Change Analysis of the Top Ten Percent of
Global Photosynthesis as Captured by the
SPOT VEGETATION (VGT) Sensor for the Time
Period: 1998 to 2008
A Thesis by
Moumita Dutta Gupta
Advisor: Dr. S. Young
2010
Submitted in partial fulfillment of the requirements
for the degree of
Master of Science in Geo Information Science
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Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
DEPARTMENT
OF
GEOGRAPHY, SALEM STATE COLLEGE, SALEM, MA
Acknowledgement
This thesis would not have been possible without the support of
many people. I wish to express my gratitude to my advisor Prof. Dr.
Stephen Young who was abundantly helpful and offered
invaluable assistance, support and excellent guidance. Deepest
gratitude is also due to the readers of the committee, Prof. Dr.
Laurence Goss and Prof. Dr. Marcos Luna without whose
knowledge and assistance this study would not have been
successful.
Special thanks also to all my friends for their support and
understanding. Not forgetting my husband Thomas Clifton who
always been there and helped me all throughout the thesis.
I would also like to convey thanks to the Graduate School and
Faculty for providing the financial means and laboratory
facilities.
Special thanks to Kym Pappathanasi and Marcie Talbot for their
unconditional help and support at the DGL.
I also wish to express my love and gratitude to my beloved families;
for their understanding & endless love, through the duration of her
studies.
Contents
Abstract: .................................................................................................................................................. 1
Introduction: ........................................................................................................................................... 1
Why is it important to study photosynthesis? .................................................................................... 4
Data and Methodology: .......................................................................................................................... 6
Base data:............................................................................................................................................ 6
Spot Vegetation (VGT) Facts: .............................................................................................................. 6
Data Quality ........................................................................................................................................ 8
Data Preparation ............................................................................................................................... 10
Results: .................................................................................................................................................. 13
Africa: ................................................................................................................................................ 14
Europe: .............................................................................................................................................. 14
North America:.................................................................................................................................. 15
Central America: ............................................................................................................................... 15
South America ................................................................................................................................... 15
North Asia: ........................................................................................................................................ 15
West Asia: ......................................................................................................................................... 15
South East Asia:................................................................................................................................. 15
Asia Islands:....................................................................................................................................... 16
Australasia:........................................................................................................................................ 16
Conclusion: ............................................................................................................................................ 17
References ..................................................................................................................................... 19
Appendix 1: ........................................................................................................................................... 21
Appendix 2: ........................................................................................................................................... 31
Temporal Profiles: ............................................................................................................................. 31
End Notes .............................................................................................................................................. 46
List of Figures:
Figure 1: A conceptual model illustrating human effect on environment. ............................................ 2
Figure 2: Global CO₂ emissions and warming compared to pre-industrial times from 2000 to 2050.... 3
Figure 3: The electromagnetic spectrum ................................................................................................ 5
Figure 4: Map produced using AVHRR .................................................................................................... 6
Figure 5- Regions of Interest ................................................................................................................... 9
Figure 6: Flowchart of the Methodology .............................................................................................. 12
Figure 7: Global percentage change image for the 11-year period (1998-2008) ................................ 13
Figure 8: Africa ...................................................................................................................................... 21
Figure 9: Europe ................................................................................................................................... 22
Figure 10: North America...................................................................................................................... 23
Figure 11: Central America ................................................................................................................... 24
Figure 12: South America ...................................................................................................................... 25
Figure 13: North Asia ............................................................................................................................ 26
Figure 14: West Asia ............................................................................................................................. 27
Figure 15 South East Asia ...................................................................................................................... 28
Figure 16: Asia Islands ........................................................................................................................... 29
Figure 17 Australasia ............................................................................................................................. 30
Figure 18: Africa decrease..................................................................................................................... 31
Figure 19: Africa increase...................................................................................................................... 32
Figure 20: Madagascar Increase ........................................................................................................... 33
Figure 21: Madagascar decrease .......................................................................................................... 34
Figure 22: Brazil (South America).......................................................................................................... 35
Figure 23: Venezuela (Bolívar)- South America .................................................................................... 36
Figure 24: Paraguay - South America .................................................................................................... 37
Figure 25: Germany............................................................................................................................... 38
Figure 26: Ireland .................................................................................................................................. 39
Figure 27: Indonesia .............................................................................................................................. 40
Figure 28: Myanmar .............................................................................................................................. 41
Figure 29: Vietnam ................................................................................................................................ 42
Figure 30: North East India ................................................................................................................... 43
Figure 31: Australia ............................................................................................................................... 44
Figure 32: Tasmania .............................................................................................................................. 45
List of Tables:
Table 1: Characteristics of SPOT VGT ...................................................................................................... 7
Table 2: Channel Description .................................................................................................................. 7
Table 3: Product Description of SPOT VGT (source: www.vgt.vito.be) .................................................. 8
Table 4: Showing the 10 regions of interestused for the research. (source: www.vgt.vito.be) ............. 9
Table 5: Showing the value slice criteria............................................................................................... 11
Table 6: Africa-Table of Change ............................................................................................................ 21
Table 7: Europe-Table of Change ......................................................................................................... 22
Table 8: North America-Table of Change .............................................................................................. 23
Table 9: Central America-Table of Change ........................................................................................... 24
Table 10: South America-Table of Change ............................................................................................ 25
Table 11: North Asia-Table of Change .................................................................................................. 26
Table 12: West Asia-Table of Change ................................................................................................... 27
Table 13: South East Asia-Table of Change ........................................................................................... 28
Table 14: Asia Islands- Table of Change ................................................................................................ 29
Table 15: Australasia-Table of Change.................................................................................................. 30
List of Graphs:
Graph 1: Temporal Profile-Africa decrease .......................................................................................... 31
Graph 2: Temporal Profile- Africa increase........................................................................................... 32
Graph 3: Temporal Profile- Madagascar increase ................................................................................ 33
Graph 4: Temporal Profile- Madagascar decrease ............................................................................... 34
Graph 5: Temporal Profile- Brazil.......................................................................................................... 35
Graph 6: Temporal Profile- Venezuela (Bolívar) ................................................................................... 36
Graph 7: Temporal Profile- South America (Paraguay) ........................................................................ 37
Graph 8: Temporal Profile-Germany .................................................................................................... 38
Graph 9: Temporal Profile-Ireland ........................................................................................................ 39
Graph 10: Temporal Profile-Indonesia.................................................................................................. 40
Graph 11: Temporal Profile-Myanmar.................................................................................................. 41
Graph 12: Temporal Profile-Vietnam.................................................................................................... 42
Graph 13: Temporal Profile-North East India ....................................................................................... 43
Graph 14: Temporal Profile-Australia ................................................................................................... 44
Graph 15: Temporal Profile-Tasmania .................................................................................................. 45
Change Analysis of the Top Ten Percent of Global Photosynthesis as Captured by the SPOT
VEGETATION (VGT) Sensor for the Time period: 1998 to 2008
Moumita DuttaGupta
Salem State College
Abstract:
The primary objective of the research was to see how and where is the highest level of
photosynthesis change between 1998 to 2008 for regions of the world. Global scale SPOT Vegetation
NDVI data were used for the research. The question for this research was “how and where did the
top 10% Global Photosynthesis change during the time period: 1998 to 2008?” A percentage change
image for the 11-year period has been produced for the regions of the entire world. The areas of
highest photosynthetic activities have been classified into 5 categories: decrease > 10%, decrease
from 3% to 10%, little change (-3 to +3%), increase 3% to 10%, and increase > 10%. Temporal profiles
for some of the areas with high photosynthetic activities have also been shown in this paper. The
results show that there has been significant increase as well as decrease in certain parts of the world
during this period of time.
Keywords: Remote sensing, SPOT vegetation, CO², photosynthesis, global change
Introduction:
the ecosystem level thus is a crucial issue in
studies of global change (Woodwell 1984).
