Journal of Geographic Information System, 2011, 3, 345-350
doi:10.4236/jgis.2011.34032 Published Online October 2011 (http://www.SciRP.org/journal/jgis)
Wetland Assessment and Monitoring Using Image
Processing Techniques: A Case Study of Ranchi, India
Meenu Rani1, Pavan Kumar2*, Manoj Yadav1, R. S. Hooda1
1Haryana Space App lications Centre (Departmen t of Science & Technology), Hisar, India
2Department of Remot e Sensing, Banasthali University, Rajasthan, India
Received May 17, 2011; revised July 2, 2011; accepted July 20, 2011
Abstract
Wetlands, the transitional zones that occupy an intermediate position between dry land and open water, re-
gulate the flow of water and nutrients, thereby facilitating optimum functioning of the physical and biologi-
cal cycles of nature. To conserve and manage wetland resources, it is important to invent and monitor wet-
lands and their adjacent uplands. Wetlands are most productive ecosystems besides being a rich repository of
biodiversity and are known to play a significant role in carbon sequestration. Wetlands are halfway world
between terrestrial and aquatic ecosystem and share properties of both. Wetlands exhibit enormous diversity
according to their genesis, geographical location, water regime, chemistry, dominant plants and soil or sedi-
ment characteristic. Wetland vegetation provides a natural barrier to fast moving water and therefore aids in
flood speed reduction. Remote sensing offers a cost effective means for identifying and monitoring wetlands
over a large area and at different moments of time. The present paper describes the methodology and results
of wetland area for Ranchi city of Jharkhand state for the year 1996-2004.The signatures of wetlands and
associated land features are identified in unsupervised classification approach based on their DN value using
Satellite data. There are drastic change in between 1996 and 2004. The spatial distributions of the NDVI
values were evaluated to determine the cut-off points for the water bodies, and wetted area.
Keywords: NDVI, DN Value, Unsupervised Classification
1. Introduction
Wetland assessment is the gathering and analysis of in-
formation needed for wetland conservation and man-
agement. Assessment criteria and procedure are critical
because the outcome of wetland protection/destruction
battles is increasingly determined by the information
available to decision maker. India, by virtue of its geo-
graphical extent, varied terrain and climatic conditions,
supports a rich diversity of inland and coastal wetland
ecosystems. The wetlands in India are distributed in var-
ious ecological regions. Although the significance of
wetlands has been known for a long time, their role in
maintaining ecological balance is less understood. [1],
LANDSAT imagery to map coastal water turbidity,
whereas [2], estimated the concentration of suspended
sediments through LANDSAT-MSS data. Remote sens-
ing offers a cost effective means for delineating wetlands
over a large area at different points of time and can pro-
vide useful information on wetland characteristics [3-6].
Based on various remote sensing data types, many
methods for delineating water bodies have been de-
scribed [7]. Wetlands, particularly those in floodplains
and in coastal areas, contribute to flood control by stor-
ing and decreasing the velocity of excess water during
heavy rainfall. Wetland vegetation also provides a natu-
ral barrier to fast moving water and therefore aids in
flood speed reduction. In the past, visual interpretations
of wetlands from maps, aerial photography, and hard
copy of satellite images have been used extensively [8,9 ].
Currently, also digital image processing is used [10].The
most distinctive feature is the energy absorption at
Near-IR wavelengths and beyond [11]. Characteristics
like water quality, turbidity and chloroph yll contents can
also be determined using optical remote sensing tech-
niques but are more complicated to assess [12,13]. Wet-
lands have very fertile soils and though drainage of wet-
lands may not be without risks of producing acid soils.
Large areas of wetland have been converted in the past
for purpose of cultivation. The importance of these wet-

M. RANI ET AL. 346
lands has long been recognized and here with also the
need to conserve and protect these wetlands.
