interest (this is one of the research field of AQUAMAR project, co- funded under the FP7, which has started in April 2010).
A last approach of the same type is followed in the GIRAC (http://www.pole-mer- bretagne.com/girac.php) project, approved by the French « Pôles Mer PACA et Bretagne », which gathers pilot sites located in PACA region (south-east of France) and Brittany for the setup of an integrated system for monitoring waste and pluvial water releases at sea. The modelling of rain water networks and catchment’s areas for each site is coupled with a coastal modelling of the discharges of these releases. There is currently no use of satellite data but this option seems promising to complement the detection of diffuse pollution sources.
With regard to vector-borne diseases, transmitted by mobile agents, various studies have been also carried out.
An early warning system has been set up for the Rift Fever Valley (RFV), transmitted to people by the Aedes mosquito. Anyamba et al. (2008) have developed a model based on earth observation data. The Spot-VEGETATION NDVI index is the main data used for the calculation of the epidemic risk. However, the water surface temperature is also taken into account as an important indicator of El Nino oscillations. Rainfall anomalies are also very linked to this vegetation index. Rift Valley fever epidemic risk indexes are monthly updated in the GEIS website (Global Emerging Infections System) http://www.geis.fhp.osd.mil/GEIS/SurveillanceActivities/RVFWeb/indexRVF.asp
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Figure 1 (left) : Oct. 2008 forecast of RVF in Africa, source GEIS. Figure 2 (right): Rainfall anomalies forecast for Malaria risk areas for the period 11-20 sept.2009, source ADDS.
Fuller et al. (2009) used ENSO index of Pacific surface temperature jointly with a vegetation index to identify the probability of development of the mosquito vector for the dengue fever.
Malaria is also a recurrent theme for epidemiologists. Studies use meteorological models results of rainfall forecasts from climatology (Thomson et al., 1999), or rainfall estimates and Cold Cloud Duration (CCD) from satellites (Rogers et al., 2002 ; WHO – Global Malaria Programme ; Grover-Kopec et al. 2005). Thompson et al. in 2005 focused on the link between an epidemic observed in Botswana and water surface temperature averaged over the Nino3.4 region (5°N-5°S ; 170-120°W) of the Pacific Ocean, indicator of climate oscillation El Nino/La Nina. The study has shown, despite a reduced study period, a significant relationship between the sea surface temperature and malaria cases.
Vegetation indexes, such as NDVI computed from AVHRR instrument, can be used as well (Thomson et al., 1999 ; Rogers et al., 2002). These data coupled with individual-based models of the vector allow forecasting the risk of disease transmission.
Access to satellite data is not always easy. This is why, for these applications in Africa, a data server ADDS (Africa Data Dissemination Service), comprising in particular an index of malaria epidemic risk has been implemented in the frame of the Global Malaria Programme of WHO (figure 2), and is accessible online at : http://igskmncnwb015.cr.usgs.gov/adds/index.php A second initiative consisted between 2004 and 2006 in defining a set of Earth observation products that could help epidemiologists in their research works in their areas of interest. This project named Epidemio (http://www.epidemio.info/) allowed computing various maps like the air temperature, important rivers and associated hydrographical network, country planning, vegetation or desert sand transportation.
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Models of epidemic risks, based on environmental parameters, cannot always be easily used from one region to another, as it is shown by the different studies on cholera. On the contrary, works at global scale seem to be more efficient. They are nevertheless limited to areas where these diseases are epidemic and not endemic. It seems then possible to develop a forecast model thanks to the time delay between certain climatic events and epidemic occurrences.
The role of remote sensing depends on the degree of knowledge of the disease. If the epidemiology of the disease is well known, remote sensing is used as a mapping tool to identify development areas with optimal conditions for the agents or pathogens associated to the disease. It might also be used to evaluate the vulnerability of populations closed to infected zones (Tran et al., 2002). If the disease is badly known, satellite data provide environmental information which can be associated with the evolution of past epidemic diseases to develop a risk model.
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Disease Parameters (Pathogen, type Reference Areas of Interest Period of observation (Measuring Instruments or Source) and/or vector) Water-borne Diseases Lobitz et al., 2000 SST (AVHRR), SSH (Topex/Poseïdon). Bangladesh 1992-1995 Mendelsohn & SST (AVHRR), Chl-a (SeaWiFS), rain (GPCP), SSH KwaZulu-Natal, South Africa. 2000-2001 Cholera Dawson, 2008 (Topex/Poseïdon). Constantin de Magny rain (GPCP v2), SST (Reynolds et al., 2002), CHL-a Kolkata, India and Matlab, 1998-2006 Bacteria: et al., 2008 (SeaWiFS). Bangladesh. Vibrio cholera Chl-a (SeaWiFS), SST (AVHRR), SSH Matlab, Bangladesh ; Hue and 1985-2003 Emch, 2008 (Topex/Poseïdon and Jason-1), rain, air temperature, Nha Trang, Vietnam. (1997-2003) for Chl river flow and height level (in situ data). Vincent et al., 2005 LANDSAT TM Spectral Bands 1-5 and 7. Lake Erie, Canada/USA. Bathing Water Measured pollution sources on water treatment Forecast project installations + biogeochemical model of 3D Copenhagen, Denmark. Real-time data (DHI) development. Real-time data GIRAC project Sources of pollutions (rain) + model of pollutant Antibes, Toulon, Brest and Duration of the (Veolia) fluxes transportation in sea. Saint Malo, France. project : Bacteria : Escherichia 2008-2011 Coli or other River Tallapoosa, Georgia, coliforms Busse et al., 2007 Turbidity, presence of coliforms. USA. River flows, conductivity, pH, temperature, dissolved Clark et al., 2000 Wyoming rivers, USA. 1990-1999 oxygen (in situ measurements), presence of coliforms. Visible imagery, airborne and temperature, salinity, conductivity, pH, dissolved O2, suspended matter, Affian et al., 2002 Laguna Ebrié, Ivory Coast. 01/10/1998 concentration in streptococcus, coliforms and clostridium (in situ measurements) Table 1 : Use of remote sensing for applications to water-borne diseases
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Disease Parameters (Pathogen, type and/or Reference Areas of Interest Period of observation (Measuring Instruments or Source) vector) Vector-borne Diseases Dengue and dengue hemorrhagic 5 indexes ENSO SST, Fuller et al., 2009 Costa Rica 2003-2007 Virus : Flavivirus NDVI and EVI (MODIS Terra) Vector : Aedes (moustique) 1982-1998 for AVHRR Rogers et al., NDVI (AVHRR), Land Surface Temperature Africa 1988-1999 for Malaria 2002 (AVHRR), CCD (MeteoSat-HRR) MeteoSat-HRR Parasite : Plasmodium Grover-Kopec et Falciparum Rainfall Anomalies (NOAA -RFE) Africa Since Sept. 2008 al., 2005 Vector : Anopheles gambiae Botswana (moustique) Thomson et al., ENSO SST index for the Nino3.4 zone (NCEP) SST over the Nino3.4 zone: 1982-2003 2005 and rainfall (CMAP v0407) [5°S ; 5°N]*[170 ;120°W]
NDVIg (AVHRR), Rift Valley Fever Anyamba et al., NDVI (SPOT VEGETATION) Africa Corn (Erythrea, Ethiopia, Virus : Phlebovirus Sept.2006-Mai 2007 2008 SST anomalies (AVHRR), rainfall anomalies Somalia, Djibouti) Vector : Aedes (moustique) (ARC)
Table 2 : Use of remote sensing for applications to vector-borne diseases related to water
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3.1 Context and main syndromes caused by harmful algae
The following table shows the main syndromes due to the presence of toxins secreted by different types of algae.