Vegetation, whether natural or humancontrolled, plays an important role in the
global carbon cycle. The process of
photosynthesis, respiration and litter
decomposition in the terrestrial plant
communities consume and/or produce large
amounts of CO₂, the predominant greenhouse
gas in global warming (Sabbe and Veroustrate
2000). The monitoring of carbon dynamics at
Human alteration of Earth is substantial and
growing (Imhoff et al., 2004). Between onethird and one-half of the land surface has
been transformed by human action; the
carbon dioxide concentration in the
atmosphere has increased by nearly 30
percent since the beginning of the Industrial
Revolution; more atmospheric nitrogen is
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Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
fixed by humanity than by all natural
terrestrial sources combined; more than half
of all accessible surface fresh water is put to
use by humanity; and about one-quarter of
the bird species on Earth have been driven to
extinction. By these and other standards, it is
clear that we live on a human dominated
planet (Vitousek et al., 1997).
Various chemical compounds found in the
Earth’s atmosphere act as "greenhouse gases"
which mainly include the following gases:iv




Carbon dioxide (CO₂)
Methane (CH₄)
Nitrous oxide (N₂O)
the Industrial Gases
 Hydro fluorocarbons (HFCs)
 Perfluorocarbons (PFCs)
 Sulfur hexafluoride (SF6)
When sunlight strikes the Earth’s surface,
some of it is re-radiated back towards space
as infrared radiation (heat). Greenhouse
gases, especially CO₂, absorb this infrared
radiation and traps its heat in the
atmosphere.
Figure 1: A conceptual model illustrating human effect
on environment.i
One of the major results of human activity
changing the surface of the Earth is the
alteration of our atmosphere and the earth’s
energy balance. Changes in the atmospheric
abundance of greenhouse gases and aerosols,
in solar radiation and in land surface
properties alter the energy balance of the
climate system. These changes are expressed
in terms of radiative forcing ii, which is used to
compare how a range of human and natural
factors drive warming or cooling influences on
global climate (IPCC, 2007).iii
Increasing greenhouse gas concentrations are
expected to have significant impacts on the
world’s climate on a timescale of decades to
centuries.
Evidence
from
long-term
monitoring studies is now accumulating and
suggests that the climate of the past few
decades is anomalous compared with past
climate variation, and that recent climatic and
atmospheric trends are already affecting
species
physiology,
distribution
and
phenology (Hugus 2000).
Life on Earth is based on carbon, and the
Carbon dioxide (CO₂) in the atmosphere is the
primary
resource
for photosynthesis.
Humanity adds CO₂ to the atmosphere by
mining and burning fossil fuels, the residue of
life from the distant past, and by converting
forests and grasslands to agricultural and
other low-biomass ecosystems. The net result
of both activities is that organic carbon from
rocks, organisms, and soils is released into the
atmosphere as CO₂ (Vitousek et al., 1997).
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Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
CO₂ plays a very important role in global
climate change. According to an article
published in Science Daily titled ‘Climate
Change: Halving Carbon Dioxide Emissions By
2050 Could Stabilize Global Warming’ (May 4,
2009). “If CO2 emissions are halved by 2050
compared to 1990, global warming can be
stabilized below two degrees. This is shown
by two studies by a co-operation of German,
Swiss and British researchers in the journal
Nature.”
very slowly. To not let the bathtub overflow,
the inflow must thus be stopped early
enough. It is wrong to believe that the
temperature will remain constant with
constant emissions.”
Vegetation constitutes a major player in
regulating the global climate through
photosynthesis and respiration. It is a major
contributor to the hydrological cycle and
carbon exchanges between the Earth’s
surface and the atmosphere (Young and
Harris 2005).
Photosynthesis is a process that converts
carbon dioxide into organic compounds,
especially sugars, using the energy from
sunlight. Photosynthesis occurs in plants,
algae, and many species of bacteria. In plants,
algae and cyanobacteria photosynthesis uses
carbon dioxide and water, releasing oxygen as
a waste product (The Encyclopedia of Earth,
2007). Photosynthesis is vital for life on Earth.
As well as maintaining the normal level of
oxygen in the atmosphere, nearly all life
either depends on it directly as a source of
energy, or indirectly as the ultimate source of
the energy in their food (Physorg.com).
Figure 2: Global CO₂ emissions and warming compared
to pre-industrial times for a scenario without climate
policy (red) and a scenario in which the emissions are
restricted to 1000 billion tones of CO₂ (blue) from 2000
to 2050. v
Here, the behavior of CO₂ in the
atmosphere has also been described as a
Bath-tub. According to Reo Knutti,
Plants convert light energy into chemical
energy during photosynthesis and in the
process extract relevant greenhouse gases
such as carbon dioxide (CO₂) from the
atmosphere and return CO₂ through
respiration. Biomass, the product of primary
production, provides chemical energy for the
beginning of food chains on Earth (Young and
Harris 2005).
professor at the Institute for Atmosphere and
Climate at ETH Zurich “The inflow of the
bathtub is large, but the drainage is small. The
CO2 emissions are increasing every year, but
the CO2 is only removed from the atmosphere
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Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Why is it important to study
photosynthesis?
Without photosynthesis there would be no
life on earth. Photosynthesis is the process
where the green plants use the energy
obtained from the Sun to convert carbon
dioxide from the air and water from the soil
into the sugars and starches which provide
them with the energy to flourish.
Another vital part of photosynthesis is that it
produces oxygen as a byproduct and so we
are able to breathe air. It is Nature's way of
creating balance on earth: plants take in the
carbon dioxide and release oxygen. Humans
and animals breathe in the oxygen and
release carbon dioxide. It is truly wondrous in
how it ensures that all life is sustained, so it is
an important topic to study.
Photosynthesis is important in regulating
atmospheric gases and providing food for life,
it is critical to monitor changes in
photosynthesis on Earth. Because of storage
of carbon and the production of
carbohydrates,
these
regions
of
photosynthesis are important to monitor. In
addition, the lack of research in this area
demands research for these regions. An
important way to monitor photosynthesis is
by understanding changes in land cover, and
where it is occurring. Land cover change can
have a tremendous impact on local
communities and the global economy as well
as influencing global levels of greenhouse
gases.
Modern techniques, such as remote sensing
through satellites have made it more reliable
and consistent to investigate and analyze the
major trends in vegetation cover at the global
scale. Remote sensing technology in
combination with geographic information
system (GIS) can render reliable information
on vegetation cover. The analysis of the
spatial extent and temporal change of
vegetation cover using remotely sensed data
is of critical importance to investigate our
changing planet.
Satellite remote sensing has been shown to
be powerful tools for local (Band et al., 1991;
Running et al., 1989), regional (Cihlar, Chen,
and Li, 1997; Liu, Chen, Cihlar, and Chen,
2002; Veroustraete and Myneni, 1996), and
global ecological applications (Hunt et al.,
1996; Potter et al., 1993; Sellers et al., 1996).
Previously, Landsat and SPOT images were
often used for local applications, and only the
Advanced Very High Resolution Radiometer
(AVHRR) images were available for regional
and global applications. With the successful
launches of new sensors, including
VEGETATION (VGT) on board SPOT-4
platform, MODIS, MISR and ASTER of the
Terra mission, and the short-lived POLDER as
part of ADEOS, our ability in ecological
monitoring and modeling has been greatly
improved. In addition, several forthcoming
high spectral resolution and hyperspectral
sensors as well as POLDER II sensor will soon
be available. Technical improvements have
been made in all remote sensing domains
including spectral, angular, spatial and
temporal resolutions.
