Munshi Khaledur Rahamn RS Project

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. Outline
 Introduction
 Study area and data used
 Methodology
 Results
 Limitation
 Conclusion and future direction
 References

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. Study Area
Figure: Los Angeles, California Forest Fire

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. Landsat5 TM Imagery
Image 2007, Source: USGS Image 2009, Source: USGS

7. Methodology
Figure: Flow chart

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. NDVI For Both Images (2007+2009)
Figure: 2007 Image after NDVI Figure: 2009 Image after NDVI

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. 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. 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. 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. 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. 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. Acknowledgement
 I would like to thank Sasha for his kind help and
guidelines and USGS for providing the Landsat data
free.

17. Questions?

18. Extraction of forest area
 Used Modeler
 Extract only forest area
 Both before and after
forest fire images

19. References