1.1. The Framework for Monitoring of Wetlands
For devising a suitable wetland classification system it is
essential that is should be simple, easy to replicate and
incorporate all or most of wetland types. In India no
suitable wetland classification existed for comprehens ive
inventory of wetlands in the country prior to the execu-
tion of Nation-wide Wetland mapping project based on
satellite remote sensing by the Space Applications Center
(SAC), Ahmedabad. The classification system is based
on Ramsar Convention definition of wetlands, which
provides a broad fr amework for de lineating wetland s and
is amenable to remote sensor data for inventory of wet-
lands. It considers all parts of a water mass including its
ecotonal area as wetland. It add ition, Ramsar conv ention ,
considers fish and shrimp ponds, saltpans, reservoirs,
gravel pits etc. as wetlands. In the present wetland in-
ventory, Modified National Wetland Classification sys-
tem is used for wetland delineation and mapping. The
classification system being used (Table 1) should be
tested for its suitability for monitoring based on spectral
classification and for use with imagery of different reso-
lution.
A number of classes represent inland wetlands, like
lakes, Ox-Blow lakes, waterlogged, salt pans etc. and
coastal wetlands, like lagoons, creeks, salt marsh and
aquaculture ponds etc. This would imply that land cover
history be recorded and that land cover actually needs to
be mapped, this would imply a rather high level of spa-
tial details.
Table 1. Wetland covers classes as adopted by NBS.
Wetland category Class Cover class
Lakes
Ox-Blow Lakes
Waterlogged
Reverine Wetlands
Natural
River/Stream
Reservoirs/Barrages
Tanks/Ponds
Waterlogged
Inland Wetlands
Man-made
Salt pans
Lagoons
Creeks
Sand Beach
Salt Marsh
Mangroves
Natural
Coral Reefs
Man-made Salt pans
Coastal Wetlands
Aquaculture Ponds
2. Methodology
2.1. Study Area
The area selected for carrying out the present research
covers the Ranchi city, the capital of Jharkhand state,
India and its environs, bounded by 85˚75' - 85˚87' East to
23˚21' - 23˚87' North (Figure 1). Ranchi is located on
the southern part of the Chota Nagpur plateau which
forms the eastern edge of the Deccan plateau. Ranchi is
referred as the “City of Waterfalls”, due to the presence
of numerous large and small falls around the close vicin-
ity of the city. The Subarnarekha River and its tributaries
constitute the local river system. The study area is char-
acterized by sub-tropical climate. Temperature ranges
from 20˚C to 37˚C during summer and 3˚C to 22˚C dur-
ing winter. The rainfall pattern is monsoonal covering
the period from middle of June to middle of October
with an average annu al rainfall of about 1530 mm.
The major land cover types that dominate the area are
viz. agricultural land, built-up land with and without
vegetation, dense and open forest, dense shrub, planta-
tion and water bodies comprising mainly reservoir,
lakes, river and its tributaries and numerous ponds.There
are three main wetlands in Ranchi i.e. Dhurwa dam,
Getalsud reservoir and Kanke. Rapid population growth
and industrialization have caused considerable change in
the weather pattern and rise in average temperatures.
This has resulted in gradual loss of this Hill Station.
2.2. Description of Satellite Data
Satellite, sensor and acquisition dates for the data used
during analysis are given in Table 2.
2.3. Data Collection and Analysis
The spatial data consisting of Survey of India toposheet
and satellite imagery were used after pre-processing.
Various digital image-processing techniques to obtain
valuable informatio n related to study and also to identify
the classes and feature. Using ERDAS EMAGINE 9.1
software, the data was loaded in the computer. Radio-
metric and stretch correction was applied for removing
radiometric defects and improving the visual impact of
Table 2. Description of satellite data used data.
Particulates
Satellite IRS 1C
Sensor LISS III
Band combination 3,2,1
Swath 141 km.
Spatial Resolution 23.5m
Year 1996 and 2004

M. RANI ET AL. 347
Figure 1. Location map of study area.
the False Color Composite (FCC) made up of the vari-
ance within individual strata and of variance within the
strata. Geometric rectification of the data was carried out
with the help of Survey of India (SOI) toposheet for
assigning geographical coordinates to keep pixel of the
image.
