estimating and mapping chlorophyll-a concentration in ... · there are roughly 200 mwra long-term...

ESTIMATING AND MAPPING CHLOROPHYLL-A CONCENTRATION IN BOSTON

HARBOR, MA USING LANDSAT DATA

A Thesis Presented

By

Qian Cao

to

The Department of Civil and Environmental Engineering

in partial fulfillment of the requirements

for the degree of

Master of Science

in the field of

Civil and Environmental Engineering

Northeastern University

Boston, Massachusetts

May 2018

ii

Abstract

A key component to ensuring our sustainable water resources is the quality of lake, river, estuary and ocean waters. Chlorophyll-a enables photosynthesis and can be used to characterize algae biomass in both fresh and salt water systems. Chlorophyll-a concentration is a common indicator of water quality and used to assess water body trophic conditions. Although Chlorophyll-a is relatively easy to measure in water samples, collection of water samples at select locations is resource intensive and limited in terms of characterizing spatial and temporal variability. The unique electromagnetic signature of Chlorophyll enables Chlorophyll-a concentration to be estimated from remote sensing platforms providing continuous spatial sampling over large portions of a water body. The goal of this study is to estimate the spatial distribution of the Chlorophyll-a concentration in Boston Harbor, based on LandSat satellite observations. A regression-based model is developed using LandSat 7 or 8 reflectance, in-situ measurements of Chlorophyll-a concentrations collected throughout Boston Harbor, precipitation, streamflow entering the harbor, and air temperature. A suite of model forms that build on existing literature is explored to determine the optimal relationship. The results indicate that the ratio of blue (0.441- 0.541 µm) to green (0.519 - 0.601 µm) reflectance in a quadratic formula combined with recent hydroclimate conditions can be used to estimate the Chlorophyll-a concentration in Boston Harbor at measurement locations (R2=0.67) and used to map concentrations throughout the Harbor. Also, sample site mean concentrations are shown to be representative of harbor-wide mean concentration.

iii

Table of Contents

Introduction ..................................................................................................................................... 1

Methods .......................................................................................................................................... 3

1. Study Area ......................................................................................................................... 3

2. Data Obtain ........................................................................................................................ 4

2.1 Sample Data ............................................................................................................ 4

2.2 LandSat Data .......................................................................................................... 5

2.3 Other Data ............................................................................................................... 5

3. Data Processing ................................................................................................................ 6

Results and Discussion ................................................................................................................ 7

Conclusions .................................................................................................................................. 15

REFERENCES ............................................................................................................................ 16

APPENDIX ................................................................................................................................... 19

1

Introduction

Water quality describes the chemical, physical, and biological characteristics of water

(Diersing & Nancy, 2009). Characterizing water quality of terrestrial and ocean

waterbodies is critical for understanding their ecosystems and the services they provide.

Water quality also is closely linked to public health. For example, microorganisms are

responsible for intestinal diseases (Mazmanian, Round, & Kasper, 2008).

Cyanobacterial toxicity could lead to large scale visual disturbances, nausea, vomiting,

and muscle weakness. (Azevedo et al., 2002) Thus, for sustainable ecosystems and

human health concerns, it is essential to monitor the quality of our water resources.

An estuary is a zone of transition with gradients in salinity, sediment characteristics,

chemical composition (i.e., nutrients, metals, etc.) and diversity and productivity of

microbial, animal and plant species (Wolanski, Andutta, & Delhez, 2013). Estuaries mix

freshwater from rivers and saline water from the ocean, resulting in a unique hydrologic

system at the margin of terrestrial and ocean boundaries. The unique water circulation

patterns enable their ecosystem to often have high levels of productivity. The balance is

easily influenced by ecosystem activities and production, input water conditions, salinity,

tide, streamflow, temperature, precipitation and other altering factors (Day, 1989). Thus,

most estuaries cannot maintain in a stable status, which result in a relative complicated

hydrologic system to model or predict.

Phytoplankton is a kind of single-celled microorganism drifting in the rivers, lakes and

ocean. It is a key part of the oceanic ecosystem, generating the organics to provide

foods. Therefore, it is taken as one of the criterion to estimate the water quality in

research studies (Gharib, El-Sherif, Abdel-Halim, & Radwan, 2011). Chlorophyll, a

pigment found in phytoplankton, allows phytoplankton to absorb the energy from sunlight

to convert carbon dioxygen and water to carbon hydrates (Kirk, 1994). It absorbs the

light in red and blue wavelengths, and reflect green wavelengths in the visible spectrum

(Mackinney, 1941). As the most abundant form of chlorophyll, chlorophyll-a is an

indicator of water quality status of the waterbody (Boyer, Kelble, Ortner, & Rudnick,

2009).

2

Remote Sensing is a technique to observe the earth surface or the atmosphere from

above using satellites (space borne) or from the air using aircrafts (airborne) (Curran,

1985). The LandSat suite of satellites has collected the longest continuous archive of

multispectral data of any land-observing space program (Irish, 2000; Landsat). Remote

sensing has been applied to build models to estimate various quantiles by correlating in-

situ measurements with reflectance values in different wavelength. For example,

previous work has been performed to estimate chlorophyll-a concentration using

LandSat 7 in Pensacola Bay, Florida. (Han & Jordan, 2005)

The chlorophyll absorbs either red or blue wavelengths but reflects green, indicating that

green should be the wavelength emphasized as the primary reflectance color. MODIS

Moderate Resolution Imaging Spectroradiometer (MODIS), another satellite sensor used

to model chlorophyll concentration, provides blue and green bands with a width of 405-

536 nm (Chen, Li, Dai, Sun, & Chen, 2011). Sea-viewing Wide Field-of-view Sensor

(SeaWiFS), the satellite to detect the ocean properties, provides quantitative marine

biological data also select those wavelength to model chlorophyll concentrations

(O'Reilly et al., 1998). These studies have proven developing a model to estimate

chlorophyll concentration using remote sensing is practical in divergent geological

conditions and hydrologic systems. Building on these studies, it is possible to develop

relationships with in-situ chlorophyll-a measurements and reflectance ratios can be used

estimate the water quality conditions in Boston Harbor without repeatedly and costly field

sampling.

