1. Wetland mapping using RS &
GIS
K Tharani
131863
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2. Contents
 Introduction
 Function of wetlands
 Remote sensing in wetland mapping
 Literature review
 Common methodology
 Case studies
 Summary
 References
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3. Introduction
 A wetland is a land area
which is completely saturated
with water either permanently
or seasonally.
 The factor that distinguishes
wetlands from other land
forms or water bodies is the
characteristic vegetation
that is adapted to its unique
soil conditions. Fig1:Amazon river basin
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4. Functions of wetland
 Flood control
 Groundwater replenishment
 Water purification
 Shoreline stability
 Climate change mitigation and adaptation
 Recreation and tourism
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5. Remote sensing in wetland mapping
 Satellite remote sensing can also provide information on
surrounding land uses and their changes over time.
 Current information on the uplands.
 Remote sensing of wetlands started in 1972 with the launch
of LANDSAT-1.
 But due to low resolution of LANDSAT, LANDSAT TM is
used for mapping of wetland changes.
 Landsat TM bands 4 (near infrared), 5 (mid-infrared), and 3
(red) were most optimal for discriminating between land-
water interface
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6. Contd..
Limitation is overlap of spectral signatures
Fig 2 : Spectral signature plotted for red and infrared bands
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7. Some of the satellites & sensors used
 Landsat MSS
 Landsat TM
 Landsat ETM+
 IRS P6 LISS III
 Panchromatic & multispectral sensors in IKONOS
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8. Literature Review
 Ozesmi et.al (2002) explained the classification techniques for
wetland classification & identification.
 Chaves et.al(2007) described the combined usage of satellite data &
ancillary data for baseline inventory & also about wetland
restoration.
 Li et.al (2007) explained the change of the Yellow river delta
wetlands. The result has significance for building & protecting eco-
environment
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9.  Jyotishman Deka et.al (2011) suggested that usage of remotely
sensed data for wetland mapping provides a cost effective method
& spatio-temporal characteristics of wetlands in terms of change
detection could serve as guiding tool, in conservation and
prioritization of wetlands
 Ghobadi et.al (2012) applied multi temporal remote sensing data &
GIS techniques to monitor changes in wetlands.
 Nidhi Nagabhatla et.al (2012) explained wetland delineation &
mapping in coastal regions. The study reflects an approach for
practical application of pro-supervised learning and pattern
recognition for the multi-spectral earth observation data
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10. 10
Topographic
maps
Satellite images
Preparation of
base map &
other maps
Supervised/Unsu
pervised
classification
Data input for
GIS editing
Change
detection map
Common Methodology

11. Case study 1
 Title : Use of Multi-Temporal Remote Sensing Data and GIS
for Wetland Change Monitoring and Degradation.
 Author : Ghobadi et.al (2012)
 Journal : Institute of Electrical & Electronic Engineers
 Objective: The main objective of this study is to assess the
wetland change and degradation using multi-temporal
satellite data, GIS and ancillary data in Hoor Al Azim
wetland.
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12.  Study area : The study
area is located in the
southwest of Iran
bordering with Iraq and
lies within the latitude
31°28′4″ N and longitude
47° 56′ 57″ E in north of
the Persian Gulf. This
wetland is mainly fed by
Karkheh River
Fig 3: Location of study area
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13. Data
Data and information on wetland and upstream,
was extracted from Multispectral Scanner (MSS) image
in 1985 and Enhance Thematic Mapper (ETM+) images
of the years 1999, 2002, and 2011
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Image Path/row Date of
acquisition
Season time
Landsat 5 MSS 166/38 25/05/1985 Before harvesting
Landsat 7 ETM+ 166/38 18/10/1999 Beginning of
growth
Landsat 7 ETM+ 166/38 03/05/2002 Before harvesting
Landsat 7 ETM+ 166/38 03/10/2011 Beginning of
growth

14. Ancillary data
Fig 5 : Distribution of precipitation and evapotranspiration in the area
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15. Fig 4 :Methodology adopted
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16. Image preprocessing & classification
 Atmospheric & geometric corrections were applied for
images with image obtained on 3rd may 2002 as
reference.
 Supervised classification was performed & six classes
were identified. But in the present study two classes
have been assessed
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17. Fig 6 : Classified images of 1985(A) 1999(B) 2002(C) 2011(D) 17

