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.

https://en.wikipedia.org/wiki/Pseudonym

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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

https://en.wikipedia.org/wiki/Honor%C3%A9_Daumier

https://www.brooklynmuseum.org/opencollection/objects/64499

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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

28

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)


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)


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



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


0.0289301 80.76% 79.85% 78.57%

Coefficients


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



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


0.0348179 80.18% 79.26% 78.10%

Coefficients


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)


+0.01164 sin(2π3t/23)- 0.02375 cos(2π3t/23)

40

Regression Analysis: Cacaulandia



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


0.0325245 81.70% 80.83% 79.62%

Coefficients


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)


+0.01351 sin(2π3t/23)- 0.02237 cos(2π3t/23)

41

Regression Analysis: Machadinho do Oeste

Analysis of Variance


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


0.0437769 31.60% 28.47% 24.20%

Coefficients


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)


-0.00260 sin(2π3t/23)- 0.01720 cos(2π3t/23)

42

Regression Analysis: Monte Negro



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


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)


+0.01260 sin(2π3t/23)- 0.01827 cos(2π3t/23)

43

Regression Analysis: Rio Crespo



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


0.0308251 79.35% 78.36% 77.03%

Coefficients


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)


+0.00407 sin(2π3t/23)- 0.01943 cos(2π3t/23)

44

Regression Analysis: Vale do Anari



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


0.0354720 48.05% 45.65% 42.07%

Coefficients


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)


+0.00214 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


0.0480104 87.65% 87.07% 86.29%

Coefficients


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



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


0.0161054 43.34% 40.68% 37.14%

Coefficients


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)


-0.00147 sin(2π3t/23)+ 0.00342 cos(2π3t/23)

47

Regression Analysis: Beykoz



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


0.0304283 87.68% 87.11% 86.34%

Coefficients


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)


+0.02064 sin(2π3t/23)+ 0.00182 cos(2π3t/23)

48

Regression Analysis: Cekmekoy



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


0.0409881 82.63% 81.82% 80.78%

Coefficients


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)


+0.01625 sin(2π3t/23)+ 0.00875 cos(2π3t/23)

49

Regression Analysis: Kadikoy



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


0.0178982 75.28% 74.12% 72.63%

Coefficients


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



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


0.0296027 40.72% 37.95% 34.03%

Coefficients


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)


-0.00272 sin(2π3t/23)+ 0.00530 cos(2π3t/23)

51

Regression Analysis: Maltepe



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


0.0301181 28.75% 25.42% 20.56%

Coefficients


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)


-0.00379 sin(2π3t/23)+ 0.00301 cos(2π3t/23)

52

Regression Analysis: Pendik



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


0.0540883 40.12% 37.34% 33.50%

Coefficients


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)


-0.00044 sin(2π3t/23)+ 0.01187 cos(2π3t/23)

53

Regression Analysis: Sancaktepe



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


0.0401725 33.53% 30.41% 25.85%

Coefficients


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)


+0.00547 sin(2π3t/23)+ 0.01041 cos(2π3t/23)

54

Regression Analysis: Sile



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


0.0319231 91.31% 90.91% 90.38%

Coefficients


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)


+0.02607 sin(2π3t/23)+ 0.00603 cos(2π3t/23)

55

Regression Analysis: Sultanbeyli



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


0.0271582 48.73% 46.29% 42.71%

Coefficients


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)


-0.00253 sin(2π3t/23)+ 0.00790 cos(2π3t/23)

56

Regression Analysis: Tuzla



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


0.0554203 41.05% 38.33% 34.62%

Coefficients


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)


-0.00633 sin(2π3t/23)+ 0.01037 cos(2π3t/23)

57

Regression Analysis: Umraniye



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


0.0360378 31.67% 28.49% 23.60%

Coefficients


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)


-0.00318 sin(2π3t/23)+ 0.00718 cos(2π3t/23)

58

Regression Analysis: Uskudar



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


0.0239916 73.33% 72.08% 70.47%

Coefficients


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)


+0.00401 sin(2π3t/23)- 0.00176 cos(2π3t/23)

59

APPENDIX B LINEAR REGRESSION MODEL USING MODIS EVI TIME SERIES

Regression Analysis: RS in Ariquemes



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


0.0294493 59.70% 57.31% 52.51%

Coefficients


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



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


0.0266175 85.85% 85.17% 84.23%

Coefficients


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)