Associated Distribution in the world Syndrome and characteristics Species Maps ©WHOI, oct. 2008 (Class) HABs listed during the last 10 years DSP : Diarrhetic Shellfish Poisoning diarrhetic syndrome
These toxins lead rapidly to Dinophysis diarrhoea and vomiting (30 min Prorocentrum after eating contaminated shellfishes), spontaneously diminishing in 2 to 3 days, without (Dinoflagellate) sequels. No deadly case recorded.
Alexandrium PSP : Paralytic Shellfish Poisoning spp. Paralysing syndrome Gymnodinium
catenatum Brutal paresthesia in 5 to 30 minutes of lips, face, arms and legs. Pyrodinium Serious cases can lead to a lack of bahamense var. motor coordination, incoherency compressum and death risk by respiratory Gonyaulax paralysis. (Dinoflagellate)
CFP : Cigüatera fish poisoning Gambierdiscus Cutaneous irritation, gastro- toxicus enteritis, and face paresthesia. Neurological and cardiovascular (Benthic sequels have been noticed as well. dinoflagellate)
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NSP : Neurotoxic shellfish poisoning
Karenia brevis Gastro-intestinal and neurological symptoms. Aerosol emissions can (Dinoflagellate) lead to respiratory symptoms similar to asthma. No deadly case recorded.
ASP : Amnesic shellfish poisoning
Pseudo-nitzchia The toxins lead rapidly (in 2 to 24 spp. hours) to diarrhoea and vomiting with possible neurological (Diatom) symptoms, or even amnesia and mortal comas.
Cutaneous irritations, fever, and respiratory difficulties caused by direct contamination or inhalation of sea sprays containing Ostreopsis ovata Originally in tropical zones, then detected in phycotoxins, and eating of Mediterranean Sea: Italy, France, Monaco. contaminated sea products. The (Benthic specie is benthic but goes up to the dinoflagellate) surface when flowering. The toxin can then be present in the food chain. Anabaena Aphanizomenon Cylindrospermo psis Cutaneous irritations, lever lesions, Microcystis World wide: North and South America, Africa, Europe and more rarely of neurons. Nodularia and China. Oscillatoria Planktothrix
(Cyanobacteria)
Table 3: Main syndromes associated to HABs; from WHOI, 2008, AFSSET-AFSSA, 2006 and INVS, 2007.
Centre National d’Etudes Spatiales Page: 17/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL In this section, we will focus on the use of remote sensing for the forecast and the monitoring of harmful algal blooms caused by organisms listed in the previous table and which could have an impact on the people and/or animals’ health. The French coasts are regularly prone to the development of toxic algae, leading to public health issues and to the closing of beaches or ban on eating shellfishes. This problem is also recurrent in many other parts of the world as it is shown on the distribution maps of Table 3.
Through the various works studied, this section addresses the main usages of satellite data for the monitoring of some algae species, and stresses for each case the difficulties met and the existing or foreseen solutions for the improvement of forecasting systems.
3.2 Presentation of some key works
Thanks to remote sensing tools, it is possible to measure the reflectance due to certain species present in the water. This reflectance is the ratio between the energy scattered by the water surface and the incident energy. Absorption and diffusion effects are linked to this reflectance which is expressed for all constituents present in the water and the atmosphere. The water absorption spectrum, an important indicator, can be accessed after inversion and atmospheric corrections with an accuracy depending on the spectral resolution of the sensor. Some authors suggest that remote sensing of toxic species could be improved by using hyper-spectral sensors (Liew et al, 2000). Thanks to the detailed absorption spectrum, algae are more easily identifiable. Some important spectral signatures are shown on figure 3 hereafter. Craig et al. (2006) have demonstrated the identification of the Karenia brevis by hyper-spectral remote sensing. The study is based on spectral reflectances obtained during an ECOHAB campaign. The presence of an absorption peak peculair to this alga for wavelengths at 444 and 469 nm allows determining the presence of this species in the sea.
Figure 3 : Absorption Spectra for different algae species, from Ruddick-HABWatch, 2003.
Centre National d’Etudes Spatiales Page: 18/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL However a high spectral resolution is not always sufficient for differentiating all species. Some spectra are very similar and do not allow a discrimination of the algae based on the spectral signature. A similarity diagram between species, from Millie et al. (2002), is shown in figure 4. According to these results, various diatoms and dinoflagellates have very similar spectra and could not be differentiated easily. On the contrary, the similarity among cyanobacteria, also called cyanophytes, and diatoms/dinoflagellates is largely less important.
Figure 4 : Diagram of similarity between different algae species, from Millie et al., 2002.
Indeed, cyanobacteria are characterised by the presence of pigments like chlorophyll-a, carotenoids and phycobilins (phycocyanins and/or phycoerythrin). The phycocyanin can be easily measured by satellite (Metsamaa et al., 2006, Simis et al., 2005) and has an absorption peak at 620 nm, and a fluorescence at ca. 650 nm (Babin, personal communication). This is not common for other algae and such a reflectance spectrum allows the determination the presence of cyanobacteria in the surface waters. Simis et al. (2005) set up a model of the phycocyanin concentration from reflectances measured for inland turbid waters and from spectral bands corresponding to those used by MERIS.
