Information Extraction Using Remote sensing
Technology to Study the Impact on Climate
Kapil Pandey
Asstt. Professor, CSE Dept.
Govt. College of Engineering & Technology, Bikaner,
pandeykapil2003@gmail.com
Abstract - Change Analysis due to climate is an important step
in many environmental studies. A wide variety of methods have
been developed in this concern. During this study, remote sensing
images and GIS techniques were used to extract information for
environmental impact prediction in Doon Valley area,
Uttarakhand, during the years of 1985~2010. It was assumed that
environmental impact could be predicted using time series
satellite imageries. Natural vegetation cover, urban areas were
chosen as main environmental elements to study. Meteorological
data, land use and land cover classes within several years were
prepared using satellite images. Supervised classification and
change detection techniques were adopted in this project.
The detected changes in land use classes were correlated with
temperature and rainfall data of twenty five years. Significant
differences were observed as increasing temperature and
decreasing rainfall. Finally, graphical models were used to
present the changes.
Keywords: Change detection technique, Remote Sensing and
GIS, Classification.
I.
INTRODUCTION
Climate change is not a new phenomenon. Any enhanced
greenhouse effect that warms the earth is natural over time
scale of over hundreds or thousands and millions of years. The
increases in global temperatures projected by scientific data on
climate trends could bring about significant changes to the
world. Global warming may also have serious implications for
forest ecosystems, especially for plantations which may be
affected by changed climatic conditions [8].
It is well established that global climate is changing day by
day as a result natural variability. This includes the change in
the atmospheric composition, hydrological changes in solar
inputs and finally changes in the land surface. Changes are
taking place in our environment. Global changes are occurring
in many ways: atmosphere, landuse and now also in climate
[6][3].
Climate change is occurring, and though there is
uncertainty about the exact magnitude, rate and regional
patterns of its impacts, it will almost certainly bring about sea
level rise and shifts in climatic zones due to increased
temperatures and changes in precipitation patterns. Also,
climate change is likely to increase the frequency and
magnitude of extreme weather events such as droughts, floods,
and storms [2].
Land use and land cover change has become a central
component in current strategies for managing natural resources
and monitoring environmental change. The rapid development
of the concept of vegetation mapping has lead to increased
studies of landuse and land cover change worldwide. Although
the terms ‘Land Use’ and ‘Land Cover’ are often used
interchangeably, their actual meanings are quite distinct. ‘Land
Use’ refers to human activities that take place on the earth’s
surface. (How the land is being used; such as residential
housing or agricultural cropping.) ‘Land Cover’ refers to the
natural or manmade physical properties of the land surface.
During the past millennium, humans have taken an
increasingly large role in the modification of the global
environment. With increasing numbers and developing
technologies, man has emerged as the major, most powerful,
and universal instrument of environmental change in the
biosphere today. Both globally and in India, land cover today is
altered primarily by direct human use. Any conception of
global change must include the pervasive influence of human
action on land surface conditions and processes. The growing
demand for new agricultural land is generally met from nearby
resources such as forest areas/scrub dominated areas [9].
The most common remote sensing applications in
environmental assessment are environmental inventory and
monitoring studies [10][11]. Satellite imagery has provided a
valuable source of information on topography, land use,
vegetation cover and habitat destruction. It has also enabled us
to quantify the rate of global and regional habitat destruction
which would otherwise be an incredibly difficult task [5][12].
Remotely sensed images have played a key role in ecosystem
classifying and mapping, particularly for regional and national
applications. Lunetta [4] used satellite images for detection of
land-cover (LC) change; overall results indicate that a
minimum of 3- to 4-year temporal data acquisition frequency
was required to monitor LC change events in a study area.
Satellite data and GIS were used to develop a land cover map
of the area to detect landscape changes through the time span.
Climate change in present day is affecting our environment
globally, due to increase in temperature of the environment;
variable pattern of rainfall has a adverse effect on our natural
resources such as water bodies and forest.