Satellite remote sensing has enabled the
acquisition of Land use/ land cover and
vegetation information at different spatial
and temporal scales. VGT instrument onboard
Spot 4 satellite with four spectral bands blue
(0.43-0.47mm), red (0.61-0.68mm), infrared
(0.78-0.89mm) and short wave infrared (1.584|Page
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
1.75mm) at a spatial resolution of 1 Km and
temporal resolution of 1 day meets the
requirement of vegetation mapping at a
continental scale.¹⁹
Over the past two decades, data from Earth
observation satellites have become important
in mapping the Earth’s features and
infrastructure, managing natural resources
and studying environmental changes. Remote
sensing and Geographic Information Systems
(GIS) provide tools for advanced ecosystem
management. The collection of remotely
sensed data facilitates the synoptic analyses
of earth-system function, patterning, and
change at local, regional, and global scales
over time. Coarse spatial resolution (1 km
resolution), combined with high temporal
resolution component would allow for the
early production of an actual global
forest/non forest status map. This effort could
provide a primary data, which can be used in
conjunction with other data sets available to
define the extent of deforestation.
Remote sensing is becoming an increasingly
important tool for agriculturalists, ecologists,
and land managers for the study of Earth’s
agricultural and natural vegetation, and can
be applied to further our understanding of
key environmental issues, including climate
change and ecosystem management. (Jones
and Vaughan, 2010)²³. Scientists use satellites
to collect information about the surface of the
Earth and about the Earth’s atmosphere. The
satellite information is used for weather
studies (meteorology), biology, geology and
many other areas of scientific study. All earthimaging satellites collect electromagnetic
radiation.
Figure 3: The electromagnetic spectrum vi
Throughout the years, various studies have
been conducted to study the continuously
changing Earth, its vegetation and climate. A
very commonly used sensor for the study of
vegetation is the primary sensor on board the
NOAA polar-orbiting satellites, the Advanced
Very High Resolution Radiometer (AVHRR).
The AVHRR sensor is also a useful tool for
monitoring vegetation, land cover, and
climate, and enables scientists to observe
how these three elements interact. AVHRR
data can be used to assess the quantity and
vigor (photosynthesis activity) of vegetation
through a measure of “greenness”, referred
to as the vegetation index or the NormalizedDifference Vegetation Index (NDVI). Using
AVHRR data, scientists can monitor the
growing season of crops – which can change
with variations in regional climates – and can
provide potentially lifesaving information to
developing countries that heavily rely on an
abundant and reliable harvest. From AVHRR
data it is relatively easy to identify green
vegetation and non-vegetated features such
as water, barren land, ice, snow, and clouds.
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Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Data and Methodology:
Base data:
Figure 4: Map produced using AVHRR
Numerous studies have been performed
regionally, locally as well as globally using
AVHRR data vegetation related studies.
An improvement over the AVHRR sensor is
the SPOT Vegetation (VGT) Sensor.
Though SPOT VGT has some similarity
compared with AVHRR, both sensors differ
due to fundamental characteristics:
 Firstly, the acquisition is based on a
push broom system which limits the
off –nadir pixel-size increase
 Secondly, the presence of a Short
Wave Infrared channel (SWIR) permits
the study of vegetation water content
 Finally, the ground segment is
organized to acquire process and
archive all daily data over land surface
at the full 1x1 km² resolution. ²
Therefore, this thesis reports on how and
where the highest level of photosynthesis are
changing globally during the time period of
1998 to 2008. As mentioned earlier, because
of storage of carbon and the production of
carbohydrates,
these
regions
of
photosynthesis are important to monitor. In
addition the lack of research in this area
demands research for these regions.
For the research reported in this thesis paper
SPOT Vegetation datasets were used. SPOT 4
with the Vegetation sensor (VGT) was
launched in 1998. It is dedicated to
monitoring terrestrial vegetation at global and
regional levels. It has a spatial resolution of
1km which is ideal for analysis at the global
scale. Other satellite-borne imaging system
such as LANDSAT ETM provides a spatial
resolution of 30 meters multispectral and 15
m panchromatic is very beneficial for local
scale analysis. In addition, SPOT Vegetation
images the world everyday which minimizes
the chances of obstacles such as cloud cover
when the data are composited at decadal or
monthly levels. Moreover, for the purpose of
this research, it is more important to get a
finer temporal resolution instead of a finer
spatial resolution. Hence SPOT Vegetation
datasets were used.
Spot Vegetation (VGT) Facts:
Spot Vegetation is a sensor on board of SPOT
4 satellite, operational since 24 March 1998. It
is a joint project of European Commission,
OSTC (Belgian Office of Scientific, Technical
and Cultural affairs), CNES (Centre National
d’Etudes Spatiales), SNSB (Swedish National
Space Board) and ASI (Agenzia Spaziale
Italiana).
The vegetation instrument is dedicated to the
daily observation of the terrestrial ecosystem
and the biosphere, particularly to address
global change and environmental issues. The
principal characteristic of the sensor are
optimized for global scale vegetation. ²
SPOT Vegetation has daily global coverage
and stable spatial resolution which makes it a
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Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
very suitable sensor for global vegetation
mapping. (de Badts, E.P.J, 2002).
Table 1: Characteristics of SPOT VGT
Spatial resolution
1 km
Temporal
resolution
At least once a day
Swath Width
2200 km
Altitude
820-830 km
Scan Method
Push-broom
Field of View
101°
Orbital Inclination
98.72°
Instantaneous Field
of View (IFOV)
Absolute pixel
positioning
Pixel geometric
superposition
1.15 km at nadir,
1.3 km at 50° off nadir
350 m
< 0.5 pixel
The SPOT VGT sensor records electromagnetic
energy in 4 channels that measure reflected
energy from the earth. The spectral bands are
B0 (Blue channel), B2 (Red channel), B3 (NIR
channel) and MIR (SWIR channel) as given in
the Table below.
Table 2: Channel Description
Channels of SPOT VGT sensor
1
B0 (Blue channel)
0.43-0.47 µm
2
B2 (Red 3 channel)
0.61-0.68 µm
3
B3 (Near Infrared
channel)
0.78-0.89 µm
4
MIR (Short Wave
Infrared channel)
1.58-1.75 µm
The B2, B3 and SWIR bands are well adapted
for the observation of plant and crop cover,
while B0 is used for atmospheric correction.
The data for this research were for the period
of April 1998 to December 2008 and were in
10 – days maximum value composites which
were further converted to monthly maximum
value composites (MVC) for the purpose of
the research. The datasets were in a
Normalized Difference Vegetation Index
(NDVI) format and are from the Vegetation
Sensor on board the SPOT satellite. SPOT
Corporation provides free NDVI data for the
entire world at a 1km resolution. The
vegetation index NDVI is a very commonly
used index to monitor vegetation presence
and properties. An 8-bit scale is used to
represent the NDVI pixel value in a range from
0 to 255 which is more convenient to be used
on 8-bit gray tone display (Njomo, Donatien,
2008).
It is a simple formula using two satellite
channels. One band is in the visible region
(VIS) and one is in the near infrared (NIR),
then the NDVI is (NIR – VIS)/ (NIR + VIS).
𝐍𝐈𝐑 – 𝐕𝐈𝐒
NDVI = 𝐍𝐈𝐑 + 𝐕𝐈𝐒
The reason NDVI is related to vegetation is
that
healthy
vegetation
absorbs
electromagnetic radiation (EMR) in the visible
spectrum and reflects very well in the near
infrared part of the spectrum (NOAA Coastal
Service center, 2007). Green leaves have a
reflectance of 20 percent or less in the 0.5 to
0.7 micron range (green to red) and about 60
percent in the 0.7 to 1.3 micron range (near
infrared). The visible channel gives us some
degree of atmospheric correction. The value is
then normalized to the range -1<=NDVI<=1 to
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Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
partially account for differences
illumination and surface slope.
in
NDVI provides a crude estimate of vegetation
health and a means of monitoring changes in
vegetation over time. The possible range of
values is between -1 and 1, but the typical
range is between about -0.1 (NIR less than VIS
for a not very green area) to 0.6 (for a very
green area) (NOAA Coastal Service center,
2007). Plant leaves have a distinctive spectral
profile because chlorophyll absorbs strongly
in portions of visible light spectrum, central at
about 0.45 µm and 0.67 µm while the
structure of the leaves’ cell (particularly
mesophyll) creates a high reflectance and
scatters in the near-infrared light (Gates et al.