Subramaniam et Carpenter (1999) have studied the remote sensing of non-toxic cyanobacteria (but nevertheless fundamental in the trophic cycle as it allows the importation of N2 in the environment medium): Trichodesmium, thanks to an interesting methodology using data from the high resolution radiometer AVHRR combined with ground truth. They have derived from this analysis a model based on the backscattering, the absorption and the fluorescence which allows detecting the presence of these cyanobacteria with concentration higher than 1mg/m3. Later in 2006, Metsamaa et al. have also assessed the possibility to detect cyanobacteria in case 1 waters above a given threshold and thanks to sensors having a sufficient spectral resolution. Another interesting study, done by Vincent et al. (2004), tried to set up a cyanobacteria forecast model from the LANDSAT TM spectral bands. The authors derived two models of presence of a characteristic pigment (phycocyanin) of cyanobacteria, which have a coefficient of determination of 0.74 and 0.78. Centre National d’Etudes Spatiales Page: 19/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL
The detection of harmful algae thanks to the Fluorescence Line Height (FLH), which represents the fraction of luminance created by the chlorophyll, is an alternative solution to the use of more common water products. This track has been studied by Hu et al. in 2005 and the first outcome is that the FLH from MODIS is higher correlated to in situ measurements than the chlorophyll concentration, in the complex coastal waters of the west coast of Florida. A high Fluorescence Line Height (FLH) is significantly linked to the presence of Karenia brevis and a bloom event has been monitored with this technique.
In 2004, the analysis from Cannizzaro et al. of the correlations between the chlorophyll concentration and the CDOM, with SeaWiFS data, and the presence of different species of phytoplankton allowed to establish an optical classification of the presence of these algae.
Later, in 2006, a monitoring system of the algae Karenia brevis based on this classification technique and on fluorescence was developed (Carder et al., 2006). The results have been successfully compared with in situ measurements and are presented in the following figure.
Bbp Bbp (550)
Figure 5 : Relation of low chl-bbp ratio in the case of a Karenia brevis bloom in situ (left) and modelled (right)
The use of fluorescence in this study reminds of the Hu et al.’s works (2005) previously mentioned where the authors have already observed a high FLH during Karenia brevis blooms thanks to FLH MODIS data. Here, the study shows (see next figure) the singular behaviour of the Fluorescence Line Height during of K.b. blooms measured on site.
Chl (mg m-3)
Figure 6 : Measured Chl-a VS Fluorescence Line Height MODIS for Karenia brevis blooms identification Centre National d’Etudes Spatiales Page: 20/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL
Kahru (internet publication) and Babin et al., 2005 nevertheless state the importance to distinguish the presence of potential harmful algae and the effective presence of toxins in the water. Numerous studies, complementary to those already mentioned, have been carried out on the identification of particular algae species from satellite data: Nodularia spumigena cyanobacteria detected by their high reflectance (Kahru et al., 1994), diatoms causing a decrease of absorption (Sathyendranath et al., 2004), dinoflagellates blooms with mycosporin (aminoacid) detected by a lower reflectance in the UV bands (Kahru and Mitchell, 1998), high concentrations in cyanobacteria thanks to the presence of phycocyanin (Simis et al, 2005; Vincent et al., 2004), etc...
Due to the difficulty to distinguish by satellite the presence of toxins from the simple presence of non-harmful algae, some researchers focused on the correlations which may exist between the environmental parameters and the presence of toxins. An interesting example is presented by Anderson et al. (2008) who leaned over the multi-criteria analysis to derive from a set of in- situ measures the link between environmental parameters and the presence, on one hand, of the Pseudo-nitzschia algae which is potentially harmful and, on the other hand, of the toxin secreted by this algae, the domoïc acid. Two different models have been derived from in-situ observations: the first one takes into account all interesting parameters, and the second one in light of a forecast by satellite data, based only on variables available by remote sensing. The forecasts are well representatives of the presence of the Pseudo-nitszchia algae, however the quantity of domoïc acid is often badly estimated.
In this study, the authors underline in parallel, as done in the VIBRIO report (2007) presented in the first section of this document, the low adequacy between SeaWiFS reflectances and those obtained during radiometric on-site measurement campaigns. As previously indicated, the use of semi-analytical algorithms like GSM (Maritorena et al., 2002) and merged products can be an asset for this kind of applications. This research emphasises also the difficulty of the forecasting of the presence of the toxin independently from the implicated algae, which is one of the reasons why the coupling of satellite and in-situ data is fundamental. Conversely, one must pay attention to the possible presence of toxins without any massive reproduction of harmful phytoplankton. In such a case, the use of remote sensing is not appropriate and regular in situ measurements are fundamental for the detection of these substances. The toxin secreted by the Alexandrium tamarense may cause serious PSP syndromes by accumulating in shellfishes which could be eaten.
Kerfoot et al. (2005) provided clarifications on one issue which can biase the use of satellite remote sensing. They dealt with the vertical migration of the Karenia brevis algae which is regularly present in large quantity in the Mexico gulf. Indeed, this algae is attracted by light, moves closer to the surface at mid-day and moves away towards the sea bed as light decreases. But satellites can only detect the upper part of the euphotic zone and thus, observations made at 12:00 and 16:00 may have different chlorophyll concentration. Moreover, in case of spectral identification of the various present species, the signal of Karenia brevis, potentially harmful, may be largely under-estimated in comparison to the one of other present algae.
These studies show that the monitoring of harmfulness due to the presence of algae cannot be done by satellite only. Direct harmfulness measures are needed for all monitoring systems. Kahru (internet publication) stresses on the limits of both sensors and remote sensing
Centre National d’Etudes Spatiales Page: 21/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL algorithms: problems of coastal waters, of shallow waters, of the low water depth which can be observed, of the geometric resolution also. One solution proposed by the author consists in using remote sensing images to complement regular in situ measurements in order to, when a toxic bloom has been identified, monitor it, derive its spatial distribution as well as its temporal evolution. The data proposed for such an application would be a “true colour” medium resolution MODIS image at 250 m and the turbidity parameter at the same resolution (Kahru et al., 2004). The next figure is a MODIS image and shows the extent (around 100 km) of the Nodularia cynobacteria annual bloom in Baltic sea.
Figure 7 : “True colour” MODIS image of Nodularia cyanobacteria bloom in Baltic sea on 30 June 2003
Independently of the production of toxins, a massive reproduction of phytoplankton may be proved to be detrimental for the ecosystem. Eutrophication induces for instance a reduction of the penetration of light in the marine medium, or even extreme situation of anoxia due to a too important consumption of oxygen during the decomposition phase of biomasses. The forecast of massive apparition of phytoplankton is also useful here, even if the direct impact on human health is limited. Unlike other applications previously mentioned, the forecast of such events does not imperatively need a coupling between remote sensing and in situ measurements to identify risky situations.
