Transcript Development of a Statistical Relationship Between GroundBased and Remotely-Sensed Damage in Windstorms Tanya M.

Development of a Statistical
Relationship Between GroundBased and Remotely-Sensed
Damage in Windstorms
Tanya M. Brown
Ph.D. Candidate Wind Science & Engineering Research Center
Daan Liang
J. Arn Womble
February 17, 2010
Outline
 Need for Research
 Objective
 Hypothesis
 Damage Scales
 Damage Assessment Methodology
 “Super Tuesday” Tornado Ground-Based Datasets
 “Super Tuesday” Tornado Remote-Sensing Datasets
 Modifications to Dataset
 Developing Regression Models
 Validating Regression Models
• Hurricane Katrina Remote-Sensing Datasets
• Hurricane Katrina Ground-Based Datasets
 Future Work
 Acknowledgements
Need for Research
 Tropical cyclones and tornadoes are responsible for 45.6%
and 26.5% of catastrophic insurance losses 1988-2007,
respectively.
 New technology
• VIEWS
• Satellite Imagery
– Quickbird 61 cm panchromatic
– WorldView 1 50 cm panchromatic
• Aerial Photographs
– NOAA hurricane surveys ~ 37 cm resolution
– Pictometry 15 cm resolution
 Damage detection is not yet automated
 Ground surveys are costly and time-consuming
Objective
Provide a tool for the prediction of windstorm
damage states at ground-level using remotesensing imagery, for site built one- or two-family
residences (FR12)
Hypothesis
Windstorm damage evaluated from ground
observations is statistically correlated to
windstorm damage evaluated from remote-sensing
imagery
Damage Scales
Table 1: Degree of Damage (DOD) states and wind speed parameters for
FR12 structures from the Enhanced Fujita (EF) Scale
Table 2: Womble’s Remote Sensing (RS) Damage Scale for Residential
Construction
Damage Assessment Methodology:
“Super Tuesday” Tornadoes
 Select areas of both ground-based and remote-
sensing damage data coverage
 Assign Degree of Damage (DOD) from EF Scale to
each structure in ground-based dataset
 Assign Remote-Sensing Damage Scale (RS) from
Womble (2005) to each structure
 Assign percentage of damage category for each RS
Scale rating
•0%
•51-75%
•1-25%
•>75%
•26-50%
Ground-Based Datasets:
“Super Tuesday” Tornadoes
Tornado outbreak February 5, 2008 in Arkansas,
Mississippi, Alabama, Tennessee, and Kentucky
VIEWS deployed to capture high-definition ground-
based photographs
32 hours of video captured; 8 hours processed to
extract still, georeferenced images
Each DI, DOD, and corresponding EF Scale value
were evaluated for structures in photos
• DIs other than FR12 are eliminated for this research
Sample size: 271 FR12 structures
Ground-Based Datasets:
“Super Tuesday” Tornadoes
<EF-0
EF-0
EF-2
EF-3
EF-1Damage
Damage
Damage
Damage
DOD<1
DOD
DOD
DOD6
284
Ground-Based Datasets:
“Super Tuesday” Tornadoes
Percentage of Ground-Based Damage
for Madison County
DOD 5
0%
DOD 7
2%
DOD 8
0%
DOD 6
4%
DOD 4
15%
DOD 3
6%
DOD <1
53%
DOD 2
15%
DOD 1
5%
Remote-Sensing Datasets:
“Super Tuesday” Tornadoes
26 square kilometers of satellite imagery in
Madison County, TN
 February 8th and March 2nd, 2008 from QuickBird
• Panchromatic 61 cm
• Multispectral 2.44 m
 February 10th, 2008 from WorldView 1
• Panchromatic 50 cm
Remote-Sensing Datasets:
“Super Tuesday” Tornadoes
QuickBird WorldView
pan-sharpened
QuickBird 1
imagery
61 cm resolution
50
Remote-Sensing Datasets:
“Super Tuesday” Tornadoes
RS Scale Evaluated from February 8th QuickBird Imagery
0%
0%
1%
0% 1%
0%
3% 3%
1%
3%
RS Scale Evaluated from February 10th WorldView 1
Imagery
1%
0%
1%
0%
0%
1%
A 0%
1%
2% 4%
1%
A 0%
B 1-25%
B 26-50%
9%
39%
B 1-25%
7%
B 26-50%
B 51-75%
B 51-75%
B >75%
B >75%
C 1- 25%
C 1- 25%
C 26-50%
C 26-50%
53%
C 51-75%
C >75%
C >75%
D 1-25%
D 1-25%
D 26-50%
D 51-75%
D >75%
40%
C 51-75%
29%
D 26-50%
D 51-75%
D >75%
Remote-Sensing Datasets:
“Super Tuesday” Tornadoes
Modification to Dataset: “Rerated” DODs
Lots of structures rated DOD 4, with very slight damage seen in
remote-sensing survey
DOD 2 –Loss of roof covering material <20%
DOD 4 –Loss of roof covering material >20% AND uplift of roof
deck
What about houses with more than 20% roof covering loss, but
NO uplift of roof decking? What do we rate these?
