Munshi Khaledur Rahman (KHALED)Department of Geography

University of Northern Iowa

Remote sensing of the Environment (970:173g)

December 16th, 2009

Change Detection of Forest Fire in Los Angeles, California; Using Landsat5 TM Satellite Imagery

Outline

Introduction

Study area and data used

Methodology

Results

Limitation

Conclusion and future direction

References

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

Study Area

Figure: Los Angeles, California Forest Fire

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.

Landsat5 TM Imagery

Image 2007, Source: USGS Image 2009, Source: USGS

Methodology

Figure: Flow chart

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

NDVI For Both Images (2007+2009)

Figure: 2007 Image after NDVI Figure: 2009 Image after NDVI

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

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

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

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

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

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

Acknowledgement

I would like to thank Sasha for his kind help and

guidelines and USGS for providing the Landsat data

free.

Questions?

Extraction of forest area

Used Modeler

Extract only forest area

Both before and after

forest fire images

References