18. Multi temporal classification :
The accuracy of the data for the years 1985, 1999, 2002, and 2011
was 76.83%, 82.84%, 76.74%, and 88.23% respectively.
The mean overall accuracy of classification was 81.16%
Table 2 : Area & percent of land cover area in study area
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Class Percent % /area (ha)
1985 1999 2002 2011
1 1.56/207 2.13/277 3.04/15849 7.92/41325
2 4.52/5902 6.811/8681 13.6/35545 14.35/70887
3 49.5/64142 52.94/274033 43.16/58545 47.16/249637
4 7.14/9218 9.39/46432 14.25/19192 9.28/48443
5 25.61/33405 24.9/130275 7.34/9555 14.68/7735
6 11.46/14944 3.75/19530 6.65/22197 17.05/34684
Classes: 1 – Water body, 2 – Farming, 3 – Rangeland, 4 – Sand dune, 5 – Smooth
sand surface,6 – Farmland

19. Fig 7 : Change detection map
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20. Fig 8 : Spatial temporal changes in Hoor Al Azim wetland
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21. Case study 2
 Title : Remote Sensing & GIS based integrated study &
analysis for mangrove - wetland restoration in Ennore Creek,
Chennai, South India.
 Author : Chaves et.al (2008)
 Conference : The 12th World Lake Conference
 Objective : To study the wetland degradation & its factors.
 Study area : Ennore creek is located 10 Km north of chennai
city between 130 11’10’’ to 130 15’00’’ north & longitudes 800
17’20’’ to 800 20’30’’
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22. 22Fig 9: Study area

23. Fig 10 : Methodology adopted
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24. Image processing
 Image processing operations are essentially meant to
substitute visual analysis of remotely sensed data with
quantitative analysis.
 The distinction between the features was achieved by
applying principal component analysis (PCA) &
minimum noise fraction analysis (MNF).
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25. Fig 11: PCA on LANDSAT TM & IRS P6 LISS-III images
Fig 12 : MNF on LANDSAT TM & IRS P6 LISS-III images 25

26. 26
Fig 13 : Base map of study area

27.  A 3D model of the study
area was prepared by
draping the Landsat TM
false color composite of
bands 4,3 & 1 over the
Shuttle Radar
Topographic Mission
elevation data
Fig 15 :Area quantification map
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28. Summary
 Multi temporal remote sensing data is complimentary to
wetland information extraction at a particular time &
monitoring change over a given period of time.
 The combined use of satellite data & ancillary data helps
to delineate coastal wetland boundary.
 GIS layers can be applied for wetland restoration.
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29. References
 Birajdara, Samee Azmia, Arun Inamdara, Tutu Sengupta and A.K.
Sinha (2009) ISPRS Archives XXXVIII-8/W3 Workshop
Proceedings: Impact of Climate Change on Agriculture at
Ahmedabad, Dec 17-18 2009,381-385
 Chaves & Lakshumanan (2008) The 12th World lake conference at
Jaipur,29th oct-2nd Nov 2007,685-690
 Deka, Om Prakash Tripathi & Mohammad Latif Khan (2011)
Journal of wetlands ecology 5(4),40-47
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30.  Ghobadi, Pradhan, Kabiri, Pirasteh, Shafri and Sayyad (2012)
IEEE colloquium humanities, science & engineering at Malaysia,
Dec 3-4 2012. Pg:103-108
 Li, Shifeng Huang, Ji-ren Li, Mei Xu (2007) Geoscience & Remote
sensing symposium at Barcelona , July 23-28 2007, 4607-4610
 Nagabhatla, C. M ax Finlayson and Sonali Seneratna Sellamuttu3
(2012) European journal of Remote sensing on wetland ecosystem,
45(3), 215-232
 Ozesmi and Marvin E. Bauer (2002) Journal on Wetlands Ecology
and Management, 10(5), 381-402
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