The objectives of this study are to: (1) investigate relationships between chlorophyll-a

concentrations measured throughout Boston Harbor on selected days at specific sites

with LandSat reflectance (i.e., LandSat 7 ETM+ or LandSat 8 OLI) on corresponding

days for which there is limited cloud cover, (2) develop a regression-based model to

estimate chlorophyll-a using LandSat reflectance, and (3) use the develop relationship to

map the spatial distribution of chlorophyll-a throughout Boston Harbor.

3

Methods

1. Study Area

Boston Harbor, located adjacent to the city of Boston, Massachusetts, is connected to

Massachusetts Bay (Figure 1) and encompasses an area of roughly 15 square miles.

The harbor mixes ocean and terrestrial water, receiving river flow from three main

tributaries: the Charles River, Mystic River and Neponset River. It is a relatively complex

estuary system with 180 miles seashore and 34 islands. Since Boston is a major city in

northeastern United States, it has had problems linked water pollution for long periods

during the 19th and 20th century (Bothner, Ten Brink, & Manheim, 1998). There are

abundant monitoring sites in Boston Harbor to collect data for this study (Figure 1).

Figure 1. Map of Boston Harbor MWRA monitoring stations

4

2. Data Obtain

2.1 Sample Data

Massachusetts Water Resources Authority (MWRA) has collected physical, clarity,

nutrients and bacteria data in segments of Boston Harbor to monitor the water quality of

Boston Harbor and Massachusetts Bay, set around stations collecting bacteria and

nutrients data since 1989. For nutrients, the long-term collected data includes surface

and bottom nitrate and nitrite, total Kjeldahl nitrogen, phosphate, total phosphorus,

chlorophyll-a, and phaeophytin. This thesis only cares about the concentration of

chlorophyll-a. MWRA report of Deer Island (Taylor, 2001) has given the coordinates of

the sites in study area collecting nutrients data (Table 1). The 11 sites shown are

distributed generally even in study area, covering the whole Boston Harbor Area.

Table 1. Coordinates of Monitoring Stations in Study Area

Station ID Longitude (°) Latitude (°)

137 -71.0633 42.3867

138 -71.0470 42.3598

130 -70.9900 42.3633

24 -71.0080 42.3432

140 -71.0405 42.3058

106 -70.9600 42.3333

139 -70.9689 42.2867

77 -70.9885 42.2752

142 -70.9315 42.3392

141 -70.9308 42.3050

124 -70.8977 42.2727

The raw concentration data for chlorophyll-a at surface and bottom, measured at 0.2 m

and 10 m, were obtained from MWRA at 11 sites. The water samples were collected

between 9:00 am and 11:00 am at each site. The surface and bottom sampling are

measured almost simultaneously at each site. The concentration of chlorophyll-a at each

site is taken as the average of surface and bottom concentrations. If either sample is

missing, no site average is used for that date.

5

2.2 LandSat Data

In this study, satellite-based reflectance measurements in difference wavelengths from

LandSat 7 and 8 sensors are used. The orbital period of LandSat 7/8 is 16 days

generating 30m reflective, 60m thermal pixels (Irish, 2000). For LandSat 8, the

wavelength spectrum is divided into more detailed band designations (Table 2). Here,

the average reflectance for pixels within a circle around each field site is used. The

optimal radius dimension is investigated.

The Worldwide Reference System (WRS) is a global notation system for LandSat data.

It enables a user to inquire about satellite imagery over any portion of the world by

specifying a nominal scene center designated by PATH and ROW numbers. (Irish, 2000)

The satellite images of 12-path, 31-row for the WRS2 overlay in LandSat 7 ETM and

LandSat 8 OLI scene are used for this research, covering the entire Boston Harbor. The

maximum cloud cover is set at 20%. Band 1 to Band 4 in LandSat 7 and Band 2 to 5 in

LandSat 8 are have similar spectrial widths and are used in this study.

Table 2. Comparison of LandSat 7 and LandSat 8 Bands

LandSat-7 ETM

Bands(μm) LandSat-8 OLI and TIRS

Bands(μm)

0.435-0.451 Band 1

Blue Band 1 0.441-0.514 0.452-0.512 Band 2

Green Band 2 0.519-0.601 0.533-0.590 Band 3

Red Band 3 0.631-0.692 0.636-0.673 Band 4

NIR Band 4 0.772-0.898 0.851-0.879 Band 5

2.3 Other Data

The estuary, which is a relatively complex system, may require more than just spectral

reflectance to estimate chlorophyll concentration. For example, previous research shows

that water temperature can influence the chlorophyll concentration through biological

synthesis (Chen et al., 2011). Streamflow from larger rivers and local tributaries, which is

largely modulated by precipitation, is a key contributor of nitrogen and phosphorus to the

coastal zone impacting chlorophyll concentration. Thus, factors related to river input are

6

also considered, specifically, streamflow and precipitations. Air temperature and

precipitation data were obtained from the National Oceanic and Atmospheric

Administration (NOAA) from 1936 until current (NOAA Site ID USW00014739). The

streamflow data, dating back to 1931, were obtained for the Charles River (USGS Site

ID 01104500), which is this largest river discharging to Boston Harbor. For all three

datasets, only data for the overlapping period were used (1936 - 2016). During this time,

average monthly precipitation temperature and streamflow is listed in Table 3.

