monitoring the eutrophication of lakes...

2013

Biol. Gustavo Leonhard

Master in Emergency Early Warning and Response Space Applications

MONITORING THE EUTROPHICATION OF LAKES AND HARMFUL ALGAL BLOOMS

USING SATELLITE DATA

Seminary – Leonhard Gustavo

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Contents

Abstract .................................................................................................................................2

Chapter 1:

Introduction …………..…………..………..…………………………...........................................................3

Objective................................................................................................................................4

Eutrophication of Lakes…………………………………..…..…………………………………………………........4-7

Harmful Algal Blooms ………………………………….………….…………………………………………………….8-9

Indicators and indexes.…………………………….……………….………………………………………………….9-12

Chapter 2:

Backgrounds………...........................................................................................................13-20

Chapter 3:

Discussion and recommendations…………….………..…..………………....................................21-22

Bibliography ...................................................................................................................23-25


2

Abstract

Eutrophication is the process by which primary production is enhanced through

the increase in the rate of supply of organic matter and thus nutrients to an ecosystem.

Of particular concern are the effects of excess nutrients introduced by human activities,

such as nitrogenous and phosphorous.

Related to this, in severe cases, eutrophication can lead to large algal blooms and

algal scum, (some of which can be toxic), enhanced benthic algal growth, cycles of

bacterial and fungal growth, oxygen depletion, and subsequent changes in the structure

and functioning of lakes and marine ecosystem with changes or loss of flora and fauna of

this ones.

The use of remote sensing can help to prevent the consequences of these algal

blooms like an early alert tool. Also can be used to recognise the cause and the effects of

these processes and to generate models to predict the behaviour.


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CHAPTER I

Introduction

Historically, the relationship between man and water has been constant and

eternal dependency. The concern of countries to have enough water in quantity and

quality for its various activities is always in growing.

Lakes and reservoirs provide water for human consumption and allow a number of

highly valuable environmental functions. These are water reservoirs used to meet the

needs of society and the environment. These needs include power generation, provision

of water for human and animal consumption and irrigation, increasing attenuation,

groundwater recharge, providing habitat for many plant and animal species

(Sriwongsitanon et al., 2011).

Coastal areas are transitional areas between the land and sea characterized by a

very high biodiversity and they include some of the richest and most fragile ecosystems on

earth, like mangroves and coral reefs. At the same time, coasts are under very high

population pressure due to rapid urbanization processes. More than half of today’s world

population live in coastal areas (within 60 km from the sea) and this number is on the rise.

Throughout history coastal areas have been important to humans in many ways—

including as a source of water, edible plants, and animals. Coasts are also gateways for

trade (via shipping). Major cities have developed along sea coasts, or along lakes and

rivers that are connected by a waterway with the ocean (Yunis, 2001).

However, these systems are exposed to environmental degradation;

eutrophication remains one of the most common problems and produces ecological

impacts, significant negative health and economic local and regional scale (Rodriguez,

1997). Because of this emerges the purpose of this study.


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Objective

Learn about the problematic of different levels of eutrophication or algal blooms in

lakes and coastal regions and the techniques and satellite sensors used for the recognition

of them.

Eutrophication of Lakes

The aging process bodies of water like lakes, ponds and streams are called

eutrophication. Eutrophication is the process by which primary production is enhanced

through the increase in the rate of supply of organic matter and thus nutrients to an

ecosystem (EEA, 2001; Nixon, 1995) this nutrient includes such substances as nitrates and

phosphorus, which encourage plant growth. (Fig. 1)

Figure 1: Eutrophic lake: San Roque Dam


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The natural process of eutrophication is simple and takes several decades or centuries.

The plants consume the nutrients, and then they age and die, releasing the nutrients back

into the water. Over time, the cumulative nutrients allow for almost unchecked plant

growth, while choking out fish, invertebrates and other such aquatic life.

Eventually, only plants and the sediment from dead organisms remain in the water. This

sediment layer gets thicker as more plants grow and die, insects drown in the water and

other organic debris accumulates. Over time, this sediment pushes up the floor of the

body of water, causing the water itself to spill over its old banks and redistribute over the

surrounding ground. It may take decades, but eventually the body will disappear

altogether, leaving behind first a marshy area and then a firm, fertile plain. (Fig. 2)

Figure 2: Natural Eutrophication process.

As mentioned in the previous paragraph, eutrophication is part of a natural process

and regardless of human activity, but the increasing urbanization, agriculture and energy

development, accelerate this process, shortening the life of the body of water. Thus,

generate an "artificial eutrophication or culture", which is much more rapid and

dangerous. (Fig. 3)


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The causes of anthropogenic eutrophication:

Industrial waste discharges, agricultural, urban and treatment plants.