The development of an operational and efficient HAB forecast system remains the objective of each of these studies. Here are some solutions which have been proposed.
Since 2004, an operational publication system of monitoring bulletins of the presence of harmful algae has been set up over Florida by the research teams of the NOAA. This project HAB-FS (Harmful Algal Bloom Forecast System) aims at being implemented in other regions in the USA. Monitoring and information systems on verified algae blooms are also published regularly at: http://tidesandcurrents.noaa.gov/hab/ The bulletin issued by the NOAA shows results of in situ measurements of the toxicity of waters done by different permanent stations, data obtained by Autonomous Underwater Vehicle (AUV) and MODIS chlorophyll concentrations from the CoastWatch project.
Centre National d’Etudes Spatiales Page: 22/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL Environmental information like the wind forecast over the zone, allows following these algal blooms.
Figure 8 : Extract of a HAB forecast bulletin for Florida – 14 Sept 2009, source HAB-FS NOAA.
Since 2004, areas located east of the Gulf of Mexico and Florida have their operational system. A demonstrator is also available online over internet for the west part of the Gulf of Mexico and the development of such forecast systems is planned in the 5 next years on the Erie lake, the Gulf of Maine, Washington and Oregon coasts, Californian coast as well as the Chesapeake bay. Indeed, in these areas, the presence of harmful algae is regularly listed. Here, forecasts are done up to 5 days and the chlorophyll concentration is completed by in situ measurements of the harmfulness of observed algae.
With the same perspective, MARCOAST project ensures a downstream service of detection of algae blooms by supplying daily chlorophyll products over all open European waters. The description of these products is available at: http://213.236.12.71:8010/marcoast/htdocs/sections/service.html#S4 Here, the chlorophyll content is not associated to toxicity.
Another algal bloom operational forecast system is proposed by DHI (Danish Hydraulic Institute) which models also the presence of algae without ruling on harmfulness. Environmental and ecological models forecast up to D+5 the oxygen concentration (Ilt), chlorophyll (klorofyl), primary production (primaerproduktion), water transparency (sigtdybde,). These predictions are shown on this another website (in Danish): http://www.waterforecast.com/Bansai/.
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Figure 9 : Example of forecast of chlorophyll concentration
These models do not assimilate satellite data nor in situ measures which could inform on the harmfulness of algae.
Remote sensing of non harmful algae is nevertheless interesting since the massive apparitions of algae may cause large imbalances of the ecosystems, and specially anoxia for the marine fauna.
3.3 Assessment of the current research state-of-the-art
The different works which have been analysed propose various solutions allowing the remote sensing of algae blooms in general or of particular species which are potentially harmful. The main parameters used are those derived from ocean colour multi-spectral sensors. Some studies use sensors with hyper-spectral or better geometric resolutions. Be that as it may, in every case, the evaluation of harmfulness cannot be done by the unique use of satellite data. Various problems occur: insufficient spectral, temporal or geometric resolutions, efficiency of algorithms applied on turbid waters, in shallow waters, detection limited to the close surface, absence of detection in case of low chlorophyll concentration, no link between harmful species and emission of toxin, etc. In order to answer these questions, in situ measurements are fundamental. These measures can be coupled to satellite data to ensure a better efficiency of alert systems. Satellite imagery offers a quasi-continuous coverage and allow the monitoring of a harmful bloom already identified in clear areas.
Centre National d’Etudes Spatiales Page: 24/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL 3.4 Summary of works dealing with this thematic field
Location and duration of Detected species Reference Parameters or sensors Description study West Coast of Florida ; Vertical migration of Kerfoot et al., 2005 in situ measurements October 2001, ECOHAB HABs and remote sensing campaign Use of fluorescence to FLH MODIS, in situ West Coast of Florida, Hu et al., 2005 detect Karenia brevis measurements October-December 2004. Karenia brevis blooms in coastal waters Identification of a Karenia Chl-a and FLH, MODIS, Gulf of Mexico, Carder et al., 2006. brevis bloom using remote in situ measurements. 2005/2006 sensing. IOP derived from in situ Use of spectral reflectance West Coast of Florida, Craig et al., 2006 measurements to identify K.b. 1999/2001/2003 Karenia b., Trichod., Chl-a and CDOM, Cannizzaro et al., Optical classification of West Coast of Florida, diatoms and SeaWiFS, in situ 2004. algae species October 2000. prochlorophytes measurements. IOP derived from Identification of Metsamaa et al., laboratory cyanobacteria thanks to 2006 measurements. their spectral signature. MODIS medium Remote detection of Kahru et al., 1994 Baltic sea resolution. cyanobacteria blooms. Identification of Subramaniam and Cyanobacteria AVHRR. cyanobacteria from their Somalian coast, May 1995. Carpenter, 1999 inherent optical properties. Remote sensing of Loosdrecht and IJsselmeer Simis et al., 2005 MERIS. cyanobacteria in turbid lakes, Pays Bas. inland waters. Detection of cyanobacteria Vincent et al., 2004 LANDSAT TM. Lake Erie, North America presence by remote sensing Centre National d’Etudes Spatiales Page: 25/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL Location and duration of Detected species Reference Parameters or sensors Description study of phycocyanin pigment using LANDSAT TM. Predictive model of Santa Barbara Channel, Pseudo-nitszchia Anderson et al, 2008 Chl-a, SeaWiFS. Pseudo-nitszchia and Nov. 2004 to June 2006. domoïc acid occurrence. Sathyendranath et al., Identification of diatoms Diatoms SeaWiFS. North-West Atlantic 2004 from their low absorption. Chl-a, O2, H2S, NH3, NO3, PO4, primary Biogeochemical forecast DHI, 2006 production and water Baltic Sea, North Sea. model. transparency. No specific species Ocean colour daily MARCOAST Chl-a, various sensors. Europe products. HAB-FS, NOOA HAB HAB-FS, NOAA Chl-a , MODIS. Gulf of Mexico, 2004-today forecast system. Kahru (internet MODIS medium Review of remote sensing
publication) resolution. use for HABs detection.