Table 3. Average Data Monthly since 1936

Month Precipitation (in/day) Temperature (F) Streamflow (ft3/s)

1 0.12 29.2 376

2 0.12 30.8 415

3 0.13 38.1 621

4 0.12 48.0 603

5 0.11 58.2 366

6 0.11 67.8 264

7 0.09 73.4 139

8 0.11 72.0 124

9 0.11 64.5 117

10 0.11 54.6 171

11 0.10 44.8 266

12 0.13 33.9 373

3. Data Processing

There are roughly 200 MWRA long-term monitoring stations in the Boston Harbor and

surrounding water bodies to detect potential water quality changes while most of them

are Harbor beach monitoring and emergency outfall monitoring sites. Harbor Chlorophyll

data are available for the period 1994 to 2016, but LandSat 7/8 data does not start until

2003. Thus, images and measured values were selected in overlapping years from 2003

through 2016. After picking the overlapping days with both field data and LandSat data,

only 12 discontinuous days were identified (2003/08/19, 2006/02/16, 2006/04/21,

2008/05/28, 2009/04/29, 2014/05/21, 2015/04/22, 2015/06/25, 2016/05/18, 2016/05/26,

2016/06/27, 2016/07/13).

7

ArcMap, a primary product of a series of ERIS products, is a tool that can process the

GIS dataset. The spatial reference of the map shown is WGS_1984_UTM_ZONE_19N.

World Geodetic System 1984 (WGS1984) is an earth-centered, earth-fixed terrestrial

reference system and geodetic datum which is one of the standard global reference

system to describe the geospatial information (Mularie, 2000). UTM (Universal

Transverse Mercator) is a type of plane rectangular coordinate system which straight

lines intersect each other at right angles to form a two-dimensional surface, dividing

globe into 60 north and south zones.(Chrisman, Cowen, Fisher, Goodchild, & Mark,

1989) The resolution of used bands is 30 meters in both satellites. To convert digital

number (DN) downloaded from original files to reflectance, the fundamental step was to

convert image data from sensor to physical meaningful radiometric scale. Then,

removing the solar variance effect by converting radiance to reflectance, the raw data

could result in the reflectance values in pixels.(Irish, 2000) Following these steps to

extract the DN to reflectance, average reflectance value surrounding stations and

corresponding Chlorophyll concentration is calculated as table like Table 1 in Appendix.

Results and Discussion

Chlorophyll reflects light in wavelengths between 400nm and 900nm, corresponding to 4

bands in LandSat (blue, green, red and NIR) (Mackinney, 1941). Chlorophyll-a has low

reflectance between 400 and 500 nm (blue absorption) and the conspicuous reflectance

minimum around 670 nm (red absorption), and the broad reflectance maximum around

550 nm (green peak) (Han & Jordan, 2005). Thus, bands reflecting green are commonly

used to assess chlorophyll. Algorithms have been proven to be applicable in many cases

(Iluz, Yacobi, & Gitelson, 2003). Band ratios, reflectance value in a pixel from a given

band divided by the value from another band, have been used to distinguish certain

targets accurately. Using algorithms of as the dependent variable, bands ratio is found to

be dominant as the independent variable to model because it removes the effects of

illuminations coming from atmosphere and the potential spectrum analysis errors in

composition difference from the reflecting objects (Jensen & Lulla, 1987). These

concepts are further explored in the Results and Discussion section.

8

The models are diverse when mapping the chlorophyll-a concentration with LandSat

image at different locations. For example, Band1 / Band3 in quadratic regression (Dogan

et al., 2016) or Band 3 / Band 1 in linear regression (Torbick et al., 2008) is proven to

work in their study region. Trying serval combinations of Bands to fit in some

fundamental formulas like quadratic equation y = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 (Narteh, 2011),

logarithmically transfer linear equation y = a ∗ log(x) (Han & Jordan, 2005) or

exponential equation y = 𝑎𝑥𝑏 (Bartholomew, 2003). Some other irregular form of model

(the ratio reflectance of Band 1 and the logarithm of Band 3) (Trescott, 2012) also

match.

To construct a more suitable model and maintain more pairs of datasets, a circle with the

radius of 400m was established at every sampling sites. The average of the pixels

values in covering circle was extracted as the reflectance value at each site. First, the

possible error came from the time delay (usually around 6h) between the image and the

sampling in rapid flowing water conditions would significantly reduce. Second, with more

pixels included, the effect of possible error from single pixels could be reduced to

minimum level. Table 5 to 9 in Appendix are detailed trails to determine the coefficient

parameters 𝑎1 ,𝑎2, 𝑎3. 𝑥 is the ratio of the different combinations while y is the logarithm

of the chlorophyll-a concentration n.

The test models include 𝑦 = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3, 𝑦 = 𝑎1 ln(𝑥) + 𝑎2, 𝑦 = 𝑎1𝑥2 + 𝑎2, 𝑦 = 𝑎1𝑥𝑎2

with 1-4 bands combinations (Band 1 / Band 2, Band 1 / Band 3, Band 1 / Band 4, Band

2 / Band 3 etc.) . Among all these formulas, the most suitable formula is quadratic

equation rationing blue to green in this case. The radius is tested every 100m until the

overall R square decreases at radius of 500m. A quadratic equation rationing Band 1 to

Band 2 with a radius of 400m around the sites shows the best matching performance in

selected 12 days. (R2 of 300m is also close to 400m) Then a model is established as

following:

Log(chlor) = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 ①

where 𝑥 = Reflectance of Band 1(blue) divided by Reflectance of Band 2(green).

Table 1 in Appendix shows the average reflectance of Band 1 to 4 (blue, green, red,

NIR) within a radius of 400m at each site. There are 98 sample sites spread over 12

9

days. One site at one day corresponds with four Bands of reflectance and one measured

chlorophyll-a concentration.

