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Use of Landsat Imagery in

Tornado Damage Detection

Matt Taraldsen and

Tom Richards

FR5262

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I. INTRODUCTION AND OBJECTIVE

The National Weather Service (NWS) is tasked with many different requirements. One of these

requirements is that NWS meteorologists be used to conduct ground surveys and assessments

of weather created ground damage. Initially, the idea of using these highly trained individuals

to conduct this type of activity appears to make perfect sense. However, a ground survey

typically necessitates the meteorologists’ absence from their office while the survey is

conducted. This absence can and often does impose an addition burden on the remaining NWS

staff. These surveys can take multiple days to conduct, particularly with large storms.

Consequently, this “outside” additional duty has the potential to seriously affect the ability of

the NWS to conduct its primary mission – that of weather analysis and forecasting.

The use of remote sensing to assess ground damage could offer a viable alternative to actual in

situ ground surveys. The objective of this project, then, is to examine remotely sensed images

of tornadic damage and compare the efficiency of this information to the data derived from

ground based surveys. This will be accomplished by a temporal comparison of pro tempestas

(pre-storm) and secundum tempestas (post-storm) remote sensed images of two large and

extremely powerful tornadoes: the Tuscaloosa Alabama (27th April 2011) and Joplin Missouri

(22nd May, 2011)

On the afternoon of April 27, 2011, the nation’s largest outbreak of tornadoes on record

occurred (NWS). One hundred and ninety nine tornadoes occurred in less than twenty four

hours, resulting in 316 fatalities. A month later on the 22nd of May 2011, another tornado struck

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Joplin, Missouri resulting in 161 fatalities. These were devastating storms, many of which were

EF-4 or EF-5 on the Enhanced Fujita (EF) scale. (For further information see Appendix) which

resulted in horrific damage. Collectively, there were over 550 fatalities and billions of dollars of

damage. In both of these situations, the paths of destruction caused by the storms were

recorded through remotely sensed imagery.

Figure 1 – Tornadic activity in Tuscaloosa and Joplin,

II. CONCEPTS

From an analytical perspective, these storms demonstrated both similarities and unique

differences. For both events, this project will focus on the use of pre-storm and post-storm

Joplin 22 May 2011

Tuscaloosa 27 April 2011

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Landsat ETM7 imagery to determine:

1) The touchdown (starting) and ending points for both tornadoes

2) Maximum swath path dimensions (width and length)

3) Damage indicators

4) Any other meteorological significant observations

The information derived from the analysis of this imagery will then be compared with the data

derived by field surveys. While both storms produced significant damage and fatalities, they

were also different event to warrant separate levels of analysis. These specific aspects will be

considered individually in the sections dealing with those storms.

Landsat ETM 7 was the source of satellite imagery for both storms. Landsat 7 is the most

recent Landsat series satellite in orbit and provides imagery in narrower bands than its

predecessors. The data gathered from Landsat 7 was relatively cloud free, cost free and

provided sufficient spatial resolution to view tornado damage.

Landsat imagery is freely available and was downloaded through the use of the US Geological

Survey’s (USGS) Global Visualization Viewer known as GLOVIS (http://glovis.usgs.gov). Images

were selected based a cloud cover of 10% or less. From a temporal perspective, imagery was

selected based upon the greatest contrast between pre-storm and post-storm. The period of

change was two months or less. The intent was to mitigate temporal changes due to growing

vegetation – or other non-tornado related phenomenon.

Once the Landsat imagery was selected, ERDAS 2011 Version 1.0 imagine image processing

software was used to import the satellite imagery and stack the various layers into one raster

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image. The downloading and importing procedures were similar for both storms. However,

because of different locations and dates, specific imagery was chosen for each storm. In the

case of Tuscaloosa, the dates of imagery were May 04, May 20, March 17, April 18, and April 2.

2011 dates acquired for Joplin: May 5 and June 11. This allowed for both pre-storm and post-

storm image analysis. In both the Tuscaloosa and Joplin storms, significant damage to

vegetation and ground cover occurred. This damage was further clarified through the use of

Normalized Difference Vegetation Index (NDVI) analysis. Finally, change detection analysis was

applied to the imagery of both storms.