DOD 3 pertains to glass breakage, so its inappropriate
Settled on DOD 2.5, to indicate worse than DOD 2 damage, but
definitely less than DOD 4 damage—all homes originally rated
DOD 4 were investigated to see if they should actually be DOD
4, or the new category of DOD 2.5, some ended up being DOD 3
because of glass breakage
Suggests that the DOD categories in the EF Scale for FR12
structures may need to be adjusted in future revisions
Modifications to Dataset
Percentage of Ground-Based Damage
for Madison County
DOD 5
0%
DOD 4
2%
DOD 7
2%
DOD 8
0%
DOD 6
4%
DOD 3
12%
DOD 2.5
7%
DOD <1
53%
DOD 2
15%
DOD 1
5%
Developing Regression Models
Table 3: Numerical representation scheme for the alphabetic and
alphanumeric RS Scales used in the regression.
Developing Regression Models
4 satellite datasets
•
•
•
•
QuickBird
WorldView 1
combined
averaged
2 RS Scales
•
•
alphabetic
alphanumeric
3 regression types
•
•
•
linear
exponential
quadratic
Original & “Rerated” DODs
4 satellite datasets
•
•
•
•
QuickBird
WorldView 1
combined
averaged
1 RS Scale
•
alphanumeric
3 regression types
•
•
•
linear
exponential
quadratic
“Rerated DOD” only
4 statistical transformations
•
•
•
•
logarithmic
exponential
square root
squared
3 variables transformed
•
•
•
x-variable (RS) alone
y-variable (DOD) alone
both x- and y-variables
=192 models!!!
Developing Regression Models
re-rated DOD vs. alphanumeric RS Scale
10
8
8
6
6
4
QuickBird
alphanumeric
comparison
2
re-rated DOD
re-rated DOD
re-rated DOD vs. alphanumeric RS Scale
10
4
0
-1
0
0
1
2
3
4
5
-1
-2
0
2
3
4
5
-2
RS Scale rating
re-rated DOD vs. alphanumeric RS Scale
DOD vs. alphanumeric RS Scale
10
10
8
8
6
6
4
combined
satellites
alphanumeric
comparison
2
4
averaged
satellites
alphanumeric
comparison
2
0
-1
1
RS Scale rating
DOD ratings
re-rated DOD
WorldView 1
alphanumeric
comparison
2
0
0
1
2
-2
3
4
5
-1
0
1
2
-2
RS Scale rating
RS Scale ratings
3
4
5
Developing Regression Models
Alphanumeric RS Scale always outperforms alphabetic
RS Scale
“Rerated” DOD dataset always outperforms original
WorldView 1 imagery generally outperforms
QuickBird imagery
Average satellite dataset generally outperforms
QuickBird, WorldView 1 & combined dataset
Quadratic regression models generally outperform
linear & exponential models
Statistical transformations generally increased model
performance slightly
DOD  2.256* RS 1.143* RS  0.3565
Developing Regression Models
The five best models were selected for validation
•
•
•
All used the alphanumeric RS Scale
All used the averaged satellite damage states
All used the “rerated” DOD
Eq. 4-5—Quadratic model with square root transformation of ‘RS’
(R2=0.6863)
•
DOD=2.256*RS-1.143*√RS+0.3565
