MONITORING VEGETATION COVER CHANGE USING MODIS...
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MONITORING VEGETATION COVER CHANGE USING MODIS NDVI AND EVI TIME SERIES FROM 2010 TO 2016 IN ARIQUEMES MICROREGION, BRAZIL AND THE
ASIAN SIDE OF ISTANBUL, TURKEY
By
OMER EKMEN
A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
2017
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© 2017 Omer Ekmen
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To My Mom
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ACKNOWLEDGMENTS
I would first like to thank my Mom for her love, support and motivation during my
Master’s journey from thousands of miles away.
I would like to convey my gratefulness to my advisor Dr. Amr Abd-Elrahman for
his generous support, coaching and helpful advices during my thesis journey. Without
his supervision and constant help, this thesis could not have been completed.
I would like to thank Dr. Wendell Cropper and Dr. Scot E. Smith for serving on
my committee. Dr. Wendell Cropper introduced me to the steps of the research process
and Dr. Scot E. Smith helped me broaden my knowledge of remote sensing.
I would also like to express my sincere gratitude to colleagues and staff in the
Geomatics Program at the Plant City Campus for their assistance and friendship.
.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .............................................................................................. 4
LIST OF FIGURES ...................................................................................................... 8
ABSTRACT ................................................................................................................. 9
CHAPTER
1 INTRODUCTION................................................................................................. 11
Remote Sensing.................................................................................................. 11
Remote Sensing for Monitoring Vegetation .......................................................... 13
Study Objectives ................................................................................................. 16
2 MATERIAL AND METHODS ............................................................................... 17
Study Areas ........................................................................................................ 17 Data Preparation ................................................................................................. 21
Municipal Boundaries .................................................................................... 21
Vegetation Indices......................................................................................... 22 Temporal Data Acquisition Using Google Earth Engine .................................. 23
Harmonic Seasonal Model and Standard Linear Regression Model ...................... 24 Population Data .................................................................................................. 25
3 RESULTS ........................................................................................................... 26
Linear Regression Model and Trends in Vegetation Cover Change ...................... 26 Trends in Vegetation Cover Change Using MODIS NDVI Time Series ............ 26
Trends in Vegetation Cover Change Using MODIS EVI Time Series .............. 27 Population and Vegetation Cover......................................................................... 27
Greenness-Based Municipal Ranking .................................................................. 29
4 DISCUSSION...................................................................................................... 32
5 CONCLUSION .................................................................................................... 36
APPENDIX
A LINEAR REGRESSION MODEL USING MODIS NDVI TIME SERIES .................. 37
Regression Analysis: RS in Ariquemes ................................................................ 37
Regression Analysis: Alto Paraiso ....................................................................... 38 Regression Analysis: Ariquemes.......................................................................... 39
Regression Analysis: Cacaulandia ....................................................................... 40
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Regression Analysis: Machadinho do Oeste ........................................................ 41 Regression Analysis: Monte Negro ...................................................................... 42
Regression Analysis: Rio Crespo ......................................................................... 43 Regression Analysis: Vale do Anari ..................................................................... 44
Regression Analysis: RS in Istanbul..................................................................... 45
Regression Analysis: Atasehir ............................................................................. 46 Regression Analysis: Beykoz ............................................................................... 47
Regression Analysis: Cekmekoy .......................................................................... 48 Regression Analysis: Kadikoy .............................................................................. 49
Regression Analysis: Kartal ................................................................................. 50
Regression Analysis: Maltepe .............................................................................. 51 Regression Analysis: Pendik ............................................................................... 52
Regression Analysis: Sancaktepe ........................................................................ 53 Regression Analysis: Sile .................................................................................... 54
Regression Analysis: Sultanbeyli ......................................................................... 55
Regression Analysis: Tuzla.................................................................................. 56 Regression Analysis: Umraniye ........................................................................... 57
Regression Analysis: Uskudar ............................................................................. 58
B LINEAR REGRESSION MODEL USING MODIS EVI TIME SERIES .................... 59
Regression Analysis: RS in Ariquemes ................................................................ 59
Regression Analysis: Alto Paraiso ....................................................................... 60 Regression Analysis: Ariquemes.......................................................................... 61
Regression Analysis: Cacaulandia ....................................................................... 62 Regression Analysis: Machadinho do Oeste ........................................................ 63
Regression Analysis: Monte Negro ...................................................................... 64
Regression Analysis: Rio Crespo ......................................................................... 65 Regression Analysis: Vale do Anari ..................................................................... 66
Regression Analysis: RS in Istanbul..................................................................... 67 Regression Analysis: Atasehir ............................................................................. 68
Regression Analysis: Beykoz ............................................................................... 69
Regression Analysis: Cekmekoy .......................................................................... 70 Regression Analysis: Kadikoy .............................................................................. 71
Regression Analysis: Kartal ................................................................................. 72 Regression Analysis: Maltepe .............................................................................. 73
Regression Analysis: Pendik ............................................................................... 74
Regression Analysis: Sancaktepe ........................................................................ 75 Regression Analysis: Sile .................................................................................... 76
Regression Analysis: Sultanbeyli ......................................................................... 77 Regression Analysis: Tuzla.................................................................................. 78
Regression Analysis: Umraniye ........................................................................... 79
Regression Analysis: Uskudar ............................................................................. 80
LIST OF REFERENCES ............................................................................................ 81
BIOGRAPHICAL SKETCH ........................................................................................ 86
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LIST OF TABLES Table page 3-1 Pearson correlation of Intercept (NDVI) and Population Density per km2 in
2010 ............................................................................................................... 28
3-2 Pearson correlation of Intercept (EVI) and Population Density per km2 in 2010 ............................................................................................................... 29
3-3 RMSD values and greenness-based municipal ranking .................................... 31
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LIST OF FIGURES
Figure page 1-1 “Nadar elevating Photography to Art” ............................................................... 12
2-1 Microregions in the State of Rondonia ............................................................. 18
2-2 Municipalities in Ariquemes microregion .......................................................... 19
2-3 The two sides of Istanbul ................................................................................. 20
2-4 Municipalities on the Asian Side of Istanbul...................................................... 21
2-5 NDVI time-series graph of Monte Negro municipality........................................ 24
3-1 EVI time series plot of Reference Site in Istanbul, Uskudar, and Atasehir municipalities .................................................................................................. 30
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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science
MONITORING VEGETATION COVER CHANGE USING MODIS NDVI AND EVI TIME SERIES FROM 2010 TO 2016 IN ARIQUEMES MICROREGION, BRAZIL AND THE
ASIAN SIDE OF ISTANBUL, TURKEY
By
Omer Ekmen
December 2017
Chair: Amr Abd-Elrahman Major: Forest Resources and Conservation
Monitoring vegetation cover change is essential to take measures against
desertification, deforestation, soil erosion, and the loss of vegetation cover. Vegetation
also helps to reduce air pollution in cities. Besides, vegetation has social and
physiological benefits.
Satellite image time series are a valuable resource for vegetation cover
monitoring. In recent years, Google Earth Engine (GEE) made satellite image time
series more accessible with its gigantic data collection.
This study used a monitoring approach based on a harmonic seasonal model
which is applicable to both NDVI and EVI time series obtained from GEE. Then, trends
in vegetation cover change were investigated for each municipality in Ariquemes
microregion and the Asian Side of Istanbul to identify trends in vegetation cover change
in these municipalities.
We also looked into the relationship between population and vegetation cover.
The results showed that although we could not find a statistically significant correlation
coefficient between the municipal populations and the intercepts in the regression
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models for Ariquemes microregion, there was a strong inverse correlation between the
municipal populations and the intercepts in the regression models for the Asian Side of
Istanbul.
However, this study demonstrated that the correlation coefficient between the
trends in vegetation cover change and the population growth rates in the municipalities
on Asian Side of Istanbul was statistically insignificant.
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CHAPTER 1 INTRODUCTION
Remote Sensing
The term ‘remote sensing’ has been defined many times. For this reason, there
are a variety of definitions of remote sensing. One definition of remote sensing is:
“remote sensing, in simplest words, means obtaining information about an object
without touching the object itself” (Gupta, 2013, p. 1). According to Barrett (2013),
“remote sensing can be defined as ‘the science of observation from a distance’” (p. 6).
Another definition of remote sensing is, “remote sensing is the science of making
inferences about objects from measurements, made at a distance, without coming into
physical contact with the objects under study” (Joseph, 2005, p. 1). In fact, as you read
this thesis, you are utilizing remote sensing. Your retina passively senses the light
reflected from this page. Next, the data your eyes acquire are analyzed and interpreted
in your brain. In the general sense, remote sensing can be considered a reading
process (Lillesand et al., 2014).
The early development of remote sensing is closely engaged to advancements in
aerial photography. The French photographer Gaspard-Félix Tournachon (1820-1910),
known by the pseudonym Nadar, took the first aerial photograph above Paris from a
tethered balloon in 1858 (Figure 1-1). In succeeding years, kites and pigeons were also
used to acquire aerial photographs. Meanwhile, it should be noted that balloons were
also used for military observation of a region to locate an enemy or ascertain strategic
features during the American Civil War (National Research Council, 1998). Airplanes
began to play an important role in aerial photography with the advent of the airplane in
the early twentieth century.
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Figure 1-1. “Nadar elevating Photography to Art”. Lithograph by Honoré Daumier, 1862. (Source: Brooklyn Museum, Frank L. Babbott Fund. Retrieved 10 October 2017, from https://www.brooklynmuseum.org/opencollection/objects/64499)
In 1967, NASA initiated the Earth Resource Technology Satellite (ERTS)
program (Markham et al., 2016). The ERTS-1 satellite which was the first Earth-
observing satellite was launched in 1972. Later, NASA decided to officially rename the
ERTS-1 to Landsat 1. This satellite operated until 1978. Landsat 2 was launched in
1975 and it operated until 1983. Most recent satellite of the Landsat series is Landsat 8.
This satellite was launched on February 11, 2013. Landsat 8 carries the Operational
Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments. OLI contains
nine spectral bands and TIRS collect data from two thermal bands.
There are two types of remote sensing based on the sensors used. Remotely
gathered data are collected using either active or passive remote sensing. Active
sensors use their own source of radiation. RADAR (Radio Detection and Ranging) and
LIDAR (Light Detection and Ranging) are widely used active remote sensing
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instruments. Passive sensors measure electromagnetic radiation (EMR) that is reflected
or emitted from the terrain of interest. The Landsat, MODIS and many other satellites
are examples of passive sensors. Cameras are passive sensors unless a photographer
uses flash.
There are four types of resolving power (resolution) defined for the satellite
remote sensing technology. Spatial resolution is a measure of the smallest size detail
that can be detected by the sensor. Radiometric resolution represents the sensitivity of
a sensor to distinguish different grey-scale values while recording imagery. For
instance, a 6-bit representation has 64 grey-scale values. In simple terms, the finer
radiometric resolution means the more accurate remotely sensed data. Spectral
resolution determines the ability to resolve features in the electromagnetic spectrum.
The higher the number of spectral bands, the finer the spectral resolution of a sensor.
Scrupulous selection of the spectral bands may boost the probability of detection and
identification of a feature (Jensen, 1996). An Earth observation satellite revisits a
particular area in specific cycles on its trajectory around the Earth (Stephan, 2015).
Temporal resolution refers to how often a satellite provides information on the same
location.
Remote Sensing for Monitoring Vegetation
Grasslands and forests are significant natural resources. These natural
resources are essential for the carbon cycle and regional economy. On land, forests are
the most common type of vegetation. Forests are a key component in providing an
ecological foundation, the environmental context and forming the roles of regional and
worldwide ecosystem processes (Banskota et al., 2014). “Forests harbor the majority of
species on Earth and provide valuable ecosystem goods and services to humanity,
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including food, fiber, timber, medicine, clean water, aesthetic and spiritual values, and
climate moderation (Jackson et al., 2005; McKinley et al., 2011)” (Pan et al., 2013, p.
594). Furthermore, there are millions of people in poverty who depend on forests for
fuel, housing, and their jobs.
On a global scale, deforestation is one of the serious environmental problems.
“Deforestation affects biological diversity in three ways: destruction of habitat, isolation
of fragments of formerly contiguous habitat, and edge effects within a boundary zone
between forest and deforested areas” (TARTICLEt, 1993, p. 1905). For example,
deforestation in Brazilian Amazon results in significant amount of greenhouse gases
(Fearnside, 1997). Therefore, monitoring deforestation is crucial in order to learn the
deforestation rates and combat it.
Grasslands and forests are facing degradation caused by human-induced
activates such as agriculture and urbanization. Because degradation happens in large
areas, the resources (money, personnel and technology) available to cope with the
problem are limited (He et al., 2005). Remote sensing techniques present a practical
and economical means to study vegetation cover changes, particularly over massive
areas (Xie et al., 2008).
Remotely sensed data has been used to improve the accuracy of datasets that
describing the geographic distribution of land cover (De Fries et al., 1998). Mapping
land use/land cover (LULC) is crucial for a wide range of applications (Reis, 2008). At
this point, remote sensing is a great source of land cover since it supplies a reliable
representation of the Earth’s surface (Marsik et al., 2011).
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Monitoring vegetation change is significant to take precautions against
desertification, deforestation, soil erosion, and the loss of vegetation cover. Vegetation
also helps to reduce air pollution in cities. In addition to this, vegetation has social and
physiological benefits as well. For instance, Kuo & Sullivan (2001) investigated the
relationship between vegetation and crime in an inner city. They found that the greener
the surroundings the fewer number of crime reports that happened. Taking all these
benefits into consideration, adequate monitoring of vegetation results in correct
environmental decision-making.
Satellite remote sensing has been used as a tool to monitor vegetated land
surfaces since the early 1980s (O’Connor et al., 2008). For example, deforestation is
difficult to quantify over large areas, but remote sensing techniques are often used to
estimate deforestation rates (Rignot et al., 1997; Sánchez‐Azofeifa et al., 2001;
Miettinen et al., 2011; Margono, 2013; Buchanan et al., 2013; Grinand et al., 2013;
Rahm et al., 2013; Sannier et al., 2014; Beuchle et al., 2015; Shermeyer & Haack,
2015; Armenta et al., 2016; Ramachandran & Reddy, 2016).
Several studies have monitored urban land cover and vegetation change in cities
by using remotely sensed data. Peijun et al. (2010) investigated urban vegetation
changes from 1987 to 2007 in Xuzhou city based on the normalized difference
vegetation indices (NDVIs) derived from four Landsat TM images. Nichol & Lee (2005)
studied urban vegetation monitoring in Hong Kong using multispectral IKONOS images.
They found that the use of satellite images was much more economical than aerial
photographs for urban vegetation monitoring.
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Study Objectives
1. To apply a harmonic seasonal model on MODIS satellite image time series to monitor vegetation cover change in each municipality in Ariquemes microregion and the Asian Side of Istanbul.
2. To assess NDVI or EVI time series fits better into the harmonic seasonal model to monitor vegetation cover change.
3. To look into trends in vegetation cover change in municipalities.
4. To rank municipalities based on their “greenness”.
5. To investigate the relationship between population and vegetation cover.
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CHAPTER 2 MATERIAL AND METHODS
Study Areas
Two study areas were selected for this research. One of the study areas was
Ariquemes microregion in the state of Rondonia and the other one was the Asian side of
Istanbul excluding the islands.
