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Chapter 4
Land Use Land Cover Dynamics Using
Remote Sensing and GIS Techniques
in Western Doon Valley, Uttarakhand,
India
Ajay Kumar Taloor, Vaibhav Kumar, Vivek Kumar Singh,
Anil Kumar Singh, Ravindra V. Kale, Rahul Sharma, Varun Khajuria,
Girish Raina, Beena Kouser and Naveed Hassan Chowdhary
Abstract Land use land cover (LULC) change analysis emerged as one of the most
significant factors which assist decision makers to ensure sustainable development
and to understand the dynamics of our changing environment. An integrated approach
of remote sensing and GIS has been used to study the land use land cover dynamics
of the Western Doon Valley, Uttarakhand. Landsat satellite imageries of two different
time periods, i.e., Landsat ETM +data of 2001 and 2010 were acquired and used
A. K. Taloor (B
)·R. Sharma ·V. K h a j u r i a ·G. Raina
Department of Remote Sensing and GIS, University of Jammu, Jammu
180006, India
e-mail: ajaytaloor@gmail.com
R. Sharma
e-mail: rahul29453@gmail.com
V. K h a j u r i a
e-mail: varunkhajuria8182@gmail.com
G. Raina
e-mail: girishrainaraina@gmail.com
V. Kumar
Centre for Urban Science and Engineering, Indian Institute of Technology,
Bombay, India
e-mail: vaibhav.iirs@gmail.com
V. K. Singh
Centre for Atmospheric Sciences, Indian Institute of Technology, Delhi, India
e-mail: december.keviv@gmail.com
A. K. Singh ·R. V. Kale ·B. Kouser ·N. H. Chowdhary
Department of Geology, University of Jammu, Jammu 180006, India
e-mail: singhanil854@gmail.com
R. V. Kale
e-mail: ravikale2610@gmail.com
B. Kouser
e-mail: beenajucryosphere@gmail.com
© Springer Nature Singapore Pte Ltd. 2020
S. Sahdev et al. (eds.), Geoecology of Landscape Dynamics,
Advances in Geographical and Environmental Sciences,
https://doi.org/10.1007/978-981- 15-2097- 6_4
37
38 A. K. Taloor et al.
to quantify the land use land cover changes in the study area from 2001 to 2010
over a period of one decade. ERDAS Imagine 10 software has been used to carry
out the supervised classification using a maximum likelihood technique. The images
of the study area were categorized into five different classes, viz., agricultural land
area, settlement area, forest cover area, wasteland area, and water body area. The
result indicates that during the decadal period, the agriculture forest and settlement
area have increased about 6.22% (i.e., 25.19 km2), 0.30% (i.e., 2.66 km2), 2.17%
(20.47 km2), respectively, while area under other land categories such as wasteland
and water bodies have decreased about 6.16% (i.e., 22.67 km2) and 2.52% (i.e.,
0.22 km2), respectively. The Shuttle Radar Topographic Mission (SRTM), digital
elevation model (DEM) data have been used for determination of slope analysis and
it is found that most of the LULC changes have occurred in the area where slope
percentage was in nearly level to gentle categories. The accuracy assessment and
Kappa coefficient of both data sets have also been determined and found that in
the 2001 accuracy assessment was 85.35% and in 2010 accuracy assessment was
89.59%. The technique used in the study shows the importance of digital data-based
change detection techniques for the nature and location of a change in the study area.
Keywords Land use land cover ·Change detection ·Landsat data ·Kappa
coefficient ·Accuracy assessment
4.1 Introduction
Human beings are one of the most destructive agents of nature who continuously
changes and modifying the landscape depends upon its suitability for survival and
wellbeing. Since the history of human being the land surface have witnessed the
many changes in the form of national boundary barrier, great walls, embankments,
urban planning, industrialization, settlement agricultural practice etc. Human
alteration of a landscape from natural vegetation to any other use typically results
in habitat loss, degradation, and fragmentation, all of which can have a devastating
effect on biodiversity. The changes in land use/land cover represent an important part
of the global change affecting the environment. These changes occurred by altering
(increasing or decreasing) the number, structure, or conditions of the elements
in the satellite image over various spatial and temporal scales (Stow et al. 1990;
Sreenivasulu and Bhaskar 2010). Although, quantifying, monitoring, and evaluating
the spatial and temporal dynamics of the land use land cover is quite critical for
better understanding many of the Earth’s land surface processes (Midekisa et al.
