IRJET - Decadal Sodic Land Change in Bewar Branch Canal Command using Isodata Algorithm

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 07 | July 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 3491
Decadal Sodic Land Change in Bewar Branch Canal Command using
Isodata Algorithm
Renu Kumari1*, Abhishek Kumar1, Narendra Kumar2, Dr. R. K. Upadhyay1
1*,1,2Remote Sensing Applications Centre Uttar Pradesh, Lucknow
--------------------------------------------------------------------***----------------------------------------------------------
Abstract— Sodic land occurs due to high sodicity in soils accumulated naturally or anthropogenically. The excessive amount of salt in
soil could lead to adverse effects, both, environmentally and socially. Bewar Branch Canal Command lies in semi-arid region of Uttar
Pradesh. The study focuses on estimation of sodic land using the ability of Landsat 5 data for 2009 and Landsat-8 data for 2019 and
Isodata algorithm to know decadal change in the branch canal command. The main benefit of Isodata algorithm is that it allows different
numbers of clusters during iterations. The net decadal decrease in sodic land area from in Bewar Branch Canal Command from 2009 to
2019 has been estimated to be 9713.9 ha.
Keywords— Sodic Land, Decadal Change, Landsat 5, Landsat 8, Isodata Algorithm.
1. INTRODUCTION
Soil represents a considerable part of natural resources.
The presence of excessive amount of dissolved salts in soil
and ground water is a characteristic feature in many parts
of the including India. These salts contain sodium, calcium
and magnesium as the main cations, and, carbonate,
bicarbonate, chloride and sulphate as associated anions.
The origin and accumulation of salts in Indo-Gangetic plain
is due to sodium chloride in the soils and ground water
coming from Himalayan catchment (both from geological
and rain water) and partly due to rainfall occurring in
plains [3]. The soils with pH higher than 7 and electrical
conductivity more than zero are characterized as sodic soils.
These soils are widespread in India.
The mapping of sodic land of an area can be done by
digitization, supervised or unsupervised classification [4]. It
can also be mapped using salinity indices based on different
spectral bands [1]. The paper adopted a collative approach
that included the use of Landsat 2 MSS False Color
Composites, top maps surveys, and restricted field controls
to map saline soils and wetlands. The result showed that
the separation of saline and waterlogged is feasible due to
their distinct coloration and peculiar pattern on false colour
composite imageries [2].
This study is focused on delineating sodic land from
Landsat data using Isodata algorithm and thus, estimating
decadal change in sodic land in Bewar Branch Canal
Command.
2. STUDY AREA AND DATASETS
Bewar branch canal command lies in Lower Ganga Canal
command with area of 200087.051 ha. The branch canal
command comes under Upper Ganga basin. The extent of
the study area is shown in Table I. Mainpuri, Etah and
Kannauj districts cover approximate 98% of the area of
branch canal command as shown in Table II.
TABLE I
EXTENT OF BEWAR BRANCH CANAL COMMAND
Command Bewar Branch Canal Command
Latitude Range 27°47’36.055” N - 27°1’43.403” N
Longitude Range 78°39’32.989” E - 79°33’2.845” E
TABLE II
DISTRICTS IN BEWAR BRANCH CANAL COMMAND
Command Districts Percentage
Bewar Branch Canal Command Farrukhabad 0.92
Kanshiram Nagar 1.30
Kannauj 10.53
Etah 32.71
Mainpuri 54.54
Fig 1-: Location Map of Study Area
Landsat series data as shown in Table III has been used
to delineate sodic land due to its high temporal availability
and free cost. Various vector datasets have also been used
in carrying out this study as given in Table IV.