As Tucker et al. (1984) support, Rondonia is an ideal place for testing remote-
sensing techniques for monitoring tropical forest clearing. From the early 1970s to the
late 1990s, dramatic changes have happened in the Brazilian Amazon because of
human-induced activities (Alves et al., 1999). Until the mid-1970s, deforestation rate in
the Brazilian Amazon was less than 1% (Guild et al., 2004). After 1975 though, forest
clearing increased severely. Estimates of primary forest clearing for Rondonia in 1975
and 1978 were 1200 km2 and 4200 km2 in the order given (Tucker et al., 1984). This
upswing continued all the way through the mid-1980s. Malingreau & Tucker (1988)
estimated that deforestation of Rondonia in 1984 and 1985 were 17000 km2 and 27000
km2 in the order given. The deforestation rate grew with the arrival of new colonists and
new settlement projects that were begun to house them. The building of BR-364
highway also eased immigration and contributed to the forest market. Consequently, it
added a pressure for forest clearing (Tucker et al., 1984). The experience of Rondonia
showed how rapidly a vast tropical forest could be cleared and used for different
purposes (Malingreau & Tucker, 1988). Ariquemes microregion is one of the 8
microregions in the state of Rondonia (Figure 2-1). This microregion consists of Alto
Paraiso, Ariquemes, Cacaulandia, Machadinho do Oeste, Monte Negro, Rio Crespo,
and, Vale do Anari municipalities (Figure 2-2). Besides, a reference site was chosen in
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the municipality of Vale do Anari to represent the characteristics of nondeforested areas
in this microregion.
Figure 2-1. Microregions in the State of Rondonia (Created in “ArcGIS”).
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Figure 2-2. Municipalities in Ariquemes microregion (Created in “ArcGIS”).
Istanbul is Turkey’s financial and historic center as well as the country’s most
populated city. According to TUIK (2017), Istanbul was home to 14 804 116 residents in
2016. The importance of Istanbul is closely related to its location. The strait of Istanbul
(The Bosphorus) links the Black Sea to the Sea of Marmara. At the same time, this
waterway is the continental boundary between Asia and Europa. It follows that while
one part of Istanbul is called “the European Side of Istanbul”, the other part is cal led “the
Asian Side of Istanbul” (Figure 2-3). The Asian Side of Istanbul is greener (Fowler,
2015). There are 13 municipalities (Atasehir, Beykoz, Cekmekoy, Kadikoy, Kartal,
Maltepe, Pendik, Sancaktepe, Sile, Sultanbeyli, Tuzla, Umraniye, Uskudar) on the Asian
Side of Istanbul (Figure 2-4). In addition, the reference site of the Asian Side of Istanbul
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is in the municipality of Sile. This reference site is forested and reflects the
characteristics of the forested areas on the Asian Side of Istanbul.
Figure 2-3. The two sides of Istanbul (Created in “ArcGIS”).
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Figure 2-4. Municipalities on the Asian Side of Istanbul (Created in “ArcGIS”).
Data Preparation
Municipal Boundaries
The polygon shapefile of the municipal boundaries for Ariquemes microregion
was downloaded from the Earthworks which is a tool for downloading Geographic
Information Systems (GIS) data owned by Stanford University Libraries (Stanford
Libraries, 2017). On the other hand, the polygon shapefile of the municipal boundaries
for the Asian Side of Istanbul was downloaded from the Database of Global
Administrative Areas which is a spatial database of the world’s administrative
boundaries for the applications in a GIS software (Global Administrative Areas, 2017).
Shapefiles cannot be used directly in Google Earth Engine. For this reason, we
converted each shapefile to the Keyhole Markup Language (KML) format. Then, a
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fusion table was created for each municipality as well as two reference sites to use in
Google Earth Engine. Fusion Tables is a web (cloud-based) service for data
management by Google (Gonzalez et al., 2010). Fusion Tables are automatically stored
online in Google Drive.
Vegetation Indices
Vegetation is a crucial part of the ecosystem and satellite remote sensing is
widely used to map and monitor vegetation cover change on various scales.
Traditionally, vegetation cover defines the ground which is covered by green vegetation.
In general, information on vegetation is gathered by computing the vegetation indices
(VIs). In our study, we used the normalized difference vegetation index (NDVI) and the
enhanced vegetation index (EVI).
Normalized difference vegetation index is the most commonly used vegetation
index (Leprieur et al., 2000). It was used to monitor desertification and explore
desertification trends (Stenberg et al., 2011; Piao et al., 2005; Liu et al., 2003), assess
soil erosion (Fu et al., 2011), and analyze seasonal changes in vegetation activity (Piao
& Fang, 2003). NDVI can be computed from remotely sensed data using the formula of
the near-infrared (NIR) band minus the red band (R) divided by the near-infrared band
plus the red band. Written mathematically, the formula is:
NDVI =(𝑁𝐼𝑅 − 𝑅)
(𝑁𝐼𝑅 + 𝑅)
(2-1)
The normalized difference vegetation index values range from +1.0 to -1.0. If
NDVI has a negative value, it usually corresponds to water. Close to zero values of
NDVI correspond to sand, rock, snow etc. While low positive values indicate shrub and
grassland, high NDVI values represent forest and dense vegetation.
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The enhanced vegetation index (EVI) is another vegetation index. EVI is less
sensitive to soil and atmospheric effects than NDVI since it includes blue spectral
wavelengths (Waring et al., 2006). Written mathematically, EVI is expressed as:
EVI = 𝐺(𝑁𝐼𝑅 − 𝑅)
(𝑁𝐼𝑅 + 𝐶1𝑅 − 𝐶2𝐵 + 𝐿)
(2-2)
where B stands for the blue band, C1 and C2 are aerosol resistance coefficients and G is
a gain factor. L refers to the canopy background adjustment in the above-mentioned
formula. As it can be understood from the formulas, EVI is derived from three bands
(near-infrared, red, and blue), while NDVI can be computed using two bands (near-
infrared and red).
Temporal Data Acquisition Using Google Earth Engine
In this study, Google Earth Engine (GEE) played a key role to acquire data. The
Google Earth Engine is a cloud-based platform to acquire Earth science data. It’s free
for educational purposes. GEE stores satellite imagery, organizes it, and makes it
accessible for the users (Google Earth Engine, 2017). There are numerous datasets in
GEE. MODIS Combined 16-Day NDVI and MODIS Combined 16-Day EVI products
were used in this study. Both products are based on the Moderate-resolution Imaging
Spectroradiometer (MODIS) Reflectance dataset (MCD43A4 Version 5). This dataset
has a ground instantaneous field of view (GIFOV) of 500m and includes data for every 8
days using the last 16 days of acquisition.
From January 1st, 2010 to December 31st, 2016 were chosen for the study
period. Temporal coverage of MODIS starts with February 18th, 2000 but Atasehir,
Cekmekoy, and Sancaktepe became districts later in 2008. NDVI and EVI data were
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obtained as a time-series graph (Figure 2-5) as well as an Excel file for each district
using the following code in GEE.
Figure 2-5. NDVI time-series graph of Monte Negro municipality (created in “Google Earth Engine”).
Finally, because all obtained data include observations for every 8 days using the
last 16 days of acquisition, every second row was removed from the Excel files to
provide independent observations (uncorrelated observations).
Harmonic Seasonal Model and Standard Linear Regression Model
According to Verbesselt et al. (2012), for the observations yt at time t, a season-
trend model is assumed with linear trend and harmonic season:
𝑦𝑡 = 𝛼1 + 𝛼2𝑡 + ∑ 𝛾𝑗
𝑘
𝑗=1
sin (2𝜋𝑗𝑡
𝑓+ 𝛿𝑗) + 휀𝑡
(2-3)
where k represents the number of harmonic terms. In this study, three harmonic terms
were applied. α1 and α2 represent intercept and slope respectively, γ1 to γk are
amplitudes of the seasonal model, δ1 to δk are phases of the seasonal model and εt is
the unobservable error term at time t. f stands for frequency. In this study, f=23 since
there were 23 observations (16-day interval) for each year. This proposed model is
based on the characteristics of the vegetation indices. In other words, it is formed of
routinely taking place seasonal changes (Verbesselt et al., 2012).
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As Verbesselt et al. (2012) stated, the model can be rewritten as a standard
regression model.
𝑦𝑡 = 𝑥𝑡𝑇𝛽 + 휀𝑡
𝑥𝑡 = {1, 𝑡, sin (2𝜋1𝑡
𝑓) , cos (
2𝜋1𝑡
𝑓) , … , sin (
2𝜋𝑘𝑡
𝑓) , cos (
2𝜋𝑘𝑡
𝑓)}T
𝛽 = {𝛼1 , 𝛼2 , 𝛾1 cos(𝛿1) , 𝛾1 sin(𝛿1) , … , 𝛾𝑘 cos(𝛿𝑘) , 𝛾𝑘sin (𝛿𝑘)}T
(2-4)
Using ordinary least square (OLS) method, also known as linear least squares,
the unknown parameters β in the regression model can be estimated.
Population Data
Population data and the results of the trend analyses were correlated to
investigate the relationships between population and vegetation cover. Areas of
municipalities to calculate population density per km2 and 2010 census data were
obtained from the Instituto Brasileiro de Geografia e Estatística (IBGE) for the
municipalities in Ariquemes microregion. On the other hand, address based population
data for the years 2010 and 2016 acquired from the Turkish Statistical Institute (TUIK)
for municipalities on the Asian Side of Istanbul. Municipal areas to calculate population
density per km2 were acquired from the General Command of Mapping (HGK).
Correlations were assessed with the use of the Pearson product-moment correlation
coefficient. Even though the Pearson correlation coefficient was discovered by Bravais,
Karl Pearson was the first person who demonstrated the standard method of its
calculation and demonstrated it to be the best one possible (Hauke & Kossowski, 2011).
It has a value between -1 and +1. −1 or +1 shows a perfect linear relationship. If the
correlation coefficient comes closer to +1 or -1, it indicates that variables are more
directly or inversely related (Mukaka, 2012).
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CHAPTER 3 RESULTS
Linear Regression Model and Trends in Vegetation Cover Change
Linear regression model was implemented for each municipality as well as the
reference sites (RSs) in Ariquemes microregion and the Asian Side of Istanbul using the
MODIS NDVI and the MODIS EVI time series (see Appendices A and B). When there
were missing observations in the time series, those observations were excluded. After
getting the regression equations, trends in vegetation cover change were examined for
each municipality in the years between 2010 and 2016.
Trends in Vegetation Cover Change Using MODIS NDVI Time Series
In the regression equations formed, the slope term expresses the overall trend
and the sign in front of the slope indicates the trend direction. In Ariquemes microregion,
results showed a downward trend in vegetation cover changes in Monte Negro and Alto
Paraiso municipalities with slopes of -0.000146 (p-value = 0.049) and -0.000139 (p-
value = 0.007) respectively. However, results demonstrated no trend in vegetation cover
change in Ariquemes, Cacaulandia, Machadinho do Oeste, Rio Crespo, and Vale do
Anari municipalities between the given dates (from January 1st, 2010 to December 31st,
2016) since the slopes of these municipalities were statistically insignificant (p-value >
0.05).
With regard to the municipalities on the Asian Side of Istanbul, results exhibited
an upward trend in vegetation cover change in Kartal municipality with a slope of
+0.000225 (p-value = 0.000) in the years between 2010 and 2016. Results indicated a
downward trend in vegetation cover change in Sancaktepe, Sultanbeyli and Atasehir
municipalities. The slopes of these municipalities were -0.000156 (p-value = 0.027),
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-0.000115 (p-value = 0.017), -0.000056 (p-value = 0.049) in the same order. Results
showed no trend in vegetation cover change in Beykoz, Cekmekoy, Kadikoy, Maltepe,
Pendik, Sile, Tuzla, Umraniye, and Uskudar municipalities in the years between 2010
and 2016 since the slopes of these municipalities were statistically insignificant (p-value
> 0.05).
Trends in Vegetation Cover Change Using MODIS EVI Time Series
According to the results, there was no trend in vegetation cover change in Alto
Paraiso, Ariquemes, Cacaulandia, Machadinho do Oeste, Monte Negro, Rio Crespo,
and Vale do Anari municipalities in Ariquemes microregion within a seven-year period
(2010-2016) since the slopes of all these municipalities were statistically not significant
(p-value > 0.05).
On the Asian Side of Istanbul, results showed an upward trend in vegetation
cover change in Kartal, Sile, Pendik, and Maltepe municipalities with slopes of
+0.000150 (p-value = 0.000), +0.000148 (p-value = 0.002), +0.000103 (p-value = 0.006)
+0.000064 (p-value = 0.022) in the same order. On the other hand, the results indicated
a downward trend in vegetation cover change in Sultanbeyli and Atasehir municipalities.
The slopes of these municipalities -0.000065 (p-value = 0.029) and -0.000040 (p-value
= 0.015) respectively. Results showed no trend in vegetation cover change in Beykoz,
Cekmekoy, Kadikoy, Sancaktepe, Tuzla, Umraniye, and Uskudar municipalities from
2010 to 2016 since the slopes of these municipalities were statistically insignificant (p-
value > 0.05).
Population and Vegetation Cover
The Pearson’s correlation coefficient between the intercepts (constants) in the
regression models using the NDVI time series and the population density per km2 in
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2010 in the municipalities in Ariquemes microregion was computed. Similarly, the
correlation coefficient between the intercepts in the models using the EVI time series
and the population density per km2 in 2010 in the same municipalities was computed.
The correlation coefficients were statistically insignificant (p-value > 0.05).
Likewise, the Pearson’s correlation coefficient between the intercepts in the
regression models using the NDVI time series and the population density per km2 in
2010 in the municipalities on the Asian Side of Istanbul was computed (Table 3-1). A
strong negative correlation was found (p-value = 0.000). Also, a correlation was
computed between the intercepts in the models using the EVI time series and the
population density per km2 in 2010 in the same municipalities (Table 3-2). A strong
negative correlation was found (p-value = 0.000). Finally, a correlation was computed
between the slopes that were found to be statistically significant in the regression
equations using the EVI time series and the population growth rates (2010-2016) in the
municipalities on the Asian Side of Istanbul. The correlation coefficient was found
statistically not significant (p-value > 0.05).
Table 3-1. Pearson correlation of Intercept (NDVI) and Population Density per km2 in 2010
Municipality Intercept (NDVI)
Population Density per km2
in 2010
Atasehir 0.24269 14431.08 Beykoz 0.65195 793.99 Cekmekoy 0.63516 1108.14 Kadikoy 0.26578 21313.40 Kartal 0.33674 11373.66 Maltepe 0.33509 8269.00 Pendik 0.49860 3079.98 Sancaktepe 0.45441 4070.51 Sile 0.63786 35.15 Sultanbeyli 0.33419 10036.66 Tuzla 0.42858 1346.51
29
Table 3-1. Continued
Municipality Intercept (NDVI)
Population Density per km2
in 2010
Umraniye 0.37842 13118.07 Uskudar 0.37253 15055.63
Pearson correlation coefficient = -0.847 P-Value = 0.000
Table 3-2. Pearson correlation of Intercept (EVI) and Population Density per km2 in
2010
Municipality Intercept
(EVI)
Population Density per km2
in 2010
Atasehir 0.13061 14431.08 Beykoz 0.37098 793.99 Cekmekoy 0.35740 1108.14 Kadikoy 0.13509 21313.40 Kartal 0.17800 11373.66 Maltepe 0.17000 8269.00 Pendik 0.25027 3079.98 Sancaktepe 0.23732 4070.51 Sile 0.35972 35.15 Sultanbeyli 0.17952 10036.66 Tuzla 0.23639 1346.51 Umraniye 0.19102 13118.07 Uskudar 0.18585 15055.63
Pearson correlation coefficient = -0.846 P-Value = 0.000
Greenness-Based Municipal Ranking
Using the fitted EVI values of the reference site and the municipalities in
Ariquemes microregion, the root mean square of the difference (RMSD) between the
reference site and each municipality EVI was calculated. In the same way, the RMSD
values for each municipality on the Asian Side of Istanbul was computed.