A study will be presented using time series analysis of
remote sensing data and there correlation with meteorological
data.
II. STUDY AREA
Doon Valley is located in northern Uttarakhand between
longitudes 77035’& 78024’ (E) and latitudes 3005’ & 30035’(N)
and stretches in NW-SE direction following the main
Himalayan trend. The valley is about 70 kms. In length; the
1
width of the valley floor varies from 10 to 25 kms. Northern
and Southern boundaries of the valley are surrounded by high
peaks of Mussoorie and Siwalik ranges. Doon valley covers
85.7% area of Dehradun Tehsil of Dehradun district. Doon
valley has a rich vegetation cover. Although the major portion
of Doon is occupied by the Sal (shorea robusta) but
miscellaneous forests are also found here. The hydrogeological
and meteorological conditions (2150 mm. annual average
rainfalls) of the valley are responsible for the condition for the
different types of forest cover [1].
III. METHODOLOGY AND DATA USED
Figure 1: Location Map of Study Area
Land use of the valley is dominated by forest and cultivated
area. During past independence era, urbanization in the valley
has become most powerful agent of transformation. After
creation of Uttarakhand state in 2002, Dehradun city became
state capital which ushered the era of rapid expansion in Built
up area beyond municipal limits. There has been massive
expansion in construction activities for industries, institutional
infrastructure and residential colonies [9].
Figure 2: Methodology Used
The information required for the study has been procured
from various sources. A summary of the data collection is
given below:
1) Meteorological Data were collected from FRI &
CSWCRTI Stations.
2) Data of study area for classified classes of year1985 &
1993 were collected from a report titled “Carrying Capacity
based Development Planning of Doon Valley,1994, NEERI,
Nagpur.
3) Software used to find out different image processing
operations is ERDAS IMAGINE 9.1 and ARC GIS 9.3
4) Topographic features were extracted from topographic
map sheets (53 F/11, F/14,F/15,F/16: 53 J/3,J/4,J/7,J/8: 53
K/1) at a scale of 1:50,000 from the Survey of India (SOI).
5) Table1 shows Pre-processed multi-temporal data in
the form of satellite images from the LANDSAT-7.
2
TABLE 1: SATELLITE IMAGES FROM THE LANDSAT-7
S.
No.
Satellite
Sensor
Resol
-ution
PathRow
Dates
1.
LAND
SAT-7
TM
30 m
146-39
21/10/1990
2.
LAND
SAT-7
TM
30 m
146-39
23/11/2005
3.
LAND
SAT-7
ETM+
30 m
146-39
20/11/2010
A. Geo Referencing of Satellite Data
Satellite data has been checked for radiometric error and
basic corrections for radiometry for line dropout and striping
have been applied. Individual scenes of the satellite data are
georeferenced with respect to the Survey of India topographic.
2nd order polynomial transformation was used to achieve
higher accuracy in georeferencing. This polynomial requires 6
or more ground control points (GCPs) for geometric
rectification of satellite data. To ensure better geometric
fidelity of the images minimum twenty GCPs, well distributed
spatially, have been used for each satellite image. UTM WGS
84 projection system and UTM zone 43 have been used for
geo-referencing satellite images.
B. Analysis of Remote Sensing Images
From remote sensed data after duly geo-referencing them, a
series of new data are generated. The boundary of Doon Valley
was extracted from the topographic maps at scale 1:250,000
and digitized to prepare a mask. The mask was then overlaid
onto satellite data to extract the Doon Valley. The FCCs
satellite data of Doon Valley for the years 1990, 2005 and 2010
are shown in Figure 3, 4 and 5 respectively. In FCCs, various
shades of pink color indicate the vigorisity of vegetation cover.
Rivers and streams appear light blue in color. Dehradun city
can be also easily located which is present in the heart of Doon
valley, giving bluish tint.