1965, Tucker 1979, Curran 1985). Reflectance
measurement can therefore be used to detect
the presence of growing vegetation. As NDVI
is related to photosynthetic activity and plant
respiration,
relationships
have
been
developed between annual variation in NDVI
and variability in CO₂.
However, it is very important to be cautious
when using NDVI as a variable for the study of
vegetation change as it is not only sensitive to
vegetation characteristics but also to
atmospheric variables, particularly the
amount and variability of water vapor in the
atmosphere and the presence of aerosols.
Other objects affecting NDVI are sensor
degradation, cloud cover, orbital drift, the
anisotropic and transmissive radiation
properties of plant canopies, soil moisture
and soil contour (Bannari et al. 1995, Mennis
2001). Moreover, for some arid and semi arid
regions, where bare soil reflectance accounts
for a large percentage of the reflectance pixel,
soil contour and conditions may cause large
NDVI variations spatially (Harris ,2003).Here in
this paper, changes in NDVI over time is used
as an indicator of changes in Photosynthesis.
Data Quality
The global SPOT Vegetation data are broken
into ten sub regions and come in a 10-day
maximum value composite (MVC) with three
images per month.
Some of the regions of interest defined by
vgt.vito.be overlap each other, so in certain
areas the pixel counts are repeated due to the
overlapped regions.
Table 3: Product Description of SPOT VGT (source: www.vgt.vito.be)
Products
Description
P
S1
Physical products
Maximum value composite image, with worldwide land cover with minimum cloud
cover
Daily synthesis product, consisting of the “calibrated ground reflectance values”
S10
D10
Ten-daily synthesis product, consisting of the “calibrated ground reflectance values”
Bi-directional composite synthesis
S
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Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Table 4: Showing the ten regions of interest used for the research. (Source: SPOT)
Figure 5- Regions of Interest
For this research, The S10 datasets were used.
The ten-day synthesis (S10) is computed from
all the passes on each location acquired
during 10-day periods. The periods are
defined according to the legal calendar: from
1st to 10th, from 11th to 20th, from 21st to the
end of each month. The synthesis between
different passes is performed selecting the
best measurement of the period defined from
the following criteria
 It does not correspond to a blind or
interpolated pixel,
 It is not flagged as cloudy in the status
map,
 It does correspond to the highest value of
Top of Atmosphere NDVI.
For all of the regions of interest of the World,
all 10-day MVC images (from April 1998 to
December 2008) were downloaded. The data
delivered by VITO were in HDF format. For the
classification they had to be imported into the
Idrisi image processing software package. In
Idrisi the 10-day MVCs were combined into
monthly MVCs and then for each year the 12
monthly MVCs were added and then divided
by 12 creating annual average images, or
annual integrated images.
To determine change the end dates were
differenced. To reduce any extreme events
such as drought, flood etc which may
drastically influence the NDVI, the end images
were averaged. So image 9899 was the
9|Page
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
averaged image of 1998 and 1999 {(1998 +
1999) ÷ 2)}. And 0708 was the averaged
image of the years 2007 and 2008 {(2007 +
2008) ÷ 2)}.
In addition, the annual averages reduce a
variety of monthly external factors. First the
data are in an NDVI format that has reduced a
number of sun angle and atmospheric
problems (Kidwell 1997). The MVC process for
the base data of the annual averages also
reduces atmospheric problems such as cloud
cover. The MVC is created on a pixel by pixel
basis where each pixel’s NDVI value is the
highest value over the monthly period. Clouds
have low NDVI values and so if there is one
day without cloud cover, the NDVI from the
vegetation will be the resulting NDVI in
monthly value composite. MVCs are
successful at removing cloud cover (Holben
1986). Cloud cover has been one of the major
problems with satellite based studies on land
cover.
The NDVI maximum value composite imagery
is highly related to green vegetation dynamics
and is ideal for large area land cover research
as the problems common to single data
remote sensing studies, such as cloud cover,
atmosphere problems and view and
elimination geometry have been minimized.
The data as an annual average composite
captures the average vegetation status over
the course of the entire year and thus reduces
the problem of capturing vegetation
information at different times of the
phenological cycle. In addition an annual
average as opposed to a growing season
average is important because some part of
the earth have agriculture throughout the
year as well as capturing winterS productivity
from evergreen trees.
However it must be noted that all possible
non-signal effects (atmospheric scatter,
orbital drift, changing, illumination etc) have
not been completely removed from that data
set and some of the conclusion below are
most likely influenced by them as well as
actual changes in vegetation.
Data Preparation
After the data were downloaded and
processed the annual average images were
geo-referenced. A mask with the highest level
of global photosynthesis (top 10%), created
by research done by Dr. S. Young, was
reformatted with the existing geo-referenced
images. This mask will be referred to as the
10% mask.
The 10% mask was used to mask out all areas
except the top 10% of global photosynthesis.
It is this area (top levels of photosynthesis)
which this research focused on.
As noted above, the annual average images of
1998 and 1999 for each of the 10 sub regions
are added together and are divided by two
creating a 1998 – 1999 average image (9899
image). The same was done for the 2007 and
2008 years (0708 image). This reduces any
extreme events in the end years of the
eleven-year period. The 1998-99 image is
then subtracted from the 2007-08 image and
then the resulting image is divided by the
1998-99 image. The resulting image is the
percent change image for the 11 year period.
The next task was to multiply the averaged
end images (9899 and 0708) with the reclassed 10 percent mask image, leaving an
image of only the highest levels of NDVI
(10%0708 and 10%9899).
10 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Now the percentage change
calculated by using this formula:
image
is
𝟏𝟎%𝟎𝟕𝟎𝟖 − 𝟏𝟎%𝟗𝟖𝟗𝟗
𝟏𝟎%𝟗𝟖𝟗𝟗
2007/08 for the regions of the Earth with top
10% of photosynthesis. The percentage
change image was further re-classed, or value
sliced, into 9 and 5 classes.
The resulting image is an image showing the
percent change from year 1998/99 to
The 9 classes represent:
Classes
Description
The 5 classes represent:
Classes
Description
1
decreased > 20 %
1
decrease > 10%
2
decreased 10 to 20 %
2
Decrease 3 to 10%
3
decreased 5 to 10%
3
No change (-3 to +3% change)
4
decreased 3 to 5%
4
Increase 3 to 10%
5
No change (-3 to +3% change)
5
Increase > 10%
6
increase 3 to 5%
7
increase 5 % to 10%
8
increase 10% to 20 %
9
increase > 20 %
Table 5: Showing the value slice criteria
Areas with high level of change were digitized
and temporal profiles were created and
temporal patterns of change were graphed.
Areas include both 10% increasing and
decreasing NDVI. These Temporal profiles
show how the vegetation has changed
throughout the time period of 1998 to 2008.
These temporal profiles are analyzed to
understand how the top 10% of
photosynthesis is changing on earth.
Temporal Profiles or Time series analysis are
the study over time and space to see the
trend of change. They are very essential to
examine change over time. For temporal
profiling, pixels of change are digitized, and
then their mean NDVI annual average values
for each year are then graphed.
The temporal and spatial vegetation dynamics
is highly dependent on many different
environmental and biophysical factors.
Among these, climate is one of the most
important factors that influence the growth
and condition of vegetation (Propastin et al.,
2006). The complexity of the relationships
between vegetation and climate; climate and
oceanic dynamics; and the impacts of the
combination of ocean-atmosphere interaction
on vegetation result in a huge challenge in
monitoring drought patterns and its temporal
and spatial effects on vegetation.
11 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
The analysis of time series of vegetation
index data is potentially a powerful tool for
discriminating between vegetation types and
classifying the plant cover according to their
phenological character (Justice et al. 1985,
Viovy et al.1988).
