Application of Remote Sensing and GIS for Modeling and Assessment
of Land Use/Land Cover Changes in Krishna Delta
V.S.S.KIRAN*, G Jai Sankar and M. Jagannadha Rao
1. Lead Faculty, IIC Academy at IIC Technologies Ltd, Visakhapatnam – 530041.
2. Professor, Dept of Geo Engineering, Andhra University, Visakhapatnam- 530017
2. Professor, Dept of Geology, Andhra University, Visakhapatnam- 530017.
*Email: vsskiran_rsgis@rediffmail.com
ABSTRACT
The fluvial activity of a river system has been a dynamic process by which the land
surface area will undergo a continuous transformation both spatially and temporarily.
The study of these changes of any river system will provide valuable scientific inputs to
understand the nature of changes that are occurring by natural process as well as the
impact of anthropogenic activities. Especially Land use and Land cover is an important
component in understanding the interaction of the human activities with the environment
and thus it is necessary to simulate environmental changes from periodically. In addition
another one factor of changes in deltaic regions is climate conditions because present
time the climatic change is one of the global environmental challenges for the significant
impact on deltaic region.
Remote Sensing has been established to be an important tool to study such dynamic
changes of any natural process. Since it gives us reasonable pictures of entire process
in spatial and temporal terms.
In this study the Krishna Delta (16°7'14.07"N to
15°47'24.29"N and 80°43'29.29"E to 81°17'3.04"E) of Andhra Pradesh has been taken
up to study for “Change Detection” using the remotely sensed data and GIS tools.
In this present study the Landsat TM & ETM data for a period of twenty seven years
from 1973 to 2000 (three images) and LISS data for a period of three year from 2009 to
2011(two images) will be used. Besides the Landsat and LISS satellite images data the
elevation data from ASTER & SRTM are also be used to understand changes in slopes.
The established methodology of DIP, Spatial database techniques and remote sensing
and GIS models (Erdas Imagine 2013 and ArcGIS10.2.1) was carried out using hybrid
classification approach NDVI techniques, Masking techniques and classifications
techniques i.e. supervised and unsupervised are used to generate data on land
use/land cover changes.
The data generated in the form of various output maps,
comparative graphs and DEMS have been presented in this paper. In addition the
Statistics generated from satellite remote sensing data helped in understanding the
physical process and changes in land use/land cover in space and time variation. The
most important geological considerations and factors controlling the fluvial system
resulted various changes have been brought out. The paper established that the right
combination of RS & GIS techniques could be perfectly used for the change detection
studies.
Keywords: Remote Sensing, GIS, DIP, Landsat TM & ETM, ASTER/SRTM.
INTRODUCTION:
out from the different periods of remote
We aware of every day the change is
sensing satellite data.
coming in environment. Faster, slower,
ABOUT THE STUDY AREA:
or going in circles, it is the biggest
The study area relates to the deltaic
constant in our world. An attained a
region located in Krishna district and
couple things that are already in Remote
Andhra Pradesh state. Study area is
sensing
covered
environment,
understanding
much
more
that
makes
at
16°7'14.07"North
imagery
and
change
15°47'24.29"North
palatable.
In
Remote
80°43'29.29"East
latitudes
to
to
and
81°17'3.04"East
sensing the change detection is an
longitudes. The mouth of Krishna River
important application to gain knowledge
is
about the geographical changes. It is a
Andhra Pradesh. The delta of this river
method to find out the changes of
is one of the most fertile regions in India
specific features within a certain time
and
period. During this period, the change
Satavahana and Ikshvaku Sun Dynasty
detection application provides spatial
kings.
distribution of features and detailed
METHODOLOGY:
information of its changes, which carried
The methodology is derived into the
Hamsaladeevi,
was
the
Krishna
home
to
district,
ancient
different parts i.e. Data collection, Geo-
referencing,
Data
Erdas Imagine 2013 was used to create
Use/Land
Cover
land use/ land cover maps of all years
Analysis,
LU/LC
and ArcCatalog 10.2.1 was used to
Area calculation, Land use/ Land cover
create GeoDatabases to store datasets
change detection using GIS statistical
and the results of analysis.
models & Image Subtraction method,
Geo Referencing & Projection:
Surface Analysis etc.
All
Data Collection:
corrected using some ground control
For this present study the several
GPS points and the SOI base points in
datasets
were
required
the
objective
and
aim.
preparation,
projection,
Land
classification,
Data
to
Data
reach
themes
collected
software
data
of
is
geometrically
ArcGIS
10.2.1.
Resourcesat and Landsat satellite data
georeferenced
cover classes like Lake, Water Body,
polynomial method and double imagery
Heavy Dense Forest, Medium Dense
rectification process.
Forest, Open Forest, Sand Deposition,
The data used for the analysis were all
Wet land, Agriculture Land, Aquaculture
projected using the Global Coordinate
Land and Settlement etc. The 1973,
System
1990 and 2000 Tm & Etm plus landsat
Projected Coordinate system “Universal
satellite datasets were downloaded from
Transverse Mercator” projection system
the
with 44 North zone and spheroid is
Global
Land
Coverage
Facility
by
"GCS
(GLCF) website and 2009 & 2011 liss-3
WGS 1984.
resourcesat
Data Preparation:
satellite
datasets
were
WGS
Archive website. The remaining data
projected in ArcGIS and it’s managed in
sets i.e. Ground Elevation Data was
ArcCatalog 10.2.1. As a vector shape
obtained from the SRTM & ASTER
file
website and Base maps SOI Reference
boundary and saved as a database file.