Table 4: Main studies related to the use of remote sensing for (H)ABs’ observation
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Other organisms are potentially dangerous for people. This is the case of zooplankton: some jellyfishes found in Australia may be deadly. In Europe, species are not so dangerous but the forecast of their apparition focuses attention of more and more researchers and local or regional authorities. Among them, Decker et al. (2007) have obtained by logistic regression a presence forecast formula of a typical jellyfish (Chrysaora) that is found in the Chesapeake bay. It gives: logit= –8.120 + (0.351 × temperature)– (0.572 × (salinity-salinity_mean)). proba = exp(logit) / [exp(logit)+1] Currently, the two parameters (salinity and temperature) are obtained from an hydrodynamical model without assimilation. The results of the model (cf next figure) are presented on the website: http://155.206.18.162/seanettles/index.php
Figure 10 : 3 days forecasts (26/09/2009) of the presence of jellyfishes in the Chesapeake bay
In the frame of the GENESIS project (2009), funded by the European Union (FP7-ICT), ACRI-ST has led a thematic work aiming at developing a forecast index of presence of jellyfish by integrating chlorophyll and sea surface temperature satellite data.
Another project, JellyWatch, led by the Oceanology Laboratoire of Villefranche-sur-Mer has started in October 2009. It focuses on the study of the biology of the Pelagia noctiluca species thanks to comparisons between its distribution in the sea and environmental events supplied by models and earth observation data.
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At European level, various laws have allowed to set up a legal framework controlling the protection of ecosystems, the prevention of marine pollutions and the reasoned use of marine resources. The first one is the water framework directive 2000/60/CE issued on the 23rd of October 2000.
5.1 Water Framework Directive (WFD) 2000/60/CE
This directive2 establishes a frame for a communitarian policy in the water domain for coastal waters (up to one marine mile off the coast), for surface inland waters and for underground waters. It relies on the need for a water management per hydrographical district. It sets an objective of good ecological status for 2015 by stressing the need of cooperation for the management of cross-border waters. The parameters allowing for a classification to a good ecological status for coastal waters are: quantity and composition of phytoplankton, marine flora and invertebrate benthic fauna, morphological conditions and tides regime, transparency, temperature, salinity, concentration in nutrients and oxygen as well as the presence of pollutants said “priority” or being discharged in important quantity in water.
The detail of the good ecological status for coastal waters is presented in the next table.
2 http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CONSLEG:2000L0060:20090625:EN:PDF
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Table 5: Definition of a good ecological status for coastal waters, source Directive 2000/60/CE, Annex V.
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The bathing water quality is one of these major directive on which the WFD relies on.
5.2 Bathing water quality
We mean by « bathing water » the waters or part of them, fresh, running or stagnant, as well as sea water, in which bathing is expressively permitted by the legal authorities of each Member State or is not prohibited and usually practised by an important number of bathers. The current European legislation is the directive 1976/160/ECC3 which controls the parameters listed in the next table:
3 http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CONSLEG:1976L0160:20081211:EN:PDF
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Table 1 : Required bathing water quality for the directive 1976/160/CEE
A second directive 2006/7/EC4 relative to the management of the bathing water quality was adopted on the 15th of February 2006 by the European Parliament and the European Council and will definitively abrogate the directive signed in 1976 on the 31st of December 2014.
The monitoring of bathing water quality is now limited to two parameters: Escherichia coli and Enterococci, with more stringent thresholds than those of the directive 76/160/ECC. These thresholds that define four quality classes (Insufficient, Sufficient, Good
4 http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2006:064:0037:0051:EN:PDF
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Table 2 : Required Quality for bathing waters for the directive 2006/60/EC
This directive introduces also an historical notion by integrating the ranking over four consecutive years instead of one.
Concerning aquaculture, the directive 2006/88/EC5 of the 24th October 2006 governs at the European level the conditions of sanitary police applicable to animals and products from aquaculture and the prevention of some diseases of aquatic animals and to the fight measures against these diseases.
These directives mentioned so far are valid for inland waters and coastal waters limited to one marine mile off European coasts, which represents less than 20% of all European waters. Another European directive complements the regulations by dealing with the marine strategy for all European waters and establishes a community framework in the field of marine environmental policy.
5 http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CONSLEG:2006L0088:20080521:FR:PDF Centre National d’Etudes Spatiales Page: 32/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL
5.3 Marine Strategy Framework Directive
6 This directive (2008/56/EC) aims at setting up a marine strategy in order to ensure the protection and the preservation of marine environment for coastal waters as defined in the directive 2000/60/EC, as well as all waters hold by European Union Member States as stated in the United Nations convention on the sea right (up to 200 marine miles off basis lines). It is first foreseen to evaluate the current ecological status of marine regions and to define homogeneously for each zone a “good ecological status” and a set of environmental objectives before July 2012. Before July 2014, an environmental monitoring programme should also be set up in order to update these objectives. The directive also plans for 2015 the determination of a set of measures in order to reach or to maintain this “good ecological status”. These measures will be enforced the next year.
6 http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:164:0019:0040:EN:PDF
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ADDS, Africa Data Dissemination Service [Url: http://igskmncnwb015.cr.usgs.gov/adds/]
Affian K., Djagoua E.V., Kouamé F.F., Gioan P., Bemi J., 2002. Etude par télédétection aéroportée d’un environnement lagunaire en zone tropicale : cas de la lagune Ebrié en Côte d’Ivoire.Télédétection, 2 (4), 233-242.
AFSSET/AFSSA, juillet 2006. Risques sanitaires liés à la présence de cyanobactéries dans l’eau. [Url : http://www.afsset.fr/upload/bibliotheque/085391856141331010617707867709/cyanobacteries.pdf]
Anderson C.R., Siegel D.A., Kudela R.M., Brzezinski M.A., 2008. Empirical models of toxigenic Pseudo-nitzschia blooms : Potential use as a remote detection tool in the Santa Barbara Channel. Harmful algae 8, 478-492.
Anyamba A., J.P. Chretien, J. Small, C.J. Tucker, P.B. Formenty, J.H. Richardson, S.C. Britch, D.C. Scnabel, R.L. Erikson and K.J. Linthicum, 2008. Prediction of a Rift Valley fever outbreak, PNAS 106(3), 955-959
Babin M., J.J. Cullen, C.S. Roesler, P.L.Donaghay, G.J. Doucette, M. Kahru, M.R. Lewis, C.A. Scholin, M.E. Sieracki and H.M. Sosik, 2005. New approaches and technologies for observing harmful algal blooms. Oceanography, 18 (2), 210-227.
Boutin J.P., H. Delorme, et L.J.Andre, 1992. Eau douce et pathologie. Médecine d’Afrique Noire, 39(3), pp.165-168.
Brown, C.W. and J.A. Yoder, 1994. Coccolithophorid blooms in the global ocean. J. Geophys. Res., 99, 7467-7482.