Deforestation that increases erosion and reduces the recycling of nutrients in the

watershed, increasing their income to the water body.

Fertilizer applied in excess.

Sewage farms.

Septic tanks.

Detergents with large amounts of phosphorus.

Contribution of pollutants from rainwater.

Sewer system of cities and towns.

Figure 3: Cultural Eutrophication process.

Many lakes show vertical stratification of their water masses, at least for some

extended time periods. Density differences in water bodies facilitate an evolution of

chemical differences with many consequences for living organisms in lakes. Temperature

and dissolved substances contribute to density differences in water. The atmosphere

imposes a temperature signal on the lake surface. As a result, thermal stratification can be

established during the warm season if a lake is sufficiently deep. On the contrary, during


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the cold period, surface cooling forces vertical circulation of water masses and removal of

gradients of water properties (Aeschbach-Hertig et al. 1999). (Fig. 4)

Figure 4: thermal stratification of lakes.

Also, lakes with marked thermal stratification tend to have a synergistic effect in

the eutrophication process. During summer, the surface of the water being heated and

expands, floating over the bottom at the top of the water column.

The lower part of the column became hypoxic due the oxygen is congsuming by

the biological activity of the flora and fauna. When levels of oxygen rich lows values, more

nutrients are concentrate in the background by the death of benthic animals and plants.

At the top of the column takes just another process also increases the organic matter of

the water body. The largest solar radiation of the warm season brings a proliferation algal

that generate organic matter when he dies and decreases the diversity of other species of

lower strata. During the winter season, the basal surface temperature is very low

temperature (4 ° C) and may or may not possess the upper layer frozen. This can cause a

greater degree of hypoxia in the water and accumulation of organic matter due to the

death of the algae surface.


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Harmful Algal Blooms

Proliferation of dinoflagellates, ciliates and bacteria, can become toxic when they

reach high concentrations. This phenomenon is known as harmful algal blooms (HABs).

The cause of these natural proliferations may be by factors such as salinity, amount of

light, temperature and turbulence (Chandy et al. 1991). Some human activities that may

have contributed to the increase in HAB occurrence and distribution include: increases in

nutrient loadings (which can change the natural nutrient regime and select for HAB

species, plus more nutrients support more growth), overfishing (which can decrease the

grazing pressure on HAB species), and ballast water discharge (which can transfer resting

stages, or cysts, of HAB species to new areas). However, of the thousands algal blooms,

only a small percentage are harmful to humans (approximately 10% of total).

The HABs have great ecological importance as the animals that feed on these toxic

microorganisms, are poisoned , sick , dying and transmit the poison through the food

chain , which in turn affects humans ingest these contaminated organisms, reduce

dissolved oxygen in the water and cause the deaths of hundreds of fish and corals (Fig. 5).

Similarly, have a great social importance as they constitute a threat to the health of

people, the economy, tourism, fisheries and aquaculture (GEOHAB, 2001).

Figure 5: Coastline covered in dead fish, Ukraine.


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In terms of health, people can suffer different poisoning as paralytic, Neurotoxic,

Diarrheal, amnesic, or Ciguatera. As a result of these, some effects and symptoms that

may occur are: pictures neurotic, oral paraesthesia, abdominal pain, headache, altered

pulse, respiratory failure, cardiac arrests and death.

Among the most common organisms that cause harmful algal blooms are:

Alexandrium catenella produces the paralytic shellfish toxin. In humans and animals

cause paralysis in the extremities and subsequently, death by cardio-respiratory arrest.

Karenia brevis blooms can potentially cause eye and respiratory irritation to swimmers,

boaters and coastal residents.

Dinophysis acuta produces diarrhetic shellfish toxin in humans and animals causes

gastrointestinal problems such as diarrhea.

Pseudo-nitzschia sp. produces amnesic shellfish and humans can cause loss of short term

memory. (Fig. 6)

Figure 6: Harmful algal from left to rigth: Alexandrium catenella, Karenia brevis, Dinophysis acuta

and Pseudo-nitzschia sp.

Indicators and indexes

There are several indicators presently used to assess eutrophication in aquatic

systems. Phosphate, ammonia and nitrate, in both river and coastal waters are the major

constituent nutrients driving eutrophication. Chlorophyll-a is an indicator of

phytoplankton biomass and concentration increases due to phytoplankton growth are a

result of eutrophication. Chemical oxygen demand and Biochemical oxygen demand are


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used to quantify the magnitude of organic pollution. Suspended solid and transparency

are used as an indicator of turbidity.

