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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
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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
Table 6. R2 at 200m where x is the bands ratio, y is the logarithm chlorophyll concentration (μg/l)
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
Table 7. R2 at 300m 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 0.454 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
Table 8. R2 at 400m 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 0.469 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
Table 9. R2 at 500m 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 0.17
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
Table 11. 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 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
Table 12. 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 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
Table 13. 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 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
Table 14. 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 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
Table 15. 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 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
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