Busse E.L., Parrish B.K., Hollabaugh C.L., and Harris R.R.,2007. The correlation of fecal colliform and turbidity of the little Tallapoosa river in the West Georgia region. 2007 GSA Denver Annual Meeting (28–31 October 2007), Paper No. 81-6.
Carder K., Cannizzaro J., Chen F.R. and Walsh J., 2006. Remote detection of HABs in the Gulf of Mexico: Alerts for resource management. In: MODIS Meeting, October 2006, poster session.
Cannizzaro J.P., Carder K.L., Chen F.R., Walsh J.J., Lee Z.and Heil C., 2004. A novel optical classification technique for detection of red tides in the gulf of Mexico. In: Steidinger, K.A., Landsberg, J.H., Tomas, C.R., and Vargo, G.A. (eds). Harmful Algae 2002. Florida Fish and Wildlife Commission, Florida Institute of Oceanography and Intergovernmental Oceanographic Commission of UNESCO, pp. 282-284.
Clark M.L. and Norris J.R., 2000. Occurrence of Fecal Coliform Bacteria in Selected Streams in Wyoming, 1990-99. Water-Resources Investigations Report 00-4198 Cheyenne, Wyoming 2000.
Centre National d’Etudes Spatiales Page: 34/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL Constantin de Magny G., R.Murtugudde, M. R. P. Sapiano, A. Nizam, C.W. Brown, A. J. Busalacchi, M. Yunus, G. B. Nair, A. I. Gil, C. F. Lanata, J. Calkins, B. Manna, K. Rajendran, M.K. Bhattacharya, A. Huq, R.B. Sack, and R.R. Colwell, 2008. Environmental signatures associated with cholera epidemics. PNAS, 105 (46), 17676-17681.
Craig S.E., Lohrenz S.E., Lee Z., Mahoney K.L., Kirkpatrick G.J., Scofield O.M., Steward R.G., 2006. Use of hyperspectral remote sensing reflectance for detection and assessment of the harmful alga Karenia brevis. App. Optics, 45 (21), 5414-5425.
Decker M.B., C.W. Brown, R.R. Hood, J.E. Purcell, T.F. Gross, J.C. Matanoski, R.O. Bannon and E.M. Setzler-Hamilton, 2007. Predicting the distribution of the scyphomedusa Chrysaora quinquecirrha in Chesapeake Bay. Mar. Ecol. Prog. Ser., Vol. 329: 99–113. [Url : http://155.206.18.162/seanettles/index.php]
Delorme H., J.P. Boutin et L.J.Andre, 1992. Eau de mer et pathologie. Médecine d’Afrique Noire, 39(3), pp.173-176.
DHI, Bathing water forecasting: [Url: http://bathingwater.dhigroup.com/services/WaterQualityForecast.html]
Emch M., Feldacker C, Yunus M, Streatfield PK, DinhThiem V, Canh do G, Ali M., 2008. Local environmental predictors of cholera in Bangladesh and Vietnam. Am J Trop Med Hyg., 78(5),823-832
Epidemio : Project : availability of remote sensing resources for epidemiology. [Url : http://www.epidemio.info/]
Ferdous Z., 2009. Survival of Vibrio cholerae O139 in association with Anabaena variabilis in four different microcosoms. Amer. Jour. of Applied Sciences 6(3): 439-444.
Fuller D.O., A.Troyo and J.C. Beier, 2009. El Nino Southern Oscillation and vegetation dynamics as predictors of dengue fever cases in Costa Rica. Env. Res. Lett. 4, 014011. doi:10.1088/1748-9326/4/1/014011.
GEIS : Monthly predictions of rift valley fever epidemic risk. [Url : http://www.geis.fhp.osd.mil/GEIS/SurveillanceActivities/RVFWeb/indexRVF.asp]
GENESIS (FP7-ICT project), 2009. D8100.1 - State of the art on Air and Coastal Water quality on French Riviera. [Url : http://www.genesis-fp7.eu]
Global Malaria Programme [Url: http://malaria.who.int/epidemicsmews.html]
Grover-Kopec E., M. Kawano, R.W. Klaver, B. Blumenthal, P. Ceccato and S.J. Connor, 2005 : An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa. Malaria Journal, 4:6doi:10.1186/1475-2875-4-6
HABWatch proceedings, 11-21 june 2003, Villefranche-sur-Mer. [Url : http://www.obs-vlfr.fr/habwatch/]
Centre National d’Etudes Spatiales Page: 35/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL Herbreteau V., G. Salem, M. Souris, J.P.Hugot and J.P. Gonzales, 2005. Sizing up human health through Remote Sensing: uses and misuses. Parassitologia 47, 63-79.
Hood M.A. and G.E. Ness, 1982. Survival of Vibrio cholerae and Escherichia coli in Estuarine Waters and Sediments. Appl Environ Microbiol. 43(3): 578–584.
Hu C., Muller-Karger F.E., Taylor C., Carder K.L., Kelble C., Johns E. and Heil C.A., 2005. Red tide monitoring and tracing using MODIS fluorescence data : A regional example in SW Florida coastal waters. Rem. Sens. of Env., 97, 311-321.
Institut Pasteur, last update : March 2009. « Fiches sur les maladies infectieuses – Cholera » [Url:http://www.pasteur.fr/ip/easysite/go/03b-00000j-0ep/presse/fiches-sur-les-maladies-infectieuses ]
INVS, 31/07/2007. Note: Risques sanitaires liés à la présence d’ Ostreopsis ovata dans les eaux de baignades ou d'activités nautiques. [Url:http://www.invs.sante.fr/presse/2008/le_point_sur/note_ostreopsis_ovata/note_ostreopsis_ovata.pdf]
Kahru, M., B.G. Mitchell, 1998. Spectral reflectance and absorption of a massive red tide off Southern California, J. Geophys. Res. Vol. 103, No. C10, 21,601-21,609.
Kahru, M., U. Horstmann, O. Rud, 1994. Satellite detection of increased cyanobacteria blooms in the Baltic Sea: Natural fluctuation or ecosystem change? Ambio, 23 (8): 469-472.
Kahru M. Satellite detection of harmful algal blooms (HABs), Scripps Photobiology Group website.[Url:[http://spg.ucsd.edu/Satellite_Projects/Various_HABs/Satellite_detection_of_H ABs.htm]
Kerfoot J., Kirkpatrick G., Lohrenz S., Mahoney K., Moline M., Schofield O. 2005. Vertical Migration of a Karenia brevis Bloom: Implications for Remote Sensing of Harmful Algal Blooms. In : Harmful Algae 2002: Proceedings of the 10th International Conference of Harmful Algae, Steidinger, K.A., Landsberg, J.H., Tomas, C.R., and Vargo, G.A. (eds). 2005. Florida Fish and Wildlife Commission, Florida Institute of Oceanography and Intergovernmental Oceanographic Commission of UNESCO, pp. 279-281.
