Wetland mapping using RS & GIS

K Tharani131863

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Contents

Introduction

Function of wetlands

Remote sensing in wetland mapping

Literature review

Common methodology

Case studies

Summary

References

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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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Functions of wetland

Flood control

Groundwater replenishment

Water purification

Shoreline stability

Climate change mitigation and adaptation

Recreation and tourism

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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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Contd..Limitation is overlap of spectral signatures

Fig 2 : Spectral signature plotted for red and infrared bands6

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Some of the satellites & sensors used Landsat MSS

Landsat TM

Landsat ETM+

IRS P6 LISS III

Panchromatic & multispectral sensors in IKONOS

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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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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Topographic maps

Satellite images

Preparation of base map & other maps

Supervised/Unsupervised classification

Data input for GIS editing

Change detection

map

Common Methodology

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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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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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

Ancillary data

Fig 5 : Distribution of precipitation and evapotranspiration in the area14

Fig 4 :Methodology adopted15

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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Fig 6 : Classified images of 1985(A) 1999(B) 2002(C) 2011(D) 17

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

Fig 7 : Change detection map19

Fig 8 : Spatial temporal changes in Hoor Al Azim wetland20

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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22Fig 9: Study area

Fig 10 : Methodology adopted 23

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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Fig 11: PCA on LANDSAT TM & IRS P6 LISS-III images

Fig 12 : MNF on LANDSAT TM & IRS P6 LISS-III images 25

26Fig 13 : Base map of study area

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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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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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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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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