Figure 3: FCC image of 1990
Figure 4: FCC image of 2005
The satellite data were subsequently analyzed digitally on
an image processing system for vegetation and land use
classification, employing maximum likelihood supervised
classification technique and accuracy assessment was done by
comparing with Google images.
Eight vegetation and land use classes were identified which
include Dense forest, Sparse forest, Agriculture, Water, Sandy
area, Urban, Uncultivated, and Scrub. The classified satellite
data for the years 1990, 2005 and 2010 are shown in Figure 6,7
and 8 respectively.
Figure 5: FCC image of 2010
3
Figure 6 : classified image of 1990
Figure 8: Classified image of 2010
Dense forests, which are red in color, are mostly found on
Siwalik range. Agriculture areas (yellow color) are
predominant in middle portion of Doon Valley which has
gentle slope. Scrubs (orange color) are mostly present on
higher reaches in the north and north-east regions of valley.
Figure 7: Classified image of 2005
TABLE 2 : MULTI-DATE DIGITAL ANALYSIS OF SATELLITE DATA FOR LANDUSE CLASSES
1985
Landuse Class
Area
sq.km.
%
1990
1993
2005
2010
Area
Area
Area
Area
sq.km.
%
sq.km.
%
sq.km.
%
sq.km.
%
Sand
54.32
2.41
102.55
4.57
149.86
6.68
159.37
7.10
193.04
8.60
Water
9.55
0.43
17.19
0.77
4.72
0.21
10.10
0.45
8.98
0.40
Urban
138.88
6.19
524.66
23.37
612.29
27.28
1024.74
45.65
1080.41
48.13
Dense Forest
442.94
19.73
313.67
13.97
288.91
12.87
262.91
11.71
253.65
11.30
Uncultivated
357.34
15.92
379.50
16.91
268.46
11.96
108.87
4.85
92.48
4.12
Agriculture
380.46
16.95
237.17
10.57
201.17
8.96
162.33
7.23
136.73
6.09
Scrub
496.28
22.11
328.87
14.65
393.89
17.55
276.58
12.32
280.24
12.48
Sparse Forest
365.01
16.26
341.13
15.20
325.48
14.50
239.85
10.68
199.19
8.87
Total
2244.78
100
2244.78
100
2244.78
100
2244.78
100
2244.78
100
4
IV. RESULTS AND DISCUSSION
It is observed from the Table 2 that Dense forest cover
about 443 sq.km. area in 1985, 314 sq.km. area in 1990, 289
sq.km. area in 1993, 263 sq.km. area in 2005 and 254 sq.km.
area in 2010. Urban cover area ranging from about 139 sq.km.
to 1080 sq.km.(1985-2010). Similarly Agriculture area covers
380.46 sq.km. to 137 sq.km. from 1985 to 2010 time series.
The total area of Doon Valley from satellite data was found to
be 2244.78 Km2.
For the analysis of multi-date satellite data, the changes in
the Landuse classes were computed as reported in Table 3.
TABLE 3: PERCENTAGE VARIATION IN CLASSES WRT. TO 1985
1990
%Area
2.15
0.34
17.19
-5.76
0.99
-6.38
-7.46
-1.06
Landuse Class
Sand
Water
Urban
Dense Forest
Uncultivated
Agriculture
Scrub
Sparse Forest
1993
%Area
4.26
-0.22
21.09
-6.86
-3.96
-7.99
-4.56
-1.76
2005
%Area
4.68
0.02
39.46
-8.02
-11.07
-9.72
-9.79
-5.58
2010
%Area
6.18
-0.03
41.94
-8.43
-11.80
-10.86
-9.62
-7.39
The analysis of above data revealed that sand area and
urban area showing the increasing pattern where as other
classes have decreasing pattern as per time scale. The changes
pattern is more for urban area between 1993 to 2005 due to
time span and creation of Uttarakhand state in 2002.
Agriculture area has decreased from 16.95% in 1985 to
6.09 % in 2010 reducing at the rate of about 0.45% per year.
The comparison of Landuse classes can be shown in Fig.14.