As can be seen in Figure 3-1, if the root mean square of the difference (RMSD)
between a reference site and a municipality EVI is small, it indicates a greener
municipality. For example, the root mean square of the difference between the
30
reference site and Uskudar EVI is 0.266, and the root mean square of the difference
between the reference site and Atasehir EVI is 0.333.
Table 3-3 lists the RMSD values for each municipality and shows their ranking
from the greenest municipality to the least green municipality considering a seven-year
analysis period (2010-2016).
Figure 3-1.EVI time series plot of Reference Site in Istanbul, Uskudar, and Atasehir municipalities (Created in “Minitab”)
31
Table 3-3. RMSD values and greenness-based municipal ranking
Ariquemes Microregion The Asian Side of Istanbul
Ranking Municipality RMSD Ranking Municipality RMSD
1 Vale do Anari 0.029 1 Sile 0.066
2 Machadinho do Oeste 0.031 2 Beykoz 0.074
3 Alto Paraiso 0.053 3 Cekmekoy 0.086
4 Rio Crespo 0.055 4 Pendik 0.205
5 Monte Negro 0.064 5 Sancaktepe 0.231
6 Cacaulandia 0.065 6 Tuzla 0.244
7 Ariquemes 0.068 7 Uskudar 0.266
8 Umraniye 0.272
9 Kartal 0.279
10 Maltepe 0.292
11 Sultanbeyli 0.294
12 Kadikoy 0.320
13 Atasehir 0.333
32
CHAPTER 4 DISCUSSION
It has been found that the MODIS EVI time series performs better than the
MODIS NDVI time series to fit into this harmonic seasonal model. For instance, the R-
squared values of regression analysis with the NDVI time series were 70.99% and
48.05% for Monte Negro and Vale do Anari municipalities respectively, while the R-
squared values of regression analysis with the EVI time series were 91.64% and
81.43% for the same municipalities in the same order. For this reason, we chose the
fitted EVI values instead of the fitted NDVI values to calculate the RMSD values.
We found that the Ariquemes municipality is the least green municipality in
Ariquemes microregion. The reason of this can be explained by the fact that this area
suffered from deforestation on a massive scale. Ariquemes is an agricultural boomtown
(Fearnside, 1989) and the timber sector plays a key role in this area (Richards &
VanWey, 2015). According to the RMSD values, Vale do Anari and Machadinho do
Oeste are respectively the greenest municipalities in this microregion. A part of Jaru
Biological Reserve is located in these municipalities. This reserve protects a dense
tropical forest (Padua & Quintao, 1982). With regard to the Asian Side of Istanbul, we
found that Atasehir is the least green municipality. Kadikoy follows this municipality as
the second-least green municipality in this region. Atasehir legally became a district of
Istanbul in 2008 and experienced a rapid urbanization and population growth in the
recent years. Regarding Kadikoy, this district is a very old settlement. It is a very busy
commercial and residential district today. Sile is the greenest municipality on the Asian
Side of Istanbul. It was an expected result because 79% of this municipality is covered
with secondary forest and bush vegetation (Baron, 2008).
33
Trends in vegetation cover change were investigated using both the NDVI and
the EVI time series. We found a downward trend in vegetation cover change in Monte
Negro and Alto Paraiso municipalities when we use the NDVI time series, but we could
not support the same results with the use of EVI time series. The EVI time series
showed no trend in vegetation cover change in these municipalities. Monte Negro and
Alto Paraiso municipalities are in a tropical area and EVI is better than NDVI in tropical
areas because of the saturation problem of NDVI. For this reason, it should be an
anomaly. Similarly, on the Asian Side of Istanbul, Sancaktepe municipality showed a
downward trend with the use of NDVI slope. However, EVI slope was statistically
insignificant. When there were contradictory differences between NDVI and EVI in terms
of monitoring the trends, the results with using the EVI values should be more
dependable. In addition, the fitted EVI values may be more reliable since these values
had the higher R-squared values.
In general, we obtained smaller RMSD values for the municipalities in Ariquemes
microregion than the municipalities on the Asian Side of Istanbul. The reason is that the
reference site (RS) in Ariquemes microregion was more similar to the municipalities in
the same microregion in terms of greenness component. That is to say, the
municipalities in Ariquemes, in general, are located in a tropical ecoregion that is was
much greener, less seasonal, and primarily agricultural than the municipalities on the
Asian Side of Istanbul, where there is a transitional climate.
Our study could not find a meaningful correlation between the municipal
populations and the intercepts of regression model for the municipalities in Ariquemes
microregion. This result emerged from the biggest driver of deforestation in this
34
microregion which is agriculture. Unlike urbanization, large areas of tropical forest can
be cleared for agriculture and population do not spike there as a direct result of this.
On the other hand, the study showed a strong correlation between municipal
populations and intercepts of regression model for the municipalities on the Asian Side
of Istanbul. Urbanization may be the direct cause of these results. Urban-induced
habitat degradation is a well-known phenomenon in the world. Urbanized areas usually
have less vegetation cover as compared to pre-urban situation of the same areas.
However, this study showed that there was not a meaningful correlation between the
slopes of regression model and population growth rates.
The plus and minus sings in front of the slope are used to define trend directions.
To illustrate, both vegetation cover and population increased in Pendik municipality in
the study period. Similarly, Kartal municipality has become greener although population
in this municipality has increased over 2010-2016. These examples show that
population growth does not necessarily cause vegetation cover decrease. In addition, in
some municipalities vegetation cover may already reach to a critical threshold and that’s
why, vegetation cover change was almost at a standstill in spite of the fact that there
was population growth.
We encountered two main limitations in this study. First, we used the raw NDVI
and the raw EVI time series which possibly had some unusual observations as well.
These unusual observations can affect the accuracy of standard linear regression
models. However, we did not want to eliminate any observations without being
completely sure that they were outliers. In addition to this, we found that these
uncommon observations were generally distributed at a balance in time series. It follows
35
that they cannot significantly change the trend directions. Another limitation is that our
accuracy was dependent on the spatial resolution of MODIS.
Future research can be done by using different imagery. Besides, future research
needs to address new concepts to describe characteristics of unusual observations.
Additionally, different places can be chosen to explore the effects of different drivers on
vegetation cover change. In our study, agriculture and urbanization were two main
drivers. Further studies can focus on the places where the main drivers are climate
change, soil erosion, and mining.
36
CHAPTER 5 CONCLUSION
The study used a monitoring approach based on a harmonic seasonal model
which is applicable to both NDVI and EVI time series. This approach has the following
advantages: It (1) is pretty fast, (2) doesn’t require any technique to fill the gaps in the
time series, (3) can produce the fitted values for missing observations as well, (4)
enables us to investigate the trends.
Overall, our study presented that the MODIS EVI time series performs better
than the MODIS NDVI time series to fit into this harmonic seasonal model.
We examined trends in vegetation cover change in each municipality in
Ariquemes microregion in the state of Rondonia, Brazil and on the Asian Side of
Istanbul, Turkey from 2010 to 2016. We found no evidence of a trend for vegetation
cover change in the municipalities in Ariquemes microregion between the years 2010
and 2016 based on EVI slopes. On the Asian Side of Istanbul, Kartal municipality had
the highest positive trend in vegetation cover change from 2010 to 2016.
According to the RMSD values, Vale do Anari municipality was the greenest
municipality in Ariquemes microregion considering a seven-year period. With regard to
the Asian Side of Istanbul, Sile and Beykoz municipalities were the greenest two
municipalities in this region.
This study also investigated the relationship between population and vegetation
cover change. It is commonly believed that there is an inverse correlation between the
population growth and the vegetation cover (Li et al., 2013). However, our study
indicated that this may not be true considering the different land use changes affecting
vegetation cover.
37
APPENDIX A LINEAR REGRESSION MODEL USING MODIS NDVI TIME SERIES
Regression Analysis: RS in Ariquemes
Missing Observations: 33 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.059709 0.008530 3.91 0.001
t 1 0.002391 0.002391 1.10 0.297
sin(2π1t/23) 1 0.001804 0.001804 0.83 0.365
cos(2π1t/23) 1 0.017720 0.017720 8.12 0.005
sin(2π2t/23) 1 0.000632 0.000632 0.29 0.591
cos(2π2t/23) 1 0.024279 0.024279 11.13 0.001
sin(2π3t/23) 1 0.000593 0.000593 0.27 0.603
cos(2π3t/23) 1 0.000578 0.000578 0.27 0.607
Error 120 0.261720 0.002181
Total 127 0.321429
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0467012 18.58% 13.83% 6.44%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.80249 0.00866 92.61 0.000
t 0.000092 0.000088 1.05 0.297 1.03
sin(2π1t/23) 0.00558 0.00614 0.91 0.365 1.10
cos(2π1t/23) -0.01762 0.00618 -2.85 0.005 1.05
sin(2π2t/23) 0.00333 0.00618 0.54 0.591 1.11
cos(2π2t/23) 0.02013 0.00603 3.34 0.001 1.08
sin(2π3t/23) -0.00309 0.00593 -0.52 0.603 1.04
cos(2π3t/23) -0.00310 0.00603 -0.52 0.607 1.06
Regression Equation
RS in Ariquemes = 0.80249 + 0.000092 t + 0.00558 sin(2π1t/23)
-0.01762 cos(2π1t/23)+ 0.00333 sin(2π2t/23)+0.02013 cos(2π2t/23)
-0.00309 sin(2π3t/23)- 0.00310 cos(2π3t/23)
38
Regression Analysis: Alto Paraiso
Missing Observations: 5 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.519865 0.074266 88.73 0.000
t 1 0.006348 0.006348 7.58 0.007
sin(2π1t/23) 1 0.196348 0.196348 234.60 0.000
cos(2π1t/23) 1 0.152736 0.152736 182.49 0.000
sin(2π2t/23) 1 0.090815 0.090815 108.51 0.000
cos(2π2t/23) 1 0.009631 0.009631 11.51 0.001
sin(2π3t/23) 1 0.009477 0.009477 11.32 0.001
cos(2π3t/23) 1 0.039072 0.039072 46.68 0.000
Error 148 0.123868 0.000837
Total 155 0.643734
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0289301 80.76% 79.85% 78.57%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.74574 0.00473 157.65 0.000
t -0.000139 0.000050 -2.75 0.007 1.02
sin(2π1t/23) 0.05054 0.00330 15.32 0.000 1.02
cos(2π1t/23) 0.04431 0.00328 13.51 0.000 1.00
sin(2π2t/23) -0.03442 0.00330 -10.42 0.000 1.01
cos(2π2t/23) 0.01105 0.00326 3.39 0.001 1.00
sin(2π3t/23) 0.01100 0.00327 3.36 0.001 1.00
cos(2π3t/23) -0.02247 0.00329 -6.83 0.000 1.00
Regression Equation
Alto Paraiso = 0.74574 - 0.000139 t + 0.05054 sin(2π1t/23)
+0.04431 cos(2π1t/23)- 0.03442 sin(2π2t/23)+0.01105 cos(2π2t/23)
+0.01100 sin(2π3t/23)- 0.02247 cos(2π3t/23)
39
Regression Analysis: Ariquemes
Missing Observations: 1 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.745292 0.106470 87.83 0.000
t 1 0.000327 0.000327 0.27 0.604
sin(2π1t/23) 1 0.307753 0.307753 253.86 0.000
cos(2π1t/23) 1 0.242791 0.242791 200.28 0.000
sin(2π2t/23) 1 0.127611 0.127611 105.26 0.000
cos(2π2t/23) 1 0.006889 0.006889 5.68 0.018
sin(2π3t/23) 1 0.010830 0.010830 8.93 0.003
cos(2π3t/23) 1 0.045046 0.045046 37.16 0.000
Error 152 0.184268 0.001212
Total 159 0.929560
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0348179 80.18% 79.26% 78.10%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.71496 0.00555 128.83 0.000