To improve the precision of the model, parameters like streamflow, precipitation and

temperature are added in equation 2. After few trails, the ratio of the precipitation in a

duration versus the monthly precipitation all through the years proves to be the best as a

coefficient of precipitation to determine the precipitation situations. Same way it is to the

temperature and streamflow. The primary reason is that there is a definite time delay for

the hydrologic system to respond to their changes. Taking an average precipitation of

duration in several days backward could reduce the possible mismatch because of their

dissimilar response time to the hydrologic system. The duration was determined based

on the circumstances and tests. Table 2 to 4 in Appendix showed statistics of

precipitation, streamflow and temperature in varied duration on 12 selected days.

Log(chlor) = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 + (𝑃

𝑃𝑎)𝑝 + (

𝑄

𝑄𝑎)𝑑 + (

𝑇

𝑇𝑎)𝑡 ②

Statistical Product and Service Solutions (SPSS), a software offering advanced

statistical analysis was applied to calibrate the coefficient of these parameters (Norusis,

1993). Using SPSS for R2, different combinations of these 3 variables are tried to decide

the most suitable coefficients in equation 2. The average of long time precipitation,

temperature and streamflow are shown in Table 2 to Table 4 in Appendix. Take

2003/08/19 as an example, averaging the precipitation in last 5 days (2003/08/15 to

2003/08/19) as P, which is called P5, Pa will be the average of historical precipitation in

August. Then, specific duration combinations between the discharge and temperature,

such as 5 days average for discharge (Q5) and 10 days for temperature (T10), where

tried. In this trail, P5, Q5, T10 is alternative to combine with each other. For a given day

in a given month, Pa, Qa, Ta remains the same. SPSS shows the dependency of

formula values and field statistics through R2. Thus, the most suitable constant in this

model could be determined based on the dependency. The R2 of all these combinations

are displayed in the table varying from 0.511 to 0.723 (Table 10 to 15 in Appendix). Even

though the previous 5-day precipitation has a higher R2, it is because the negative

exponent p results in a value that makes no mathematical sense in (𝑃

𝑃𝑎)p. In SPSS,

10

these values will be skipped. And if the p exponent is constrained to positive p, the R2

drops. Therefore, searching the largest R2 in these tables without 0 values in

precipitation results in the combination of 25 days for precipitation, 25 days for

streamflow and 30 days for temperature.

Note, the results show that temperature and precipitation have smaller impact comparing

to streamflow in the regression model but both still improve the R2 (Table 4). To prove

that all 3 variables are important in the model, all other forms of model are tested to

determine the most suitable model (Tables 4). As what table has shown, R2 is largest in

the model with all three variables. Thus, to improve the precision of model, streamflow,

temperature and precipitation are included in the final model.

Table 4. R2 with different variables P25D25T30 (best match) where x is the ratio of band 1:band 2, y is the logarithm chlorophyll concentration (μg/l), and P,D,T are

(𝑃

𝑃𝑎)𝑝, (

𝑄

𝑄𝑎)𝑑, (

𝑇

𝑇𝑎)𝑡, respectively.

Model R2

𝑦 = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 0469

𝑦 = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 + 𝑇 0.532

𝑦 = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 + 𝐷 0.536

𝑦 = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 + 𝑃 0.469

𝑦 = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 + 𝑇 + 𝐷 0.629

𝑦 = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 + 𝑇 + 𝑃 0.540

𝑦 = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 + 𝑃 + 𝐷 0.607

𝑦 = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 + 𝑇 + 𝑃 + 𝐷 0.667

Equation 3 is the final model developed in this thesis. After trying different combinations

of days number of days to average precipitations and streamflow, 30 days for

precipitation, 30 days for streamflow and 5 days for temperature were the best

combinations. However, the precipitation on 2009/4/29, 2016/5/18, 2016/6/27 is zero

while the coefficient of P5 is negative, leading to a mathematically meaningless result.

Thus, 25 days for precipitation, 25 days for streamflow and 30 days for temperature are

applied in equation 3.

11

The final regression equation is:

Log(chlor) = −24.111𝑥2 + 63.609𝑥 − 44.258 + (𝑃

𝑃𝑎)−0.247 + (

𝑄

𝑄𝑎)0.370 + (

𝑇

𝑇𝑎)−2.051 ③

where

𝑥 = Reflectance of Band 1(blue)/ Reflectance of Band 2(green)

P= average precipitation backward 25days

Pa=average precipitation this month

Q= average streamflow backward 25days

Qa=average streamflow this month

T= average temperature backward 30days

Ta=average temperature this month

Figures 2-3 show comparison between model and sample data. Figure 2 shows results

for individual days. Figure 3 shows the model and sample results match relatively well

with 98 points distributed over 12 days (R2=0.67), especially for those days with the

logarithm of concentrations in the 0 to 1 range. However, even 1-day variation in this

study area could not fit into this equation. Although different standards had been

established, there was still no solution to discriminate the available data to expand the

sample size and apply the equation to more situations in this case.

12

Figure 2. The field data and modeling data for days with image and sampling

data on same days (R2=0.67)

Figure 3. The modeled and measured concentration for days with image and sampling data on same days

When chlorophyll-a concentration increases, the reflectance of ETM+1 (blue) decreases

and the reflectance of ETM+2 (green) increases. Although the ETM+2 (green) could be

affected by vegetations, the research area in Boston Harbor extracted is all covered with

ocean. Thus, vegetation effect is reduced to the minimum scale.

0

2

4

6

8

10

12

14

16

0 20 40 60 80 100 120

chl-

a (µ

g/L)

sites in days

Modeled

Measured

0

2

4

6

8

10

12

14

0 2 4 6 8 10 12 14

Mo

del

ed c

hl-

a(µ

g/L)

Measured chl-a (µg/L)

13

Figure 4 is as an example of chlorophyll-a concentration on 2014/05/21 mapped by

ArcGIS using the above model (equation 3), which shows that the concentration in the

ocean far from the coast remains low and stable while the high concentration level is all

display in the coastline. The higher concentration along the coastline indicates a higher

possibility to produce more algae which could impair the potential balance of the

ecosystem.