2017). Besides this, to understand these changes allow us to quantify and monitor
trends in agriculture (Ramankutty and Foley 2011), freshwater resources (Costa
et al. 2003), forest cover (Hansen et al. 2014), and disease transmission (Patz and
Norris 2004; Midekisa et al. 2014). Moreover, we are aware that land conversion is
N. H. Chowdhary
e-mail: hassannavid4@gmail.com
4 Land Use Land Cover Dynamics Using Remote Sensing … 39
the greatest cause of extinction of terrestrial species, of which particular concerns
are deforestation, expansion of urban centers, industrial expansions, major roads,
and railways network corridors have really created a great impact on the ecology
and survival of many species that previously existed (Tripathy et al. 1996).
Large number of researchers around the world are monitoring these changes of
land use which is a product of interactions between a society’s cultural background,
state, and its physical needs on the one hand and the natural potential of land on
the other, so that better understanding can be made among man, nature, and natural
resources (Balak and Kolarkar 1993; Chaurasia et al. 1996; Agarwal et al. 2002;
Jasrotia et al. 2012; Jasrotia et al. 2013; Taloor et al. 2018). Researchers around the
world have started to monitor land use land cover changes by involving traditional
surveys and inventories from the nineteenth century. With the passage of time and an
enhancement in the technology, remote sensing and GIS are quite advantageous as
it is economically billable and time saving for micro to macro scale LULC changes
with geographic spatial information (William et al. 1994; Yuan et al. 2005;Xiao
et al. 2006; Shalaby and Tateishi 2007; Noor et al. 2008; Prakasam 2010; Friedl et al.
2010; Dong et al. 2012;Girietal.2013; Yan and Roy 2015; Xiong et al. 2017).
The classification of the image is not completed until its accuracy assessment is not
assessed although, the applications of LULC classification is increasing day by day
with the enhancement in remote sensing technology (Congalton and Green 2008;
Martellozzo and Clarke 2011).
In recent years, there has been tremendous increase in the availability of high
performance cloud computing such as the NASA Earth Exchange (NEX) platform
which allows the processing and analysis of NASA earth observation data (Nemani
2011), Amazon Web Service (AWS) also now provides access to the Landsat data
archive, enabling analysis of this dataset on the cloud. In the recent times, Google
Earth Engine (GEE) has enhanced the scientific capability to explore and analyze as
it is a new high performance computing platform which gives access to a vast and
growing amount of earth observation data. In the recent times, Google Earth Engine
(GEE) has enhanced the scientific ability to explore and analyses of the earth surface,
as it is a new high-performance computing platform which gives access to a vast and
growing amount of earth observation data and also the processing power to analyze
these data at planetary as well as micro-scale (Midekisa et al. 2017).
The main objectives of the present study are to examine the land use/land cover
temporal changes during 2001–2010, determination of accuracy assessment, kappa
coefficient, and role of slope in land use land cover change dynamics. The study also
highlights the importance of digital change detection techniques for the nature and
location of change in the Western Doon valley.
4.2 Study Area
The Western Doon valley lies between latitude 30° 141 to 30° 3051 and longitude
77° 3805to 78° 0550 covers the total area of 898.33 km2(Fig. 4.1). The Western
40 A. K. Taloor et al.
Fig. 4.1 Location map of the study area (Source Landsat-7, ETM+)
Doon valley is an intermountain valley that lies between two intermittent ranges of
the Himalayas. It is bounded on all sides by mountains, with one range running from
the west to the east in a semi-circular arc; and one running at the south from Paonta
Sahib to Haridwar. The valley also forms a watershed between the Yamuna and Bindal
River in the systems. Doon or Dun is a local word for valley, particularly an open
valley in between the Siwaliks and higher Himalayan foothills. The average annual
rainfall is 2200 mm out of which 1700 mm is monsoonal. Geologically, Western
Doon valley is an asymmetrically, longitudinal structurally synclinal valley formed
4 Land Use Land Cover Dynamics Using Remote Sensing … 41
of Siwalik rocks of sedimentary origin having the trend of the northwest to southeast
of Upper Tertiary Age (Jasrotia et al. 2018).