t 0.000031 0.000060 0.52 0.604 1.02
sin(2π1t/23) 0.06245 0.00392 15.93 0.000 1.01
cos(2π1t/23) 0.05509 0.00389 14.15 0.000 1.00
sin(2π2t/23) -0.04014 0.00391 -10.26 0.000 1.00
cos(2π2t/23) 0.00925 0.00388 2.38 0.018 1.00
sin(2π3t/23) 0.01164 0.00389 2.99 0.003 1.00
cos(2π3t/23) -0.02375 0.00390 -6.10 0.000 1.00
Regression Equation
Ariquemes = 0.71496 + 0.000031 t + 0.06245 sin(2π1t/23)
+0.05509 cos(2π1t/23)- 0.04014 sin(2π2t/23)+0.00925 cos(2π2t/23)
+0.01164 sin(2π3t/23)- 0.02375 cos(2π3t/23)
40
Regression Analysis: Cacaulandia
Missing Observations: 7 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.689659 0.098523 93.14 0.000
t 1 0.000081 0.000081 0.08 0.782
sin(2π1t/23) 1 0.234629 0.234629 221.80 0.000
cos(2π1t/23) 1 0.209775 0.209775 198.30 0.000
sin(2π2t/23) 1 0.149009 0.149009 140.86 0.000
cos(2π2t/23) 1 0.017051 0.017051 16.12 0.000
sin(2π3t/23) 1 0.014018 0.014018 13.25 0.000
cos(2π3t/23) 1 0.038356 0.038356 36.26 0.000
Error 146 0.154445 0.001058
Total 153 0.844104
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0325245 81.70% 80.83% 79.62%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.72332 0.00528 137.06 0.000
t -0.000016 0.000057 -0.28 0.782 1.03
sin(2π1t/23) 0.05618 0.00377 14.89 0.000 1.03
cos(2π1t/23) 0.05207 0.00370 14.08 0.000 1.00
sin(2π2t/23) -0.04476 0.00377 -11.87 0.000 1.01
cos(2π2t/23) 0.01474 0.00367 4.01 0.000 1.00
sin(2π3t/23) 0.01351 0.00371 3.64 0.000 1.00
cos(2π3t/23) -0.02237 0.00372 -6.02 0.000 1.00
Regression Equation
Cacaulandia = 0.72332 - 0.000016 t + 0.05618 sin(2π1t/23)
+0.05207 cos(2π1t/23)- 0.04476 sin(2π2t/23)+0.01474 cos(2π2t/23)
+0.01351 sin(2π3t/23)- 0.02237 cos(2π3t/23)
41
Regression Analysis: Machadinho do Oeste
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.135448 0.019350 10.10 0.000
t 1 0.001908 0.001908 1.00 0.320
sin(2π1t/23) 1 0.039478 0.039478 20.60 0.000
cos(2π1t/23) 1 0.006401 0.006401 3.34 0.070
sin(2π2t/23) 1 0.052945 0.052945 27.63 0.000
cos(2π2t/23) 1 0.010404 0.010404 5.43 0.021
sin(2π3t/23) 1 0.000543 0.000543 0.28 0.595
cos(2π3t/23) 1 0.023813 0.023813 12.43 0.001
Error 153 0.293212 0.001916
Total 160 0.428660
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0437769 31.60% 28.47% 24.20%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.75862 0.00698 108.72 0.000
t 0.000075 0.000075 1.00 0.320 1.02
sin(2π1t/23) 0.02228 0.00491 4.54 0.000 1.01
cos(2π1t/23) 0.00892 0.00488 1.83 0.070 1.00
sin(2π2t/23) -0.02568 0.00489 -5.26 0.000 1.00
cos(2π2t/23) 0.01137 0.00488 2.33 0.021 1.00
sin(2π3t/23) -0.00260 0.00488 -0.53 0.595 1.00
cos(2π3t/23) -0.01720 0.00488 -3.52 0.001 1.00
Regression Equation
Machadinho do Oeste = 0.75862 + 0.000075 t+ 0.02228 sin(2π1t/23)
+0.00892 cos(2π1t/23)- 0.02568 sin(2π2t/23)+0.01137 cos(2π2t/23)
-0.00260 sin(2π3t/23)- 0.01720 cos(2π3t/23)
42
Regression Analysis: Monte Negro
Missing Observations: 8 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.625862 0.089409 50.69 0.000
t 1 0.006933 0.006933 3.93 0.049
sin(2π1t/23) 1 0.222382 0.222382 126.08 0.000
cos(2π1t/23) 1 0.164226 0.164226 93.11 0.000
sin(2π2t/23) 1 0.149990 0.149990 85.04 0.000
cos(2π2t/23) 1 0.015954 0.015954 9.04 0.003
sin(2π3t/23) 1 0.012249 0.012249 6.94 0.009
cos(2π3t/23) 1 0.025114 0.025114 14.24 0.000
Error 145 0.255758 0.001764
Total 152 0.881620
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0419982 70.99% 69.59% 67.57%
Coefficients Term Coef SE Coef T-Value P-Value VIF
Constant 0.72942 0.00689 105.94 0.000
t -0.000146 0.000074 -1.98 0.049 1.02
sin(2π1t/23) 0.05424 0.00483 11.23 0.000 1.02
cos(2π1t/23) 0.04662 0.00483 9.65 0.000 1.00
sin(2π2t/23) -0.04467 0.00484 -9.22 0.000 1.01
cos(2π2t/23) 0.01441 0.00479 3.01 0.003 1.00
sin(2π3t/23) 0.01260 0.00478 2.64 0.009 1.00
cos(2π3t/23) -0.01827 0.00484 -3.77 0.000 1.00
Regression Equation
Monte Negro = 0.72942 - 0.000146 t + 0.05424 sin(2π1t/23)
+0.04662 cos(2π1t/23)- 0.04467 sin(2π2t/23)+0.01441 cos(2π2t/23)
+0.01260 sin(2π3t/23)- 0.01827 cos(2π3t/23)
43
Regression Analysis: Rio Crespo
Missing Observations: 7 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.533149 0.076164 80.16 0.000
t 1 0.000004 0.000004 0.00 0.949
sin(2π1t/23) 1 0.170597 0.170597 179.54 0.000
cos(2π1t/23) 1 0.171436 0.171436 180.42 0.000
sin(2π2t/23) 1 0.137642 0.137642 144.86 0.000
cos(2π2t/23) 1 0.007598 0.007598 8.00 0.005
sin(2π3t/23) 1 0.001268 0.001268 1.33 0.250
cos(2π3t/23) 1 0.029027 0.029027 30.55 0.000
Error 146 0.138728 0.000950
Total 153 0.671877
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0308251 79.35% 78.36% 77.03%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.73243 0.00505 145.15 0.000
t -0.000003 0.000054 -0.06 0.949 1.02
sin(2π1t/23) 0.04724 0.00353 13.40 0.000 1.02
cos(2π1t/23) 0.04750 0.00354 13.43 0.000 1.00
sin(2π2t/23) -0.04270 0.00355 -12.04 0.000 1.01
cos(2π2t/23) 0.00988 0.00350 2.83 0.005 1.00
sin(2π3t/23) 0.00407 0.00352 1.16 0.250 1.00
cos(2π3t/23) -0.01943 0.00352 -5.53 0.000 1.00
Regression Equation
Rio Crespo = 0.73243 - 0.000003 t + 0.04724 sin(2π1t/23)
+0.04750 cos(2π1t/23)- 0.04270 sin(2π2t/23)+0.00988 cos(2π2t/23)
+0.00407 sin(2π3t/23)- 0.01943 cos(2π3t/23)
44
Regression Analysis: Vale do Anari
Missing Observations: 1 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.176868 0.025267 20.08 0.000
t 1 0.002901 0.002901 2.31 0.131
sin(2π1t/23) 1 0.071179 0.071179 56.57 0.000
cos(2π1t/23) 1 0.031930 0.031930 25.38 0.000
sin(2π2t/23) 1 0.039327 0.039327 31.25 0.000
cos(2π2t/23) 1 0.013268 0.013268 10.54 0.001
sin(2π3t/23) 1 0.000363 0.000363 0.29 0.592
cos(2π3t/23) 1 0.018246 0.018246 14.50 0.000
Error 152 0.191256 0.001258
Total 159 0.368124
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0354720 48.05% 45.65% 42.07%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.76099 0.00567 134.19 0.000
t 0.000092 0.000061 1.52 0.131 1.02
sin(2π1t/23) 0.02998 0.00399 7.52 0.000 1.01
cos(2π1t/23) 0.02001 0.00397 5.04 0.000 1.00
sin(2π2t/23) -0.02225 0.00398 -5.59 0.000 1.00
cos(2π2t/23) 0.01286 0.00396 3.25 0.001 1.00
sin(2π3t/23) 0.00214 0.00398 0.54 0.592 1.00
cos(2π3t/23) -0.01506 0.00395 -3.81 0.000 1.00
Regression Equation
Vale do Anari = 0.76099 + 0.000092 t + 0.02998 sin(2π1t/23)
+0.02001 cos(2π1t/23)- 0.02225 sin(2π2t/23)+0.01286 cos(2π2t/23)
+0.00214 sin(2π3t/23)- 0.01506 cos(2π3t/23)
45
Regression Analysis: RS in Istanbul
Missing Observations: 4 Analysis of Variance Source DF Adj SS Adj MS F-Value P-Value
Regression 7 2.43732 0.34819 151.06 0.000
t 1 0.00773 0.00773 3.36 0.069
sin(2π1t/23) 1 0.57967 0.57967 251.49 0.000
cos(2π1t/23) 1 1.62978 1.62978 707.06 0.000
sin(2π2t/23) 1 0.11929 0.11929 51.75 0.000
cos(2π2t/23) 1 0.04378 0.04378 18.99 0.000
sin(2π3t/23) 1 0.06291 0.06291 27.29 0.000
cos(2π3t/23) 1 0.01332 0.01332 5.78 0.017
Error 149 0.34344 0.00230
Total 156 2.78076
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0480104 87.65% 87.07% 86.29%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.69664 0.00776 89.76 0.000
t 0.000152 0.000083 1.83 0.069 1.01
sin(2π1t/23) -0.08649 0.00545 -15.86 0.000 1.01
cos(2π1t/23) -0.14416 0.00542 -26.59 0.000 1.00
sin(2π2t/23) -0.03923 0.00545 -7.19 0.000 1.00
cos(2π2t/23) 0.02352 0.00540 4.36 0.000 1.00
sin(2π3t/23) 0.02852 0.00546 5.22 0.000 1.00
cos(2π3t/23) 0.01294 0.00538 2.40 0.017 1.00
Regression Equation
RS in Istanbul = 0.69664 + 0.000152 t - 0.08649 sin(2π1t/23)
-0.14416 cos(2π1t/23)- 0.03923 sin(2π2t/23)+0.02352 cos(2π2t/23)
+0.02852 sin(2π3t/23)+ 0.01294 cos(2π3t/23)
46
Regression Analysis: Atasehir
Missing Observations: 4 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.029566 0.004224 16.28 0.000
t 1 0.001022 0.001022 3.94 0.049
sin(2π1t/23) 1 0.003369 0.003369 12.99 0.000
cos(2π1t/23) 1 0.005989 0.005989 23.09 0.000
sin(2π2t/23) 1 0.017438 0.017438 67.23 0.000
cos(2π2t/23) 1 0.000317 0.000317 1.22 0.271
sin(2π3t/23) 1 0.000167 0.000167 0.64 0.424
cos(2π3t/23) 1 0.000926 0.000926 3.57 0.061
Error 149 0.038648 0.000259
Total 156 0.068214
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0161054 43.34% 40.68% 37.14%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.24269 0.00260 93.42 0.000
t -0.000056 0.000028 -1.98 0.049 1.01
sin(2π1t/23) 0.00656 0.00182 3.60 0.000 1.01
cos(2π1t/23) -0.00879 0.00183 -4.81 0.000 1.00
sin(2π2t/23) -0.01502 0.00183 -8.20 0.000 1.00
cos(2π2t/23) 0.00200 0.00181 1.11 0.271 1.00
sin(2π3t/23) -0.00147 0.00183 -0.80 0.424 1.00
cos(2π3t/23) 0.00342 0.00181 1.89 0.061 1.00
Regression Equation
Atasehir = 0.24269 - 0.000056 t + 0.00656 sin(2π1t/23)
-0.00879 cos(2π1t/23)- 0.01502 sin(2π2t/23)+0.00200 cos(2π2t/23)
-0.00147 sin(2π3t/23)+ 0.00342 cos(2π3t/23)
47
Regression Analysis: Beykoz
Missing Observations: 1 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 1.00149 0.143070 154.52 0.000
t 1 0.00020 0.000201 0.22 0.642
sin(2π1t/23) 1 0.23245 0.232448 251.06 0.000
cos(2π1t/23) 1 0.63762 0.637621 688.66 0.000
sin(2π2t/23) 1 0.06146 0.061460 66.38 0.000
cos(2π2t/23) 1 0.02742 0.027416 29.61 0.000
sin(2π3t/23) 1 0.03381 0.033812 36.52 0.000
cos(2π3t/23) 1 0.00027 0.000268 0.29 0.591
Error 152 0.14073 0.000926
Total 159 1.14222
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0304283 87.68% 87.11% 86.34%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.65195 0.00485 134.42 0.000
t -0.000024 0.000052 -0.47 0.642 1.02
sin(2π1t/23) -0.05415 0.00342 -15.84 0.000 1.01
cos(2π1t/23) -0.08943 0.00341 -26.24 0.000 1.00
sin(2π2t/23) -0.02781 0.00341 -8.15 0.000 1.00
cos(2π2t/23) 0.01848 0.00340 5.44 0.000 1.00
sin(2π3t/23) 0.02064 0.00341 6.04 0.000 1.00
cos(2π3t/23) 0.00182 0.00339 0.54 0.591 1.00
Regression Equation
Beykoz = 0.65195 - 0.000024 t - 0.05415 sin(2π1t/23)
-0.08943 cos(2π1t/23)- 0.02781 sin(2π2t/23)+0.01848 cos(2π2t/23)
+0.02064 sin(2π3t/23)+ 0.00182 cos(2π3t/23)
48
Regression Analysis: Cekmekoy
Missing Observations: 2 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 1.20672 0.172389 102.61 0.000
t 1 0.00624 0.006245 3.72 0.056
sin(2π1t/23) 1 0.20563 0.205631 122.40 0.000
cos(2π1t/23) 1 0.83215 0.832152 495.32 0.000
sin(2π2t/23) 1 0.11748 0.117476 69.93 0.000
cos(2π2t/23) 1 0.02737 0.027369 16.29 0.000
sin(2π3t/23) 1 0.02080 0.020798 12.38 0.001
cos(2π3t/23) 1 0.00613 0.006135 3.65 0.058
Error 151 0.25368 0.001680
Total 158 1.46041
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0409881 82.63% 81.82% 80.78%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.63516 0.00654 97.17 0.000
t -0.000136 0.000070 -1.93 0.056 1.02
sin(2π1t/23) -0.05130 0.00464 -11.06 0.000 1.01
cos(2π1t/23) -0.10219 0.00459 -22.26 0.000 1.00
sin(2π2t/23) -0.03849 0.00460 -8.36 0.000 1.00
cos(2π2t/23) 0.01857 0.00460 4.04 0.000 1.00
sin(2π3t/23) 0.01625 0.00462 3.52 0.001 1.00
cos(2π3t/23) 0.00875 0.00458 1.91 0.058 1.00
Regression Equation
Cekmekoy = 0.63516 - 0.000136 t - 0.05130 sin(2π1t/23)
-0.10219 cos(2π1t/23)- 0.03849 sin(2π2t/23)+0.01857 cos(2π2t/23)
+0.01625 sin(2π3t/23)+ 0.00875 cos(2π3t/23)
49
Regression Analysis: Kadikoy
Missing Observations: 4 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.145394 0.020771 64.84 0.000
t 1 0.000112 0.000112 0.35 0.556
sin(2π1t/23) 1 0.015452 0.015452 48.23 0.000
cos(2π1t/23) 1 0.109934 0.109934 343.17 0.000
sin(2π2t/23) 1 0.020715 0.020715 64.66 0.000
cos(2π2t/23) 1 0.000114 0.000114 0.36 0.551
sin(2π3t/23) 1 0.000394 0.000394 1.23 0.269
cos(2π3t/23) 1 0.000295 0.000295 0.92 0.339
Error 149 0.047732 0.000320
Total 156 0.193126
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0178982 75.28% 74.12% 72.63%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.26578 0.00290 91.61 0.000
t -0.000018 0.000031 -0.59 0.556 1.01
sin(2π1t/23) -0.01417 0.00204 -6.95 0.000 1.01
cos(2π1t/23) -0.03732 0.00201 -18.52 0.000 1.00
sin(2π2t/23) -0.01633 0.00203 -8.04 0.000 1.00
cos(2π2t/23) -0.00120 0.00201 -0.60 0.551 1.00
sin(2π3t/23) 0.00224 0.00202 1.11 0.269 1.00
cos(2π3t/23) -0.00194 0.00202 -0.96 0.339 1.00
Regression Equation Kadikoy = 0.26578 - 0.000018 t - 0.01417 sin(2π1t/23)