One of the most widely index used for measuring the degree of eutrophication is

the Carlson's eutrophication index also called Trophic State index (TSI) (Fig. 7) and is

defined as the total weight of biomass in a given water body at the time of measurement.

Three variables can be used to calculate the Carlson Index: chlorophyll pigments, total

phosphorus and Secchi depth. Of these three, chlorophyll will probably yield the most

accurate measures, as it is the most accurate predictor of biomass. Phosphorus may be a

more accurate estimation of a water body's summer trophic status than chlorophyll if the

measurements are made during the winter. Finally, the Secchi depth is probably the least

accurate measure, but also the most affordable and expedient one (Carlson & Simpsons,

1996). Because this tree variables tend to correlate and citizen monitoring programs and

other volunteer or large-scale surveys will often use the Secchi depth the relationship of

this index is expressed by the following equation:

Where

z = the depth at which the disk disappears,

I0 is the intensity of light striking the water's surface,

Iz is about 10% of I0 and is considered a constant,

kw is a coefficient for the attenuation of light by water and dissolved substances,

α is treated as a constant with the units of square meters per milligram and

C is the concentration of particulate matter in units for milligrams per cubic meter.

(Carlson, 1977)


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Figure 7: Comparison between indexes

All these indicators can be measured directly through ship-based water sampling.

Collecting water samples through ships is often costly and provides data limited in space

and time. In contrast, satellite remote sensing can cover large areas with relatively high

resolution and without restriction of national boundaries. (Cearac, 2007)

Remote sensing involves measuring some property of an object of interest from

the distance. This allows us to extrapolate a characteristic of a particular area relieved by

local measurements analysis to a larger scale area in less time and cost. Remote sensing

also enables simultaneous comparison of the water quality of all lakes within an image. In

lakes with a large spatial variation in water quality, the use of spatially high-resolution

remote sensing data improves the accuracy of water quality estimation compared to

conventional monitoring methods (kallio et al, 2008).

In order to extrapolate the ship-based measurements of water sampling is necessary

to know the main optical properties that influence the reflectance spectrum of natural

waters, such as:

Absorption coefficient due to the presence of coloured organic matter.

Absorption coefficient due to the presence of droppings of aquatic flora and fauna,

Absorption coefficient due to the presence of phytoplankton, which includes

chlorophyll-a, phycocyanin, carotenoids and others.

Backscattering coefficient caused primarily by matter in suspension.


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Each of these properties are taken into account depending on the wavelength at which it

carries out the measurement of reflectance.

The land surface temperature (TS) is one of the physical properties more used as

input variable in climate models, hydrodynamics, panoramic epidemiology, among others.

There is now a substantial variety of remote sensors that allow as calculate TS, providing a

good spatial and temporal coverage (Jacoba et al. 2004; Li et al. 2004)

For example, the GOES satellite, has a sensor with a resolution of 4 km in the thermal

infrared, while the sensors AVHRR from NOAA and MODIS from TERRA and AQUA, have a

spatial resolution of 1 km in that area of the spectrum. On the other hand there are

thermal-infrared sensors with better spatial resolution as the TM, Thematic Mapper,

Landsat-5 which has 120 m and ASTER, from TERRA satellite with 90 m. the ETM from

LANDSAT-7 with 60 m of spatial resolution. However, these instruments have a revisit

period of 16 days.

Water quality parameters retrievable from remote imagery include the

concentration of chlorophyll-a, transparency, coloured dissolved organic matter (CDOM),

Secchi disk depth (SDD), and Aquatic vegetation. Most studies of this type are based on

the correlation between the spectral information obtained by the satellite and samples

obtained in situ, in order to extrapolate the results to larger areas. Also are based in the

similarity that exist between different sensors that allows create and apply a modelled

relationship from different satellites images and field data.


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CHAPTER II

Backgrounds

For the study of eutrophication and water quality with remote sensing there have

been used different types of sensors (satellites and airborne) to measure water bodies like

lakes, rivers, estuaries, marshes, oceans and coastal areas. The following is a review of

different sensors and techniques for various problematic events related to eutrophication.

LANDSAT-5TM was used for Zhou et al. in 2006 to develop and apply multi-

temporal models to estimate and map the concentrations of total suspended matter

(TSM) in Lakes. They use 2 LANDSAT-5TM with contemporaneous measurements in situ of

total suspended matter. Results of their study show that the ratio TM4/TM1 has a strong

relationship with TSM concentrations for lake waters with relatively low concentrations of

phytoplankton algae. Also TM3 provided a strong predictive relationship with TSM

concentrations despite varied water quality conditions. The validation of the multi-

temporal capability of the best models indicated that it is feasible to apply the linear

regression model using TM3 to estimate TSM concentrations across time in Lake Taihu

(Fig. 8), even if no in situ data were available.

