Waldo Canyon Fire 2012

Discussion of basic remote sensing techniques to detect landcover change caused by wildfires.

CONTENTS :

Introduction to the Waldo Canyon Fire, and Principles of Remote Sensing

True and False Color Composites

Image Processing in Google Earth Engine

Vegetation regeneration

Difficulties of Remote Sensing: Burn Severity and Mountain Shadow Discrepancy

Cartographic Posters

Conclusion

1

Colorado Springs, Colorado

2

Waldo Canyon

Waldo Canyon was a recreation destination with a 6.5 mile trail and elevated views of the city.

3

Waldo Canyon Fire

In June of 2012, a fire broke out and burned over 15,000 acres and nearly 350 homes, closing the area for restoration.

4

Data For the Fire Investigation

Our investigation uses data gathered from Landsat5 & 8 imagery

5

Landsat 5 Satellite

Launched in March of 1984 from Vandenberg Air Force Base. The rocket dropped its payload at roughly 500miles above the earths surface.

6

Fire Investigation usingLandsat 5 Imagery from 2011

This investigations first multispectral image was taken in June of 2011 by Landsat 5.

7

Landsat 8 Satellite

Launched in February 2013 from Vandenberg Air Force Base. Its first images were relayed from orbit in March of 2013.

8

Landsat 8 imagery from 2013

This investigations second multispectral image was taken in September of 2013 by Landsat 8.

9

USGS Eros Data Center

This investigations data came from the USGS Eros data center in Sioux Falls, South Dakota, where Landsat Imagery is processed and archived to be found and downloaded from Earth Explorer, GloVis, and other online viewers.

True Color Composite

Multispectral satellites detect light across nearly the entire electromagnetic spectrum. Scientists have developed ways to interpret wavelengths of light not visible to our eyes using color composites.

True Color Composite

True color composites are made by assigning the red, green and blue spectral bands to the red, green and blue displays on a computer monitor respectively.

False Color Composite

When bands outside of our visibility are assigned to one of the red, green or blue color displays the image is called a false color composite.

False Color Composite

This particular false color composite has the near infrared band assigned to the red display. Vegetation is bright in red hues, wildfire burn scars are not.

Multitemporal False Color Composite

The multi-temporal aspect comes about by assigning images from different dates to the monitor display colors.

Multitemporal False Color Composite

The green colored areas show a change in reflectance of the red wavelength (0.64-0.67micrometers) between 2011 and 2013, charred areas reflect more red light than vegetation.


Image processing in Google Earth Engine

Google Earth Engine is a powerful web based analytics tool. Users code in Java and program google earths engine to filter imagery databases for specific areas, times and cloud cover to begin analysis.

Enhanced Vegetation Index processed in Google Earth Engine.

The enhanced vegetation index (EVI) came about through the evolution of the vegetation index. This index uses vegetation reflectance properties in the red and near-infrared wavelengths to saturate vegetation in an image.

Histogram of 2011 EVI image pixel values

Together the image and associated histogram show a snap shot of vegetation health in the Waldo Canyon area prior to the Waldo Canyon Fire in June of 2012.

The full extent of the fire is seen by a dramatic change in EVI from the previous 2011 image.

2013 EVI Histogram

Histogram of 2013 EVI image pixel values

Normalized burn ratios processed in Google Earth Engine.

The normalized burn ratio uses the near infrared and shortwave infrared wavelengths to emphasize charred areas in an image. The image to the left is from 2011, before the Waldo Canyon fire happened.

This image from 2013 was taken about a year after the Waldo Canyon fire.

The low reflectance in the near infrared and high reflectance in the shortwave infrared wavelengths of charred areas is the opposite reflectance pattern of vegetation, and is drawn to attention by the blue color.

The delta normalized burned ratio compares the normalized burn ratios of before and after the fire and is used to analyze the severity of the burn. This image has had the color palette changed for visual purposes.

Histogram of the delta normalized burn ratio between images from 2011 and 2013.

Vegetation Regeneration

Remote Sensing Challenges: Interpreting shadows in the Waldo Canyon images.

First, observe both images by moving the slider to one side or the other.

The left image is a typicalities classification with vegetated and burned areas in mountain shadows assigned to different categories. The image on the right is the delta normalized burn ratio image with classes of burn severity assigned as different categories.

Comparative slider of land cover classification and burn severity classification.

These two images were compared using cross tabulation inTerrSet, which compares categories designated to a location in one image to the categories designated in the other. The result of the cross tabulation showed high burn severity in the delta normalized burn ratio image coincided with burned areas in mountain shadows.

Cross tabulation of land cover classification and burn severity classification.

This discrepancy comes about because of the way the index normalizes information from an image. Heres more on why.

Reflectance values for shadowed and non-shadowed areas in the pre-fire 2011 image.

The shadowed areas, represented by the blue dotted lines, had lower reflectance values than the non-shadowed areas in both the 2011 and 2013 images; which is an important note when normalizing. Larger numbers, when normalized, become smaller decimals and the opposite is true for smaller numbers. Take for example [3-2]/[3+2] =0.2 while a smaller normalized ratio like, [2-1]/[2+1]= 0.3.

Normalized Burn Ratio of shadowed and non-shadowed areas.

The result of normalizing the reflectance values in both the near infrared and shortwave infrared using the normalized burn ratio ([NIR-SWIR2]/[NIR+SWIR2]) of the shadowed and non-shadowed areas returned values in the shadowed areas to be be higher than the non shadowed areas.

When the pre-fire image was subtracted from the post-fire image to calculate the change (delta, dNBR) in the Normalized Burn Ratios (NBRpre-fire - NBRpost-fire) the difference of the apparent higher valued shadowed area remained higher than the non-shadowed areas and gave a false impression that the burn severity was different. In fact it is unclear what the difference in the burn severity is.

Histogram of 2011 EVI image pixel values

Histogram of 2013 EVI image pixel values

Histogram of the delta normalized burn ratio between images from 2011 and 2013.

Comparative slider of land cover classification and burn severity classification.

Cross tabulation of land cover classification and burn severity classification.

Reflectance values for shadowed and non-shadowed areas in the pre-fire 2011 image.

Normalized Burn Ratio of shadowed and non-shadowed areas.