Maps was obtained from Department of
The satellite datasets was subsetted
SOI. Combined, these sources provided
from its original size to represent the
the
the
study location from the Krishna Deltaic
comprehensive analysis of this project.
region of the Andhra Pradesh state in
used
for
defines
geocorrected
and
The
using
were
1984"
order
downloaded from the Bhuvan Data
datasets
data
the
2nd
including slope, aspect, land use/land
the
study
and
area
India using the raster "clip‟ tool in "Data
stretch, filtering techniques etc. The
Management Tools‟ within ArcToolbox.
Land use/Land cover classification gave
Figure 1 shows the study area of
a rather broad classification where the
Krishna Delta.
land use land cover was identified by
specific digits. The used classification
methods is supervised classification with
minimum parallelepiped & maximum
likelihood classification were assigned in
Erdas Imagine 2013.
TNDVI= SQRT((NIR-RED)/(NIR+RED)+0.5) -------(1)
The LU/LC maps prepared for all of the
five
2011.
years
1973,1990,2000,2009
&
All Land Use and Land Cover
datasets were divided into 10 different
pre-determined categories by the USGS
(Figure-2).
Area Calculation:
This aspect of analysis examined the
Figure-1: Study Area
area and percentage change for each
year (1973, 1990, 2000, 2009 and 2011)
Land Use Land Cover Classification:
For this present study, the classification
scheme is derived in different parts
because the input satellite data is
having some of atmospheric error and a
few areas are covered by the clouds.
For this purpose we applying the TNDVI
technique (equation -1) and Image clear
interpretation purpose a few of Digital
image processing techniques applied
i.e.
histogram
equalization,
linear
for each land use land cover type. On
the display attribute table we can add
one area column and the count that
attribute field, which makes up the
number of cells in a particular raster
class, was used to compute the area in
square miles for each individual land
use/land cover category. The following
results in percentage of Land Use /
Land Cover for all five year samples
1973, 1990, 2000, 2009 and 2011 is
in
represented in figure 3.
between LULC 1973 and 2011 were
Land
Use
Land
Cover
ArcGIS,
changes
that
occurred
Change
identified from where ever areas is
Detection Using Image Subtraction
changed and also identified unchanged
and GIS Statistical Model:
area and minimum changed area.
In the change detection, multi-band
Specially in Image Subtraction method
remote sensing image is usually used
is depend on the gained from the
due to it provides enough information.
subtraction
The change detection methods of multi-
corresponding pixels of images after
band remote sensing image can be
image registration. The gray values of
divided into image subtraction method
the subtraction image is to show the
and the method of change detection
extent of changes of two images. But
after land use/ land cover classification,
the present generated model is first we
apart from this change detection based
classified the all land use/land cover
on elevation change information of
classes and we are using these output
digital surface model. Some of the
from image subtracted method in the
change detection methods are Image
base of predefined values of each pixel
subtraction method, changing vector
in
analysis method, Principal component
equation
analysis
Images is represented in figure 4.
method,
Pixel
based
of
particular
2).
the
gray
values
class(flowchart-1
The
resultant
of
and
Output
classification method, and cell statistical
Results & Discussion:
method and spectral features variance
The resulted most significant changes
method are usually used in the former
noted were within the agricultural land,
change detection method.
medium dense forest, open forest and
In this present research we processed
aquaculture
the multi band remote sensing data to
difference shows a substantial increase
create an Image segmentation model in
in Wet land dense forest to agriculture
GIS
change
land between 1973 and 1990, especially
subtraction
both agricultural land and forest cover
method in Erdas Imagine and GIS cell
area both increases in aquaculture land
Statistics function in Spatial Analyst tool
at the time period from 1990 to 2000,
and
detection
also
model
using
image
the
area.
The
percentage
2009 and 2011. There was a decrease
area within the study area. Wet and
in Wet land from 1973 (146.661 sq.
forest land decreased significantly by
miles) to 2011 (8,631 sq. miles) and an
25-30 % from 1973 to 2011. Figures 4&
increases in Aquaculture land from 1990
5 illustrate the changes that occurred
(85.764 sq miles) to 2011 (152.698).
over the 38 years of time period and
This change may be somewhat related
also its gives a tabulated illustration of
to the decrease of wet land and forest
the percentage change.
Image-1
Output-1
Image-2
Output-2
Image-3
Classification
Image Subtraction
Method
Validation
Geo Statistics for
Area Calculation
Output-3
Image-4
Output-4
Image-4
Output-5
Geo Database
Flow Chart -1: Present the Change detection Model of Image Subtraction and GIS
Statistics method of the present study
Image Segmentation based Subtraction Method:
Base on the above flow chart model we are procession the all five image in one by
one process i.e. (output 2-output 1), (output 3- output 2), (output 4- output-3)
(output 5- output 4) and finally overlay of all outputs in GIS and generate the final
out put with the help of layer stacking method. The formula which used for Image
Subtraction method:
Dxk ij= xk ij（t2）- xk ij（t1）+ C
-------------------------------------- equation 2
Where i, j as pixel coordinates, k for the classified image, xk ij（t1）for the pixel (i, j) value
of k-class of image, t1, t2 for the time of the first and the second image, C is constant
value 255 because 8 bit satellite image value is 0 to 255.
Figure-2: Land Use/ Land Cover Classification Map of All Years
Figure-3: Land Use/ Land Cover Area Percentage Table & Bar Graph
Figure 4: Final Change Detection Resulting Image