Change Detection Of Forest Fire In Los Angeles


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Munshi Khaledur Rahamn RS Project

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Change Detection Of Forest Fire In Los Angeles

  1. 1. Change Detection of Forest Fire in Los Angeles, California; Using Landsat5 TM Satellite Imagery Munshi Khaledur Rahman (KHALED) Department of Geography University of Northern Iowa Remote sensing of the Environment (970:173g) December 16th, 2009
  2. 2. Outline  Introduction  Study area and data used  Methodology  Results  Limitation  Conclusion and future direction  References
  3. 3. Introduction  Forest fire is a frequent and constant natural disaster in California, USA  The Station Fire (26 August -16 October, 160,577 acres (251 sq mi; 64,983 ha)  209 structures destroyed, including 89 homes  Two firefighters were killed on 30 August  The blaze threatened 12,000 structures in the National Forest and the nearby communities
  4. 4. Study Area Figure: Los Angeles, California Forest Fire
  5. 5. Data Used  Landsat-5 TM image of October, 2007 and Landsat5 TM image of October, 2009  with 0% cloud  Landsat5Thematic Mapper (TM), from USGS Landsat achieve  5 bands excluding band 6 and band 7.
  6. 6. Landsat5 TM Imagery Image 2007, Source: USGS Image 2009, Source: USGS
  7. 7. Methodology Figure: Flow chart
  8. 8. Methodology continue  Clip target area for before and after fire both the 2007 and 2009 Image  Normalized Difference in Vegetation Index (NDVI)  Supervised Classification  Unsupervised Classification  Extraction of Forest  Change Detection  Final output
  9. 9. NDVI For Both Images (2007+2009) Figure: 2007 Image after NDVI Figure: 2009 Image after NDVI
  10. 10. Unsupervised Classification  I performed the unsupervised classification classified as 15 classes  Recoded as 4 classes for the image 2009 (after fire) Figure: Unsupervised Classification of 2009 Image
  11. 11. Supervised Classification  Supervised Classification for 2007 image only  Took helpe of Unsupervised Class attribute of 2009 Image  Recoded as 3 classes Figure: Supervised classification of 2007 image
  12. 12. Perform Change Detection  Change detection is a process that measures how the attributes of a particular area have changed between two or more time periods  Year ‘07 and ’08 extracted forest area as input file Figure: Changedection
  13. 13. Result  The Los Angles forest fire has occurred in September, 2009.  Using NDVI clearly showed the real land cover in the Los Angels forest area and showed the areas affected by fire.  Results of image classification and change detection show very clearly the location affected by forest fire  Multi spectral Landsat data can be used for delineating the forest fires
  14. 14. Limitations  Multispectral imagery availability and real time data availability  If it is possible then I would like to go to the field and observe the real situation and then can predict the accuracy in full confident  It was hard to differentiate between classification errors and areas of forest spread
  15. 15. Conclusions and Future Directions  This project has helped me to learn a lot about image processing, classification of images, extracting target data and information, change detection techniques, and many more  The output of my project shows highly satisfactory result for forest fire change detection but some areas that are identified as growing region in forest fire area which was unexpected  In future, continue my analysis, use accuracy assessment and validation supervised classification for distinguishing urban, forest, different trees species, vegetation, and water bodies in the study area  Burn severity would be really a good thing for analysis
  16. 16. Acknowledgement  I would like to thank Sasha for his kind help and guidelines and USGS for providing the Landsat data free.
  17. 17. Questions?
  18. 18. Extraction of forest area  Used Modeler  Extract only forest area  Both before and after forest fire images
  19. 19. References