Supervised classification is a method in which the ana-
lyst defines small areas, called training sites, on the im-
age which are representative of each desired land cover
category. In the present study, IRS LISS-III satellite data
has been digitally interpreted and classified in land use
and land cover classification and wetland mapping has
been done using image processing technique, viz, unsu-
pervised classification and NDVI. In this classification
system of land use/land cover different categories have
been taken, because Remote Sensing and Geographical
Information System (GIS) techniques give us broad tool
for better identification. The classes which I have identi-
fied are forest, Agriculture land, Fallow land, Drainage,
Water bodies and Settlement.
The three images of August 1996 (dry period), De-
cember 2004 (wet period) were used from which each
frequency distribution of the DNs were derived. There
were clear distinctions between the water bodies and wet
areas (lower DN) against the dry areas (higher DN).
Thresholds were obtained for each of the periods
examined.
3. Result and Discussion
Land use/land cover classification was done on the basis
of spectral signatures. Five main classes were identified
elaborated in Table 3 and shown in Figure 2.
3.1. Land Use and Cover Type Classification
According to the land use/cover map (Figure 2), agri-
cultural area is predominantly covered by forest and fal-
low land of the study area where human activities are
relatively less intense. Fallow land is commonly associ-
ated with settlement, while crop land is scattered around
the build-up land (Table 3).
3.2. Wetland Classification
In the present study we used remote sensing based ap-
proach to observe changes in flow resistance in wetlands
marshes using a remote sensing approach. The developed
methodology was applied for estimation of the decrease
or increase of the wetland in the Ranchi city. The ob-
tained value of NDVI of the stud y area ranges form –1 to
+1. Wetlands with Fresh water have NDVI value at zero
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M. RANI ET AL. 348
Land Use and Land Cover Map
Figure 2. Land use / Land cover map of Ranchi city.
or nearby. On the basis of NDVI values and visual inter-
pretation three wetland classes h ave been differentiate in
the study area. There was large area covered by Dhurwa
dam, Getalsud reservoir and kanke dam in 1996 i.e. 474,
1579.44, 112.30 fallowed by 366, 1 392.38 and 108 .49 in
2004.
There are drastic change in between 1996 and 2004.
Maximum % changes occur in Dhurwa dam follow by
Getalsud reservoir i.e. 22.78% and 11.84%. Very less
change occurs in Kanke dam (Figures 3-8) and (Table
4).
The spatial distributions of the NDVI values were
evaluated to determine the cut-off points for the water
bodies, and wetted area. Thresholds of the water indices
were in most cases easier to be sliced. Thresholds used to
delimit the two cover classes for each of the acquisition
dates are summarized. (Table 5). A 3*3 filter was then
used to integrate the pixels with a large number of small
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M. RANI ET AL.349
Figure 3. Dhurwa dam (2004).
Figure 4. Dhurwa dam (1996).
Figure 5. Getalsud reservoir (2004).
Figure 6. Getalsud Reservoir (1996).
Figure 7. Kanke dam (2004).
Figure 8. Kanke Dam (1996).

M. RANI ET AL. 350
Table 3. Calculated areas with LULC classes (2004).
Features Area (ha.)
Water bodies 5363.29
Forest 21269.89
Built-up land 10041.49
Agricultural are a 24620.44
Fallow land 3826.76
Table 4. Wetland cla ssified area .
Area(ha)
Category 1996 2004 % Change
Dhurwa Dam 474 366 -22.78
Getalsud Reservoir 1579.44 1392.38 -11.84
Kanke Dam 112.30 108.49 -3.39
Table 5. DN value of two acquisition dates.
Type\Year Aug, 1996 (DN) Dec, 2004 (DN)
Water Wet area Water Wet area
Dhurwa dam 22 66 21 68
Getalsud
reservoir 28 61 25 63
Kanke dam 20 68 23 61
scattered, areas to calculate DN value.
4. Acknowledgements
We are thankful to HARSAC, Hisar, Haryana, India for
providing nece ssary s upp ort.
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