4.3 Materials and Methods
The present study was carried out using the various primary and secondary data.
These include Survey of India (SoI) topographic sheet of 1:50000 scale. Landsat
ETM +satellite images of Western Doon Valley were acquired for 2001 and 2010,
respectively, with the spatial resolution of 30 m. These datasets were obtained from
the Global Land Cover Facility (GLCF) an earth science data interface. To find out
the changes, Landsat ETM +data of 2001 and 2010 were geo-referenced and super-
vised classification was used to determine the change detection analysis by using
the maximum likelihood algorithm in ERDAS Imagine 10 software. The supervised
classification depends on the accuracy of the user, techniques, experience, and accu-
racy of his optical capability to define and detect the different signatures among the
various patterns in the satellite images. Spectral information represented by the one
spectral band is used to classify each individual pixel. The Arc GIS 10 software
was used for the integration of spatial data and the preparation of thematic maps.
Adequate field checks have been made before finalizing of thematic maps. Slope
map was prepared from SRTM, DEM data to envisage the role of slope in landscape
change dynamics. The approach used in the present study is shown in Fig. 4.2.
Fig. 4.2 Methodology
adopted in the present study Satellite data
Landsat ETM+ (2001)
Geometric and radiometric correction
Supervised classification using
maximum likelihood classification
(MLC)
Land use/land cover (2001) Land use/land cover (2010)
Change detection analysis
Landsat ETM + (2010)
Field work (Ground Truth Collection)
42 A. K. Taloor et al.
4.4 Results and Discussions
4.4.1 Slope Map
The slope is a measure of the steepness of a line, or a section of a line, connecting two
points and is also one of the indicators of human development in many cases. Level
and gentle slope areas are mostly developed with agricultural activities or human
settlements compared to moderate and steep slopes. The Shuttle Radar Topographic
Mission (SRTM), Digital elevation model (DEM) data were used to prepare the slope
map of the study area. The derived slope map was classified into seven categories
(Taloor et al. 2017;) such as nearly level (0–1%), very gentle (1–3%), gentle (3–5%),
moderate (5–10%), steep (10–15%), moderately steep (15–35%), and very steep
(>35%) (Fig. 4.3). It is found in the study by comparing the slope map with change
detection map that most of the changes were made in the area which has a level to
gentle slope due to human activities which suggest that anthropogenic activities play
a vital role in changing the landscape surface in the Western Doon Valley.
4.4.2 Land Use/Cover Status
The study area is classified into five major classes from Landsat TM satellite images of
2001 and 2010 are shown in Fig. 4.4 and Fig. 4.5, respectively. The different classes
analyzed from the satellite data are shown in Table 4.1. The land use land cover
study depicts that there is a positive growth in agriculture, settlement, forest cover;
negative growth in water bodies and wasteland (Fig. 4.6). The detail description of
the different classes is given in the following subheading.
Settlement area: Settlement included the area under residential, commercial,
industrial, parking and transportation facilities. In the satellite imagery, the class
was identified by blocky appearance, light bluish colored, fine to medium texture
with regular shape and varying size. An increase in the settlement area means the
expansion of mankind which has positive, as well as negative impact on the land it
surges. In the 2001 thematic layer, the area covered by settlement class is 175.07 km2
(19.49%) and increased 2.17% of the total area in 2010 as 194.54 km2(21.66%). In
the study area, it is found that most of the expansion in the settlement is in the fringes
of the earlier built up area and generally in the area with level to the gentle slope.