III. TUSCALOOSA TORNADO

A. Event Overview

On the 27th of April 2011, the largest series of tornadoes in US history occurred. The Tuscaloosa

tornado was spawned from the longest track thunderstorm of the outbreak, which formed over

Newton County, Mississippi, and persisted for over seven hours. The thunderstorm eventually

dissipated in Macon County, North Carolina, after traveling 380 miles (Figure 2). The tornado

that struck the Tuscaloosa area was on the ground for 80 miles. The tornado then lifted, but the

storm continued to produce multiple violent tornadoes further East in Alabama and Georgia.

In addition to the incredibly long track, the tornado was also unique in that it struck two major

metropolitan areas (Birmingham and Tuscaloosa, AL). As it crossed Alabama, the National

Weather Service surveys indicate that the tornado was approximately 1.50 miles in diameter as

it crossed through Tuscaloosa, with a peak wind speed of 190mph (NWS BMX). While crossing

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through these cities, 65 people were killed with an additional 1500+ injured. For these reasons

this tornado was selected as one for further investigation in the project.

B. Specific Procedures for Tuscaloosa

Following the downloading and importing Landsat 7 ETM+ data for Tuscaloosa, a Shapefile of

the Tuscaloosa tornado was downloaded from the Alabama Emergency Management

Department (Figure 3). ArcGIS 10.0 was utilized to create shapefiles of the tornado paths, and

also to create the Area Of Interest (AOI) images. This shape file was buffered by 10 miles and

then loaded into ERDAS as AOI file. The image was then clipped for this AOI (Figure 4).

The clipped files were then loaded into Mosaic Pro (Figure 5). When the initial Landsat imagery

was downloaded, black lines from the failing Landsat 7 ETM sensor appeared on the imagery.

Mosaic Pro was used to remove those lines.

The March 17 and April 2 imagers were stitched together to form a mosaic before the tornado.

May 04 and 20 were used to form a mosaic for after the tornado (Figure 6).

To gauge the impact on vegetation – NDVI data sets were then generated from the mosaic files

(Figure 7)

Figure 2 – Radar screenshot of the Tuscaloosa Tornado from MS to NC. From National Center for Atmospheric Research (NCAR).

Tuscaloosa

Birmingham

80 Miles

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The NDVI images were then subtracted from one another and highlighted (figure 7) to show

areas of changes.

Several different change thresholds were utilized to find the maximum amount and areas of

change (Figure 8).

Figure 3 – Area of Interest a ten mile buffer (Yellow Outline) of the Tuscaloosa Tornado. Also included is a basemap, tornado path (red stripe) and city outlines of Birmingham and Tuscaloosa, AL (from ESRI)

Figure 4 - Four times of imagery clipped to the AOI (from top left to bottom right: March 17, April 02, May 04, and May 20)

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C. Discussion

During this project, several skills in remote sensing were employed. The first was the use of

spatial analysis to visually assess the area of the tornado. The second was the use of multi-

Figure 5 - Mosiac Pro

Figure 6 - Maximum overlay mosaics: before (left), after (right)

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temporal data to gauge the amount of change before and after the tornado. Finally, the images

were analyzed and compared to real world data.

Once the data was downloaded, a visual observation of that data indicated a distinct change in

the appearance of the imagery (Figure 4). However, a quantitative estimate of change was also

needed. NDVIs were calculated and then subtracted from one another in ERDAS 2011. This gave

an empirical calculation of the amount of change per area.

There were several issues to deal with while completing this project. The first was the random

black lines associated with the failing Landsat 7 ETM + sensor. I purposely took four images

that I stitched together into composite mosaic images. The first of these was the pre-storm

image comprised of March 17 and April 2. Subsequently, the images from May 04 and 20 were

used to form a post-storm mosaic (Figure 4). The first visual analysis of the images, showed a

noticeable change between the amount reflectance in the near IR between March 17 and April

2. This was can most likely be attributed to the start of the growing season in the area, which

increased the plant coverage, and thus the reflectance of the surface.

Using the maximum overlap tool in MosiacPro I stacked the two images from before the storm

(March 17, April 2) and the two images from after the storm (May 4, May 20) together (figure

5). Maximum value was utilized due to the assumption that the black bars of missing data

would have a value of zero. The maximum overlay tool would then replace these zero values

with values from the other image to create a mosaic of the two images with no black lines. The

tool then generated two new images that combine the previous four; one image represented

before the tornado, the other after (figure 6).

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Once the mosaics were complete, it was determined that a better way to contrast the damaged

areas was needed. I decided to convert the two mosaics into NDVI. I followed the methodology

from University of Minnesota course FR 5262 Lab 9 to combine the bands in the mosaics into a

NDVI image. The NDVI created an image with a much more distinct spatial change (Figure 7).