Eq. 4-6—Quadratic model with natural logarithm transformation o f ‘RS+1’
(R2=0.6859)
•
DOD=2.737*(ln(RS+1))2-0.2846*ln(RS+1)+0.3465
Eq. 4-7—Quadratic model (R2=0.6795)
•
DOD=0.1656*RS2+1.070*RS+0.2654
Eq. 4-8—Exponential model, natural logarithm transformation of ‘RS+1’
(R2=0.6767)
•
DOD=0.4224*exp(1.772*ln(RS+1))
Eq. 4-9—Quadratic model, squared transformation of ‘RS’ (R2=0.6740)
•
DOD=-0.03206*RS4+0.8830*RS2+0.4650
Validating Regression Models
Test the performance of the models against a Hurricane
Katrina dataset
• Evaluate the wind damage ONLY
• Do not evaluate the surge damage
Methodology:
• Assign alphanumeric RS Scale rating to each FR12 structure
• Predict the ground-level damage state using the developed
models
• Assign DOD from EF Scale to each structure in ground-based
dataset
• Compare the predicted ground-level damage state to the actual
ground-level damage state obtained by the ground-based
survey
Remote-Sensing Datasets: Hurricane Katrina
Aerial imagery obtained in Harrison, Hancock, and
Jackson Counties in Mississippi
 September 6th-11th, 13th, and October 9th, 2005
Pictometry images
• 15 cm resolution
• Sample size: 517 FR12 structures
 August 30th-31st, and September 2nd, and 4th, 2005 NOAA
images
• 37 cm resolution
• Sample size: 1008 FR12 structures
 Averaged aerial imagery dataset
• Sample size: 505 FR12 structures
Remote-Sensing Datasets: Hurricane Katrina
NOAA
37 cm resolution
Pictometry
6 in. resolution
Remote-Sensing Datasets: Hurricane Katrina
RS-scale Evaluated from NOAA Imagery
RS-scale Evaluated from Pictometry Imagery
0%
0%
2%
0%
0%
0%
0%
1%
A 0%
3%
8%
0%
21%
0%
0%
B 1-25%
B 26-50%
0%
2%
0%
A 0%
0%
B 1-25%
B 26-50%
2%
B 51-75%
10%
25%
B 51-75%
4%
0%
B >75%
B >75%
C 1-25%
C 1-25%
C 26-50%
C 26-50%
19%
C 51-75%
C 51-75%
C >75%
C >75%
D 1-25%
D 1-25%
D 26-50%
D 26-50%
D 51-75%
D 51-75%
D > 75%
43%
D > 75%
60%
Ground-Based Datasets: Hurricane Katrina
Made landfall at Buras, Louisiana at a Category 3
hurricane on August 29th, 2005
• Wind damage
• Surge damage
VIEWS deployed to capture high-definition ground-
based photographs
Video captured in Hancock, Harrison, and Jackson
Counties in Mississippi; processed to extract still,
georeferenced images
Ground-Based Datasets: Hurricane Katrina
EF-1
EF-2 Damage
Damage
DOD
DOD2.5
6
Validating Regression Models
Predicted Ground-Based Damage States Determined
from Averaged Satellite Imagery and Eq. 4-5
DOD 5
1%
DOD 6
0%
DOD 7
0%
DOD 5
1%
DOD 8
0%
DOD 4
3%
DOD 2.5
6%
Predicted Ground-Based Damage States Determined
from Averaged Satellite Imagery and Eq. 4-6
DOD 6
0%
DOD 4
4%
DOD <1
11%
Predicted Ground-Based Damage States
Determined from Averaged Satellite Imagery and Eq.