Figure 4. Map of chlorophyll-a concentration (µg/L) on 2014/05/21

Averaging the sites concentrations and the harbor-wide pixels concentrations based on

equation 3, Table 5 and Figure 5 shows the comparison between the mean of all pixels

and site specific sample data. In general, the sample site measurements provide mean

concentrations that are similar to the mean of all pixels values based on equation 3

distributed throughout the entire harbor.

14

Table 5. Mean chlorophyll-a concentration for in-situ samples and harbor-wide pixels

values derived based on equation 3.

Date Samples Chl-a (µg/L)

Model Chl-a (µg/L)

2003/8/19 4.58 3.44

2006/2/16 0.46 0.58

2006/4/21 3.62 0.09

2008/5/28 5.88 4.33

2009/4/29 3.28 2.89

2014/5/21 7.99 7.25

2015/4/22 13.49 4.75

2015/6/25 4.00 2.48

2016/5/18 3.80 4.91

2016/5/26 2.40 3.94

2016/6/27 4.02 2.48

2016/7/13 3.11 3.99

Excluding 2 extremely high concentrations at the sites of that day to draw the figure to

show the relationship between the harbor-wide model concentrations and the sites

sampling concentration. Figure 5 shows the relation between the average sample sites

data and average of all pixels within the harbor. The key finding from this analysis is that

the mean of in-situ sample measurements and harbor-wide estimates from the model

are similar in magnitude (i.e., the harbor-wide mean is 86% of the sample site mean).

Thus, the sample sites provide representative concentrations for the harbor.

Figure 5. Average of all pixel values based on the model (eq. 3) and sample sites values

y = 0.86xR² = 0.62

0

2

4

6

8

10

0 2 4 6 8 10

Mo

del

(µg/

L)

sample (µg/L)

15

Conclusions

Chlorophyll-a is a significant indicator of algae biomass, the main symptom of water

quality condition, playing an important role in overall monitoring waterbody. Therefore, to

estimate the concentration of chlorophyll-a by remote sensing could seriously reduce the

collective work burden and promote the efficiency of water quality estimation. The

regression model provides a relatively effective formula to estimate the chlorophyll-a

concentration, basing on the ratio of ETM+1/ETM+2, precipitation, temperature and

streamflow data. Considering 98 points as the whole sample size, R2 = 0.67 is

acceptable come out although it cannot match every point exactly, which could be

referred in potential research work. One key finding from this study is that the in-situ

sample measurements and harbor-wide estimates from the model are similar in

magnitude (i.e., the harbor-wide mean is 86% of the sample site mean). Thus, the

sample sites provide representative concentrations for the harbor as a whole.

iv

Acknowledgements

I am so grateful that Professor Edward Beighley could offer the opportunity for this

remote sensing modeling project. Gaining so much rewarding in the way to think and

solve the potential problem, I would like to thank again for his great guidance and patient

given as a supervisor during the research. Many thanks to my parents and friends, who

have encouraged me spiritually when I feel tired.

16

REFERENCES

Azevedo, S. M., Carmichael, W. W., Jochimsen, E. M., Rinehart, K. L., Lau, S., Shaw, G.

R., & Eaglesham, G. K. (2002). Human intoxication by microcystins during renal

dialysis treatment in Caruaru—Brazil. Toxicology, 181, 441-446.

Bartholomew, P. J. (2003). Mapping and modeling chlorophyll-a concentrations in the

Lake Manassas Reservoir using Landsat Thematic Mapper satellite imagery.

Virginia Tech.

Bothner, M. H., Ten Brink, M. B., & Manheim, F. (1998). Metal concentrations in surface

sediments of Boston Harbor—changes with time. Marine Environmental

Research, 45(2), 127-155.

Boyer, J. N., Kelble, C. R., Ortner, P. B., & Rudnick, D. T. (2009). Phytoplankton bloom

status: Chlorophyll a biomass as an indicator of water quality condition in the

southern estuaries of Florida, USA. Ecological Indicators, 9, S56-S67.

doi:10.1016/j.ecolind.2008.11.013

Chen, M., Li, J., Dai, X., Sun, Y., & Chen, F. (2011). Effect of phosphorus and

temperature on chlorophyll a contents and cell sizes of Scenedesmus obliquus

and Microcystis aeruginosa. Limnology, 12(2), 187-192.

Chrisman, N. R., Cowen, D. J., Fisher, P. F., Goodchild, M. F., & Mark, D. M. (1989).

Geographic information systems. Geography in America, 353-375.

Comly, H. H. (1945). Cyanosis in infants caused by nitrates in well water. Jama, 129(2),

112-116.

Curran, P. J. (1985). Principles of remote sensing: Longman Inc.

Day, J. W. (1989). Estuarine ecology: John Wiley & Sons.

Diersing, N., & Nancy, F. (2009). Water quality: Frequently asked questions. Florida

Brooks National Marine Sanctuary, Key West, FL.

Dogan, H., Polat, F., Buhan, E., Kılıç, O., Yılmaz, D., & Buhan, S. (2016). Modeling and

mapping temperature, secchi depth, and chlorophyll-a distributions of Zinav Lake

by using GIS and Landsat-7 ETM+ imagery. Gaziosmanpașa Üniversitesi Ziraat

Fakültesi Dergisi, 33(3), 55-60.

17

Gharib, S. M., El-Sherif, Z. M., Abdel-Halim, A. M., & Radwan, A. A. (2011).

Phytoplankton and environmental variables as a water quality indicator for the

beaches at Matrouh, south-eastern Mediterranean Sea, Egypt: an assessment.