Liew S.C., Kwoh L.K., Lim H, 2000. Classification of algal bloom types from remote sensing reflectance. In : 21st Asian Conference on Remote Sensing, 4-8 dec., Taipei, Taiwan.
Lleo M.M., M. Lafaye et A. Guell, 2008. Application of space technologies to the surveillance and modelling of waterborne diseases. Cur. Op. in Biotech., 19, pp 307-312
Lobitz, B., Beck, L., Huq, A., Wood, B., Fuchs, G., Faruque, A.S., Colwell, R., 2000. Climate and infectious disease: use of remote sensing for detection of Vibrio cholerae by indirect measurement. Proc Natl Acad Sci U S A. 97, 1438-1443.
Maritorena, S., D.A. Siegel, and A.R. Peterson, 2002. Optimization of a semianalytical ocean color model for global-scale applications. Appl. Opt., 41, 2705– 2714.
Mendelsohn J. et Dawson T., 2008. Climate and cholera in KwaZulu-Natal, South Africa: The role of environmental factors and implications for epidemic preparedness. Int. J. Hyg. Environ. Health, 211, 156-162. Centre National d’Etudes Spatiales Page: 36/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL
Metsamaa L., Kutser T. and Strömbeck N., 2006. Recognising cyanobacterial blooms based on their optical signature: a modelling study. Boreal Env. Res., 11, 493-506. Millie D.F., O.M.E. Schofield, G.J. Kirkpatrick, G. Johnsen and T.J. Evens, 2002. Using absorbance and fluorescence spectra to discriminate microalgae. Eur. J. Phycology , 37(3):313-322
Reynolds R.W., Rayner N.A., Smith T.M., Stokes D.C., Wang W.Q., 2002. An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609-1625.
Rogers D.J., S.E. Randolph, R.W. Snow and S.I. Hay, 2002. Satellite imagery in the study and forecast of malaria. Nature, Vol 415 pp. 710-715.
Ruddick K., 2003. Overview of Ocean Colour theoretical background, sensors, and applicability for the detection and monitoring of HABs (capabilities, limitations). In : HABWatch workshop, Villefranche-sur-Mer, 11-21 june 2003. [Url : http://www.obs- vlfr.fr/habwatch/]
Sathyendranath, S., L. Watts, E.Devred, T. Platt, C. Caverhill, and H. Maass, 2004. Discrimination of diatoms from other phytoplankton using ocean color data. Mar. Ecol. Prog. Ser., 272, 59-68.
Simis, S.G.H, S.W.M. Peters, and H.J. Gons, 2005. Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water, Limnol. Oceanogr., 50(1), 237–245.
Subramanian A. and Carpenter E.J., 1999. Bio-optical properties of the marine diazotrophic cyanobacteria Trichodesmium spp. II. A reflectance model for remote sensing. Limnol. Oceanogr. , 44 (3), 618-627
Subramaniam, A., C.W. Brown, R.R. Hood, E.J. Carpenter, and D.G. Capone, 2002. Detecting Trichodesmium blooms in SeaWiFS imagery, Deep-Sea Res. II, 49, 107-121.
Thomson, M.C., S.J. Mason, T. Phindela, and S.J. Connor, 2005: Use of rainfall and sea surface temperature monitoring for malaria early warning in Botswana. Am. J. of Tropic. Med. and Hyg., 73, pp. 214-221
Thomson M.C., Connor S.J., D'Alessandro U., Rowlingson B., Diggle P., Cresswell M. and Greenwood B., 1999. Predicting malaria infection in Gambian children from satellite data and bed net use surveys : The importance of spatial correlation in the interpretation of results. Am J. of Tropic Med. and Hyg, 61 (1), pp. 2-8.
Tran A., J. Gardon, S. Weber and L. Polidori, 2002. Mapping disease influence in suburban areas using remotely sensed data. Am. J. Epidemiol., 156 (7), pp. 662-668.
VIBRIO (project) - CLS, 2007. Corrélations entre les mesures historiques in situ et les données MERIS pour le projet VIBRIO. Ref : CLS-DOT-NT-07-149. 03/08/2007.
Vincent R.K., R.M. McKay, M.M.D. Al-Rshaidat, K. Czajkowski, T. Bridgeman, and J. Savino, 2005. Mapping the bacterial content of surface waters with LandSat TM data :
Centre National d’Etudes Spatiales Page: 37/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL Importance for monitoring global surface sources of potable water, 16th Pecora Symposium, October 23-27, 2005, Sioux Falls, South Dakota.
Vincent, R. K., X. M. Qin, R. M. L. McKay, J. Miner, K. Czajkowski, J. Savino, and T. Bridgeman, 2004. Phycocyanin detection from LANDSATTM data for mapping cyanobacterial blooms in Lake Erie. Rem. Sens. Environ. 89: 381–392.