This made sense, as the NDVI is especially sensitive to the change in vegetative cover. While

this tornado not only created prolific structural damage, it also created widespread damage to

trees and other vegetation. Consequently, the stark NDVI contrast was expected.

Figure 7 - NDVI over the AOI, before (left) and after (right)

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Once the NDVIs were calculated, I then followed the methodologies from University of

Minnesota course FR 5262 Lab 12 to determine image differences. This included the use of the

change detection tool to generate highlighted fields of positive and negative change based on

image differencing. For the first attempt, I used a default highlight value of 10%. This means

that areas with more than 10% positive change were colored green, and negative colored red.

For the first set of image differences I conducted an image difference on both the NDVI images

and the mosaic images (Figure8).

Once the image comparison was complete, I noted that the mosaic image (top row of figure 8)

showed a stark visual change. However, the highlight fields of strongest change did not

correspond to the tornado path. The strongest areas of negative change for the mosaic seem

Figure 8 - Image Differences. Top: change (left) and highlight (right) from the false color imagery. Bottom: difference (left) and highlight (right) of the NDVI. Both set to 10%.

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to be centered on the areas that the mosaic tool had overlaid images, which was not

representative of the tornadic damage. Therefore, I decided that I would not pursue the usage

of the mosaic (false color) imagery in further analysis.

For the NDVI imagery (bottom of figure 8), there was a stark change between pre-storm and

post-storm imagery. The bottom left row of figure 8 clearly illustrates the difference in the

NDVI imagery, where the tornado path is represented by the large black line. The highlight field

(Bottom Right) had a strong decrease (red highlights) in the areas that correspond to the

tornado path. What was surprising was the large area of positive change noted in the highlight

field of the NDVI (bottom right of Figure 8). This could have been a result of the month that

passed from before to the after image, which allowed for further growth of vegetation. The first

image difference used the default value of 10%. To see the extent of the changes, running the

NDVI images with highlight changes of both 50% and 100% will be conducted.

Figure 9- NDVI Changes with 25% Highlight

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When the 25% difference and highlight was created (Figure 9), there was only a small cluster of

pixels found in the top right of the image. This area corresponded to the area of most scan line

errors, and is not representative of the tornado path. Therefore, it was determined to use a

highlight of 25% instead of the planned 100% (Figure 9). The 100% and 50% layers were

therefore excluded from analysis.

D. Results

Figure 9 illustrates the changes in the NDVI imagery, with +/- 25% highlighted in green/red. As

with the 10%, there is a large amount of green (positive) changes that corresponds to the

growth of vegetation. However, there is a strong negative response that specifically

corresponds to the tornado track area. Figure 11 illustrates the strong negative changes in the

city limits of Tuscaloosa.

Figure 10 – Twenty-five % Highlight with the city of Tuscaloosa (yellow) and tornado path (purple) plotted

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The negative 25% highlight corresponds highly with the most extensive damage to the western

side of Tuscaloosa. This large area of negative change just on the SW corner of the city

corresponds to the area which sustained multiple fatalities and widespread structural damage

(NWS BMX). What was interesting was the widespread negative pixels outside of the tornado

path. I am not sure of the purpose of these pixels. There were two meteorological possibilities

for these images. As indicated in the survey, this storm produced a large multi-vortex tornado.

Tornadoes which are multivortex can often spawn smaller satellite tornadoes (Fujita 1971) from

the parent tornado, resulting in damage. The pixels of negative highlight also have a cyclonic

curve, which further indicates the damage associated with the storm. Fujita also notes cyclonic

damage curves in association with a phenomenon called a rear flank downdraft, which is the

downdraft of the thunderstorm being wrapped around the backside of the storm. Unlike a

tornado this damage is characterized by vegetation being flattened in a cyclonic pattern away

from the parent thunderstorm (Fujita 1971). It appears that the negative pixels match this

description.

While not as apparent as the large negative highlights in SW Tuscaloosa, small negative changes

can be seen in the tornado path across Tuscaloosa and into Birmingham. Also of note, is that in

the tornado path there is a rather linear clump of positive (green) highlighted areas, but a

distinct absence of any positive highlights. The fact there is a distinct lack of positive growth

makes the tornado path identifiable in the imagery without the damage path.