-0.03732 cos(2π1t/23)- 0.01633 sin(2π2t/23)-0.00120 cos(2π2t/23)
+0.00224 sin(2π3t/23)- 0.00194 cos(2π3t/23)
50
Regression Analysis: Kartal
Missing Observations: 3 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.090291 0.012899 14.72 0.000
t 1 0.016868 0.016868 19.25 0.000
sin(2π1t/23) 1 0.011231 0.011231 12.82 0.000
cos(2π1t/23) 1 0.000060 0.000060 0.07 0.795
sin(2π2t/23) 1 0.051258 0.051258 58.49 0.000
cos(2π2t/23) 1 0.007088 0.007088 8.09 0.005
sin(2π3t/23) 1 0.000580 0.000580 0.66 0.417
cos(2π3t/23) 1 0.002230 0.002230 2.54 0.113
Error 150 0.131448 0.000876
Total 157 0.221739
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0296027 40.72% 37.95% 34.03%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.33674 0.00477 70.61 0.000
t 0.000225 0.000051 4.39 0.000 1.01
sin(2π1t/23) 0.01202 0.00336 3.58 0.000 1.01
cos(2π1t/23) 0.00087 0.00333 0.26 0.795 1.00
sin(2π2t/23) -0.02558 0.00334 -7.65 0.000 1.00
cos(2π2t/23) 0.00945 0.00332 2.84 0.005 1.00
sin(2π3t/23) -0.00272 0.00334 -0.81 0.417 1.00
cos(2π3t/23) 0.00530 0.00332 1.60 0.113 1.00
Regression Equation
Kartal = 0.33674 + 0.000225 t + 0.01202 sin(2π1t/23)
+0.00087 cos(2π1t/23)- 0.02558 sin(2π2t/23)+0.00945 cos(2π2t/23)
-0.00272 sin(2π3t/23)+ 0.00530 cos(2π3t/23)
51
Regression Analysis: Maltepe
Missing Observations: 3 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.054893 0.007842 8.64 0.000
t 1 0.001263 0.001263 1.39 0.240
sin(2π1t/23) 1 0.004512 0.004512 4.97 0.027
cos(2π1t/23) 1 0.000221 0.000221 0.24 0.622
sin(2π2t/23) 1 0.042295 0.042295 46.63 0.000
cos(2π2t/23) 1 0.004254 0.004254 4.69 0.032
sin(2π3t/23) 1 0.001127 0.001127 1.24 0.267
cos(2π3t/23) 1 0.000722 0.000722 0.80 0.374
Error 150 0.136065 0.000907
Total 157 0.190958
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0301181 28.75% 25.42% 20.56%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.33509 0.00485 69.06 0.000
t 0.000061 0.000052 1.18 0.240 1.01
sin(2π1t/23) 0.00762 0.00342 2.23 0.027 1.01
cos(2π1t/23) 0.00167 0.00338 0.49 0.622 1.00
sin(2π2t/23) -0.02323 0.00340 -6.83 0.000 1.00
cos(2π2t/23) 0.00732 0.00338 2.17 0.032 1.00
sin(2π3t/23) -0.00379 0.00340 -1.11 0.267 1.00
cos(2π3t/23) 0.00301 0.00338 0.89 0.374 1.00
Regression Equation
Maltepe = 0.33509 + 0.000061 t + 0.00762 sin(2π1t/23)
+0.00167 cos(2π1t/23)- 0.02323 sin(2π2t/23)+0.00732 cos(2π2t/23)
-0.00379 sin(2π3t/23)+ 0.00301 cos(2π3t/23)
52
Regression Analysis: Pendik
Missing Observations: 2 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.295943 0.042278 14.45 0.000
t 1 0.000582 0.000582 0.20 0.656
sin(2π1t/23) 1 0.014278 0.014278 4.88 0.029
cos(2π1t/23) 1 0.107140 0.107140 36.62 0.000
sin(2π2t/23) 1 0.142402 0.142402 48.68 0.000
cos(2π2t/23) 1 0.021788 0.021788 7.45 0.007
sin(2π3t/23) 1 0.000015 0.000015 0.01 0.943
cos(2π3t/23) 1 0.011257 0.011257 3.85 0.052
Error 151 0.441757 0.002926
Total 158 0.737700
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0540883 40.12% 37.34% 33.50%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.49860 0.00871 57.25 0.000
t 0.000042 0.000093 0.45 0.656 1.01
sin(2π1t/23) -0.01345 0.00609 -2.21 0.029 1.01
cos(2π1t/23) -0.03677 0.00608 -6.05 0.000 1.00
sin(2π2t/23) -0.04258 0.00610 -6.98 0.000 1.00
cos(2π2t/23) 0.01648 0.00604 2.73 0.007 1.00
sin(2π3t/23) -0.00044 0.00608 -0.07 0.943 1.00
cos(2π3t/23) 0.01187 0.00605 1.96 0.052 1.00
Regression Equation
Pendik = 0.49860 + 0.000042 t - 0.01345 sin(2π1t/23)
-0.03677 cos(2π1t/23)- 0.04258 sin(2π2t/23)+0.01648 cos(2π2t/23)
-0.00044 sin(2π3t/23)+ 0.01187 cos(2π3t/23)
53
Regression Analysis: Sancaktepe
Missing Observations: 4 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.121313 0.017330 10.74 0.000
t 1 0.008006 0.008006 4.96 0.027
sin(2π1t/23) 1 0.003983 0.003983 2.47 0.118
cos(2π1t/23) 1 0.015786 0.015786 9.78 0.002
sin(2π2t/23) 1 0.056314 0.056314 34.89 0.000
cos(2π2t/23) 1 0.026654 0.026654 16.52 0.000
sin(2π3t/23) 1 0.002315 0.002315 1.43 0.233
cos(2π3t/23) 1 0.008609 0.008609 5.33 0.022
Error 149 0.240461 0.001614
Total 156 0.361774
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0401725 33.53% 30.41% 25.85%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.45441 0.00648 70.09 0.000
t -0.000156 0.000070 -2.23 0.027 1.02
sin(2π1t/23) 0.00718 0.00457 1.57 0.118 1.01
cos(2π1t/23) -0.01418 0.00454 -3.13 0.002 1.00
sin(2π2t/23) -0.02697 0.00457 -5.91 0.000 1.00
cos(2π2t/23) 0.01835 0.00451 4.06 0.000 1.00
sin(2π3t/23) 0.00547 0.00457 1.20 0.233 1.00
cos(2π3t/23) 0.01041 0.00451 2.31 0.022 1.00
Regression Equation
Sancaktepe = 0.45441 - 0.000156 t + 0.00718 sin(2π1t/23)
-0.01418 cos(2π1t/23)- 0.02697 sin(2π2t/23)+0.01835 cos(2π2t/23)
+0.00547 sin(2π3t/23)+ 0.01041 cos(2π3t/23)
54
Regression Analysis: Sile
Missing Observations: 1 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 1.62756 0.23251 228.15 0.000
t 1 0.00319 0.00319 3.13 0.079
sin(2π1t/23) 1 0.35384 0.35384 347.21 0.000
cos(2π1t/23) 1 1.03517 1.03517 1015.79 0.000
sin(2π2t/23) 1 0.11196 0.11196 109.86 0.000
cos(2π2t/23) 1 0.04309 0.04309 42.28 0.000
sin(2π3t/23) 1 0.05395 0.05395 52.94 0.000
cos(2π3t/23) 1 0.00293 0.00293 2.87 0.092
Error 152 0.15490 0.00102
Total 159 1.78246
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0319231 91.31% 90.91% 90.38%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.63786 0.00509 125.35 0.000
t 0.000097 0.000055 1.77 0.079 1.02
sin(2π1t/23) -0.06681 0.00359 -18.63 0.000 1.01
cos(2π1t/23) -0.11394 0.00358 -31.87 0.000 1.00
sin(2π2t/23) -0.03753 0.00358 -10.48 0.000 1.00
cos(2π2t/23) 0.02317 0.00356 6.50 0.000 1.00
sin(2π3t/23) 0.02607 0.00358 7.28 0.000 1.00
cos(2π3t/23) 0.00603 0.00356 1.70 0.092 1.00
Regression Equation
Sile = 0.63786 + 0.000097 t - 0.06681 sin(2π1t/23)
-0.11394 cos(2π1t/23)- 0.03753 sin(2π2t/23)+0.02317 cos(2π2t/23)
+0.02607 sin(2π3t/23)+ 0.00603 cos(2π3t/23)
55
Regression Analysis: Sultanbeyli
Missing Observations: 6 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.103047 0.014721 19.96 0.000
t 1 0.004271 0.004271 5.79 0.017
sin(2π1t/23) 1 0.032781 0.032781 44.44 0.000
cos(2π1t/23) 1 0.000970 0.000970 1.32 0.253
sin(2π2t/23) 1 0.050513 0.050513 68.49 0.000
cos(2π2t/23) 1 0.004086 0.004086 5.54 0.020
sin(2π3t/23) 1 0.000484 0.000484 0.66 0.419
cos(2π3t/23) 1 0.004930 0.004930 6.68 0.011
Error 147 0.108422 0.000738
Total 154 0.211469
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0271582 48.73% 46.29% 42.71%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.33419 0.00443 75.37 0.000
t -0.000115 0.000048 -2.41 0.017 1.01
sin(2π1t/23) 0.02064 0.00310 6.67 0.000 1.01
cos(2π1t/23) 0.00356 0.00310 1.15 0.253 1.00
sin(2π2t/23) -0.02575 0.00311 -8.28 0.000 1.01
cos(2π2t/23) 0.00723 0.00307 2.35 0.020 1.00
sin(2π3t/23) -0.00253 0.00312 -0.81 0.419 1.00
cos(2π3t/23) 0.00790 0.00305 2.59 0.011 1.00
Regression Equation
Sultanbeyli = 0.33419 - 0.000115 t + 0.02064 sin(2π1t/23)
+0.00356 cos(2π1t/23)- 0.02575 sin(2π2t/23)+0.00723 cos(2π2t/23)
-0.00253 sin(2π3t/23)+ 0.00790 cos(2π3t/23)
56
Regression Analysis: Tuzla
Missing Observations: 1 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.325047 0.046435 15.12 0.000
t 1 0.000893 0.000893 0.29 0.591
sin(2π1t/23) 1 0.118920 0.118920 38.72 0.000
cos(2π1t/23) 1 0.008351 0.008351 2.72 0.101
sin(2π2t/23) 1 0.167765 0.167765 54.62 0.000
cos(2π2t/23) 1 0.014098 0.014098 4.59 0.034
sin(2π3t/23) 1 0.003182 0.003182 1.04 0.310
cos(2π3t/23) 1 0.008653 0.008653 2.82 0.095
Error 152 0.466855 0.003071
Total 159 0.791902
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0554203 41.05% 38.33% 34.62%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.42858 0.00883 48.52 0.000
t -0.000051 0.000095 -0.54 0.591 1.02
sin(2π1t/23) 0.03873 0.00622 6.22 0.000 1.01
cos(2π1t/23) 0.01023 0.00621 1.65 0.101 1.00
sin(2π2t/23) -0.04594 0.00622 -7.39 0.000 1.00
cos(2π2t/23) 0.01325 0.00619 2.14 0.034 1.00
sin(2π3t/23) -0.00633 0.00622 -1.02 0.310 1.00
cos(2π3t/23) 0.01037 0.00618 1.68 0.095 1.00
Regression Equation
Tuzla = 0.42858 - 0.000051 t + 0.03873 sin(2π1t/23)
+0.01023 cos(2π1t/23)- 0.04594 sin(2π2t/23)+0.01325 cos(2π2t/23)
-0.00633 sin(2π3t/23)+ 0.01037 cos(2π3t/23)
57
Regression Analysis: Umraniye
Missing Observations: 3 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.090309 0.012901 9.93 0.000
t 1 0.001412 0.001412 1.09 0.299
sin(2π1t/23) 1 0.006253 0.006253 4.82 0.030
cos(2π1t/23) 1 0.017309 0.017309 13.33 0.000
sin(2π2t/23) 1 0.049488 0.049488 38.11 0.000
cos(2π2t/23) 1 0.014495 0.014495 11.16 0.001
sin(2π3t/23) 1 0.000793 0.000793 0.61 0.436
cos(2π3t/23) 1 0.004097 0.004097 3.15 0.078
Error 150 0.194808 0.001299
Total 157 0.285117
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0360378 31.67% 28.49% 23.60%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.37842 0.00581 65.18 0.000
t -0.000065 0.000062 -1.04 0.299 1.01
sin(2π1t/23) -0.00897 0.00409 -2.19 0.030 1.01
cos(2π1t/23) -0.01478 0.00405 -3.65 0.000 1.00
sin(2π2t/23) -0.02513 0.00407 -6.17 0.000 1.00
cos(2π2t/23) 0.01351 0.00405 3.34 0.001 1.00
sin(2π3t/23) -0.00318 0.00407 -0.78 0.436 1.00
cos(2π3t/23) 0.00718 0.00404 1.78 0.078 1.00
Regression Equation
Umraniye = 0.37842 - 0.000065 t - 0.00897 sin(2π1t/23)
-0.01478 cos(2π1t/23)- 0.02513 sin(2π2t/23)+0.01351 cos(2π2t/23)
-0.00318 sin(2π3t/23)+ 0.00718 cos(2π3t/23)
58
Regression Analysis: Uskudar
Missing Observations: 4 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.235845 0.033692 58.53 0.000
t 1 0.000645 0.000645 1.12 0.291
sin(2π1t/23) 1 0.019908 0.019908 34.59 0.000
cos(2π1t/23) 1 0.176301 0.176301 306.29 0.000
sin(2π2t/23) 1 0.038907 0.038907 67.59 0.000
cos(2π2t/23) 1 0.001202 0.001202 2.09 0.151
sin(2π3t/23) 1 0.001261 0.001261 2.19 0.141
cos(2π3t/23) 1 0.000242 0.000242 0.42 0.517
Error 149 0.085764 0.000576
Total 156 0.321609
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0239916 73.33% 72.08% 70.47%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.37253 0.00389 95.79 0.000
t 0.000044 0.000042 1.06 0.291 1.01
sin(2π1t/23) -0.01608 0.00273 -5.88 0.000 1.01
cos(2π1t/23) -0.04726 0.00270 -17.50 0.000 1.00
sin(2π2t/23) -0.02239 0.00272 -8.22 0.000 1.00
cos(2π2t/23) 0.00390 0.00270 1.44 0.151 1.00
sin(2π3t/23) 0.00401 0.00271 1.48 0.141 1.00
cos(2π3t/23) -0.00176 0.00271 -0.65 0.517 1.00
Regression Equation
Uskudar = 0.37253 + 0.000044 t - 0.01608 sin(2π1t/23)
-0.04726 cos(2π1t/23)- 0.02239 sin(2π2t/23)+0.00390 cos(2π2t/23)
+0.00401 sin(2π3t/23)- 0.00176 cos(2π3t/23)
59
APPENDIX B LINEAR REGRESSION MODEL USING MODIS EVI TIME SERIES
Regression Analysis: RS in Ariquemes
Missing Observations: 35 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.151611 0.021659 24.97 0.000
t 1 0.002306 0.002306 2.66 0.106
sin(2π1t/23) 1 0.001717 0.001717 1.98 0.162
cos(2π1t/23) 1 0.127703 0.127703 147.25 0.000
sin(2π2t/23) 1 0.000224 0.000224 0.26 0.612
cos(2π2t/23) 1 0.002072 0.002072 2.39 0.125
sin(2π3t/23) 1 0.000077 0.000077 0.09 0.766
cos(2π3t/23) 1 0.003078 0.003078 3.55 0.062
Error 118 0.102337 0.000867
Total 125 0.253948
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0294493 59.70% 57.31% 52.51%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.55024 0.00548 100.37 0.000
t -0.000091 0.000056 -1.63 0.106 1.02
sin(2π1t/23) -0.00549 0.00390 -1.41 0.162 1.12
cos(2π1t/23) 0.04871 0.00401 12.13 0.000 1.06
sin(2π2t/23) -0.00202 0.00396 -0.51 0.612 1.13
cos(2π2t/23) -0.00599 0.00388 -1.55 0.125 1.10
sin(2π3t/23) 0.00113 0.00380 0.30 0.766 1.05
cos(2π3t/23) -0.00724 0.00385 -1.88 0.062 1.07
Regression Equation
RS in Ariquemes = 0.55024 - 0.000091 t - 0.00549 sin(2π1t/23)
+0.04871 cos(2π1t/23)- 0.00202 sin(2π2t/23)-0.00599 cos(2π2t/23)
+0.00113 sin(2π3t/23)- 0.00724 cos(2π3t/23)
60
Regression Analysis: Alto Paraiso
Missing Observations: 7 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.627612 0.089659 126.55 0.000
t 1 0.000303 0.000303 0.43 0.514
sin(2π1t/23) 1 0.128401 0.128401 181.23 0.000
cos(2π1t/23) 1 0.378862 0.378862 534.74 0.000
sin(2π2t/23) 1 0.083974 0.083974 118.53 0.000
cos(2π2t/23) 1 0.000329 0.000329 0.46 0.497
sin(2π3t/23) 1 0.004098 0.004098 5.78 0.017