TABLE III
DIFFERENT SATELLITE DATASETS
SNo SATELLITE SENSOR PATH ROW DATE SOURCE
1 Landsat 5 TM 145 41
2009-
02-04
Earth
Explorer,
USGS
2 Landsat 8 OLI 145 41
2019-
04-05
Earth
Explorer,
USGS
TABLE IV
DIFFERENT VECTOR DATASETS
S
No
Name Type Source
1 Bewar Branch Canal
Command Boundary
Polygon RSAC-UP, Lucknow
2 Lower Ganga Canal
Command Boundary
Polygon RSAC-UP, Lucknow
3 Uttar Pradesh Boundary Polygon RSAC-UP, Lucknow
4 India Boundary Polygon RSAC-UP, Lucknow
5 Canals Polyline RSAC-UP, Lucknow
3. METHODOLOGY
The flow chart explaining the methodology of the study is
shown in Figure 2. It can be explained in following steps:
A. Data Preparation
Landsat 5 and Landsat 8 satellite data (as given in Table
III) was ordered, downloaded, preprocessed, stacked and
clipped for study area in ERDAS Imagine environment.
B. Mask Preparation
In ArcGIS environment, settlement mask for Bewar
branch canal command was digitized using visual image
interpretation using Landsat 5 and Landsat 8 data for 2009
and 2019 respectively.
C. Unsupervised classification
The unsupervised classification technique is better than
supervised and digitization to estimate the sodic land
categories (Yadav et.al, 2019). Using ISODATA cluster
algorithm in ERDAS Imagine environment, an unsupervised
classification was performed on stacked Landsat 5 TM data
and Landsat 8 OLI data to extract sodic land for 2009 and
2019 respectively.
D. Decadal Change Analysis
The decadal change in sodic land in Bewar branch canal
command is estimated from sodic land of branch canal
command for 2009 and 2019 respectively.
E. Decadal Change Map preparation
The map showing change in sodic land area in the branch
canal command is prepared in arcGIS environment.
Fig 2-: Flow Chart for Methodology
4. RESULTS & DISCUSSIONS
A. Landsat 5 and Landsat 8 Data:
The False colour composite of Landsat 5 for 2009 and
Landsat 8 for 2019 is shown in Fig 3 and Fig 4 respectively.
These composites have been prepared after preprocessing,
layer stacking and clipping of satellite data.
B. Settlement Masks for 2009 and 2019:
Settlement masks digitized from Landsat 5 for 2009 and
Landsat 8 for 2019 in Bewar branch canal command are
shown in Fig 5 and Fig 6 respectively.
C. Sodic Land Extraction for 2009 and 2019:
Isodata algorithm is one of most useful clustering
algorithm which can be used for classification of different
landuse/landcover classes due to allowance of different
clusters during iterations. In this study, sodic land has been
classified from 100 clusters, each for 2009 and 2019. Sodic
land was overlapping with settlement in few areas.
Settlement mask has been used to improve it. Sodic land
extracted using Isodata algorithm is shown in Fig 7 and Fig
8 for 2009 and 2019 respectively.
Sodic Land area of Bewar branch canal command for
2009 and 2019 is 30890.5 and 21176.6 ha. This means a
net decrease of 9713.9 ha in sodic land has changed in the
decade.
D. Decadal Change Analysis
Sodic Land area of Bewar branch canal command for
2009 and 2019 is 30890.5 ha and 21176.6 ha. This means a
net decrease of 9713.9 ha in sodic land has occurred in the
decade. The map shown in Fig 9 shows increase as well as
decreased sodic land in the branch canal command. The
chart 1 shows amount of increased as well as decreased
sodic land in the study area.

Fig 3-: Landsat 5 False Colour Composite of Bewar
Branch Canal Command for year 2009
Fig 4-: Landsat 5 False Colour Composite of Bewar
Branch Canal Command for year 2019
Fig 5-: Settlement Mask of Bewar Branch Canal
Command for year 2009
Fig 6-: Settlement Mask of Bewar Branch Canal
Command for year 2019

Fig 7-: Sodic Land Map of Bewar Branch Canal Command
for year 2009
Fig 8-: Sodic Land Map of Bewar Branch Canal Command
for year 2019
Fig 9-: Decadal Change Map of sodic land in Bewar
Branch Canal Command
Chart 1-: Decadal Change of sodic land area in hactare in
Bewar Branch Canal Command
5. CONCLUSIONS
Isodata Algorithm is capable of extracting sodic land
from Landsat series dataset. The sodic land area of Bewar
branch canal command for year 2009 and 2019 are 30890.5
ha and 21176.6 ha respectively. There is trend of decrease
in sodic land in Bewar branch canal command. This
indicates intervention of agencies for reclamation of sodic
land in the branch canal command area. There is also
redistribution of sodic land in the branch canal command.

ACKNOWLEDGMENT
The authors are thankful to the Director, RSAC UP, Dr.
Sudhakar Shukla (Head, School of Geoinformatics) and the
staff of Agriculture Resources Division of RSAC UP for help
and guidance in carrying out this study. The authors are
also thankful to Mr. Anubhav Srivastava for his support.
REFERENCES
[1] A. Bannari, A. Guedon, A. El Harti, F. Cherkaoui and A.
El Ghmari (2008). "Characterization of Slightly and
Moderately Saline and Sodic Soils in Irrigated
Agricultural Land using Simulated Data of Advanced
Land Imaging (EO‐1) Sensor." Communications in soil
science and plant analysis 39(19-20): 2795-2811.
[2] R. C. Sharma & G. P. Bhargava (1988) Landsat imagery
for mapping saline soils and wet lands in north-west
India, International Journal of Remote Sensing, 9:1, 39-
44.
[3] Tyagi, N. K. and Minhas, P. S., 1998. Agricultural
salinity management in India. 14. USGS, 2016. Landsat
8 (L8) data users handbook. Department of the
Interior US Geological Survey, LSDS-1574.
[4] Yadav, P. K., Singh, P., Kumar, N., Upadhyay, R. K., and
Jadaun, S. P. S (2019).: “Impact of Canal Restructuring
on Agricultural land use in 23 Down Haidergarh canal
command system, Uttar Pradesh, India”. Int. Arch.
Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W6,
345–350, https://doi.org/10.5194/isprs-archives-
XLII-3-W6-345-2019.
BIBLIOGRAPHIES
Name: Renu Kumari
Designation: M. Tech. (Final Year)
Branch: Remote Sensing & GIS
Department: School of
Geoinformatics
Organization: Remote Sensing
Applications Centre Uttar Pradesh,
Lucknow
Name: Abhishek Kumar
Designation: M. Tech. (Final Year)
Branch: Remote Sensing & GIS
Department: School of
Geoinformatics
Lucknow
Name: Narendra Kumar
Designation: Scientist-‘SE’
Department: Agriculture
Resources Division
Lucknow
Name: Dr. R. K. Upadhyay
Designation: Scientist-‘SE’ & Head
Department: Agriculture
Resources Division
Lucknow

IRJET - Decadal Sodic Land Change in Bewar Branch Canal Command using Isodata Algorithm

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