Agriculture land area: Agriculture appears light pink in the FCC image character-
ized by the shades of red color and textural variability including the areas cultivated
with various cultures of corn, wheat, barley, oat, potatoes, tea plantation etc. In the
land use classes of 2001, the agriculture land covers area covers 131.31 km2(14.62%)
of the total area whereas in 2010 this agricultural land covers 187.19 km2(20.84%)
of total area with an increase of 6.22%. The increase in agriculture due to popula-
tion pressure and availability of a large amount of fallow land in the Western Doon
4 Land Use Land Cover Dynamics Using Remote Sensing … 43
Fig. 4.3 Slope map of the study area (Source SRTM, DEM)
Valley. A certain portion of the forest land is also converted into the agricultural land
by making the reckless cutting of the trees in the area adjoining to the water bodies.
Forest cover area: Forest cover includes the evergreen forests, deciduous forests,
mixed forests, shrubs (hazelnuts, willow trees) open forest in the study area Open
forest is identified by dull red-greenish color in false color composite (FCC), the
dense forest bright red color, deciduous forest shows light gray color in the image. A
complete stretch from southwest to southeast covered by the forest cover and there
major patches of forest are lying in the central parts of the study area. In 2001, LULC
the area covered by the forest cover was 89.56 km2(9.97%) and in 2010 it increases
44 A. K. Taloor et al.
Fig. 4.4 Land use land cover map 2001 (Source Landsat-7, ETM+)
to 92.22 km2. It is also a well-established fact that despite the increase in population
pressure and an increase in the agriculture growth in the Western Doon Valley forest
cover has a positive growth.
Wasteland area: The wasteland appears light white in FCC and fine to medium
texture covers including the uncultivated agricultural lands, fallow land, pasture,
arid land with short vegetations, stony and rocky land with no vegetation cover. The
wasteland in the study area has been decreased over the period of 2001 to 2010 by
6.6% which is a positive trend in human development. In the Western Doon valley,
4 Land Use Land Cover Dynamics Using Remote Sensing … 45
Fig. 4.5 Land use land cover map 2010 (Source Landsat-7, ETM+)
the wasteland area was mixed with agriculture and settlement and it maybe further
reduced with temporal changes in the future course of time. In the study area, the
wasteland has been converted into agriculture land, settlement, and forest covers. In
2001 the area cover by this class was 169.03 (18.82) which decreases in 2010 as
113.67 (12.65%) of the total study area with a negative growth of 6.16%.
Water bodies area: The water bodies appear cyan in color and light dark in deep
water conditions. The Yamuna and the Bindal are the two major rivers fallows in the
Western Doon Valley with a large number of seasonal tributaries that joins them from
46 A. K. Taloor et al.
Table 4.1 Statistical information of land use land cover of the study area
Classes Description Area (2001) Area (2010) Growth rate (%)
Km2%Km2%
Settlement Residential,
commercial,
industrial,
parking,
transportation,
and facilities
175.07 19.49 194.54 21.66 2.17
Agricultural land Areas cultivated
with various
cultures of corn,
wheat, barley,
oat, potatoes, tea
plantation
131.31 14.62 187.19 20.84 6.22
Forest cover Evergreen
forests,
deciduous
forests, mixed
forests, shrubs
(hazelnuts,
willow trees)
89.56 9.97 92.22 10.27 0.30
Wasteland Uncultivated
agricultural lands
pasture and
consisting of arid
land with short
vegetations or no
vegetation cover
169.03 18.82 113.67 12.65 −6.16
Water bodies Rivers, lakes and
other water
bodies
333.37 37.11 310.70 34.59 −2.52
Tot a l 898.33 100 898.33 100
Source Landsat-7, ETM+
all over the study area. The Yamuna flows in the western side of the study area as
northeast to the southwest whereas Bindal flows from northeast to west. In the land
use land cover maps, the area covered by water bodies was 333.37 km2(37.11%) in
2001 and 310.70 km2(34.59%) in 2010 showing a negative growth of 2.52% over
the period of 2001 to 2010.