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Since a path can clearly be seen on the imagery, the next step was to draw a polygon in ArcGIS

that outlines what I interpreted as the path (Figure 11), and compare it with the official

shapefile of the path (Figure 12). The purpose was to gauge how accurately a tornado path can

be delineated with Landsat data compared to a field survey. Depending on the accuracy of the

drawn polygon, it may be feasible to use remotely sensed data to gauge a tornado’s path length

and ending/starting points. This would be in place of tying up resources with sending

meteorologists into the field to gauge the damage in person.

Figure 11- Hand Drawn Tornado Polygon (Yellow)

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A polygon was constructed (Figure 12) on what I thought was the outline of the polygon. The

polygon was created in ArcGIS, using the projection and datum of the mosaic image from after

the storm (WSG84 UTM16). The polygon was then overlaid with the actual observed polygon

(Table 1), and the error was observed.

Accuracy of Tornado Path

Max

Width

(Miles)

Length

(Miles)

Starting X

(Meters)

Starting Y

(Meters)

Ending X

(Meters)

Ending Y

(Meters)

Average

(From

100%)

Hand

Drawn 1.35 80 412545 3654800 520235 3719341

Actual 1.5 80.68 413502 3654379 523887 3721272

Error 90% 99.16% 99.77% 100.01% 99.30% 99.95% 1.8%

Table 1- Quantitative Analysis of Hand Drawn Path

Figure 12 - Tornado Path (Red) with comparison to hand drawn data (Yellow)

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The average error in the hand drawn analysis was found to be 98.2%. On average the error was

less than 1%, with the exception of the maximum width of the path. This raised the lowered the

error to 1.8%. To derive the error, I converted all the polygons into the same datum/projection,

and then calculated geometry in the attribute table to create the separate fields. I was actually

surprised that the errors were relatively small. The damage surveys are very precise, and the

fact that the starting position of the storms (even though they were noticeable wrong on the

overlay) had an average error of less than 2%. This indicates that satellite information could

potentially be useful for mapping long track tornadoes.

D. Tuscaloosa Findings

After completing the analysis of the Tuscaloosa storm, it was apparent that tornado paths are

visible on imagery such as Landsat. The largest error to overcome with the data was the black

scan lines from the Landsat ETM sensor. To overcome this I used the mosaic function in Erdas

Imagine, and overlaid the two images on top of one another. Using the maximum value option,

it in effect eliminated the most significant missing lines. Once the mosaics were created, they

were then converted to NDVI. The NDVI images were then compared, and found that most

changes were less than 50%. This is surprising, and I then conducted a change analysis of 10%

and 25%. As expected, the NDVI showed a negative correlation from before and after the

storm. The area of greatest negative change directly corresponded to the area of most intense

damage found in the ground survey. The NDVI change also revealed several other areas of

change that are likely related to the storm, but not listed in the survey details. To verify the

accuracy of the satellite data I then digitized the areas I thought were impacted by the tornado

from the NDVI image.

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Now to assess the stated objectives of the project:

1) Tornado path ending and starting points (in comparison to ground based surveys)

The analysis has shown a less than one percent error in finding the path ending and

starting points. This is an indication that satellite imagery could in theory be used to find

the end/beginning points of a tornado – especially in long track tornadoes.

2) Maximum path width

Path width was not as accurate as the path ending or starting point. Compared to the

ground survey, which found a maximum width of 1.50 miles, the hand drawn path only

showed 1.35 miles. This is a 10% error. Given the nature of this tornado (multi-vortex) it

is possible some smaller paths were not visualized in the satellite imagery, which would

have been possible from the ground. The maximum width is still relatively close, and

may still be useful.

3) Changes in image properties from before and after the tornado

There are several changes noted in the imagery from before and after the tornado. First

are the qualitative visual changes. I attempted to quantify these changes by subtracting

two NDVI images, but the results were not as conclusive as the qualitative analysis. That

being said, just using the qualitative image analysis to draw the polygon still resulted in

an overall 6% error.

4) Any damage indicators

Unlike ground based surveys, the largest weakness of satellite imagery is that there are

not conclusive ways to evaluate damage. Figure 11 does show a patch of strong

negative changes that directly corresponds to area of most intense damage in

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Tuscaloosa. However, the imagery does not point to which types of structures were

damaged, how extensive the damage was, or damage to ancillary structures or areas. All

of these variables can be directly measured during a ground survey. These are also the

basis for how tornadoes are classified – severely limiting how useful satellite imagery

can be. This is one major weakness of satellite data – with no simple solution unless

other data is used in conjunction with the satellite imagery.