Figure 6: Flowchart of the Methodology
12 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Results:
The results clearly show patterns of both
increase and decrease in photosynthesis over
certain parts of the world. As noted in method
section, I have analyzed areas of top 10% of
global photosynthesis Certain areas like
Brazil,
Venezuela,
Ireland,
Tasmania,
Germany, north east India etc. show a
tremendous increase in photosynthesis.
Areas like Myanmar, Vietnam, Australia,
Paraguay,
etc
show
decreases
in
photosynthetic activities. Certain areas such
as parts of Africa including Madagascar, South
America show both increasing and decreasing
NDVI, throughout the time frame of 1998 and
2008.
Figure 7 is the global percent change image of
areas with the highest level of increasing and
decreasing photosynthesis.
The image has been value sliced into 5
categories: decrease more than 10%,
decrease between 3 to 10%, area with little
change i.e. negative 3 to positive 3 %, increase
between 3 to 10% and increase more than
10% of the highest level of change in
photosynthesis of the entire world.
As mentioned in the methodology section,
parts of the regions of interest analyzed
overlap each other. The following discussion is
bases on the predefined regions of interest as
defined by SPOT.
Figure 7: Global percentage change image for the 11-year period (1998-2008)
13 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Africa:
Figure 8 (Appendix 1) shows changes in
regions of Africa with the highest level of
photosynthesis. Table 6 (a) and 6 (b) lists
change for Africa with 9 and 5 class intervals
respectively. These Tables show the
approximate number of pixels in each
category according to the value slicing
performed. The data in these Tables are
derived from the histogram of the region.
Here we see that approximately 6.62% of the
pixels show decreasing NDVI, whereas,
approximately 24.45% of the total pixels are
increasing NDVI.
Figure 18 (Appendix 2) is the percentage
change of Africa with decreasing NDVI
overlaid with Google Earth®. Graph 1 is the
temporal profile of three regions in
Mozambique with decreasing NDVI. Samples
1, 2 and 3 refer to areas with decreasing NDVI
which have been digitized for creating the
temporal profiles. In other words, to find out
the trend of change, pixels were collected
from the areas of increasing as well as
decreasing values separately. After digitizing
these sample areas, temporal profiles for the
time period of 1998 to 2008 were graphed.
The graph displayed in Graph 1 is a three year
running average graph which shows the
smooth pattern of change. Here we see that
during the year 2000, photosynthetic activity
seems to be and then declines. The graphs
gradually drop down in value of NDVI towards
the recent years which is 2008 in our case.
Figure 19 (Appendix 2) shows the region of
Africa with increasing NDVI overlaid with
Google Earth®. Graph 2, which is the temporal
profile of the increasing level of
photosynthesis in Africa (parts of Gabon and
Congo) shows a gradual increase in
photosynthesis which has also been graphed
to display the changing trend. Three samples
were collected and digitized. Temporal
profiles were created with those sample
pixels with high NDVI. This three year running
average of the temporal profiles, shows a
gradual increase of vegetation in these areas.
From 1998 to 2000, the vegetation was
decreasing but since 2001 the graph shows a
positive trend of increase.
Figure 20 and figure 21 (Appendix 2) show the
area of Madagascar with increasing and
decreasing NDVI respectively, overlaid with
Google Earth®. Graph 3 shows the temporal
profile of Madagascar with increasing
photosynthesis which is somewhat uneven
but reflects a gradual increase since the year
2002. Graph 4, which is the temporal profile
for Madagascar, shows a steep decrease from
1999 to 2002, but there is a small period of
increase until 2005, which is again followed by
gradual decrease in the level of NDVI.
Europe:
Figure 9 (Appendix 1) shows percent change
in NDVI for the areas of Europe with the
highest level of photosynthesis. Table 7(a) and
7(b) are the Table of change for Europe with 9
and 5 class intervals respectively. The data in
these Tables are derived from the histogram
of the region. These Tables show the
approximate number of pixels in each
category according to the value slicing
performed. According to Table 7(b), the
maximum number of pixel change is in the
Increase 3 to 10%. These areas show
significant amounts of increase in their
photosynthetic activity. Figure 25 shows the
Germany and its surrounding area with
increasing levels of photosynthesis. A
temporal profile created with three sample
14 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
areas collected from this area reflects a
gradual graph (Graph 8). We can see a
significant rise in the graph from 1998 to 2008
which
reflects
increasing
levels
of
photosynthesis. Most of the changes occurred
between 1999 and 2003. Figure 26 (Appendix
2), shows Ireland with increasing levels of
photosynthesis. A temporal profile for Ireland
(Graph 9) shows a smooth and consistent rise
in vegetation occurring throughout most of
the time period.
North America:
The region of North America with increasing
level of photosynthesis is shown in Figure 10
(Appendix 1). According to Table 8,
approximately 19.31 % of pixels have
decreasing NDVI and approximately 32.71% of
pixels have increasing NDVI. This region
doesn’t show much change.
Central America:
Figure 11(Appendix 1) shows the percent
change image of Central America. Changed
photosynthesis along with their pixel counts
are shown in Table 9. This Table shows that
there is more increase in photosynthesis than
decrease in this region of the world.
Approximately 44.38% of were increasing
with 39.88% in the category of high increase
(i.e. 3 to 10%). Only 6.38% decreased during
this time period.
South America
Figure 12 (Appendix 1) is the percent change
image of South America. Here we see that the
maximum number of pixels that show change
is in the category of increase 3 to 10%
(34.85% approximately) (Table 10). Brazil
shows a significant increase in photosynthesis
according to the analysis made in this
research. Figure 22 (Appendix 2) is the region
of Brazil where we can see a lot of yellow and
green colored pixels which reflect increase in
vegetation according to the legend created
for this research. The temporal profile for
Brazil (Graph 5) reflects a smooth increase in
the level of photosynthesis from 1998 to
2008, the highest being the year 2005.
Figure 23 (Appendix 2) is the image of Bolívar,
Venezuela which shows significant increase in
areas with the highest level of photosynthesis
as well. The temporal profile for this region
which is shown in graph 6 reflects a smooth
and gradual increase in vegetation. Similar to
Brazil, this area also has its highest level of
NDVI in the 2005. Paraguay on the other hand
shows
a
significant
decrease
in
photosynthesis (Figure 24, Appendix 2). The
temporal profile of this region shows a
significant decrease in vegetation (graph 7)
which is almost consistently decreasing from
1998 to 2008.
North Asia:
Figure 13 (Appendix 1) shows the region of
north Asia and Table of change for this region
is shown in Table 11. This region has hardly
any pixels of high levels of photosynthesis and
hardly had any change according to the
parameter used for this research to define top
10% change in photosynthesis.
West Asia:
Figure 14 (Appendix 1) shows the region of
West Asia. Table 12 is the Table of change of
the same. According to Table 12, increase 3
to 10% category has approximate 41.16 % of
the entire number of pixels of the region.
South East Asia:
The region of South East Asia is shown in
Figure 15 (Appendix 1). Table 13 shows the
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Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
table of change of the same. According to the
table, approximately 4.85% of pixels have
decreasing NDVI and approximately 62.89% of
pixels have increasing levels of NDVI.
One of the areas here with decreasing NDVI is
in the Myanmar region (Figure 28, Appendix
2). Two samples were collected from the area
with decreasing NDVI and the temporal
profile for the same was created (Graph 11) to
analyze the trend of change in this area. This
area showed a constant level of
photosynthesis from 1998 to 2006 but from
2006 to 2008 it drops drastically. For later
research on this, a closer look at this region
and its activity from the year period of 2006
to 2008 should be considered. There should a
explanation for this sudden and drastic drop
in the level of photosynthesis in the region of
Myanmar.
Another significant area which reflects
decrease is Vietnam (Figure 29, Appendix 2). A
look at its temporal profile which is graph 12
reveals the same patterns as Myanmar where
we see a sudden decrease in the level of
photosynthesis in the end years.
The North East region of the Indian subcontinent shows significant and gradual
increase in the level of photosynthesis. The
temporal profile for this region is shown in
graph 13 where we can clearly see the gradual
increase in vegetation.