Figure 8: Map of TSM concentration in Lake Taihu on 4 March 2004. Zhou et al. in 2006


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Olmanson et al. in 2008 measured over 20 years and with the different types of

sensors landsant (4, 5 and 7) the clarity of ten thousands lakes of Minnesota, USA (Fig. 9).

They used multiple linear regressions between the sensor values and the depth measured

by the Secchi disk. The same methodology was used by Brezonik et al. in 2006, in both

cases, Chlorophyll- a, TSS, turbidity, and SDT were highly correlated with each other and

all act as direct or indirect measures of algal abundance.

Figure 9: Minnesota lake clarity with county and ecoregion boundaries. Olmanson et al. in 2008

Zhang & He (2006) worked comparing TSI obtained in situ with the same index

obtained with the band 5 and band 7 of LANDSAT-7 ETM. They concluded that, the remote

sensing results obtained agreed with those by other methods (Fig. 10), and the applied

model can be modified to match the real work; it can provide information on different

trophic status in several areas by sampling ETM images in grey scale. The method can be

also used to evaluate large scale eutrophication in much less time and cost.


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Figure 10: Comparison on the evaluation results between two methods. Zhang & He (2006)

LANDSAT-7 ETM also was used by Onderka & Pekarova (2008) to map the spatial

patterns of suspended particulate matter (SPM) in the Danube River. In this study they

highlight the benefits of remote sensing for the monitoring al large areas of the river.

Based on a strong relationship between the Landsat near-infrared band (TM4) and field

measurements, they developed a map of SPM with a very low standard error (2.92 mg/L)

(Fig. 11).

Figure 11: Map of suspended particulate matter in the Danube River. Onderka & Pekarova (2008).


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Another work that analyses water quality but employing MERIS sensor (15 spectral

bands and 1200 m. of resolution) on board the ENVISAT satellite, is Campbell et al. 2011.

They applied a Matrix Inversion Method algorithm and found significant improvements in

the accuracy and precision of retrieved water quality parameter values (such as

chlorophyll- a), can be obtained by using differentially weighted, over determined systems

of equations, rather than exact solutions.

The study of coastal regions and open sea represents a major problem because of

the high scale of work. SEAWIFS was specifically designed to monitor ocean characteristics

such as chlorophyll-a concentration and water clarity. The sensor has a resolution of 1.1

km pixel by which allows large-scale studies. Shanmugamet et al. (2008) used this satellite

for sensing hazardous algal blooms and their underlying mechanisms in shelf-slope waters

(Fig.12). Heim et al. in 2005 also worked with this sensor measuring the variation of the

distribution of phytoplankton, solid suspended matter and yellow substance in a Siberian

lake. They concluded that a SeaWiFS time series are able to show the seasonal variations

of chlorophyll of specified bio-optical provinces of Lake.

Figure 12: The RCA-Chl (relative units) from the SeaWiFS. Shanmugamet et al. (2008).

Generally, time series of MODIS images are used to study eutrophication processes

in lakes. Martinez et al. (2011) calibrated them with field samples to discriminate different

concentrations of chlorophyll-a in different time periods. Zhang et al. in 2010, used it in a

similar way to analyze the distribution of the total Suspended Matter Concentrations in

Lake Taihu (China) (Fig. 13).


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Figure 13: Distribution of TSM concentration (mg L−1) from January to December, 2007 in Lake

Taihu from MODIS images. Zhang et al. in 2010.

Hu (2009) developed and used a simple ocean color index, namely Floating Algae

Index (FAI), to detect floating algae in open ocean environments using data from MODIS

instruments. FAI is represented by the difference between reflectance at 859 nm

(vegetation “red edge”) and a linear baseline between the red band (645 nm) and short-


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wave infrared band (1240 or 1640 nm). Through data comparison and model simulations,

FAI has shown advantages over other traditional indexes like NDVI or EVI.

Chavula et al. (2012), estimated chlorophyll-a in combination with lake surface

temperature data obtained from Advanced Very High Resolution Radiometer (AVHRR) and

MODIS/Terra satellite sensors to delineate potential fishing grounds in Lake Malawi.