cos(2π3t/23) 1 0.018838 0.018838 26.59 0.000
Error 146 0.103440 0.000708
Total 153 0.731052
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0266175 85.85% 85.17% 84.23%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.51495 0.00442 116.53 0.000
t -0.000031 0.000047 -0.65 0.514 1.02
sin(2π1t/23) 0.04108 0.00305 13.46 0.000 1.02
cos(2π1t/23) 0.07056 0.00305 23.12 0.000 1.00
sin(2π2t/23) -0.03351 0.00308 -10.89 0.000 1.01
cos(2π2t/23) -0.00205 0.00301 -0.68 0.497 1.00
sin(2π3t/23) 0.00734 0.00305 2.40 0.017 1.01
cos(2π3t/23) -0.01560 0.00303 -5.16 0.000 1.00
Regression Equation
Alto Paraiso = 0.51495 - 0.000031 t + 0.04108 sin(2π1t/23)
+0.07056 cos(2π1t/23)- 0.03351 sin(2π2t/23)-0.00205 cos(2π2t/23)
+ 0.00734 sin(2π3t/23)- 0.01560 cos(2π3t/23)
61
Regression Analysis: Ariquemes
Missing Observations: 3 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.99543 0.142204 325.11 0.000
t 1 0.00015 0.000153 0.35 0.555
sin(2π1t/23) 1 0.19967 0.199674 456.49 0.000
cos(2π1t/23) 1 0.61510 0.615099 1406.23 0.000
sin(2π2t/23) 1 0.12895 0.128953 294.81 0.000
cos(2π2t/23) 1 0.00094 0.000940 2.15 0.145
sin(2π3t/23) 1 0.00070 0.000703 1.61 0.207
cos(2π3t/23) 1 0.03694 0.036942 84.46 0.000
Error 150 0.06561 0.000437
Total 157 1.06104
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0209143 93.82% 93.53% 93.15%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.50757 0.00337 150.44 0.000
t -0.000021 0.000036 -0.59 0.555 1.02
sin(2π1t/23) 0.05066 0.00237 21.37 0.000 1.02
cos(2π1t/23) 0.08835 0.00236 37.50 0.000 1.00
sin(2π2t/23) -0.04063 0.00237 -17.17 0.000 1.01
cos(2π2t/23) -0.00344 0.00235 -1.47 0.145 1.00
sin(2π3t/23) 0.00298 0.00235 1.27 0.207 1.00
cos(2π3t/23) -0.02170 0.00236 -9.19 0.000 1.00
Regression Equation
Ariquemes = 0.50757 - 0.000021 t + 0.05066 sin(2π1t/23)
+0.08835 cos(2π1t/23)- 0.04063 sin(2π2t/23)-0.00344 cos(2π2t/23)
+0.00298 sin(2π3t/23)- 0.02170 cos(2π3t/23)
62
Regression Analysis: Cacaulandia
Missing Observations: 10 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.97443 0.139204 202.62 0.000
t 1 0.00002 0.000023 0.03 0.855
sin(2π1t/23) 1 0.18270 0.182704 265.94 0.000
cos(2π1t/23) 1 0.60213 0.602130 876.44 0.000
sin(2π2t/23) 1 0.13326 0.133256 193.96 0.000
cos(2π2t/23) 1 0.00021 0.000209 0.30 0.582
sin(2π3t/23) 1 0.00070 0.000696 1.01 0.316
cos(2π3t/23) 1 0.02774 0.027735 40.37 0.000
Error 143 0.09824 0.000687
Total 150 1.07267
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0262111 90.84% 90.39% 89.80%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.51189 0.00430 119.03 0.000
t 0.000008 0.000046 0.18 0.855 1.03
sin(2π1t/23) 0.04990 0.00306 16.31 0.000 1.03
cos(2π1t/23) 0.08971 0.00303 29.60 0.000 1.00
sin(2π2t/23) -0.04313 0.00310 -13.93 0.000 1.02
cos(2π2t/23) 0.00164 0.00297 0.55 0.582 1.00
sin(2π3t/23) 0.00308 0.00306 1.01 0.316 1.01
cos(2π3t/23) -0.01903 0.00300 -6.35 0.000 1.00
Regression Equation
Cacaulandia = 0.51189 + 0.000008 t + 0.04990 sin(2π1t/23)
+0.08971 cos(2π1t/23)- 0.04313 sin(2π2t/23)+0.00164 cos(2π2t/23)
+0.00308 sin(2π3t/23)- 0.01903 cos(2π3t/23)
63
Regression Analysis: Machadinho do Oeste
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.246187 0.035170 81.09 0.000
t 1 0.000051 0.000051 0.12 0.733
sin(2π1t/23) 1 0.008750 0.008750 20.17 0.000
cos(2π1t/23) 1 0.171795 0.171795 396.09 0.000
sin(2π2t/23) 1 0.048356 0.048356 111.49 0.000
cos(2π2t/23) 1 0.008011 0.008011 18.47 0.000
sin(2π3t/23) 1 0.000007 0.000007 0.02 0.899
cos(2π3t/23) 1 0.008992 0.008992 20.73 0.000
Error 153 0.066360 0.000434
Total 160 0.312548
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0208261 78.77% 77.80% 76.42%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.52025 0.00332 156.72 0.000
t -0.000012 0.000036 -0.34 0.733 1.02
sin(2π1t/23) 0.01049 0.00234 4.49 0.000 1.01
cos(2π1t/23) 0.04620 0.00232 19.90 0.000 1.00
sin(2π2t/23) -0.02455 0.00232 -10.56 0.000 1.00
cos(2π2t/23) -0.00998 0.00232 -4.30 0.000 1.00
sin(2π3t/23) -0.00030 0.00232 -0.13 0.899 1.00
cos(2π3t/23) -0.01057 0.00232 -4.55 0.000 1.00
Regression Equation
Machadinho do Oeste = 0.52025 - 0.000012 t+ 0.01049 sin(2π1t/23)
+0.04620 cos(2π1t/23)- 0.02455 sin(2π2t/23)-0.00998 cos(2π2t/23)
-0.00030 sin(2π3t/23)- 0.01057 cos(2π3t/23)
64
Regression Analysis: Monte Negro
Missing Observations: 14 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.871263 0.124466 217.76 0.000
t 1 0.000025 0.000025 0.04 0.835
sin(2π1t/23) 1 0.211433 0.211433 369.91 0.000
cos(2π1t/23) 1 0.501649 0.501649 877.65 0.000
sin(2π2t/23) 1 0.105820 0.105820 185.13 0.000
cos(2π2t/23) 1 0.000138 0.000138 0.24 0.624
sin(2π3t/23) 1 0.001570 0.001570 2.75 0.100
cos(2π3t/23) 1 0.019916 0.019916 34.84 0.000
Error 139 0.079450 0.000572
Total 146 0.950713
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0239078 91.64% 91.22% 90.65%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.51089 0.00405 126.30 0.000
t 0.000009 0.000043 0.21 0.835 1.03
sin(2π1t/23) 0.05466 0.00284 19.23 0.000 1.04
cos(2π1t/23) 0.08318 0.00281 29.63 0.000 1.00
sin(2π2t/23) -0.03885 0.00286 -13.61 0.000 1.03
cos(2π2t/23) -0.00136 0.00277 -0.49 0.624 1.01
sin(2π3t/23) 0.00464 0.00280 1.66 0.100 1.01
cos(2π3t/23) -0.01656 0.00280 -5.90 0.000 1.01
Regression Equation
Monte Negro = 0.51089 + 0.000009 t + 0.05466 sin(2π1t/23)
+0.08318 cos(2π1t/23)- 0.03885 sin(2π2t/23)-0.00136 cos(2π2t/23)
+0.00464 sin(2π3t/23)- 0.01656 cos(2π3t/23)
65
Regression Analysis: Rio Crespo
Missing Observations: 12 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.679428 0.097061 255.21 0.000
t 1 0.000037 0.000037 0.10 0.755
sin(2π1t/23) 1 0.120589 0.120589 317.07 0.000
cos(2π1t/23) 1 0.406653 0.406653 1069.25 0.000
sin(2π2t/23) 1 0.095074 0.095074 249.99 0.000
cos(2π2t/23) 1 0.004189 0.004189 11.01 0.001
sin(2π3t/23) 1 0.000956 0.000956 2.51 0.115
cos(2π3t/23) 1 0.023616 0.023616 62.10 0.000
Error 141 0.053625 0.000380
Total 148 0.733053
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0195017 92.68% 92.32% 91.84%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.51188 0.00325 157.53 0.000
t -0.000011 0.000034 -0.31 0.755 1.01
sin(2π1t/23) 0.04023 0.00226 17.81 0.000 1.02
cos(2π1t/23) 0.07523 0.00230 32.70 0.000 1.01
sin(2π2t/23) -0.03655 0.00231 -15.81 0.000 1.02
cos(2π2t/23) -0.00742 0.00224 -3.32 0.001 1.00
sin(2π3t/23) 0.00361 0.00228 1.59 0.115 1.01
cos(2π3t/23) -0.01782 0.00226 -7.88 0.000 1.00
Regression Equation
Rio Crespo = 0.51188 - 0.000011 t + 0.04023 sin(2π1t/23)
+0.07523 cos(2π1t/23)- 0.03655 sin(2π2t/23)-0.00742 cos(2π2t/23)
+0.00361 sin(2π3t/23)- 0.01782 cos(2π3t/23)
66
Regression Analysis: Vale do Anari
Missing Observations: 3 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.399077 0.057011 125.67 0.000
t 1 0.000496 0.000496 1.09 0.297
sin(2π1t/23) 1 0.016755 0.016755 36.93 0.000
cos(2π1t/23) 1 0.314516 0.314516 693.29 0.000
sin(2π2t/23) 1 0.050123 0.050123 110.49 0.000
cos(2π2t/23) 1 0.001869 0.001869 4.12 0.044
sin(2π3t/23) 1 0.000559 0.000559 1.23 0.269
cos(2π3t/23) 1 0.011020 0.011020 24.29 0.000
Error 150 0.068048 0.000454
Total 157 0.467125
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0212992 85.43% 84.75% 83.79%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.53093 0.00341 155.72 0.000
t -0.000038 0.000036 -1.05 0.297 1.02
sin(2π1t/23) 0.01457 0.00240 6.08 0.000 1.01
cos(2π1t/23) 0.06353 0.00241 26.33 0.000 1.00
sin(2π2t/23) -0.02525 0.00240 -10.51 0.000 1.00
cos(2π2t/23) -0.00486 0.00240 -2.03 0.044 1.00
sin(2π3t/23) 0.00267 0.00241 1.11 0.269 1.00
cos(2π3t/23) -0.01178 0.00239 -4.93 0.000 1.00
Regression Equation
Vale do Anari = 0.53093 - 0.000038 t + 0.01457 sin(2π1t/23)
+0.06353 cos(2π1t/23)- 0.02525 sin(2π2t/23)-0.00486 cos(2π2t/23)
+0.00267 sin(2π3t/23)- 0.01178 cos(2π3t/23)
67
Regression Analysis: RS in Istanbul
Missing Observations: 5 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 4.29623 0.61375 487.39 0.000
t 1 0.01622 0.01622 12.88 0.000
sin(2π1t/23) 1 0.36695 0.36695 291.40 0.000
cos(2π1t/23) 1 3.62207 3.62207 2876.34 0.000
sin(2π2t/23) 1 0.06667 0.06667 52.94 0.000
cos(2π2t/23) 1 0.10489 0.10489 83.29 0.000
sin(2π3t/23) 1 0.12112 0.12112 96.18 0.000
cos(2π3t/23) 1 0.00048 0.00048 0.38 0.540
Error 148 0.18637 0.00126
Total 155 4.48260
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0354861 95.84% 95.65% 95.37%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.40743 0.00575 70.92 0.000
t 0.000222 0.000062 3.59 0.000 1.02
sin(2π1t/23) -0.06902 0.00404 -17.07 0.000 1.01
cos(2π1t/23) -0.21602 0.00403 -53.63 0.000 1.00
sin(2π2t/23) -0.02952 0.00406 -7.28 0.000 1.01
cos(2π2t/23) 0.03645 0.00399 9.13 0.000 1.00
sin(2π3t/23) 0.03986 0.00406 9.81 0.000 1.00
cos(2π3t/23) 0.00245 0.00398 0.61 0.540 1.00
Regression Equation
RS in Istanbul = 0.40743 + 0.000222 t - 0.06902 sin(2π1t/23)
-0.21602 cos(2π1t/23)- 0.02952 sin(2π2t/23)+0.03645 cos(2π2t/23)
+0.03986 sin(2π3t/23)+ 0.00245 cos(2π3t/23)
68
Regression Analysis: Atasehir
Missing Observations: 6 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.059514 0.008502 99.90 0.000
t 1 0.000517 0.000517 6.08 0.015
sin(2π1t/23) 1 0.000327 0.000327 3.84 0.052
cos(2π1t/23) 1 0.053130 0.053130 624.31 0.000
sin(2π2t/23) 1 0.005255 0.005255 61.75 0.000
cos(2π2t/23) 1 0.000163 0.000163 1.92 0.168
sin(2π3t/23) 1 0.000020 0.000020 0.24 0.627
cos(2π3t/23) 1 0.000221 0.000221 2.60 0.109
Error 147 0.012510 0.000085
Total 154 0.072024
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0092250 82.63% 81.80% 80.70%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.13061 0.00150 86.95 0.000
t -0.000040 0.000016 -2.46 0.015 1.01
sin(2π1t/23) 0.00206 0.00105 1.96 0.052 1.01
cos(2π1t/23) -0.02631 0.00105 -24.99 0.000 1.00
sin(2π2t/23) -0.00835 0.00106 -7.86 0.000 1.01
cos(2π2t/23) 0.00144 0.00104 1.39 0.168 1.00
sin(2π3t/23) 0.00051 0.00105 0.49 0.627 1.00
cos(2π3t/23) 0.00169 0.00105 1.61 0.109 1.00
Regression Equation
Atasehir = 0.13061 - 0.000040 t + 0.00206 sin(2π1t/23)
-0.02631 cos(2π1t/23)- 0.00835 sin(2π2t/23)+0.00144 cos(2π2t/23)
+0.00051 sin(2π3t/23)+ 0.00169 cos(2π3t/23)
69
Regression Analysis: Beykoz
Missing Observations: 1 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 2.25364 0.32195 471.28 0.000
t 1 0.00000 0.00000 0.00 0.949
sin(2π1t/23) 1 0.20038 0.20038 293.33 0.000
cos(2π1t/23) 1 1.86453 1.86453 2729.36 0.000
sin(2π2t/23) 1 0.04714 0.04714 69.00 0.000
cos(2π2t/23) 1 0.06938 0.06938 101.57 0.000
sin(2π3t/23) 1 0.05718 0.05718 83.70 0.000
cos(2π3t/23) 1 0.00176 0.00176 2.57 0.111
Error 152 0.10384 0.00068
Total 159 2.35747
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0261369 95.60% 95.39% 95.11%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.37098 0.00417 89.04 0.000
t -0.000003 0.000045 -0.06 0.949 1.02
sin(2π1t/23) -0.05028 0.00294 -17.13 0.000 1.01
cos(2π1t/23) -0.15292 0.00293 -52.24 0.000 1.00
sin(2π2t/23) -0.02435 0.00293 -8.31 0.000 1.00
cos(2π2t/23) 0.02940 0.00292 10.08 0.000 1.00
sin(2π3t/23) 0.02684 0.00293 9.15 0.000 1.00
cos(2π3t/23) -0.00467 0.00291 -1.60 0.111 1.00
Regression Equation
Beykoz = 0.37098 - 0.000003 t - 0.05028 sin(2π1t/23)
-0.15292 cos(2π1t/23)- 0.02435 sin(2π2t/23)+0.02940 cos(2π2t/23)
+0.02684 sin(2π3t/23)- 0.00467 cos(2π3t/23)
70
Regression Analysis: Cekmekoy
Missing Observations: 2 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 2.15413 0.30773 421.35 0.000
t 1 0.00001 0.00001 0.01 0.920
sin(2π1t/23) 1 0.12387 0.12387 169.60 0.000
cos(2π1t/23) 1 1.84791 1.84791 2530.16 0.000
sin(2π2t/23) 1 0.07281 0.07281 99.69 0.000
cos(2π2t/23) 1 0.05804 0.05804 79.46 0.000
sin(2π3t/23) 1 0.05206 0.05206 71.28 0.000
cos(2π3t/23) 1 0.00000 0.00000 0.01 0.939
Error 151 0.11028 0.00073
Total 158 2.26442
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0270250 95.13% 94.90% 94.59%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.35740 0.00431 82.93 0.000
t -0.000005 0.000046 -0.10 0.920 1.02
sin(2π1t/23) -0.03981 0.00306 -13.02 0.000 1.01
cos(2π1t/23) -0.15228 0.00303 -50.30 0.000 1.00
sin(2π2t/23) -0.03030 0.00304 -9.98 0.000 1.00
cos(2π2t/23) 0.02704 0.00303 8.91 0.000 1.00
sin(2π3t/23) 0.02571 0.00305 8.44 0.000 1.00
