REYNOSA: Central American Migration & US Politics

Volume XX | June 2019

Extreme weather across the United States caused

over $306 billion in total damage in 2017—the most

ever recorded in a single year. Since 1980, the United

States has experienced 219 weather-related disasters

in which overall damages exceeded $1 billion (adjusted

for inflation). The frequency of these significant

weather-related disasters has steadily increased in

each subsequent decade.

This article demonstrates how Maxar’s unique

geospatial tools can be used to analyze and classify

a population’s vulnerability to extreme weather-

related events.

Part I discusses the surging costs associated

with Atlantic hurricanes and how strong coastal

growth has increased susceptibility to storm surge

and flooding.

Part II discusses the 2017 wildfire season and how

abnormal weather and vegetation health contribute to

overall risk. Part II also highlights how analyzing past

wildfire activity and vegetation health in the context

of our human landscape can provide valuable data to

classify future vulnerability and help align resources.

National Urban Change Indicator (NUCI) is Maxar’s unique

National Urban Change Indicator (NUCI) incorporates Landsat

imagery and change detection technology to identify areas of

new construction, such as parking lots or concrete structures.

NUCI is particularly valuable in showing urbanization and areas

vulnerable to flooding. For this study, NUCI data for Greater

Houston was used to show the amount of development over the

past 15 years—which has a direct effect on the city’s vulnerability

to flooding events.

Global Weather Interactive (GWI) is a Maxar application

within Weather Desk™ that provides custom access to over

900 domestic and 6,000 international weather stations in

more than 150 countries worldwide. GWI analyzes temperature

extrema, precipitation, and derived parameters such as average

temperature, average precipitation, and normal departures for

each. For the purpose of this study, GWI was used to analyze

temperature and precipitation data across the southwestern

United States for the past year.

Normalized Difference Vegetation Index (NDVI) The Normalized

Difference Vegetation Index (NDVI) is a method used to extract

and model characteristics of vegetation in remotely-sensed data.

NDVI is calculated using the visible and near-infrared light

reflected by vegetation. Healthy vegetation generally absorbs

more visible light, and reflects more near-infrared light, with an

inverse relationship occurring in unhealthy vegetation. For the

purpose of this study, the NDVI was applied to multispectral

WorldView-2 and GeoEye-1 satellite imagery (both with a sub

2-meter resolution) to provide insights into how abnormal and

extreme weather impacted vegetation health and density over

the past year.

Signature Analyst™ is Maxar’s statistical geospatial

pattern analysis tool that predicts where events are likely to

occur in the future based on the signatures of previous events.

For this study, the Signature Analyst model incorporated over 40

human landscape data layers and a fuel rank (potential fire

behavior) index to evaluate wildfire ignition risk.

EXECUTIVE SUMMARY

Summary of Unique Tools & Applications

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12

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'80-'89 '90-'99 '00-'09 '10-'17

Billion-Dollar Disasters Yearly Average for Decade

BILLION-DOLLAR NATURAL DISASTERS IN THE U.S.

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S P O T L I G H T

PART I. HURRICANES

Hurricanes are by far the most damaging and costly

natural disasters in the United States. The bulk of 2017’s

total damage came from Hurricane Harvey ($125 billion),

Hurricane Irma ($50 billion), and Hurricane Maria ($90

billion). All three of the storms rank among the top-5 most

costly hurricanes in United States history. In fact, 11 of the

top-12 most costly hurricanes have occurred since 2004.

As depicted below, hurricanes are responsible for drastic

spikes in weather-related damages.

Considering the Relationship between Climate Change and the Atlantic Hurricane Season The intensity of the Atlantic storm season has risen noticeably

over the past three decades, and some of the most active years

have occurred since 2005. The frequency of major (category-3 or

greater) hurricanes in each season has also increased. However,

despite the recent trends, high levels of hurricane activity were

also seen during the 1950s and 1960s.

While climate change has contributed to an increase in sea

surface temperatures (which favors hurricane development),

the tropical Atlantic Ocean has also experienced higher

wind shear and a drop in mid-tropospheric relative humidity

(both of which are unfavorable to hurricane development).

