WETLAND MAPPING USING RS AND GIS

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WETLAND MAPPING USING RS AND GIS

  1. 1. Wetland mapping using RS & GIS K Tharani 131863 1
  2. 2. Contents  Introduction  Function of wetlands  Remote sensing in wetland mapping  Literature review  Common methodology  Case studies  Summary  References 2
  3. 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 3
  4. 4. Functions of wetland  Flood control  Groundwater replenishment  Water purification  Shoreline stability  Climate change mitigation and adaptation  Recreation and tourism 4
  5. 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 5
  6. 6. Contd.. Limitation is overlap of spectral signatures Fig 2 : Spectral signature plotted for red and infrared bands 6
  7. 7. Some of the satellites & sensors used  Landsat MSS  Landsat TM  Landsat ETM+  IRS P6 LISS III  Panchromatic & multispectral sensors in IKONOS 7
  8. 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 8
  9. 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 9
  10. 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. 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. 11
  12. 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 12
  13. 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 13 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. 14. Ancillary data Fig 5 : Distribution of precipitation and evapotranspiration in the area 14
  15. 15. Fig 4 :Methodology adopted 15
  16. 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 16
  17. 17. Fig 6 : Classified images of 1985(A) 1999(B) 2002(C) 2011(D) 17
  18. 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 18 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. 19. Fig 7 : Change detection map 19
  20. 20. Fig 8 : Spatial temporal changes in Hoor Al Azim wetland 20
  21. 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’’ 21
  22. 22. 22Fig 9: Study area
  23. 23. Fig 10 : Methodology adopted 23
  24. 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). 24
  25. 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. 26 Fig 13 : Base map of study area
  27. 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 27
  28. 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. 28
  29. 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 29
  30. 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 30

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