cos(2π3t/23) 0.00023 0.00302 0.08 0.939 1.00
Regression Equation
Cekmekoy = 0.35740 - 0.000005 t - 0.03981 sin(2π1t/23)
-0.15228 cos(2π1t/23)- 0.03030 sin(2π2t/23)+0.02704 cos(2π2t/23)
+0.02571 sin(2π3t/23)+ 0.00023 cos(2π3t/23)
71
Regression Analysis: Kadikoy
Missing Observations: 6 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.135789 0.019398 293.04 0.000
t 1 0.000087 0.000087 1.31 0.254
sin(2π1t/23) 1 0.006517 0.006517 98.45 0.000
cos(2π1t/23) 1 0.123283 0.123283 1862.39 0.000
sin(2π2t/23) 1 0.006258 0.006258 94.54 0.000
cos(2π2t/23) 1 0.000674 0.000674 10.18 0.002
sin(2π3t/23) 1 0.000094 0.000094 1.42 0.235
cos(2π3t/23) 1 0.000009 0.000009 0.14 0.711
Error 147 0.009731 0.000066
Total 154 0.145520
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0081361 93.31% 92.99% 92.55%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.13509 0.00132 102.27 0.000
t 0.000016 0.000014 1.15 0.254 1.01
sin(2π1t/23) -0.009231 0.000930 -9.92 0.000 1.01
cos(2π1t/23) -0.039950 0.000926 -43.16 0.000 1.00
sin(2π2t/23) -0.009051 0.000931 -9.72 0.000 1.01
cos(2π2t/23) 0.002938 0.000921 3.19 0.002 1.00
sin(2π3t/23) 0.001107 0.000929 1.19 0.235 1.00
cos(2π3t/23) -0.000342 0.000922 -0.37 0.711 1.00
Regression Equation
Kadikoy = 0.13509 + 0.000016 t - 0.009231 sin(2π1t/23)
-0.03995 cos(2π1t/23)0.009051 sin(2π2t/23)+0.002938 cos(2π2t/23)
+0.001107 sin(2π3t/23)- 0.000342 cos(2π3t/23)
72
Regression Analysis: Kartal
Missing Observations: 5 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.091613 0.013088 54.01 0.000
t 1 0.007454 0.007454 30.76 0.000
sin(2π1t/23) 1 0.003888 0.003888 16.04 0.000
cos(2π1t/23) 1 0.053832 0.053832 222.15 0.000
sin(2π2t/23) 1 0.023298 0.023298 96.15 0.000
cos(2π2t/23) 1 0.003286 0.003286 13.56 0.000
sin(2π3t/23) 1 0.000023 0.000023 0.10 0.757
cos(2π3t/23) 1 0.000248 0.000248 1.02 0.313
Error 148 0.035863 0.000242
Total 155 0.127476
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0155666 71.87% 70.54% 68.53%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.17800 0.00252 70.53 0.000
t 0.000150 0.000027 5.55 0.000 1.01
sin(2π1t/23) 0.00711 0.00178 4.01 0.000 1.01
cos(2π1t/23) -0.02627 0.00176 -14.90 0.000 1.00
sin(2π2t/23) -0.01736 0.00177 -9.81 0.000 1.00
cos(2π2t/23) 0.00648 0.00176 3.68 0.000 1.00
sin(2π3t/23) -0.00055 0.00176 -0.31 0.757 1.00
cos(2π3t/23) 0.00178 0.00176 1.01 0.313 1.00
Regression Equation
Kartal = 0.17800 + 0.000150 t + 0.00711 sin(2π1t/23)
-0.02627 cos(2π1t/23)- 0.01736 sin(2π2t/23)+0.00648 cos(2π2t/23)
-0.00055 sin(2π3t/23)+ 0.00178 cos(2π3t/23)
73
Regression Analysis: Maltepe
Missing Observations: 5 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.068210 0.009744 38.87 0.000
t 1 0.001343 0.001343 5.36 0.022
sin(2π1t/23) 1 0.001236 0.001236 4.93 0.028
cos(2π1t/23) 1 0.049489 0.049489 197.39 0.000
sin(2π2t/23) 1 0.013456 0.013456 53.67 0.000
cos(2π2t/23) 1 0.002354 0.002354 9.39 0.003
sin(2π3t/23) 1 0.000099 0.000099 0.40 0.530
cos(2π3t/23) 1 0.000100 0.000100 0.40 0.529
Error 148 0.037106 0.000251
Total 155 0.105316
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0158340 64.77% 63.10% 60.66%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.17000 0.00257 66.23 0.000
t 0.000064 0.000028 2.31 0.022 1.01
sin(2π1t/23) 0.00401 0.00181 2.22 0.028 1.01
cos(2π1t/23) -0.02519 0.00179 -14.05 0.000 1.00
sin(2π2t/23) -0.01319 0.00180 -7.33 0.000 1.00
cos(2π2t/23) 0.00549 0.00179 3.06 0.003 1.00
sin(2π3t/23) 0.00113 0.00180 0.63 0.530 1.00
cos(2π3t/23) 0.00113 0.00179 0.63 0.529 1.00
Regression Equation
Maltepe = 0.17000 + 0.000064 t + 0.00401 sin(2π1t/23)
-0.02519 cos(2π1t/23)- 0.01319 sin(2π2t/23)+0.00549 cos(2π2t/23)
+0.00113 sin(2π3t/23)+ 0.00113 cos(2π3t/23)
74
Regression Analysis: Pendik
Missing Observations: 2 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.412266 0.058895 129.14 0.000
t 1 0.003579 0.003579 7.85 0.006
sin(2π1t/23) 1 0.001485 0.001485 3.26 0.073
cos(2π1t/23) 1 0.332961 0.332961 730.10 0.000
sin(2π2t/23) 1 0.046909 0.046909 102.86 0.000
cos(2π2t/23) 1 0.019463 0.019463 42.68 0.000
sin(2π3t/23) 1 0.006588 0.006588 14.45 0.000
cos(2π3t/23) 1 0.000627 0.000627 1.38 0.243
Error 151 0.068863 0.000456
Total 158 0.481130
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0213553 85.69% 85.02% 84.10%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.25027 0.00344 72.78 0.000
t 0.000103 0.000037 2.80 0.006 1.01
sin(2π1t/23) -0.00434 0.00240 -1.80 0.073 1.01
cos(2π1t/23) -0.06482 0.00240 -27.02 0.000 1.00
sin(2π2t/23) -0.02444 0.00241 -10.14 0.000 1.00
cos(2π2t/23) 0.01557 0.00238 6.53 0.000 1.00
sin(2π3t/23) 0.00913 0.00240 3.80 0.000 1.00
cos(2π3t/23) 0.00280 0.00239 1.17 0.243 1.00
Regression Equation
Pendik = 0.25027 + 0.000103 t - 0.00434 sin(2π1t/23)
-0.06482 cos(2π1t/23)- 0.02444 sin(2π2t/23)+0.01557 cos(2π2t/23)
+0.00913 sin(2π3t/23)+ 0.00280 cos(2π3t/23)
75
Regression Analysis: Sancaktepe
Missing Observations: 4 Analysis of Variance Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.245541 0.035077 64.65 0.000
t 1 0.000034 0.000034 0.06 0.803
sin(2π1t/23) 1 0.002899 0.002899 5.34 0.022
cos(2π1t/23) 1 0.191286 0.191286 352.54 0.000
sin(2π2t/23) 1 0.020574 0.020574 37.92 0.000
cos(2π2t/23) 1 0.021947 0.021947 40.45 0.000
sin(2π3t/23) 1 0.006750 0.006750 12.44 0.001
cos(2π3t/23) 1 0.001191 0.001191 2.20 0.141
Error 149 0.080848 0.000543
Total 156 0.326389
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0232938 75.23% 74.07% 72.18%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.23732 0.00376 63.13 0.000
t -0.000010 0.000041 -0.25 0.803 1.02
sin(2π1t/23) 0.00612 0.00265 2.31 0.022 1.01
cos(2π1t/23) -0.04938 0.00263 -18.78 0.000 1.00
sin(2π2t/23) -0.01630 0.00265 -6.16 0.000 1.00
cos(2π2t/23) 0.01665 0.00262 6.36 0.000 1.00
sin(2π3t/23) 0.00934 0.00265 3.53 0.001 1.00
cos(2π3t/23) 0.00387 0.00261 1.48 0.141 1.00
Regression Equation
Sancaktepe = 0.23732 - 0.000010 t + 0.00612 sin(2π1t/23)
-0.04938 cos(2π1t/23)- 0.01630 sin(2π2t/23)+0.01665 cos(2π2t/23)
+0.00934 sin(2π3t/23)+ 0.00387 cos(2π3t/23)
76
Regression Analysis: Sile
Missing Observations: 1 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 2.65524 0.37932 499.94 0.000
t 1 0.00749 0.00749 9.87 0.002
sin(2π1t/23) 1 0.19290 0.19290 254.23 0.000
cos(2π1t/23) 1 2.20319 2.20319 2903.78 0.000
sin(2π2t/23) 1 0.06911 0.06911 91.09 0.000
cos(2π2t/23) 1 0.08165 0.08165 107.62 0.000
sin(2π3t/23) 1 0.07848 0.07848 103.44 0.000
cos(2π3t/23) 1 0.00027 0.00027 0.36 0.550
Error 152 0.11533 0.00076
Total 159 2.77057
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0275451 95.84% 95.65% 95.38%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.35972 0.00439 81.93 0.000
t 0.000148 0.000047 3.14 0.002 1.02
sin(2π1t/23) -0.04933 0.00309 -15.94 0.000 1.01
cos(2π1t/23) -0.16623 0.00308 -53.89 0.000 1.00
sin(2π2t/23) -0.02949 0.00309 -9.54 0.000 1.00
cos(2π2t/23) 0.03189 0.00307 10.37 0.000 1.00
sin(2π3t/23) 0.03144 0.00309 10.17 0.000 1.00
cos(2π3t/23) -0.00184 0.00307 -0.60 0.550 1.00
Regression Equation
Sile = 0.35972 + 0.000148 t - 0.04933 sin(2π1t/23)
-0.16623 cos(2π1t/23)- 0.02949 sin(2π2t/23)+0.03189 cos(2π2t/23)
+0.03144 sin(2π3t/23)- 0.00184 cos(2π3t/23)
77
Regression Analysis: Sultanbeyli
Missing Observations: 6 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.096457 0.013780 48.82 0.000
t 1 0.001367 0.001367 4.84 0.029
sin(2π1t/23) 1 0.014145 0.014145 50.12 0.000
cos(2π1t/23) 1 0.053999 0.053999 191.32 0.000
sin(2π2t/23) 1 0.022864 0.022864 81.01 0.000
cos(2π2t/23) 1 0.000698 0.000698 2.47 0.118
sin(2π3t/23) 1 0.000000 0.000000 0.00 0.993
cos(2π3t/23) 1 0.001413 0.001413 5.01 0.027
Error 147 0.041489 0.000282
Total 154 0.137946
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0168000 69.92% 68.49% 66.44%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.17952 0.00274 65.45 0.000
t -0.000065 0.000030 -2.20 0.029 1.01
sin(2π1t/23) 0.01356 0.00192 7.08 0.000 1.01
cos(2π1t/23) -0.02656 0.00192 -13.83 0.000 1.00
sin(2π2t/23) -0.01732 0.00192 -9.00 0.000 1.01
cos(2π2t/23) 0.00299 0.00190 1.57 0.118 1.00
sin(2π3t/23) -0.00002 0.00193 -0.01 0.993 1.00
cos(2π3t/23) 0.00423 0.00189 2.24 0.027 1.00
Regression Equation
Sultanbeyli = 0.17952 - 0.000065 t + 0.01356 sin(2π1t/23)
-0.02656 cos(2π1t/23)- 0.01732 sin(2π2t/23)+0.00299 cos(2π2t/23)
-0.00002 sin(2π3t/23)+ 0.00423 cos(2π3t/23)
78
Regression Analysis: Tuzla
Missing Observations: 3 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.214773 0.030682 43.55 0.000
t 1 0.000194 0.000194 0.28 0.601
sin(2π1t/23) 1 0.073447 0.073447 104.26 0.000
cos(2π1t/23) 1 0.053536 0.053536 75.99 0.000
sin(2π2t/23) 1 0.076726 0.076726 108.91 0.000
cos(2π2t/23) 1 0.004446 0.004446 6.31 0.013
sin(2π3t/23) 1 0.000164 0.000164 0.23 0.630
cos(2π3t/23) 1 0.002903 0.002903 4.12 0.044
Error 150 0.105670 0.000704
Total 157 0.320443
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0265418 67.02% 65.48% 63.34%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.23639 0.00427 55.30 0.000
t 0.000024 0.000046 0.52 0.601 1.01
sin(2π1t/23) 0.03054 0.00299 10.21 0.000 1.01
cos(2π1t/23) -0.02615 0.00300 -8.72 0.000 1.00
sin(2π2t/23) -0.03132 0.00300 -10.44 0.000 1.00
cos(2π2t/23) 0.00748 0.00298 2.51 0.013 1.00
sin(2π3t/23) 0.00145 0.00300 0.48 0.630 1.00
cos(2π3t/23) 0.00605 0.00298 2.03 0.044 1.00
Regression Equation
Tuzla = 0.23639 + 0.000024 t + 0.03054 sin(2π1t/23)
-0.02615 cos(2π1t/23)- 0.03132 sin(2π2t/23)+0.00748 cos(2π2t/23)
+0.00145 sin(2π3t/23)+ 0.00605 cos(2π3t/23)
79
Regression Analysis: Umraniye
Missing Observations: 3 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.176308 0.025187 96.17 0.000
t 1 0.000150 0.000150 0.57 0.451
sin(2π1t/23) 1 0.002551 0.002551 9.74 0.002
cos(2π1t/23) 1 0.155015 0.155015 591.90 0.000
sin(2π2t/23) 1 0.013008 0.013008 49.67 0.000
cos(2π2t/23) 1 0.005470 0.005470 20.89 0.000
sin(2π3t/23) 1 0.000715 0.000715 2.73 0.101
cos(2π3t/23) 1 0.000022 0.000022 0.08 0.771
Error 150 0.039284 0.000262
Total 157 0.215592
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0161831 81.78% 80.93% 79.73%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.19102 0.00261 73.27 0.000
t -0.000021 0.000028 -0.76 0.451 1.01
sin(2π1t/23) -0.00573 0.00184 -3.12 0.002 1.01
cos(2π1t/23) -0.04424 0.00182 -24.33 0.000 1.00
sin(2π2t/23) -0.01288 0.00183 -7.05 0.000 1.00
cos(2π2t/23) 0.00830 0.00182 4.57 0.000 1.00
sin(2π3t/23) 0.00302 0.00183 1.65 0.101 1.00
cos(2π3t/23) 0.00053 0.00182 0.29 0.771 1.00
Regression Equation
Umraniye = 0.19102 - 0.000021 t - 0.00573 sin(2π1t/23)
-0.04424 cos(2π1t/23)- 0.01288 sin(2π2t/23)+0.00830 cos(2π2t/23)
+0.00302 sin(2π3t/23)+ 0.00053 cos(2π3t/23)
80
Regression Analysis: Uskudar
Missing Observations: 4 Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 7 0.342159 0.048880 275.73 0.000
t 1 0.000312 0.000312 1.76 0.186
sin(2π1t/23) 1 0.007034 0.007034 39.68 0.000
cos(2π1t/23) 1 0.320905 0.320905 1810.23 0.000
sin(2π2t/23) 1 0.011249 0.011249 63.46 0.000
cos(2π2t/23) 1 0.002571 0.002571 14.50 0.000
sin(2π3t/23) 1 0.002260 0.002260 12.75 0.000
cos(2π3t/23) 1 0.000052 0.000052 0.29 0.590
Error 149 0.026414 0.000177
Total 156 0.368573
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.0133144 92.83% 92.50% 92.05%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 0.18585 0.00216 86.11 0.000
t 0.000031 0.000023 1.33 0.186 1.01
sin(2π1t/23) -0.00956 0.00152 -6.30 0.000 1.01
cos(2π1t/23) -0.06376 0.00150 -42.55 0.000 1.00
sin(2π2t/23) -0.01204 0.00151 -7.97 0.000 1.00
cos(2π2t/23) 0.00570 0.00150 3.81 0.000 1.00
sin(2π3t/23) 0.00537 0.00150 3.57 0.000 1.00
cos(2π3t/23) -0.00081 0.00150 -0.54 0.590 1.00
Regression Equation
Uskudar = 0.18585 + 0.000031 t - 0.00956 sin(2π1t/23)
-0.06376 cos(2π1t/23)- 0.01204 sin(2π2t/23)+0.00570 cos(2π2t/23)
+0.00537 sin(2π3t/23)- 0.00081 cos(2π3t/23)
81
LIST OF REFERENCES
1. Alves, D. S., Pereira, J. L. G., De Sousa, C. L., Soares, J. V., & Yamaguchi, F. (1999). Characterizing landscape changes in central Rondonia using Landsat TM imagery. International Journal of Remote Sensing, 20(14), 2877-2882.