Oceanologia, 53(3), 819-836. doi:10.5697/oc.53-3.819

Han, L., & Jordan, K. J. (2005). Estimating and mapping chlorophyll‐a concentration in

Pensacola Bay, Florida using Landsat ETM+ data. International Journal of

Remote Sensing, 26(23), 5245-5254.

Iluz, D., Yacobi, Y. Z., & Gitelson, A. (2003). Adaptation of an algorithm for chlorophyll-a

estimation by optical data in the oligotrophic Gulf of Eilat. International Journal of

Remote Sensing, 24(5), 1157-1163. doi:10.1080/0143116021000044797

Irish, R. R. (2000). Landsat 7 science data users handbook. National Aeronautics and

Space Administration, Report, 2000, 415-430.

Järup, L. (2003). Hazards of heavy metal contamination. British medical bulletin, 68(1),

167-182.

Jensen, J. R., & Lulla, K. (1987). Introductory digital image processing: a remote sensing

perspective.

Kirk, J. T. (1994). Light and photosynthesis in aquatic ecosystems: Cambridge university

press.

Landsat, N. Science Data Users Handbook.

Mackinney, G. (1941). Absorption of light by chlorophyll solutions. J. biol. Chem, 140(2),

315-322.

Mazmanian, S. K., Round, J. L., & Kasper, D. L. (2008). A microbial symbiosis factor

prevents intestinal inflammatory disease. Nature, 453(7195), 620.

Mularie, W. (2000). Department of defense world geodetic system 1984, its definition

and relationships with local geodetic systems. National Geospatial-Intelligence

Agency, Tech. Rep, 152.

Narteh, V. N. A. (2011). Mapping and Modeling Chlorophyll-a Concentrations in Utah

Lake Using Landsat 7 ETM+ Imagery.

Norusis, M. J. (1993). SPSS: SPSS for Windows, base system user's guide release 6.0:

SPSS Inc.

18

O'Reilly, J. E., Maritorena, S., Mitchell, B. G., Siegel, D. A., Carder, K. L., Garver, S.

A., . . . McClain, C. (1998). Ocean color chlorophyll algorithms for SeaWiFS.

Journal of Geophysical Research: Oceans, 103(C11), 24937-24953.

Taylor, D. I. (2001). Trends in water quality in Boston Harbor during the 8 years before

offshore transfer of Deer Island flows: Massachusetts Water Resources

Authority, Environmental Quality Department.

Torbick, N., Hu, F., Zhang, J., Qi, J., Zhang, H., & Becker, B. (2008). Mapping

chlorophyll-a concentrations in West Lake, China using Landsat 7 ETM+. Journal

of Great Lakes Research, 34(3), 559-565.

Trescott, A. (2012). Remote Sensing Models of Algal Blooms and Cyanobacteria in Lake

Champlain: Environmental & Water Resources Engineering Department,

University of Massachusetts, Amherst.

Wolanski, E., Andutta, F., & Delhez, E. (2013). Estuarine Hydrology.

19

APPENDIX

Table 1. Average Reflectance 400m Around the stations and Chlorophyll concentration

DATE site ETM+1 ETM+2 ETM+3 ETM+4 Chlo-a (μg/l)