4.4.3 Accuracy Assessment
Accuracy assessment has become vital with the passage of time as remote sensing
techniques emerged as one of the most powerful tools in the classification of land
4 Land Use Land Cover Dynamics Using Remote Sensing … 47
Fig. 4.6 Growth rate between the periods of 2001–2010 (Source Landsat-7, ETM+)
use land cover. This process defines the degree of coherence of the classified image
with the ground truth of an image classification of samples reference images used
for analysis. The accuracy assessment usually evaluates the effectiveness of classi-
fiers with the help of statistical significance computation of overall accuracies. A
considerable number of references (pixels) are taken from the classified image and
made a field check visit to evaluate the correctness of the classification process. The
kappa coefficient ranges from 0 to 1; values higher than 0.7 is considered acceptable,
while those equal to or lower than 0.4 identify a very low correlation between the
classified image and the ground truth as a reference available images and maps of the
respective time period. This process was supplemented with previous knowledge and
ground checks. In the present study, the overall accuracy of the different classes was
achieved 85.35% and kappa coefficient 0.88 for 2001 dataset whereas for the data
set of 2010 the accuracy was 89.59% and Kappa coefficient was 0.91 (Table 5.2).
Table 4.2 Accuracy assessment and kappa coefficient
Time period (2001 data) (2010 data)
Classes Total accuracy
(%)
Kappa
coefficient
Total accuracy
(%)
Kappa
coefficient
Settlement 85.96 0.93 88.22 0.95
Agricultural
land
89.95 0.94 93.45 0.92
Forest cover 88.12 0.88 93.56 0.93
Wasteland 82.76 0.83 84.76 0.89
Water bodies 79.98 0.81 87.98 0.87
Tot a l 85.35 0.88 89.59 0.91
Source Landsat-7, ETM+
48 A. K. Taloor et al.
4.4.4 Change Detection
Based on the post-classification comparison (PCC) method was applied to change
detection analysis, which is recognized as the most accurate change detection tech-
nique, detects LULC changes by comparing independently produced classifications
of images from different data sets. In PCC each date of rectified imagery is inde-
pendently classified to fit a common land type schema (equal number and type of
land cover classes). The resulting land cover maps are then overlaid and compared
on a pixel-by-pixel basis. The change detection analysis was performed by using a
simple pixel-by-pixel mathematical combination of images for two different time
periods. The change map produced by overlaying the two classified images assisted
in locating the changes occurring in LULC classes (Fig. 4.7).
The formula used for the caluclation of rate of change has been derived from the
formula (Puyravaud et al. 2003)
r=1
t2 −t1 ×In At2
At1
where, r is the rate of land cover change, and At1 and At2 are the forest cover at time
t1 and t2 respectively, In is the logarithm.
Fig. 4.7 Change detection map (Source Landsat-7, ETM+)
4 Land Use Land Cover Dynamics Using Remote Sensing … 49
Table 4.3 Change detection
percentages Classes Change detection (%)
Settlement 11.12
Agricultural land 42.56
Forest cover 2.96
Barren land −32.75
Water bodies −6.80
Source Landsat-7, ETM+
4.5 Conclusion
The study conducted in one of the most important and vital regions of India located
in the Lesser Himalayas of the Uttarakhand State. The study reveals that the major
land use in Western Doon Valley is the built-up area. During one decade, the area
under built-up land has been increased by 2.17% (19.47 km2) due to the construction
of new buildings on fallow land and wasteland and in the area adjoining to the river
beds which was earlier a part of water bodies. The agricultural and vegetation land
have been increased by 6.22% (55.88 km2) tremendously due to population pressure
and high inflation rate during the period of (2001–2010) in the Western Doon Valley
and it is also observed that most of these changes have occurred in the area which is
flat wasteland and having slope very level to gentle. Another significant fact of the
study is that the water bodies have been decreased by 2.52% (2.67 km2) which is
one of the major concerns for ecology and environment of the Western Doon Valley
where more than 2 lakhs migratory birds visit annually. Although, the forest cover
has been also increased by 2.62 km2due to the effective and efficient policies of the
administration, which is a positive sign for the growth of ecology and habitat. The
results of the present study clearly demonstrated the potential of remote sensing and
remote sensing techniques in deciphering the changing pattern of land use/cover in
a study area.
Acknowledgements The authors are grateful to NASA for making the Landsat and SRTM, DEM
datasets freely available under the umbrella of USGS web server. The authors are highly thankful
to the Head, Department of Remote Sensing and GIS, University of Jammu, Jammu for providing
the facility to carry out the research work timely.
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