WHO, 2004. « Using Climate to Predict Infectious Disease Outbreaks : A Review » Communicable Diseases Surveillance and Response, Protection of the Human Environment, Roll Back Malaria, Geneva. WHO/SDE/OEH/04.01
WHOI, « Harmful Algae », Presentation of the various syndromes and regularly updated bulletins of events among the ”HAB communauty”. [Url : http://www.whoi.edu/redtide/]
Zimmerman A. M, A. DePaola, J. C. Bowers, J. A. Krantz, J. L. Nordstrom, C. N. Johnson, and D. J. Grimes, 2007. Variability of Total and Pathogenic Vibrio parahaemolyticus Densities in Northern Gulf of Mexico Water and Oysters. App. Env. Microbiol., 73 (23), 7589-7596
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Abbreviations Definitions ADSS Africa Data Dissemination ARC Active Radar Calibrator ASP Amnesic Shellfish Poisoning AUV Automated Underwater Vehicle AVHRR Advanced Very High Resolution Radiometer (NOAA) CCD Cold Cloud Duration CFP Cigüatera Fish Poisoning Chl Chlorophyll CMAP CPC Merged Analysis of Precipitation CPC Climate Prediction Center DSP Diarrheic Shellfish Poisoning ENSO El Nino Southern Oscillation EUROHAB European Initiative on Harmful Algal Blooms EVI Enhanced Vegetation Index FAO Food and Agriculture Organisation FLH Fluorescence Line Height GEOHAB Global Ecology and oceanography of Harmful Algal Blooms GIRAC Gestion Intégrée des Rejets d’Assainissement Côtiers GPCP Global Precipitation Climatology Project HAB Harmful Algal Bloom IOC Intergovernmental Oceanographic Commission LANDSAT- TM LANDSAT- Thematic Mapper LST Land Surface Temperature MERHAB-LGL Monitoring and Event Response for Harmful Algal Blooms- Lower Great Lakes Centre National d’Etudes Spatiales Page: 39/43 State of the art: remote sensing for health applications Date: 15 July 2010 Ref. 044-R755-v1.1-a All rights reserved - 2009-2010 ACRI-ST – All rights reserved CONFIDENTIAL Abbreviations Definitions MERIS MEdium Resolution Imaging Spectrometer MeteoSat-HRR MeteoSat – High Resolution Radiometer MODIS Moderate Resolution Imaging Spectroradiometer NASA National Aeronautics and Space Administration NCEP National Center for Environmental Prediction NDVI Normalized Difference Vegetation Index NOAA National Oceanic and Atmospheric Administration NSP Neurotoxic Shellfish Poisining PSP Paralytic Shellfish Poisining RFE Rainfall Estimates (NOAA) SCOR Scientific Committee on Oceanic Research SeaWiFS Sea-viewing Wide Field-of-view Sensor SSH Sea Surface Height SST Sea Surface Temperature WHOI Woods Hole Oceanographic Institute
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Main information related to HABs can be found on the following websites:
WHOI, US website on HABs: http://www.whoi.edu/redtide/ It gathers information on various harmful species, on cases recorded and consequences on health. A page is dedicated to the main events and meetings dealing with HABs.
ECOHAB (Ecology of Harmful Algal Blooms) programme website: http://www.cop.noaa.gov/Fact_Sheets/ECOHAB.html The American programme ECOHAB is managed by NOAA and axed on research in Physics, Biology, Chemistry and Oceanography in order to improve knowledge related to the behaviour and consequence of Algal blooms on fishing, ecosystems and in public health.
GEOHAB (Global Ecology and Oceanography of Harmful Algal Blooms) programme website: http://iodeweb6.vliz.be/geohab/index.php?option=com_frontpage&Itemid=1 The GEOHAB programme, organised by the SCOR (Scientific Committee on Oceanic Research) and the IOC (Intergovernmental Oceanographic Commission) from UNESCO, is an international programme that aims at promoting the cooperation at a global scale in order to improve knowledge and prediction of harmful algal blooms.
The IOC regularly releases a newsletter « Harmful Algae News » presenting the latest main news about HABs. Subscription details and archived news can be found following this link: http://www.ioc- unesco.org/hab/index.php?option=com_content&task=view&id=22&Itemid=0
EUROHAB is a European initiative on HABs created in 1999. Its aim is to coordinate researches led at European level in order to propose a better management of HABs and cyanobacteria impact within European waters. The EUROHAB programme is described in the report: European Commission Research in Enclosed Seas-5, EUR 18592, ISBN 92-828-6612- 2, 1999. The programme, which gathers 6 projects, is described online: http://cordis.europa.eu/eesd/ka3/cluster5.htm
MERHAB-LGL (Monitoring and Event Response for Harmful Algal Blooms in the Lower Great Lakes) is a multi-disciplinary programme which aims at developing strategies for HAB management for three great lakes in North America. Workgroups are dedicated to each study site, while other research teams focus on remote sensing, on hydrodynamic modelling, on toxins’ analysis or on the diffusion of information toward public. Its website is: http://www.esf.edu/merhab/index.asp
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The « EU-US Scientific Initiative on Harmful Algal Blooms » workshop was organised on the 5-8 september 2002, in Trieste, Italy. Its proceedings gathers the various studies on HABs and collaborations between European and American scientists, that had taken place at the time or which were foreseen. This report can be found here: ftp://ftp.cordis.europa.eu/pub/sustdev/docs/environment/ki-na-20578-en-c_eu- us_algal_blooms.pdf
The HABWatch symposium, where most important scientists dealing with HABs has been organized in Villefranche-sur-Mer on 11-21 June 2003. Lectures from oral presentations are available here: http://www.obs-vlfr.fr/habwatch/
A ISOC-HAB (International Symposium on Cyanobacterial Harmful Algal Blooms) symposium waas hold in theSheraton Imperial in Research Triangle Park, NC, USA on the 6- 10 September 2005. http://www.epa.gov/cyano_habs_symposium/
In the frame of the GEOHAB programme, two Open Science Meeting “HABs in Eutrophic Systems” were organized , the first took place in Baltimore, USA in 2005, the second was hold on 18-21 october 2009 in Beijing, China. http://www.geohab-osm- bj.ac.cn/default.html
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Sensor Mission Agency Dates Track Resolution # (km) (m) Bands CZCS Nimbus-7 / NASA / USA December 78 1556 825 6 USA – July 86 OCTS ADEOS-1 / NASDA / Japan November 96 1400 700 12 Japan – July 97 POLDER ADEOS-1 / NASDA / Japan November 96 2400 6000 9 Japan CNES (sensor) – July 97 POLDER 2 ADEOS-2 / NASDA / Japan Dec. 2002 – 2400 6000 9 Japan CNES (sensor) Oct. 2003 SeaWiFS Orbview-2 NASA / USA September 97 2806 1100 8 / USA – today MERIS Envisat / ESA / EU April 2002 – 1150 300 / 1200 15 EU today MODIS Aqua/EOS- NASA / USA July 2002 – 2330 1000 36 PM / USA today OCM-2 Oceansat-2 ISRO / India September 1420 1000-4000 8 / India 2009 – today POLDER 3 Parasol CNES / France 18 Dec. 2004 2100 6000 9 – today COCTS HY1B CAST / China May 2002 1400 1100 10 GOCI COMS KORDI/ Corea 2010 2500 500 8 VIIRS NPP / USA DoD/NOAA/NA June 2010 3000 370 / 740 22 SA / USA OLCI Sentinel- ESA / EU October 2012 1120 250 / 1000 19 3A / EU VIIRS NPOESS- DoD/NOAA / January 2013 3000 370 / 740 22 C1 / USA USA S-GLI GCOM-C / JAXA / Japan Early 2014 1150 / 250 / 1000 19 Japan 1400 OLCI Sentinel- ESA / EU April 2015 1120 250 / 1000 19 3B / EU Geo-oculus ESA / EU OCAPI CNES / France
Spatial Missions past actual future projected Table 3: Caracteristics of Regional and Global Ocean Colour Missions’ (Sources IOCCG, ACRI-ST)
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