2003/8/19 137 0.05 0.04 0.02 0.02 6.23

138 0.05 0.04 0.02 0.02 5.69

130 0.06 0.05 0.04 0.03 4.43

024 0.05 0.04 0.02 0.01 4.50

140 0.05 0.04 0.02 0.02 4.99

106 0.05 0.03 0.02 0.01 4.37

139 0.05 0.04 0.02 0.01 3.18

077 0.05 0.04 0.02 0.01 4.40

141 0.05 0.03 0.02 0.01 3.31

124 0.05 0.04 0.02 0.01 3.40

038 0.05 0.04 0.02 0.01 5.89

2006/2/16 137 0.02 0.02 0.01 0.01 0.69

138 0.02 0.01 0.01 0.01 0.36

130 0.02 0.02 0.01 0.01 0.44

024 0.02 0.02 0.01 0.01 0.48

140 0.02 0.01 0.01 0.01 0.47

106 0.02 0.01 0.01 0.00 0.41

139 0.02 0.01 0.01 0.00 0.45

077 0.02 0.01 0.01 0.00 0.35

141 0.02 0.01 0.01 0.00 0.47

124 0.02 0.01 0.01 0.00 0.47

038 0.02 0.01 0.01 0.00 0.44

2006/4/21 137 0.05 0.03 0.02 0.02 3.62

2008/5/28 137 0.06 0.05 0.04 0.04 6.58

138 0.06 0.05 0.04 0.03 6.80

024 0.06 0.05 0.03 0.03 6.46

140 0.06 0.05 0.04 0.03 7.53

106 0.06 0.05 0.04 0.03 5.60

139 0.07 0.05 0.04 0.03 4.14

141 0.07 0.05 0.04 0.04 3.46

038 0.06 0.05 0.03 0.03 6.48

2009/4/29 137 0.07 0.05 0.07 0.03 1.44

138 0.06 0.05 0.06 0.03 4.24

024 0.08 0.06 0.08 0.04 3.78

140 0.08 0.06 0.08 0.04 5.36

106 0.06 0.04 0.05 0.02 3.11

139 0.06 0.05 0.05 0.02 2.07

20

141 0.07 0.05 0.06 0.03 2.70

038 0.08 0.06 0.08 0.04 3.58

2014/5/21 137 0.14 0.10 0.07 0.06 8.29

138 0.16 0.12 0.09 0.08 8.52

024 0.16 0.12 0.09 0.07 8.78

140 0.14 0.10 0.08 0.07 13.00

106 0.14 0.11 0.07 0.05 8.07

139 0.15 0.12 0.08 0.05 3.54

141 0.15 0.12 0.08 0.06 6.60

124 0.15 0.12 0.08 0.04 5.52

038 0.16 0.13 0.09 0.08 9.57

2015/4/22 137 0.16 0.11 0.08 0.05 13.55

138 0.16 0.11 0.08 0.05 6.57

024 0.15 0.11 0.07 0.04 8.95

140 0.15 0.11 0.07 0.04 47.90

106 0.15 0.11 0.06 0.03 7.59

139 0.16 0.11 0.07 0.04 1.97

141 0.16 0.11 0.07 0.04 2.08

124 0.16 0.12 0.07 0.04 2.46

038 0.15 0.11 0.07 0.04 30.38

2015/6/25 137 0.15 0.11 0.08 0.08 5.79

138 0.17 0.13 0.11 0.10 4.62

024 0.15 0.11 0.08 0.06 4.69

140 0.16 0.12 0.09 0.07 4.99

106 0.15 0.11 0.07 0.05 3.98

139 0.15 0.11 0.07 0.05 2.85

141 0.15 0.11 0.07 0.05 2.57

124 0.14 0.11 0.06 0.04 2.13

038 0.15 0.11 0.08 0.06 4.44

2016/5/18 137 0.06 0.05 0.05 0.02 2.90

138 0.09 0.07 0.09 0.05 3.84

024 0.09 0.07 0.09 0.04 3.99

140 0.07 0.05 0.06 0.03 4.20

106 0.08 0.06 0.07 0.03 4.30

139 0.06 0.05 0.05 0.02 3.01

141 0.10 0.08 0.10 0.05 3.76

124 0.07 0.06 0.06 0.03 4.14

038 0.08 0.07 0.08 0.04 4.09

2016/5/26 137 0.16 0.11 0.08 0.07 2.66

138 0.18 0.14 0.11 0.09 2.12

024 0.17 0.13 0.08 0.06 2.21

106 0.17 0.13 0.09 0.07 2.44

038 0.16 0.12 0.07 0.05 2.56

21

2016/6/27 137 0.15 0.11 0.08 0.07 4.18

138 0.16 0.12 0.09 0.08 6.23

024 0.15 0.11 0.07 0.05 6.03

140 0.16 0.13 0.10 0.08 4.50

106 0.15 0.11 0.07 0.06 4.31

139 0.15 0.11 0.08 0.05 2.15

141 0.15 0.11 0.08 0.06 2.81

124 0.17 0.14 0.10 0.07 2.63

038 0.15 0.11 0.07 0.05 3.33

2016/7/13 137 0.15 0.11 0.07 0.05 3.70

138 0.16 0.12 0.08 0.06 3.42

024 0.14 0.10 0.06 0.03 3.63

140 0.15 0.12 0.08 0.04 3.97

106 0.15 0.10 0.06 0.04 2.68

139 0.14 0.10 0.06 0.03 2.64

141 0.14 0.10 0.06 0.04 1.89

124 0.15 0.12 0.07 0.04 2.57

038 0.14 0.10 0.06 0.03 3.48

Table 2. Average precipitation on 12 different dates based on previous 5,10,15,20,25 and 30 day periods.

date 5d 10d 15d 20d 25d 30d

2003/8/19 0.01 0.03 0.11 0.14 0.12 0.14

2006/2/16 0.20 0.10 0.16 0.14 0.16 0.16

2006/4/21 0.00 0.01 0.02 0.05 0.04 0.03

2008/5/28 0.20 0.13 0.13 0.11 0.10 0.12

2009/4/29 0.00 0.14 0.10 0.11 0.13 0.14

2014/5/21 0.14 0.08 0.07 0.06 0.09 0.09

2015/4/22 0.23 0.12 0.11 0.11 0.08 0.11

2015/6/25 0.35 0.18 0.14 0.11 0.14 0.14

2016/5/18 0.00 0.01 0.06 0.07 0.07 0.06

2016/5/26 0.05 0.03 0.02 0.02 0.06 0.06

2016/6/27 0.00 0.00 0.01 0.01 0.05 0.08

2016/7/13 0.04 0.05 0.05 0.04 0.03 0.03

Table 3. Average temperature on 12 different dates based on previous 5,10,15,20,25 and 30 day periods.

date 5d 10d 15d 20d 25d 30d

2003/8/19 75.2 76.7 76.3 74.4 74.7 75.0

22

2006/2/16 33.3 30.0 34.0 35.2 34.7 36.1

2006/4/21 52.9 55.2 52.1 50.3 50.4 49.1

2008/5/28 64.3 60.7 60.0 58.2 58.0 56.4

2009/4/29 64.5 58.0 54.8 52.3 51.0 50.0

2014/5/21 60.7 60.9 61.0 60.2 57.4 56.4

2015/4/22 50.7 53.2 50.2 48.4 46.7 45.2

2015/6/25 71.8 69.4 69.5 68.5 65.0 65.8

2016/5/18 57.1 58.5 55.3 53.3 52.1 52.8

2016/5/26 62.7 60.8 60.2 59.1 56.7 55.2

2016/6/27 69.5 70.1 69.5 68.1 68.1 67.8

2016/7/13 68.8 70.8 72.2 71.8 71.6 71.2

Table 4. Average streamflow on 12 different dates based on previous 5,10,15,20,25 and 30 day periods.

date 5day 10day 15day 20day 25day 30day

2003/8/19 287 337 331 300 284 273

2006/2/16 725 820 840 820 827 836

2006/4/21 163 197 227 224 218 221

2008/5/28 227 261 269 293 334 363

2009/4/29 510 514 523 550 556 534

2014/5/21 247 284 340 391 410 421

2015/4/22 287 337 331 300 284 273

2015/6/25 725 820 840 820 827 836

2016/5/18 163 197 227 224 218 221

2016/5/26 227 261 269 293 334 363

2016/6/27 510 514 523 550 556 534

2016/7/13 247 284 340 391 410 421

Table 5. R2 at 100m where x is the bands ratio, y is the logarithm chlorophyll concentration (μg/l)