Asia Islands:
Figure 16 (Appendix 1) is the percent change
image of region of Asia Islands which primarily
includes parts of Thailand, Malaysia, Sumatra
and Indonesia. According to the table of
change of Asia Islands (Table 14),
approximately 44.80% of the pixels show
increase between 3 to 10%. Figure 27
(Appendix 2) is the percent change image of
Indonesia we see a noticeable amount of
increase in NDVI. A temporal profiling for this
area shows a gradual and constant increase in
its vegetation (Graph 10).
Australasia:
The area of Australasia includes parts of Asia,
Australia and its surrounding. Figure 17
(Appendix 1) is the percent change image of
Australasia and Table 15 is the table of change
for the same. According to Table 15, which is
the Table of change of Australasia, 35.52% of
the pixels are increasing between 3 to 10%.
But this mainly covers parts of Asia Islands.
Australia shows a tremendous decrease in the
level of photosynthesis. Figure 31 (Appendix
2) is the percent change image of Australia
overlaid with Google Earth®. A temporal
profile for this region shows a constant
decreasing NDVI pattern. The lines are
smoother from the year 1998 to 2003 where
we see a gradual pattern. But from 2004 to
2006 has a sudden increase which drops
significantly from 2006 to 2008.
Figure 32 (Appendix 2) shows the region of
Tasmania which shows both increase as well
as decrease. Three samples have been
collected from the area with increasing NDVI
and a temporal profile has been graphed
(Graph 15). This area shows a constant and
consistent increase in the level of
photosynthesis.
16 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Conclusion:
SPOT Vegetation data with its high temporal
resolution has potential for use with regional
as well as global land cover mapping and the
high frequency of coverage enhances the
likelihood for cloud free observations and
makes it possible to monitor change in land
cover conditions over short periods, such as a
growing season as well as long time periods.
These datasets are useful for monitoring land
cover transformations at a wider scale.
The role of photosynthesis plays a very
important role in controlling the environment.
An important and controversial issue today is
regarding how photosynthesis in temperate
and tropical forests and in the sea affects the
quantity of greenhouse gases in the
atmosphere. Photosynthesis by plants
removes carbon dioxide from the atmosphere
and replaces it with oxygen. Thus, it would
tend to better the effects of carbon dioxide
released by the burning of fossil fuels.
However, the question is complicated by the
fact that plants themselves react to the
amount of carbon dioxide in the atmosphere
which makes it more important to monitor
the photosynthesis activities on earth. Some
plants, appear to grow more rapidly in an
atmosphere rich in carbon dioxide, but this
may not be true of all species. To better
understanding the effect of greenhouse gases
requires a much better knowledge of the
interaction of the plant kingdom with carbon
dioxide than we have today.
Satellite Earth observation data are now
available for sufficiently long time periods to
allow analysis of environmental change over
the whole planet. With suitable processing,
the Normalized Difference Vegetation Index
has been found by many scientists to be a
robust indicator of vegetation photosynthesis,
especially for large areas and yearly time
periods.
The main purpose of this paper was to
explore change in the areas of the globe with
the highest level of photosynthesis (top 10 % 90th percentile). The next phase of this
research would be to look into regions with
greater details to find out the reasons behind
the changing levels of photosynthesis.
Here we have seen that the photosynthetic
activity on the earth has shown both increase
as well as decrease. As this research was
basically targeted to highlight the areas of
high photosynthesis activities, the findings in
this research would help further detailed
research into the regions which are in the
threshold of top 10 % change (increase and
decrease).
A more detailed (regional as well as local)
study along with ground truthing would be
helpful in understanding the factors which
impact these changing patterns of
Photosynthesis. With the help of the results
found in this research, the areas of
importance in regards to changing
photosynthesis can be obtained.
This thesis also illustrates the importance of
photosynthesis as a natural process and the
impact that it has on all of our lives. Research
into the nature of photosynthesis is crucial
because only by understanding and
monitoring the changing trends of
photosynthesis on earth, can we control it,
and harness its principles for the betterment
of mankind. Science has only recently
developed the basic tools and techniques
17 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
such as Geographic Information Systems and
remote sensing, needed to investigate the
intricate details of global photosynthesis. It is
now time to apply these tools and techniques
to the problem, and to begin to reap the
benefits of this research.
18 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
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Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Appendix 1:
The 10 sub –regions with their respective Table of change with 9 and 5 classes:
Figure 8: Africa
Table 6: Africa-Table of Change
(a) Class Interval 9
Pixel
% of total pixels
(b) Class Interval 5
602
0.02
decrease > 10%
9117
0.33
Decrease 3 to 10%
decreased 5 to 10%
61124
2.20
Little change (-3 to +3% change)
decreased 3 to 5%
113349
4.08
Increase 3 to 10%
1916746
68.92
Increase > 10%
increase 3 to 5%
463458
16.66
Total
increase 5 % to 10%
201048
7.23
increase 10% to 20 %
15348
0.55
254
0.01
2781046
100.00
decreased > 20 %
decreased 10 to 20 %
Little change (-3 to +3% change)
increase > 20 %
Total
Pixel
% of total pixels
9719
0.35
174473
6.27
1916746
68.92
664506
23.89
15602
0.56
2781046
100.00
21 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 9: Europe
Table 7: Europe-Table of change
Pixel
% of total pixels
(b) Class Interval 5
Pixel
% of total pixels
decreased > 20 %
181
0.03
decrease > 10%
945
0.17
decreased 10 to 20 %
764
0.14
Decrease 3 to 10%
9183
1.70
decreased 5 to 10%
3537
0.65
Little change (-3 to +3% change)
155592
28.74
decreased 3 to 5%
5646
1.04
Increase 3 to 10%
350427
64.74
Little change (-3 to +3% change)
155592
28.74
Increase > 10%
25170
4.65
increase 3 to 5%
132698
24.51
Total
541317
100.00
increase 5 % to 10%
217729
40.22
increase 10% to 20 %