5) Any other meteorological significant observations

Not only was the tornado path and width visible on satellite imagery, but some other

important features were visible as well. The most significant was the appearance of the

rear flank downdraft right around Tuscaloosa. This downdraft likely accompanied the

tornado as it grew from a relatively small and weak tornado into a large violent one. As

noted above, the intensification of the rear flank downdraft can often lead to a stronger

tornado. This appears to be the case – as this signature is very near the location of the

most intense tornadic damage. Other than the rear flank downdraft – there appears to

be no other significant meteorological data viewed in the imagery. This is still a

surprising and unexpected development – and gives extra confidence into the accuracy

of the satellite imagery.

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I. JOPLIN TORNADO

Figure 8 Radar echo of Joplin Tornado(NWS Joplin Assessment)

A. Event Overview

The Joplin Tornado was a catastrophic EF-5 multi-vortex tornado that struck Joplin, Missouri

late in the afternoon of 22 May 2011. The storm touched down just east of the Kansas/Missouri

state line. It then generally tracked eastward for a distance of 7.3 miles reaching a maximum

swath width of 1 mile. The storm resulted in 161 fatalities as well as the loss of nearly 7,000

houses and other buildings totaling over $2.8 billion. When compared to the Tuscaloosa series

of tornadoes the Joplin storm may appear to be a microcosm. However, it should be noted that

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the Joplin tornado is considered the eighth most deadly tornado in US history, and by far the

deadliest tornado since official records began in 1950 (NWS SGF).

B. Specific Procedures for Joplin

From an analytical perspective, there were similarities in the procedures used for the Joplin and

the Tuscaloosa tornadoes. However, because the Joplin storm differed considerably from the

Tuscaloosa storm there were several unique analytical procedures employed with Joplin.

Similar to the Tuscaloosa tornado, Landsat ETM 7 imagery was used to conduct a temporal

analysis of pre-storm and post storm imagery. However, unlike the Tuscaloosa tornado,

shapefiles were not readily available. Fortunately, digital orthophotographs of both pre-storm

(Figure 9) and post-storm Joplin (Figure 10) were available and were used to conduct an initial

visual analysis. (ArcGIS on line –Joplin, Tornado)

Figure 9 2008 (Pre-storm) Digital Ortho Photo of Joplin

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Figure 10 22 May 2011 Post-storm Joplin - damage in red additional areas of tornadic damage in yellow

Visually the damage to Joplin is clearly noticeable. Subsequent analysis, however, shows that

the actual tornado’s path included several smaller areas shown in yellow southeast of the town.

These were areas of vegetation and no inhabitation that were affected as the tornado “hop-

scotched” its way across the Joplin area.

The following images (Figure 11) are the pre-storm and post-storm Landsat images used as a

temporal comparison. The image on the left is the pre-storm image (5 May 2011). The image on

the right is the post-storm image. The post storm image was sensed on the 11th of June 2011,

approximately three weeks after the 22 May storm. The tornado’s 7 mile swath is still clearly

visible.

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C. Change Detection

Figure 11 Landsat 7 ETM temporal comparison of Pre-storm (5 May) and Post-storm (11 Jun) Joplin

The path of the tornado can be seen in the area highlighted in purple

One of the concerns with Landsat imagery are the black scan lines shown in the far right

sections. In the Tuscaloosa series imagery, these lines were “mosaiced” out. In this case,

however, because the lines do not fetter the immediate imagery of Joplin, the decision was

made to retain them.

This next series of images shows change detection at the 10% level before the NDVI analysis

was completed. Interestingly, change detection at higher levels (25% and 50%) did not show

the same level of intensity. Consequently, they were not included.

To facilitate a more thorough analysis, NDVI analysis was applied to these same images. These

images show conclusively that in addition to the damage to structures, there was significant

damage to vegetation and ground cover. (As shown in purple) Notice that in this image the

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additional remnants of the tornado’s path are visible. (yellow arrow).

Figure 12 Temporal NDVI comparison of Pre and Post Storm Joplin

Following the application of NDVI, change detection analytical processes were completed. The

following images show change detection analysis at the 10%, 25%, and 50% levels. The 10%

level shows the most significant change to vegetation and land cover. This image clearly shows

not only the 7.3 mile swath through Joplin but the southeastern part of the tornado’s path as

well. Although the damage path of the tornado is visible in the 10% change images, there are

virtually no indications of this path at the 25% level and even fewer indications at the 50% level.