4-7
DOD 7
0%
DOD 8
0%
DOD 5
0%
DOD 4
3%
DOD <1
11%
DOD 3
DOD 2.5
4%
6%
DOD 3
7%
DOD 1
23%
DOD 2.5 DOD 3
7%
4%
DOD 1
23%
Predicted Ground-Based Damage States Determined
from Averaged Satellite Imagery and Eq. 4-8
DOD 5
0%
DOD 6
0%
DOD 4
2%
DOD 2.5
8%
DOD 7
0%
DOD 8
0%
DOD 2
52%
Predicted Ground-Based Damage States Determined
from Averaged Satellite Imagery and Eq. 4-9
DOD 5
2%
DOD 3
4%
DOD 2.5
6%
DOD 7
0%
DOD 8
0%
DOD <1
11%
DOD 3
4%
DOD 1
23%
DOD 2
52%
DOD 6
1%
DOD 4
4%
DOD <1
11%
DOD 7
0%
DOD 8
0%
DOD <1
11%
DOD 1
23%
DOD 2
51%
DOD 2
49%
DOD 6
0%
DOD 1
24%
DOD 2
48%
Validating Regression Models
Actual Ground-Based Damage States for the Homes
in the Averaged Aerial Dataset
DOD 5
0%
DOD 6
2%
DOD 7
0%
DOD 8
0%
DOD 4
5%
DOD 3
4%
DOD <1
33%
DOD 2.5
17%
DOD 2
24%
DOD 1
15%
Validating Regression Models
Comparison of Actual Ground-Level Damage States
vs. Predictions from Eq. 4-6 Using Averaged Aerials
Comparison of Actual Ground-Level Damage States
vs. Predictions from Eq. 4-7 Using Averaged Aerials
10
10
8
8
8
6
6
6
4
4
4
2
2
Eq. 4-5
predictions
1:1 ratio
0
-2
Predicted DOD
10
Predicted DOD
Predicted DOD
Comparison of Actual Ground-Level Damage States
vs. Predictions from Eq. 4-5 Using Averaged Aerials
0
2
4
6
8
2
Eq. 4-6
predictions
1:1 ratio
0
Linear10(1:1
ratio)
-2
0
0
2
4
6
8
Linear10(1:1
ratio)
-2
-2
-2
Eq. 4-7
predictions
1:1 ratio
0
2
-2
Actual DOD
Actual DOD
Actual DOD
Comparison of Actual Ground-Level Damage States
vs. Predictions from Eq. 4-9 Using Averaged Aerials
10
10
8
8
6
6
Predicted DOD
Predicted DOD
Comparison of Actual Ground-Level Damage States
vs. Predictions from Eq. 4-8 Using Averaged Aerials
4
4
2
2
Eq. 4-8
predictions
1:1 ratio
0
-2
0
2
4
6
8
Linear10(1:1
ratio)
Eq. 4-9
predictions
1:1 ratio
0
-2
0
2
4
-2
-2
Actual DOD
4
Actual DOD
6
8
Linear10(1:1
ratio)
6
8
Linear10(1:1
ratio)
Validating Regression Models
Frequency analysis: Given an actual DOD
rating, with what frequency does the model
predict it exactly? To within one DOD category?
To within two DOD categories?
Models can predict DOD<1 through DOD 3 to
within two DOD categories for at least 86% of
the observations
Results are not as good for DOD 4 and higher,
but data are more limited at these levels
Future Work
Inclusion of datasets with higher levels of damage
• Lots of DOD<1 through DOD 4 damage
• Less than 7% of structures from “Super Tuesday” were rated > DOD 4
• Less than 2% of structures from Hurricane Katrina were rated > DOD 4
Include additional variables
•
•
•
•
•
Tornadoes—proximity to track
Hurricanes—H*Wind wind speed
Presence of debris
Condition of surrounding structures
Resolution of remote-sensing imagery
Investigate economic recovery, by utilizing surveys from
different dates
Expand the study to include additional DI’s (mobile homes,
apartments, shopping malls, schools, etc.)
Acknowledgements
ImageCat Inc.
 Anneley McMillan
 Paul Amyx
 Ron Eguchi & Beverley Adams
Texas Tech University
 Amber Reynolds
 Rich Krupar
NSS & Associates
NSF (IGERT DGE-0221688)
MCEER
Contact Information: [email protected]