According to a study by the Geophysical Fluid Dynamics

Laboratory (GFDL), there is little current evidence that climate

change will cause a detectable increase in Atlantic hurricane

activity in the coming century. The GFDL study does, however,

predict that Atlantic hurricanes and tropical storms will likely

exhibit higher rainfall rates, which is a significant concern

for areas already prone to flooding.

Hurricane Andrew: $48B

Hurricanes Katrina, Rita, and Wilma: $210B

Hurricanes Ike and Gustav: $42B

Hurricanes Harvey,Irma, and Maria

Hurricane Sandy: $71BDrought across central agricultural states: $33B

‘90 ‘92 ‘94 ‘96 ‘98 ‘00 ‘02 ‘04 ‘06 ‘08 ‘10 ‘12 ‘14 ‘16

$300B

$250B

$200B

$150B

$100B

$50B

0

TOTAL DAMAGE OF WEATHER-RELATED DISASTERS EXCEEDING $1 BILLION (1990-2017)

NUMBER OF MAJOR HURRICANES BY SEASON (1960 - 2017)

Major Hurricanes 5-Year Mean Costliest Hurricanes in US History$

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0‘60 ’70 ‘80 ’90 ‘00 ’10 ‘17

$$

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$

$

$

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development in storm-prone areas increases susceptibility to hurricane damage

Widespread research and analysis identifies population

growth and coastal development as the most significant

factors contributing to the rising costs associated with

hurricanes. Between 2000 and 2010, the population of

counties susceptible to hurricane damage grew 22 percent

faster than the rest of the country. Analysis of US Census

data and Federal Emergency Management Agency (FEMA)

data further validates this alarming trend.

The following maps show the relationship between county-

level flood losses (1978-2015) and population growth (2010-

2015). As depicted, the areas with the highest concentration

of flood loss correspond with the greatest population

growth. Harris County, Texas (which includes the Houston

metro area) is the fastest growing region in the country. It

ranks highest in the United States in both population growth

and flood loss. Miami-Dade County and Broward County

(both in Florida) are the only other two counties in the

United States to fall within the top-20 on both measures.

MAXAR STORM

TRACK DATA SHOWS

THE TWO COSTLIEST

HURRICANES IN

US HISTORY (HARVEY

AND KATRINA)

TOTAL COST

$233BILLION

Hurricane HarveyHurricane Katrina

Harris CountyMiami-Dade County and Broward County

Low High

Low High

Harris County

Miami-Dade County and Broward County

POPULATION GROWTH BY COUNTY (2010-2015)

FLOOD LOSSES BY COUNTY (1978-2015)

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S P O T L I G H T

According to Census data, 25 percent of all residential

units in Harris County have been constructed since 2000.

This significant urbanization has increased Houston’s

risk of flooding because there are fewer areas where

the ground can absorb and hold water. By incorporating

Maxar’s National Urban Change Indicator (NUCI), the map

to the right displays this new development across the

Greater Houston area. The rapid population growth and

accompanying development also explains the magnitude

of damage caused by Hurricane Harvey. The following

image series blends color imagery with Sentinel-1A (SAR)

data to highlight some of the flooding in Houston during

the hurricane.

Conclusion: There is a strong correlation between the

expanding human footprint along thes over the past

10-15 years. Stronger-than-usual storms have also

exacerbated the vulnerability caused by rapid population

growth and urbanization in storm-prone areas. This article

New Development

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Water

Water Water

Water

demonstrates how Maxar’s unique tools and geospatial

applications can be used to analyze population change,

urbanization, weather patterns, and historic storms in

order to classify population vulnerability and flood risk.

CYPRESS BEFORE HARVEY (AUGUST 4, 2017)

CYPRESS AFTER HARVEY (AUGUST 28, 2017)

NUCI: HOUSTON’S DEVELOPMENT IN THE PAST 15 YEARS

NORTH OF ROSHARON BEFORE HARVEY (AUGUST 4, 2017)

NORTH OF ROSHARON AFTER HARVEY (AUGUST 28, 2017)

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PART II. WILDFIRES

In addition to being the costliest year recorded for

hurricane damage, 2017 also marked the costliest

wildfire season. Wildfires across several western states

(particularly California) caused $18 billion in damage with

the destruction of more than 15,000 homes, businesses,

and other structures.