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Figure 13 NDVI 10% Change

Figure 14 Post NDVI 25% Change

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Figure 15 Post NDVI 50% Change

Interestingly, the University of Missouri, Geospatial Intelligence center posted the following

image. This image was accomplished using GEOCDX change detection software. Unfortunately,

the metadata concerning the levels of intensity were not posted. However, this image does

underscore the Landsat imagery and provide further evidence of the change to the vegetation

and ground cover following the Joplin tornado.

Figure 15 Composite Geo-spatial image of damage path University of Missouri Geospatial Intelligence Center

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D. Path Accuracy

Figure 16 Hand drawn damage path

Joplin Tornado Statistics

Starting Lat.

Starting Lon

Ending Lat.

Ending Lon

Max Width

Path Length

Average Error (From 100%)

Hand Draw 37.06 94.58 37.01 94.41 0.5 12.4

Actual 37.05 94.59 36.98 94.22 0.88 22.1 Error 100.01% 99.99% 100.08% 100.20% 56.82% 56.11% 14.47%

Table 2 - Joplin Tornado Accuracy Information

E. Joplin Findings

Similar to the findings of the Tuscaloosa tornado series, a comparison of the pre-storm and

post-storm Landsat imagery of the Joplin tornado shows the 7.3 mile path of damage to

vegetation and land cover. This was true for both the imagery before and after the NVDI

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analysis procedures. Also similar to the Tuscaloosa tornado series was a level of change of less

than 50%.

There are some unique temporal factors that need to be considered in this case. Three weeks

transpired from the time of the storm (22 May) to the next pass (11 June) of the Landsat

satellite. Late May is the onset of the vegetation growth season at this latitude. While the

tornado’s swath is clearly visible, it is highly likely that the vegetation damaged by the tornado

had already begun to recover. This is further borne out by the imagery showing 10% level of

change. The level of change and contrast between the images may not have been as significant

as it would have been if the Landsat imagery had occurred immediately following the storm.

The error rate between the damage path of the tornado as shown on the digital ortho photo

(Figure 10) and the hand drawn path shown on the Landsat imagery (Figure 16) is 85.53%.

Again, some of this can be attributed to the elapsed time between images. The comparison of

the damage path (as shown in red on the aerial ortho photo image) and the Landsat imagery

also indicate some discrepancies. The red damage area shows the area in the Joplin

metropolitan area. In fact, after it had gone through Joplin, the tornado actually touched down

again several times in areas southeast of the city but in non-populated areas. These remnants

are not conclusively visible on the aerial digital ortho photos but are visible on Figures 12 and

13.

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II. FINDINGS AND CONCLUSION

For Tuscaloosa, the Landsat imagery utilized clearly indicated where the tornado path was

located. To heighten this change, NDVI and change highlighting were utilized. A hand drawn

path was then drawn in ArcGIS and compared to the official National Weather Service path. The

end result was an overall error of 1.8%. While the ending and starting point of the tornado

were fairly accurate, there were some large errors associated with the maximum width of the

tornado. There was also no way to detect damage to the structures, or any other damage

indicators that would make rating tornado strength possible.

Could these images be used to supplement NWS personal to conduct surveys? The satellite

data provided a robust aerial view of the affected area. It was easy to identify the starting and

ending point, and the path of tornado. This tornado in particular had such a long path; the use

of large scale synoptic imagery (like Landsat) may actually be easier than surveying the entire

90 miles with personnel on the ground. While these data may be possible to establish

starting/ending point and path width – there were some errors in the data. The largest of these

– was the inability to give sufficient spatial detail to rank the damage of the tornado on the

Enhanced Fujita Scale. This would make the sole of use of Landsat data for tornado survey

impractical. However, the data could easily be used as a supplement to ground based surveys

and utilized to shorten the duration meteorologist must be in the field.

In the case of the Joplin tornado, both the digital ortho photos and the Landsat imagery clearly

show at least part of the damage path of the tornado. The damage path on the digital ortho

photo, unfortunately, does not show the entire damage path. There were several patches of

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damage southeast of the city of Joplin that were excluded from the damage path. Interestingly,

although the spatial resolution of the Landsat imagery was nowhere near as crisp as it was in

the digital ortho photos, the Landsat imagery actually provided a better depiction of the

damage path southeast of Joplin. This is a clear example that underscores the importance of

having both spectral and spatial resolution for effective damage assessment. This is particularly

true in a temporal comparison such as this in which the Landsat imagery provided a spectral

comparison of the damage to vegetation and ground cover. However, it should also be noted

that the Landsat imagery does not provide a clear depiction of the damage to structures.