Band Ratios, x 1/2 1/3 1/4 2/3 2/4 3/4

𝑦 = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 0.379 0.156 0.294 0.081 0.201 0.091

𝑦 = 𝑎1 ln(𝑥) + 𝑎2 0.162 0.044 0.156 0.022 0.135 0.072

𝑦 = 𝑎1𝑥2 + 𝑎2 0.174 0.068 0.19 0.032 0.153 0.064

𝑦 = 𝑎1𝑥𝑎2 0.119 0.035 0.13 0.018 0.119 0.078


Band Ratios, x 1/2 1/3w 1/4 2/3 2/4 3/4

23

𝑦 = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 0.434 0.180 0.291 0.099 0.216 0.103

𝑦 = 𝑎1 ln(𝑥) + 𝑎2 0.188 0.052 0.181 0.026 0.157 0.081

𝑦 = 𝑎1𝑥2 + 𝑎2 0.201 0.081 0.214 0.039 0.175 0.072


Band Ratios, x 1/2 1/3 1/4 2/3 2/4 3/4

𝑦 = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 0.454 0.193 0.274 0.108 0.200 0.108

𝑦 = 𝑎1 ln(𝑥) + 𝑎2 0.211 0.058 0.191 0.030 0.163 0.081

𝑦 = 𝑎1𝑥2 + 𝑎2 0.225 0.089 0.220 0.044 0.177 0.071


Band Ratios, x 1/2 1/3 1/4 2/3 2/4 3/4

𝑦 = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 0.469 0.210 0.309 0.122 0.232 0.129

𝑦 = 𝑎1 ln(𝑥) + 𝑎2 0.224 0.061 0.217 0.031 0.188 0.094

𝑦 = 𝑎1𝑥2 + 𝑎2 0.240 0.095 0.249 0.047 0.205 0.082


Band Ratios, x 1/2 1/3 1/4 2/3 2/4 3/4

𝑦 = 𝑎1𝑥2 + 𝑎2𝑥 + 𝑎3 0.17

1 0.211 0.321 0.126 0.246 0.141

𝑦 = 𝑎1 ln(𝑥) + 𝑎2 0.17

0 0.064 0.232 0.029 0.199 0.098

𝑦 = 𝑎1𝑥2 + 𝑎2 0.17

0 0.099 0.264 0.045 0.125 0.084

Table 10. R2 for different previous day averaging durations (5,10,15,20,25,30 days) for precipitation and streamflow when temperature is the average temperature over the

previous 5 days

d=5 d=10 d=15 d=20 d=25 d=30

p=5 0.639 0.643 0.649 0.653 0.653 0.650

p=10 0.581 0.582 0.590 0.596 0.595 0.598

p=15 0.585 0.586 0.592 0.597 0.597 0.591

p=20 0.581 0.583 0.591 0.595 0.593 0.587

p=25 0.60 0.598 0.605 0.615 0.620 0.608

p=30 0.576 0.579 0.589 0.595 0.594 0.587

24


previous 10 days

d=5 d=10 d=15 d=20 d=25 d=30

p=5 0.697 0.698 0.698 0.698 0.698 0.701

p=10 0.54 0.546 0.511 0.551 0.55 0.553

p=15 0.534 0.541 0.547 0.545 0.543 0.546

p=20 0.529 0.538 0.544 0.542 0.539 0.542

p=25 0.589 0.589 0.594 0.602 0.611 0.604

p=30 0.542 0.548 0.557 0.562 0.566 0.564


previous 15 days

d=5 d=10 d=15 d=20 d=25 d=30

p=5 0.644 0.648 0.658 0.664 0.663 0.658

p=10 0.568 0.57 0.58 0.588 0.587 0.582

p=15 0.575 0.576 0.585 0.593 0.593 0.587

p=20 0.573 0.574 0.584 0.591 0.589 0.583

p=25 0.598 0.594 0.604 0.618 0.624 0.61

p=30 0.562 0.566 0.578 0.588 0.589 0.581


previous 20 days

d=5 d=10 d=15 d=20 d=25 d=30

p=5 0.701 0.704 0.707 0.723 0.712 0.707

p=10 0.589 0.59 0.601 0.611 0.611 0.607

p=15 0.597 0.596 0.606 0.617 0.618 0.607

p=20 0.599 0.598 0.607 0.617 0.618 0.612

p=25 0.613 0.608 0.619 0.635 0.643 0.612

p=30 0.585 0.587 0.6 0.612 0.613 0.607


previous 25 days

d=5 d=10 d=15 d=20 d=25 d=30

p=5 0.701 0.713 0.721 0.72 0.721 0.729

25

p=10 0.587 0.59 0.599 0.606 0.605 0.605

p=15 0.596 0.598 0.606 0.612 0.613 0.612

p=20 0.596 0.599 0.605 0.61 0.642 0.608

p=25 0.621 0.617 0.627 0.541 0.65 0.638

p=30 0.577 0.582 0.595 0.604 0.605 0.6


previous 30 days

d=5 d=10 d=15 d=20 d=25 d=30

p=5 0.736 0.737 0.737 0.737 0.741 0.741

p=10 0.629 0.627 0.634 0.642 0.643 0.64

p=15 0.636 0.634 0.639 0.645 0.647 0.644

p=20 0.636 0.634 0.638 0.642 0.643 0.64

p=25 0.644 0.638 0.646 0.66 0.667 0.656

p=30 0.611 0.615 0.625 0.632 0.632 0.629

estimating and mapping chlorophyll-a concentration in ... · there are roughly 200 mwra long-term...

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