1. CHANGE DETECTION ANALYSIS USING REMOTE SENSING TECHNIQUES Change in Urban area from 1992 to 2001 in COIMBATORE, INDIA. FNRM 5262 FINAL PROJECT PRESENTATION – SHOUMITH JEYAKUMAR

2. PROJECT GOAL:  To use Remote Sensing techniques to study Land Use change.  To perform Classification and Change Detection on Satellite Images.  Specifically, to identify the change in Urban land use class for the city of Coimbatore, India.

3. STUDY AREA  The area of focus – Coimbatore, India.  Also called the Manchester of the south.  Population: 1. 1991 – 1,100,746 2. 2001 – 1,461,139 3. 2011 – 2,136,916 [ Source: http://www.citypopulation.de/India-TamilNadu.html ]  Textile, industrial, commercial, educational, information technology, healthcare and manufacturing hub of Tamil Nadu.

4. STUDY AREA

5. PROCEDURE FOLLOWED  Obtaining the imagery.  Subset.  Classification.  Recoding.  Change Detection.  Summary report.

6. OBTAINING THE IMAGERY  Images used: obtained from the USGS Website.  Land sat 4-5 TM 1992 and Land sat 7 ETM+ images for 2001.  Downloaded using the earth explorer option of USGS.  The dates :14 th Jan 1992 and 14 th Jan 2001.  The path and row number - 144 and 52.  Image processed in ERDAS Imagine.

7. OBTAINING THE IMAGERY

8. SUBSET  Bands 3, 2 and 1 from the Multi Spectral Imagery were set to the colours, red, green and blue respectively.  Subset tool is found at - Raster – Subset and Chip – Create subset image.  Images were placed in 2 2D viewers side to side and the viewers were linked.  Coordinates of the bounding box surrounding the area of interest was given.

9. SUBSET

10. CLASSIFICATION  Method Chosen – Unsupervised Classification.  Raster – Classification – Unsupervised Classification.  Lack of proper knowledge of the area – Training pixels could not be created – Hence Supervised classification was not done.  Number of Classes specified - 20.  Number of iterations – 25.

11. CLASSIFICATION

12. RECODING  Process of reclassification of the classes.  Classes with similar characteristics are combined by the user and given a new code.  Required Classes – Urban & Non – Urban.  Non – Urban classes were given the value 0.  Urban classes were given the value 1.

13. RECODING

14.  Defined as the process to identify the change that occurred in a specific area over a span of time.  Post Classification Comparison method was used.  Reason - Images were already classified before performing Change detection.  An image showing pixels that changed between the images taken at the two time periods is produced as a result. CHANGE DETECTION

16. SUMMARY REPORT Summary report of the matrix – Zone wise summary showing count of changed pixels, Percentage of pixels changed, Area of the changed pixels.

17. RESULT & DISCUSSION

18.  Number of pixels that has changed from one class to another –  The change from the non-urban class to the urban class - 3.71 hectares.  Possible to visually interpret that there is more densification of the urban class pixels within the same area when compared to any expansion of the urban cluster.

19. SOURCES  http://earthexplorer.usgs.gov/ – Used to obtain Landsat imagery. http://earthexplorer.usgs.gov/  www.maps.google.com – Used to obtain the map for the study area. www.maps.google.com  www.wikipedia.com – Used to obtain general information about the city. www.wikipedia.com  http://www.citypopulation.de/India-TamilNadu.html - Obtained population information. http://www.citypopulation.de/India-TamilNadu.html  ERDAS Imagine Help – Used to figure out how to use the tools within the software.

20. THANK YOU!