Could these images be used to supplant the need for NWS personnel to conduct an actual field

survey? These images do provide a very effective “big picture” synoptic view of the damage

path particularly to vegetation and ground cover. Realistically, however, it is unlikely these

images will provide sufficient damage assessment to buildings and structures sufficient to offset

the need for in person ground surveys.

Overall, these data provided by Landsat can be of great use to tornado classification and

detection. While not nearly as precise as ground based surveys, these data can be used to help

assist ground based surveys – and shorten the time personnel must be away from the forecast

office. The conclusion of this project is that Landsat data cannot exclusively be used for tornadic

classification – but it can be of great use to aid ground based surveys. The use of satellite data

could reduce the amount of time National Weather Service personnel need to be away from

the office conducting surveys, and also gives a synoptic view of the damage path length and

width. These data will not eliminate, but will reduce the strain on National Weather Service

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personnel conducting ground surveys. Therefore it is advisable that Landsat data be utilized in

future surveys of violent, high profile tornadoes.

Appendix

A tornado is a meteorological phenomenon described as “A violently rotating column of air, in

contact with the ground, either pendant from a cumuliform cloud or underneath a cumuliform

cloud, and often (but not always) visible as a funnel cloud.” (What is a tornado? Charles A.

Doswell III, Cooperative Institute for Mesoscale Meteorological Studies*Norman, OK)

Thousands of tornadoes occur every year and on every continent except Antarctica.

Interestingly enough, the area most prone to tornadic activity is the central United States.

In an average year, approximately 1400 tornadoes occur with most in the April through May

timeframe.

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Figure 137 – Preliminary tornado reports from 2011. From the Storm Prediction Center

On occasion, tornadic activities occur that far exceed the predicted norms. Such events

took place in 2011. There were 1706 tornadoes that year (Figure 17). In a normal year, 22

tornadoes are categorized as killer tornadoes so named because they cause fatalities. In 2011,

there were 59 killer tornadoes, more than twice the normal level. However, what is even more

significant however is the number of actual fatalities caused by these tornadoes. Normally, 64

individuals are killed either directly or as a result of tornados. In 2011, there were 551 fatalities

due to tornadic activity, an eight fold increase.

These fatalities are primarily due to two iconic events. First was the series of tornadoes

that took place from 25 – 28 April. Known as the 2011 Super Outbreak, this was a series of

tornadoes that affected the Southern, Midwestern and Northeastern United States, with

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Alabama being particularly hard hit. This series of tornadoes resulted in 359 fatalities. This was

followed a month later by the tornado in Joplin Missouri which lead to the deaths of 161

people.

Tornado Classification:

When a tornado touches down, it leaves a swath of destruction. The amount of destruction

caused is what leads to the damage classification of the tornado. This is done through the

Enhanced Fujita (EF) scale (Table 3), which ranges from EF 0 (wind speeds 65-85 mph) to EF5

(wind speeds >200mph). The swath of a tornado varies considerably depending upon the

strength and intensity of the storm. Generally speaking, the average length of the swath is

approximately 4 miles and the width of the swath is approximately 250 yards. (weather

explained)

Enhanced EF Scale

Scale Wind Speed (mph) Damage Example

0 65 -85 Trees Pushed Over, Windows Broken

1 86-110 Roofs Significantly Damaged and/or Removed

2 111-135 Removal of Exterior Walls of Well Built Houses

3 136-165 Destructions of Most Interior Walls of Well Built House

4 166-199 Well Built Homes Reduced to Rubble, but Remain on Foundation

5 200+ Foundations of Well Built Houses Swept Clean

Table 3 - Enhanced Fujita Scale (From National Weather Service)

Tornados ranked EF-3 or EF-5 are considered to be violent tornadoes, and climatologically are

extremely rare (Figure 18). EF-5 tornadoes are considered to be the rarest, with one occurring

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every 3-5 years on average (Storm Prediction Center). The 2011 season had 6 EF-5 tornadoes

alone.

