This document discusses using remote sensing and GIS for wetland mapping. It first provides background on wetlands and their functions. It then discusses how remote sensing, beginning with Landsat in 1972, has been used to map and monitor wetlands over time. The document reviews common remote sensing and image processing methods used in wetland mapping and summarizes two case studies on mapping wetland change in Iran and India using multi-temporal satellite data and GIS analysis.

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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