Figure 14: Potential fishing grounds. Chavula et al. 2012

The high spatial resolution satellites like QUICKBIRD (60cm panchromatic and 2.4

m multispectral) is not often used because the low swath that usualy has and the cost of

the images. Also due to the high spatial resolution may present too much information to

analyze in areas of considerable size. Dogan et al. (2009) use it to mapping semi-

submerged vegetation with good results, performing an unsupervised classification.

Sebastia et al. (2012) estimated the concentration of chlorophyll-a in a coastal region

obtained by linear regression a R2 value of 0.90, suggesting that the use of QuickBird

sensor can be an effective technique for monitoring the ecological status of coastal areas

with high inherent variability (Fig. 15)


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Other high resolution satellite is the IKONOS, Tian et al. (2010) used it in a

eutrophic river to evaluate water quality. Three vegetation indices were generate,

Normalized Difference Vegetation Index (NDVI), Near-Infrared (NIR)-Green Angle Index

(NGAI) and Normalized Water Absorption Depth (DH) and compared with hyperspectral

samples acquired in situ to analize the diversity of aquatic plant species, associations and

densities. Their results confirmed that aquatic species composition alters spectral

signatures and affects the accuracy of remote sensing of aquatic plant density. Also

demonstrated that the NGAI has apparent advantages in estimating density over the NDVI

and the DH.

Figure 15: Estimation of chlorophyll-a concentration in a coastal region. Sebastia et al. 2012.

Giardino et al. (2007) used the HYPERION hyperspectral sensor to measure water

quality of Lake Garda in Italy. Due to the low concentration of organic material from the

lake, it was not possible to obtain results of colored organic matter dissolved in water. The

correlation coefficients of the concentrations of chlorophyll-a and Tryptone with sensor


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data, gave acceptable results beyond the inconvenience caused by the low lake organic

matter and for the atmospheric adjacency effects contamination.

As we see the use of optical satellites is widely extended to the study of the

eutrophication of water bodies. Recently, Ermakov et al. 2013 studied on the possibilities

of radar probing the eutrophication zones in water reservoirs. Comparing data on the

concentration and group composition of the phytoplankton in the Gorky Reservoir during

its eutrophication with data from a microwave Doppler radar. They found that, under the

same weather conditions, the intensity of a radar signal decreases in the areas where the

phytoplankton concentration is high (Fig.16). This study presents the possibility of

continuing with this methodology applied to airborne and satellite radar, in order to work

on a grater scale.

Figure 16: Measured (1) and theoretically calculated (2) intensities of a backscattered radar signal as a function of phytoplankton concentration. Ermakov et al. 2013


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CHAPTER III

Discussion and Recommendation

The eutrophication problem illustrates how anthropogenic activities on land and

oceans can affect the quality of lakes, rivers and coastal ecosystem in general, issuing

potential economic and ecological costs. Also, it displays how these activities bring

negative consequences on human and animal health (Fig. 17).

Figure 17: Mortality of sardines in California harbor

In this context, space technology plays a key role providing additional data to those

obtained by in situ measurements and, due to the possibility of a permanent

environmental monitoring, implementing the creation of large databases. To investigate

the level of correlation and individuate possible limitations of space tools, it is necessary to

conduct parallel studies comparing data obtained via the different technologies. Moreover,

specific spectral signatures might be produced for different environmental conditions,


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phytoplankton species and physiology enabling the association between biological data

and spectrophotometric characteristics.

As discussed throughout this report, the advantages of remote sensing for

characterizing eutrophic environments are being used for a long time now. There are

great numbers of satellite sensors, which are used for monitoring aquatic environments.

The premise for this kind of study is to correlate the information obtained remotely with

data obtained in situ, in order to be able to extrapolate the results to a larger scale. The

aim of this is to try to predict the causes and possible consequences of these events. The

development of new technologies or the implementation of some existing ones (like

radars) may provide new approaches to generating data for the detection and prevention

of this issue.

Solutions for these events require changes in all these activities related to water

environment, therefore it is urgent to bring the real causes-and-effect relationship of

eutrophication to our attention. Protecting aquatic environments from the harmful

consequences of nutrient enrichment is a challenge to resource managers because the

sources and delivery routes of nitrogen and phosphorus are diverse. Proposed solutions to

the eutrophication problem are multidimensional and include actions to restore wetlands

and riparian buffer zones between farms and surface waters, reduce livestock densities,

improve efficiencies of fertilizer applications, treat urban run-off from streets and storm

drains, reduce the emissions from vehicles and power plants, and further increase the

efficiency of nitrogen and phosphorus removal from municipal wastewater. It is also

necessary to optimize sampling protocols and field data collection, to increase efficiency

and reduce costs, in order to promote continuity controls for the development of a

reliable database that fosters the realization of monitoring and research work


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