Data Sources Tuscaloosa

Image Type Location Landsat 7 ETM +Image: May 4 2011 USGS GloVis Landsat 7 ETM +Image: May 20 2011 USGS GloVis Landsat 7 ETM +Image: March 27 2011 USGS GloVis Landsat 7 ETM +Image: April 2 2011 USGS GloVis Landsat 7 ETM +Image: April 18 USGS GloVis Landsat 7 ETM +Image: May 5 2011 USGS GloVis Landsat 7 ETM +Image: June 11 2011 0.USGS GloVis

Figure 148 - Annual frequency of violent tornadoes by state (from National Weather Service).

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Tuscaloosa Tornado Shapefile

http://www.srh.noaa.gov/bmx/?n=event_04272011gis

Alabama BaseMap http://www.arcgis.com/home/item.html?id=3b93337983e9436f8db950e38a8629af

Tuscaloosa City Outline http://arcdata.esri.com/data/tiger2000/tiger_statelayer.cfm?sfips=01

Table 4 – Tuscaloosa Data Utilized (with internet location)

Joplin

Image Types Location

Landsat 7 ETM 5 May 2011 USGS GloVis

Landsat 7 ETM 11 June 2011 USGS GloVis

National Agricultural Imagery Program (NAIP) ArcGIS on line –Joplin, Tornado)

Digital Aerial Ortho Photos: ArcGIS on line –Joplin, Tornado)

Pre-storm Joplin 2008

Post-storm Joplin with damage path

University of Missouri Center for Geo-spatial Intelligence r http://geoint.missouri.edu/

Table 5 – Joplin Data Utilized (with internet location)

References:

"Average Number of EF3 - EF5 Tornadoes." National Center of Climatic Data. National Weather

Service, 2010. Web. 24 Apr. 2012.

<http://www1.ncdc.noaa.gov/pub/data/cmb/images/tornado/clim/totavg-ef3-ef5-torn1991-

2010.gif>.

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"The Enhanced Fujita Tornado Scale." NCDC: Educational Topics: Enhanced Fujita Scale. National

Center of Climatic Data (NCDC), 20 Aug. 2008. Web. 24 Apr. 2012.

<http://www.ncdc.noaa.gov/oa/satellite/satelliteseye/educational/fujita.html>.

"ESRI World Street Maps." ESRI Online Basemaps. ESRI, 22 Feb. 2012. Web. 24 Apr. 2012.

<http://www.arcgis.com/home/item.html?id=3b93337983e9436f8db950e38a8629af>.

Fujita, T. T., and B. E. Smith. "Aerial Survey and Photography of Tornado and Microburst Damage."

Google Books. American Geophysical Union, 1971. Web. 24 Apr. 2012.

<http://books.google.com/books?hl=en>.

"Geography Network - Download Census 2000 TIGER/Line Shapefiles." ESRI GIS and Mapping

Software. ESRI. Web. 24 Apr. 2012.

<http://arcdata.esri.com/data/tiger2000/tiger_statelayer.cfm?sfips=01>.

Hayes, John L. "The Historic Tornadoes of April 2011." National Weather Service Service Assessments.

US Department of Commerce, Dec. 2011. Web. 24 Apr. 2012.

<http://www.nws.noaa.gov/os/assessments/pdfs/historic_tornadoes.pdf>.

"Joplin Tornado Event Summary." National Weather Service Springfield, MO (NWS SGF). National

Weather Service, 17 June 2011. Web. 24 Apr. 2012.

<http://www.crh.noaa.gov/sgf/?n=event_2011may22_summary>.

"National Weather Service Weather Forecast Office." Geographic Information Systems (GIS) Data from

April 27th, 2011 Outbreak. National Weather Service Birmingham, AL (NWS BMX), 11 Dec.

2011. Web. 24 Apr. 2012. <http://www.srh.noaa.gov/bmx/?n=event_04272011gis>.

"National Weather Service Weather Forecast Office." Historic Outbreak of April 27, 2011. National

Weather Service Birmingham (NWS BMX), 9 Apr. 2012. Web. 24 Apr. 2012.

<http://www.srh.noaa.gov/bmx/?n=event_04272011>.

"National Weather Service Weather Forecast Office." Tuscaloosa-Birmingham Tornado. National

Weather Service Birmingham AL (NWS BMX), 22 Mar. 2012. Web. 24 Apr. 2012.

<http://www.srh.noaa.gov/bmx/?n=event_04272011tuscbirm>.

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"Radar Time-lapse of Tornadic Supercell." National Center of Atmospheric Research (NCAR). NCAR.

Web. 24 Apr. 2012. <http://www.daculaweather.com/images/2011_04/storm_track_04-27-

11.jpg>.

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