The pattern of bars shows the significant increasing pattern of
urban area where as water bodies are nearly negligible with
respect to other classes.
1985
1990
1993
2005
2010
50
45
40
Figure 15: Rainfall and Temperature Pattern
With regard to mean maximum temperature and mean
minimum temperature, the mean maximum temperature varies
from 27.4 to 29.10C. On an average it fluctuates around
27.50C but if we compare 1985 with that of 2010 there is an
increase of 0.650C. However sudden rise in mean maximum
temperature during 1985, 2005 and 2010 is the clear indication
of deforestation followed by variable trend of mean minimum
temperature in subsequent years.
The Annual rainfall pattern during different decades of the
period showed highest 2599.3 mm during 1985-1994 and
lowest 1454 mm during last years of study. Thus, a decrease of
Annual rainfall intensity during whole period of 25 years is a
significant of climatic change. Rawat (1998) also made similar
observation while compiling climatological data of Doon
Valley.
The landuse changes can be correlated with the climate
variability in Doon Valley area because of the above pattern of
climatic variables (Temperature and Rainfall) have significant
effect on landuse classes. Fig.14 shows the main landuse types
occurring in study location that effect or affected by the
climate. The changes in those classes can be analyzed with the
help of climate variable as:
35
% Area
30
25
20
15
10
5
0
nd
Sa
W
r
ate
n
ba
Ur
D
se
en
t
d
ure
res
ate
ult
Fo
ltiv
ric
cu
Ag
Un
Sc
rub
e
ars
Sp
Figure 14: Comparison of Land-use Classes
Rainfall and Temperature Pattern shows the variation in
climatic condition of Doon Valley. The pattern of mean, max
and mean min. temperature and rainfall of valley of 25 years
(1985-2010) can be shown in Fig.15
Figure 16: Urban and Rainfall
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Supervised classification and change detection techniques
were used to investigate the situation of surface water in the
study area. The results revealed that there was a significant
decrease in the surface water body area during the study
period. Water has always been a scarce commodity in Doon
Valley. As the scarcity of surface water there is very less
evaporation that decrease the rainfall and increase the
temperature. This pattern leads to hot climatic condition at
valley area and decrease the amount of surface water.
Figure 17: Urban and Temperature
Figure 16 and 17 clearly depict the behavior of urban
change due to increasing pattern of temperature and decreasing
pattern of rainfall. It is a linear pattern that clearly indicates that
due to increasing the urbanization the earth surface temperature
has increased, industrialization causes the changes in
atmospheric composition that are the main component to
increase the global warming.
This increasing pattern of temperature with urban lead
decrease vegetation on earth surface that lead to decreasing
rainfall and this can be seen easily in Fig.16.
CONCLUSION
The results of present study revealed that time series
remotely sensed data could be used to predict environmental
impacts.GIS and Remote sensing images and techniques were
used to obtain Landuse changes on time series data and by
correlating these changes with climatic variables the effect of
climatic conditions on these can be analyzed. Temperature data
and rainfall data of 25 years were analyzed. It was observed
that there is an increase of 0.650C in mean maximum
temperature and total rainfall has declined pattern that gives the
evidence for climate change.
REFERENCES
A report on “Carryin Capacity Based Developmental planning of Doon
Valley” by National environmental Engineering Research
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Energy and Resources Institute,UK.
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past five centuries reconstructed from borehole temperatures.
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[1]
Figure 18: Dense forest and Rainfall
Figure 19: Dense forest and temperature
The Valley has a great potential for forests. Several
centuries ago it must have been covered by dense forests
interspersed with grasslands. The transformation of Doon
Valley from sparsely populated dense forests and flowing
streams to its current state, characterized by a residential
population, fast expanding urban-industrial areas and the rapid
disappearance of water has caused great degradation of the
environment of the valley. This deforestation affects the
rainfall pattern and causes the temp. to increase and this can be
easily matched with above pattern of climate variable in Fig.
18 and 19 respectively.
6