24990
4.62
increase > 20 %
180
0.03
Total
541317
100.00
(a) Class Interval 9
22 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 10: North America
Table 8 : North America-Table of Change
(a) Class Interval 9
Pixel
% of total pixels
(b) Class Interval 5
Pixel
% of total pixels
decreased > 20 %
1234
0.37
decrease > 10%
9258
2.77
decreased 10 to 20 %
8024
2.40
Decrease 3 to 10%
55259
16.54
decreased 5 to 10%
26380
7.90
No change (-3 to +3% change)
160246
47.98
decreased 3 to 5%
28879
8.65
Increase 3 to 10%
91288
27.33
No change (-3 to +3% change)
160246
47.98
Increase > 10%
17960
5.38
increase 3 to 5%
40913
12.25
Total
334011
100.00
increase 5 % to 10%
50375
15.08
increase 10% to 20 %
17440
5.22
increase > 20 %
520
0.16
Total
334011
100.00
23 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 11: Central America
Table 9: Central America-Table of Change
(a) Class Interval 9
Pixel
% of total pixels
decreased > 20 %
1194
0.03
decreased 10 to 20 %
12239
decreased 5 to 10%
(b) Class Interval 9
Pixel
% of total pixels
decrease > 10%
13433
0.35
0.32
Decrease 3 to 10%
232622
6.01
85368
2.21
No change (-3 to +3% change)
1906220
49.26
decreased 3 to 5%
147254
3.81
Increase 3 to 10%
1543426
39.88
No change (-3 to +3% change)
1906220
49.26
Increase > 10%
174181
4.50
increase 3 to 5%
748191
19.33
Total
3869882
100.00
increase 5 % to 10%
795235
20.55
increase 10% to 20 %
169567
4.38
increase > 20 %
4614
0.12
Total
3869882
100.00
24 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 12: South America
Table 10: South America-Table of Change
(a) Class Interval 9
Pixel
% of total pixels
(b) Class Interval 5
Pixel
% of total pixels
decreased > 20 %
12164
0.13
decrease > 10%
129947
1.38
decreased 10 to 20 %
117783
1.25
Decrease 3 to 10%
906224
9.62
decreased 5 to 10%
403137
4.28
No change (-3 to +3% change)
4867946
51.66
decreased 3 to 5%
503087
5.34
Increase 3 to 10%
3284208
34.85
No change (-3 to +3% change)
4867946
51.66
Increase > 10%
234700
2.49
increase 3 to 5%
1739180
18.46
Total
9423025
100.00
increase 5 % to 10%
1545028
16.40
increase 10% to 20 %
228218
2.42
increase > 20 %
6482
0.07
Total
9423025
100.00
25 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 13: North Asia
Table 11: North Asia-Table of Change
(a) Class Interval 9
Pixel
% of total pixels
(b) Class Interval 5
Pixel
% of total pixels
decreased > 20 %
0
0.00
decrease > 10%
3
0.09
decreased 10 to 20 %
3
0.09
Decrease 3 to 10%
48
1.49
decreased 5 to 10%
21
0.65
No change (-3 to +3% change)
555
17.20
decreased 3 to 5%
27
0.84
Increase 3 to 10%
2237
69.34
No change (-3 to +3% change)
555
17.20
Increase > 10%
383
11.87
increase 3 to 5%
596
18.47
Total
3226
100.00
increase 5 % to 10%
1641
50.87
increase 10% to 20 %
383
11.87
increase > 20 %
0
0.00
Total
3226
100.00
26 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 14: West Asia
Table 12: West Asia-Table of Change
(a) Class Interval 9
Pixel
% of total pixels
(b) Class Interval 5
Pixel
% of total pixels
decreased > 20 %
299
0.05
decrease > 10%
5587
0.86
decreased 10 to 20 %
5288
0.82
Decrease 3 to 10%
40425
6.25
decreased 5 to 10%
17692
2.73
No change (-3 to +3% change)
277725
42.92
decreased 3 to 5%
22733
3.51
Increase 3 to 10%
266308
41.16
No change (-3 to +3% change)
277725
42.92
Increase > 10%
56983
8.81
increase 3 to 5%
103556
16.00
Total
647028
100.00
increase 5 % to 10%
162752
25.15
increase 10% to 20 %
55311
8.55
increase > 20 %
1672
0.26
Total
647028
100.00
27 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 15 South East Asia
Table 13: South East Asia-Table of Change
(a) Class Interval 9
Pixel
% of total pixels
(b) Class Interval 5
Pixel
% of total pixels
decreased > 20 %
1101
0.06
decrease > 10%
10620
0.58
decreased 10 to 20 %
9519
0.52
Decrease 3 to 10%
77739
4.27
decreased 5 to 10%
33896
1.86
No change (-3 to +3% change)
586873
32.26
decreased 3 to 5%
43843
2.41
Increase 3 to 10%
954590
52.47
No change (-3 to +3% change)
586873
32.26
Increase > 10%
189600
10.42
increase 3 to 5%
364868
20.05
Total
1819422
100.00
increase 5 % to 10%
589722
32.41
increase 10% to 20 %
177750
9.77
increase > 20 %
11850
0.65
Total
1819422
100.00
28 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 16: Asia Islands
Table 14: Asia Islands- Table of Change
Pixel
% of total pixels
decrease > 10%
27076
0.73
0.63
Decrease 3 to 10%
208463
5.58
86344
2.31
No change (-3 to +3% change)
1532687
41.05
decreased 3 to 5%
122119
3.27
Increase 3 to 10%
1672848
44.80
No change (-3 to +3% change)
1532687
41.05
Increase > 10%
292898
7.84
increase 3 to 5%
708719
18.98
Total
3733972
100.00
increase 5 % to 10%
964129
25.82
increase 10% to 20 %
257224
6.89
increase > 20 %
35674
0.96
Total
3733972
100.00
(a) Class Interval 9
Pixel
% of total pixels
decreased > 20 %
3492
0.09
decreased 10 to 20 %
23584
decreased 5 to 10%
(b) Class Interval 5
29 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 17 Australasia
Table 15: Australasia-Table of Change
(a) Class Interval 9
Pixel
% of total pixels
(b) Class Interval 5
Pixel
% of total pixels
decreased > 20 %
11961
0.38
decrease > 10%
59450
1.91
decreased 10 to 20 %
47489
1.53
Decrease 3 to 10%
306101
9.85
decreased 5 to 10%
144293
4.64
No change (-3 to +3% change)
1409786
45.36
decreased 3 to 5%
161808
5.21
Increase 3 to 10%
1104166
35.52
No change (-3 to +3% change)
1409786
45.36
Increase > 10%
228757
7.36
increase 3 to 5%
489603
15.75
Total
3108260
100.00
increase 5 % to 10%
614563
19.77
increase 10% to 20 %
184808
5.95
increase > 20 %
43949
1.41
Total
11961
0.38
30 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Appendix 2:
Temporal Profiles:
Figure 18: Africa decrease
220
Scaled NDVI
215
210
205
200
195
190
Year
1998
Year
1999
Year
2000
Year
2001
Year
2002
Year
2003
Year
2004
Year
2005
Year
2006
Year
2007
Year
2008
Sample 1 200.83 204.99 206.45 206.40 204.52 203.71 205.42 201.17 200.63 197.65 194.27
Sample 2 212.11 212.30 212.71 209.76 208.54 208.73 211.81 209.17 204.50 199.92 195.27
Sample 3 214.90 216.89 217.47 215.01 212.27 211.41 212.59 211.84 208.27 207.76 203.06
Graph 1: Temporal Profile-Africa decrease
31 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 19: Africa increase
220
215
Scaled NDVI
210
205
200
195
190
185
180
175
Year
1998
Year
1999
Year
2000
Year
2001
Year
2002
Year
2003
Year
2004
Year
2005
Year
2006
Year
2007
Year
2008
Sample 1 191.38 187.69 185.06 189.70 198.87 209.84 209.68 208.90 208.29 211.90 210.45
Sample 2 184.43 184.50 187.09 193.13 199.37 203.77 206.92 209.80 205.81 207.11 203.46
Sample 3 189.93 187.41 187.71 188.09 194.38 199.92 207.39 208.44 208.10 208.05 207.20
Graph 2: Temporal Profile- Africa increase
.