2. Armenta, S., Angulo, C., Rocha, W., Barraza, G., Andrade, R., & Gonzalez, J. (2016). Determination and analysis of hot spot areas of deforestation using remote sensing and geographic information system techniques. Case study: State Sinaloa, México. Open Journal of Forestry, 6(4), 295-304.
3. Banskota, A., Kayastha, N., Falkowski, M. J., Wulder, M. A., Froese, R. E., & White, J. C. (2014). Forest monitoring using Landsat time series data: a review. Canadian Journal of Remote Sensing, 40(5), 362-384.
4. Baron, T. (2008). The Moths Fauna (Lepidoptera) of Şile in the Asian Part of Istanbul Province, Turkey. Esperiana, 14, 545-558.
5. Barrett, E. C. (2013). Introduction to environmental remote sensing. Routledge.
6. Beuchle, R., Grecchi, R. C., Shimabukuro, Y. E., Seliger, R., Eva, H. D., Sano, E., & Achard, F. (2015). Land cover changes in the Brazilian Cerrado and Caatinga biomes from 1990 to 2010 based on a systematic remote sensing sampling approach. Applied Geography, 58, 116-127.
7. Buchanan, G. M., Fishpool, L. D., Evans, M. I., & Butchart, S. H. (2013).
Comparing field-based monitoring and remote-sensing, using deforestation from logging at Important Bird Areas as a case study. Biological conservation, 167, 334-338.
8. De Fries, R. S., Hansen, M., Townshend, J. R. G., & Sohlberg, R. (1998). Global
land cover classifications at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers. International Journal of Remote Sensing, 19(16), 3141-3168.
9. EarthWorks. (2017). Stanford Libraries. Retrieved 28 August 2017, from
https://library.stanford.edu/search-services/earthworks
10. Fearnside, P. M. (1989). A prescription for slowing deforestation in Amazonia. Environment: Science and Policy for Sustainable Development, 31(4), 16-40.
11. Fearnside, P. M. (1997). Greenhouse gases from deforestation in Brazilian Amazonia: net committed emissions. Climatic Change, 35(3), 321-360.
82
12. Fowler, S. (2015). 36 Hours in Istanbul, Asian Side. Nytimes.com. Retrieved 28 August 2017, from https://www.nytimes.com/2015/08/30/travel/what-to-do-in-36-hours-in-istanbul.html?mcubz=3
13. Fu, B., Liu, Y., Lü, Y., He, C., Zeng, Y., & Wu, B. (2011). Assessing the soil erosion control service of ecosystems change in the Loess Plateau of China. Ecological Complexity, 8(4), 284-293.
14. General Command of Mapping (HGK). Retrieved 28 September 2017, from https://www.hgk.msb.gov.tr/il-ve-ilce-yuzolcumleri
15. Global Administrative Areas. (2017). Gadm.org. Retrieved 28 August 2017, from http://www.gadm.org/about
16. Gonzalez, H., Halevy, A., Jensen, C. S., Langen, A., Madhavan, J., Shapley, R.,
& Shen, W. (2010, June). Google fusion tables: data management, integration and collaboration in the cloud. In Proceedings of the 1st ACM symposium on Cloud computing (pp. 175-180). ACM.
17. Google Earth Engine, 2017. Google Earth Engine: A planetary-scale platform for
environmental data & analysis. Retrieved 6 September 2017, from https://earthengine.google.com/faq/
18. Grinand, C., Rakotomalala, F., Gond, V., Vaudry, R., Bernoux, M., & Vieilledent,
G. (2013). Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier. Remote Sensing of Environment, 139, 68-80.
19. Guild, L. S., Cohen, W. B., & Kauffman, J. B. (2004). Detection of deforestation
and land conversion in Rondonia, Brazil using change detection techniques. International Journal of Remote Sensing, 25(4), 731-750.
20. Gupta, R. P. (2013). Remote sensing geology. Springer Science & Business
Media.
21. Hauke, J., & Kossowski, T. (2011). Comparison of values of Pearson's and Spearman's correlation coefficients on the same sets of data. Quaestiones geographicae, 30(2), 87.
22. He, C., Zhang, Q., Li, Y., Li, X., & Shi, P. (2005). Zoning grassland protection area using remote sensing and cellular automata modeling—a case study in Xilingol steppe grassland in northern China. Journal of Arid Environments, 63(4), 814-826.
23. Instituto Brasileiro de Geografia e Estatística (IBGE). Retrieved 26 September
2017, from http://cidades.ibge.gov.br/xtras/uf.php?coduf=11
83
24. Jensen, J.R. (1996). Introductory digital image processing: a remote sensing perspective. Upper Saddle River, NJ: Prentice-Hall.
25. Joseph, G. (2005). Fundamentals of remote sensing. Universities Press.
26. Kuo, F. E., & Sullivan, W. C. (2001). Environment and crime in the inner city: Does vegetation reduce crime?. Environment and behavior, 33(3), 343-367.
27. Leprieur, C., Kerr, Y. H., Mastorchio, S., & Meunier, J. C. (2000). Monitoring
vegetation cover across semi-arid regions: comparison of remote observations from various scales. International Journal of Remote Sensing, 21(2), 281-300.
28. Li, C., Kuang, Y., Huang, N., & Zhang, C. (2013). The long-term relationship
between population growth and vegetation cover: an empirical analysis based on the panel data of 21 cities in Guangdong Province, China. International journal of environmental research and public health, 10(2), 660-677.
29. Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image
interpretation. John Wiley & Sons.
30. Liu, A. X., Liu, Z. J., Wang, C. Y., Niu, Z., & Yan, D. M. (2003, July). Monitoring of desertification in central Asia and western China using long term NOAA-AVHRR NDVI time-series data. In Geoscience and Remote Sensing Symposium, 2003. IGARSS'03. Proceedings. 2003 IEEE International (Vol. 4, pp. 2278-2280). Ieee.
31. Malingreau, J. P., & Tucker, C. J. (1988). Large-scale deforestation in the
southeastern Amazon basin of Brazil. Ambio, 49-55.
32. Margono, B. A. (2013). Mapping deforestation and forest degradation using Landsat time series: a case of Sumatra—Indonesia.
33. Markham, B. L., Arvidson, T., Barsi, J. A., Lubke, M., Choate, M., Kaita, E., ... &
Masek, J. G. (2016). Landsat Program.
34. Marsik, M., Stevens, F. R., & Southworth, J. (2011). Amazon deforestation: Rates and patterns of land cover change and fragmentation in Pando, northern Bolivia, 1986 to 2005. Progress in Physical Geography, 35(3), 353-374.
35. Miettinen, J., Shi, C., & Liew, S. C. (2011). Deforestation rates in insular
Southeast Asia between 2000 and 2010. Global Change Biology, 17(7), 2261-2270.
36. Mukaka, M. M. (2012). A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal, 24(3), 69-71.
84
37. National Research Council. (1998). People and pixels: Linking remote sensing and social science. National Academies Press.
38. Nichol, J., & Lee, C. M. (2005). Urban vegetation monitoring in Hong Kong using
high resolution multispectral images. International Journal of Remote Sensing, 26(5), 903-918.
39. O’Connor, B. A., Dwyera, N., & Cawkwellb, F. (2008, October). Satellite remote sensing as a tool for monitoring vegetation seasonality. In SPIE Remote Sensing (pp. 71040A-71040A). International Society for Optics and Photonics.
40. Padua, M. T. J., & Quintao, A. T. B. (1982). Parks and biological reserves in the Brazilian Amazon. Ambio, 309-314.
41. Pan, Y., Birdsey, R. A., Phillips, O. L., & Jackson, R. B. (2013). The structure, distribution, and biomass of the world's forests. Annual Review of Ecology, Evolution, and Systematics, 44, 593-622.
42. Peijun, D. U., Xingli, L. I., Wen, C. A. O., Yan, L. U. O., & Zhang, H. (2010).
Monitoring urban land cover and vegetation change by multi-temporal remote sensing information. Mining Science and Technology (China), 20(6), 922-932.
43. Piao, S., & Fang, J. (2003). Seasonal Changes in Vegetation Activity in
Response to Climate Changes in China between 1982 and 1999 [J]. Acta Geographica Sinica, 1, 014.
44. Piao, S., Fang, J., Liu, H., & Zhu, B. (2005). NDVI‐indicated decline in
desertification in China in the past two decades. Geophysical Research Letters, 32(6).
45. Rahm, M., Cayet, L., Anton, V., & Mertons, B. (2013, December). Detecting
forest degradation in the Congo Basin by optical remote sensing. In Proceedings of ESA’s Living Planet Symposium.
46. Ramachandran, R. M., & Reddy, C. S. (2017). Monitoring of deforestation and
land use changes (1925–2012) in Idukki district, Kerala, India using remote sensing and GIS. Journal of the Indian Society of Remote Sensing, 45(1), 163-170.
47. Reis, S. (2008). Analyzing land use/land cover changes using remote sensing and GIS in Rize, North-East Turkey. Sensors, 8(10), 6188-6202.
48. Richards, P., & VanWey, L. (2015). Where deforestation leads to urbanization: How resource extraction is leading to urban growth in the Brazilian Amazon. Annals of the Association of American Geographers, 105(4), 806-823.
85
49. Rignot, E., Salas, W. A., & Skole, D. L. (1997). Mapping deforestation and secondary growth in Rondônia, Brazil, using imaging radar and Thematic Mapper data. Remote Sensing of Environment, 59(2), 167-179.
50. Sánchez-Azofeifa, G. A., Harriss, R. C., & Skole, D. L. (2001). Deforestation in Costa Rica: a quantitative analysis using remote sensing imagery. Biotropica, 33(3), 378-384.
51. Sannier, C., McRoberts, R. E., Fichet, L. V., & Makaga, E. M. K. (2014). Using
the regression estimator with Landsat data to estimate proportion forest cover and net proportion deforestation in Gabon. Remote sensing of environment, 151, 138-148.
52. Shermeyer, J., & Haack, B. (2015). Remote sensing change detection methods
to track deforestation and growth in threatened rainforests in Madre de Dios, Peru. Journal of Applied Remote Sensing, 9(1), 096040-096040.
53. Stephan, C. (2015). Automating Near Real-Time Deforestation Monitoring With
Satellite Image Time Series (Doctoral dissertation, Institute for Geoinformatics).
54. Sternberg, T., Tsolmon, R., Middleton, N., & Thomas, D. (2011). Tracking desertification on the Mongolian steppe through NDVI and field-survey data. International Journal of Digital Earth, 4(1), 50-64.
55. TARTICLEt, A. (1993). Tropical deforestation and habitat fragmentation in the
Amazon: satellite data from 1978 to 1988. Science, 260, 1905.
56. Tucker, C. J., Holben, B. N., & Goff, T. E. (1984). Intensive forest clearing in Rondonia, Brazil, as detected by satellite remote sensing. Remote Sensing of Environment, 15(3), 255-261.
57. Turkish Statistical Institute (TUIK). Retrieved 22 August 2017, from
https://biruni.tuik.gov.tr/medas/?kn=95&locale=tr
58. Verbesselt, J., Zeileis, A., & Herold, M. (2012). Near real-time disturbance detection using satellite image time series. Remote Sensing of Environment, 123, 98-108.
59. Waring, R. H., Coops, N. C., Fan, W., & Nightingale, J. M. (2006). MODIS
enhanced vegetation index predicts tree species richness across forested ecoregions in the contiguous USA. Remote Sensing of Environment, 103(2), 218-226.
60. Xie, Y., Sha, Z., & Yu, M. (2008). Remote sensing imagery in vegetation
mapping: a review. Journal of plant ecology, 1(1), 9-23.
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BIOGRAPHICAL SKETCH
Omer Ekmen was born in Turkey. He believes that engineering cannot be
separated from humanity, society or environment. It is this conviction which led him to
pursue degrees in both engineering and sociology. He graduated from Ondokuz Mayıs
University where he studied Geomatics Engineering in 2012. During his undergraduate
studies in Geomatics Engineering, he served as an intern during the summers of 2009
and 2010. It is during this time that he accrued a great deal of practical experience in
engineering field. Simultaneously, he excelled in the field of sociology, ultimately
graduating with high honors from Anadolu University in 2013.
After his undergraduate career in both fields, he was the recipient of a prestigious
full scholarship that entitled him to study abroad. This scholarship is from the Ministry of
National Education, and it is given to graduate students who fulfill the academic
requirements and pass the oral examination. This academic honor gave him the
opportunity to begin his graduate studies at the University of Florida in January 2016.
His graduate concentration was in geomatics due to the fact that this department fosters
a harmonious balance between geomatics and forestry. In the summer of 2016, he was
fortunate enough to take a course called Practicum in UAS (Unmanned Aerial Systems)
Mapping which contributed to his practical experience in this field. He graduated in
December 2017.
.