+ 0.00734 sin(2π3t/23)- 0.01560 cos(2π3t/23)

61

Regression Analysis: Ariquemes



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


0.0209143 93.82% 93.53% 93.15%

Coefficients


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)


+0.00298 sin(2π3t/23)- 0.02170 cos(2π3t/23)

62

Regression Analysis: Cacaulandia



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


0.0262111 90.84% 90.39% 89.80%

Coefficients


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)


+0.00308 sin(2π3t/23)- 0.01903 cos(2π3t/23)

63

Regression Analysis: Machadinho do Oeste

Analysis of Variance


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


0.0208261 78.77% 77.80% 76.42%

Coefficients


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)


-0.00030 sin(2π3t/23)- 0.01057 cos(2π3t/23)

64

Regression Analysis: Monte Negro



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


0.0239078 91.64% 91.22% 90.65%

Coefficients


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)


+0.00464 sin(2π3t/23)- 0.01656 cos(2π3t/23)

65

Regression Analysis: Rio Crespo



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


0.0195017 92.68% 92.32% 91.84%

Coefficients


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)


+0.00361 sin(2π3t/23)- 0.01782 cos(2π3t/23)

66

Regression Analysis: Vale do Anari



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


0.0212992 85.43% 84.75% 83.79%

Coefficients


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)


+0.00267 sin(2π3t/23)- 0.01178 cos(2π3t/23)

67

Regression Analysis: RS in Istanbul



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


0.0354861 95.84% 95.65% 95.37%

Coefficients


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)


+0.03986 sin(2π3t/23)+ 0.00245 cos(2π3t/23)

68

Regression Analysis: Atasehir



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


0.0092250 82.63% 81.80% 80.70%

Coefficients


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)


+0.00051 sin(2π3t/23)+ 0.00169 cos(2π3t/23)

69

Regression Analysis: Beykoz



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


0.0261369 95.60% 95.39% 95.11%

Coefficients


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)


+0.02684 sin(2π3t/23)- 0.00467 cos(2π3t/23)

70

Regression Analysis: Cekmekoy



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


0.0270250 95.13% 94.90% 94.59%

Coefficients


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)


+0.02571 sin(2π3t/23)+ 0.00023 cos(2π3t/23)

71

Regression Analysis: Kadikoy



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


0.0081361 93.31% 92.99% 92.55%

Coefficients


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



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


0.0155666 71.87% 70.54% 68.53%

Coefficients


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)


-0.00055 sin(2π3t/23)+ 0.00178 cos(2π3t/23)

73

Regression Analysis: Maltepe



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


0.0158340 64.77% 63.10% 60.66%

Coefficients


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)


+0.00113 sin(2π3t/23)+ 0.00113 cos(2π3t/23)

74

Regression Analysis: Pendik



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


0.0213553 85.69% 85.02% 84.10%

Coefficients


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)


+0.00913 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


0.0232938 75.23% 74.07% 72.18%

Coefficients


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)


+0.00934 sin(2π3t/23)+ 0.00387 cos(2π3t/23)

76

Regression Analysis: Sile



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


0.0275451 95.84% 95.65% 95.38%

Coefficients


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)


+0.03144 sin(2π3t/23)- 0.00184 cos(2π3t/23)

77

Regression Analysis: Sultanbeyli



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


0.0168000 69.92% 68.49% 66.44%

Coefficients


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)


-0.00002 sin(2π3t/23)+ 0.00423 cos(2π3t/23)

78

Regression Analysis: Tuzla



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


0.0265418 67.02% 65.48% 63.34%

Coefficients


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)


+0.00145 sin(2π3t/23)+ 0.00605 cos(2π3t/23)

79

Regression Analysis: Umraniye



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


0.0161831 81.78% 80.93% 79.73%

Coefficients


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)


+0.00302 sin(2π3t/23)+ 0.00053 cos(2π3t/23)

80

Regression Analysis: Uskudar



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


0.0133144 92.83% 92.50% 92.05%

Coefficients


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)


+0.00537 sin(2π3t/23)- 0.00081 cos(2π3t/23)

81

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86

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.

.

monitoring vegetation cover change using modis...

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