32 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 20: Madagascar Increase
230
Scaled NDVI
225
220
215
210
205
200
Year
1998
Year
1999
Year
2000
Year
2001
Year
2002
Year
2003
Year
2004
Year
2005
Year
2006
Year
2007
Year
2008
Sample 1 215.06 208.47 202.56 203.00 208.85 220.85 223.88 226.94 224.98 224.75 220.22
Sample 2 203.36 204.47 203.78 206.48 209.63 217.15 219.74 223.00 218.66 219.99 215.50
Sample 3 211.84 213.91 210.91 210.75 209.34 214.67 218.02 221.71 221.19 222.51 221.74
Graph 3: Temporal Profile- Madagascar increase
33 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 21: Madagascar decrease
235
Scaled NDVI
230
225
220
215
210
205
Year
1998
Year
1999
Year
2000
Year
2001
Year
2002
Year
2003
Year
2004
Year
2005
Year
2006
Year
2007
Year
2008
Sample 1 227.60
227.24
224.99
220.94
216.99
217.09
218.34
218.08
216.02
213.02
212.35
Sample 2 223.66
223.07
220.48
216.65
212.91
214.05
215.79
216.18
213.94
211.00
208.46
Sample 3 230.10
229.70
225.86
221.75
217.62
220.45
222.80
224.78
223.60
221.62
219.99
Graph 4: Temporal Profile- Madagascar decrease
34 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 22: Brazil (South America)
230
Scaled NDVI
225
220
215
210
205
200
195
190
Year
1998
Year
1999
Year
2000
Year
2001
Year
2002
Year
2003
Year
2004
Year
2005
Year
2006
Year
2007
Year
2008
Sample 1 201.74 203.47 205.70 209.11 211.88 215.22 219.18 221.93 222.03 220.67 221.64
Sample 2 199.25 202.02 204.24 208.57 211.16 216.46 220.50 223.45 222.48 220.48 220.90
Sample 3 191.98 196.19 201.17 206.46 210.50 213.56 218.90 221.85 221.60 219.96 220.46
Graph 5: Temporal Profile- Brazil
35 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 23: Venezuela (Bolívar)- South America
235
Scaled NDVI
230
225
220
215
210
205
200
195
190
Year
1998
Year
1999
Year
2000
Year
2001
Year
2002
Year
2003
Year
2004
Year
2005
Year
2006
Year
2007
Year
2008
Sample 1 197.65 201.00 201.67 205.51 207.25 214.36 220.62 224.31 223.60 222.28 223.93
Sample 2 195.17 197.33 198.50 203.50 205.22 212.47 218.36 224.04 222.26 220.13 219.04
Sample 3 202.39 204.55 208.12 213.98 218.70 222.53 225.79 228.19 226.88 224.63 223.62
Graph 6: Temporal Profile- Venezuela (Bolívar)
36 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 24: Paraguay - South America
240
Scaled NDVI
230
220
210
200
190
180
Year
1998
Year
1999
Year
2000
Year
2001
Year
2002
Year
2003
Year
2004
Year
2005
Year
2006
Year
2007
Year
2008
Sample 1 220.71 213.64 212.59 206.17 200.40 194.97 197.62 202.52 207.34 201.35 201.64
Sample 2 232.46 226.67 223.71 217.44 214.52 215.37 216.74 216.80 214.49 208.51 207.17
Sample 3 236.37 230.90 227.90 221.89 219.93 220.35 221.25 219.92 217.53 211.82 209.38
Graph 7: Temporal Profile- South America (Paraguay)
37 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 25: Germany
215
Scaled NDVI
210
205
200
195
190
185
180
Year
1998
Year
1999
Year
2000
Year
2001
Year
2002
Year
2003
Year
2004
Year
2005
Year
2006
Year
2007
Year
2008
Sample 1 185.40
186.62
190.83
196.43
202.15
203.58
205.48
205.97
203.98
204.88
204.28
Sample 2 187.49
187.40
189.32
194.79
199.58
205.10
205.98
208.73
206.19
207.36
207.64
Sample 3 196.36
192.78
198.16
201.00
208.03
205.08
204.59
201.20
196.64
203.15
207.54
Graph 8: Temporal Profile-Germany
38 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 26: Ireland
225
Scaled NDVI
220
215
210
205
200
195
Year
1998
Year
1999
Year
2000
Year
2001
Year
2002
Year
2003
Year
2004
Year
2005
Year
2006
Year
2007
Year
2008
Sample 1 198.23
198.88
201.21
202.90
203.91
205.54
209.32
212.72
214.19
215.44
214.23
Sample 2 198.14
198.81
200.47
201.88
202.74
204.69
208.96
213.11
214.54
215.64
214.17
Sample 3 204.83
205.15
207.01
208.74
209.46
211.10
214.28
219.58
221.01
221.97
219.62
Graph 9: Temporal Profile-Ireland
39 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 27: Indonesia
230
220
Scaled NDVI
210
200
190
180
170
160
150
Year
1998
Year
1999
Year
2000
Year
2001
Year
2002
Year
2003
Year
2004
Year
2005
Year
2006
Year
2007
Year
2008
Sample 1 160.12 178.97 188.53 205.48 210.35 215.65 219.60 223.40 221.96 221.68 222.09
Sample 2 158.38 174.45 182.76 197.57 202.98 208.83 214.13 218.68 217.75 216.85 215.28
Sample 3 185.87 191.54 196.18 202.97 203.42 208.33 214.73 223.39 218.49 217.10 215.92
Graph 10: Temporal Profile-Indonesia
40 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 28: Myanmar
230
Scaled NDVI
225
220
215
210
205
200
195
Year
1998
Year
1999
Year
2000
Year
2001
Year
2002
Year
2003
Year
2004
Year
2005
Year
2006
Year
2007
Year
2008
Sample 1 225.03 223.74 225.81 221.15 219.00 218.68 223.99 225.39 223.50 205.16 199.52
Sample 2 224.84 223.43 225.11 222.67 220.17 219.14 221.33 221.17 220.09 204.63 199.43
Graph 11: Temporal Profile-Myanmar
41 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 29: Vietnam
230
Scaled NDVI
225
220
215
210
205
200
195
Year
1998
Year
1999
Year
2000
Year
2001
Year
2002
Year
2003
Year
2004
Year
2005
Year
2006
Year
2007
Year
2008
Sample 1 223.37 219.53 216.33 214.85 217.58 223.73 226.09 223.21 220.37 212.95 206.10
Sample 2 219.00 217.27 215.52 215.90 217.31 220.81 223.16 221.39 220.31 215.67 207.58
Sample 3 223.08 219.23 216.64 214.19 215.42 217.36 219.90 217.77 219.33 215.57 209.28
Graph 12: Temporal Profile-Vietnam
42 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 30: North East India
230
Scaled NDVI
220
210
200
190
180
170
Year
1998
Year
1999
Year
2000
Year
2001
Year
2002
Year
2003
Year
2004
Year
2005
Year
2006
Year
2007
Year
2008
Sample 1 181.30 190.04 193.82 201.70 203.52 206.99 207.63 209.33 210.99 214.87 217.37
Sample 2 179.44 190.40 194.60 204.03 205.81 211.01 214.34 217.43 217.07 217.81 217.56
Graph 13: Temporal Profile-North East India
43 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 31: Australia
240
235
Scaled NDVI
230
225
220
215
210
205
200
195
190
Year
1998
Year
1999
Year
2000
Year
2001
Year
2002
Year
2003
Year
2004
Year
2005
Year
2006
Year
2007
Year
2008
Sample 1 231.75
233.04
232.21
227.96
219.98
212.23
209.38
214.69
218.13
207.45
202.34
Sample 2 233.56
233.66
233.51
230.26
225.12
219.37
218.60
222.44
224.87
209.15
201.54
Sample 3 223.88
228.42
229.30
226.96
221.72
214.33
215.38
218.55
221.22
204.81
199.30
Graph 14: Temporal Profile-Australia
44 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
Figure 32: Tasmania
Scaled NDVI
250
240
230
220
210
200
190
180
170
160
150
Year
1998
Year
1999
Year
2000
Year
2001
Year
2002
Year
2003
Year
2004
Year
2005
Year
2006
Year
2007
Year
2008
Sample 1 202.60 200.64 204.83 205.40 207.88 211.80 215.88 223.38 226.70 233.65 234.40
Sample 2 191.90 197.91 209.49 216.96 216.33 212.86 212.57 219.50 219.50 223.92 224.89
Sample 3 161.94 172.64 192.73 212.13 219.14 214.97 212.03 218.21 219.89 224.37 224.22
Graph 15: Temporal Profile-Tasmania
45 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta
End Notes
i
Visousek, Peter M., Harold A. Mooney, Jane Lubchenco, Jerry M. Melillo. 1997. Human Domination of Earth’s
Ecosystems. Science 277 (July).
ii
Radiative forcing is a measure of the influence that a factor has in altering the balance of incoming and
outgoing energy in the Earth-atmosphere system
iii
Intergovernmental Panel on Climatic Change
Derived from US Energy Information Administration, 2009
v
This image has been derived from Science Daily, May 4, 2009
vi
Derived from NASA Goddard Space Flight Center
(http://imagine.gsfc.nasa.gov/docs/science/know_l1/emspectrum.html)
iv
46 | P a g e
Change Analysis of the top 10 percent of Global Photosynthesis as captured by the SPOT VEGETATION (VGT) Sensor for
the time period: 1998 to 2008
Moumita DuttaGupta