The 2017 wildfire season was much longer than normal as

well. California experienced six large wildfires in the month

of December, normally the calmest time of year for large

fire activity. These December wildfires burned over 308,000

acres. By comparison, California had just seven large

wildfires ignite in the month of December over the previous

17 years (2000-2016), and the total area of those wildfires

amounts to only 7 percent of what burned in 2017 alone.

The Thomas Fire, pictured above, began in early-December

2017 and is now considered California’s largest wildfire

in modern history. It torched over 273,000 acres across

Ventura and Santa Barbara Counties.

Deviation From Normal Precipitation62% Increase

Santa Barbara

San Francisco

Los Angeles

PRECIPITATION DEVIATION FROM NORMAL (JANUARY - JUNE 2017)

EUROPEAN SPACE AGENCY SENTINEL-2 (ACQUIRED FROM NASA.GOV)

Abnormal and Extreme Weather Increases Susceptibility to WildfiresBy combining weather data, remote sensing measurements of

vegetation health, and high-resolution satellite imagery, Maxar

can show how weather-related factors have contributed to

these extreme wildfires. Late-2016 and early-2017 was an

abnormally wet period for most of California. As depicted

in the map below, Santa Barbara experienced a 62 percent

increase from normal precipitation levels between January

and June 2017. These wet conditions caused an abundance

of new vegetation and increased the vegetation density

throughout the Santa Ynez Mountains north of Santa Barbara.

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S P O T L I G H T

Abnormal hot and dry conditions in the summer and fall

dried out large swaths of vegetation, significantly increasing

the area’s hazardous fuel potential. Between September

and December 2017, Santa Barbara County experienced a

92 percent decrease from normal precipitation levels and a

four degree (Fahrenheit) increase in maximum temperature

(see maps below). Additionally, the average maximum

temperature in the two weeks preceding the Thomas Fire

was six degrees (Fahrenheit) higher than it was in 2016.

The area also did not receive any rain during the two weeks

preceding the fire, whereas 2016 experienced .89 inches of

rain during this same time-period.

Deviation From Normal Precipitation

Maximum Temperature Deviation From Normal

92% Decrease

Increase by 4° Fahrenheit

Santa Barbara

San Francisco

Los Angeles

Santa Barbara

San Francisco

Los Angeles

PRECIPITATION DEVIATION FROM NORMAL (SEPTEMBER - DECEMBER 2017)

MAXIMUM TEMPERATURE DEVIATION FROM NORMAL (SEPTEMBER - DECEMBER 2017)

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The image series below depicts the Normalized Difference

Vegetation Index (NDVI) for the Santa Ynez Mountains just

north of Montecito, Santa Barbara County. The NDVI is

a method of extracting and modeling characteristics of

vegetation in remotely sensed data. The NDVI image series

and accompanying high-resolution satellite imagery shows

how abnormal and extreme weather affected the area’s

vegetation health and density over the past year.

October 25, 2016: Depicting Normal Conditions

April 8, 2017: Conditions After a Strong Rain Season

December 19, 2017: Post-Fire Conditions; Dry/Burned

San Ysidro Creek (inset) | October 25, 2016 | WorldView-2

Unhealthy Healthy

San Ysidro Creek (inset) | April 8, 2017 | WorldView-2

San Ysidro Creek (inset) | January 26, 2018 | WorldView-2

NDVI AND SATELLITE IMAGERY TIME-SERIES FOR THE SANTA YNEZ MOUNTAINS NORTH OF MONTECITO

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Increased Risk of Mudslides Following WildfiresMudslides are another natural disaster than can immediately

follow a wildfire because of destroyed vegetation and

the soil’s inability to soak up water. On January 9, 2018,

Montecito experienced near-record rainfall (2.34”). The

charred landscape of the Santa Ynez mountain range

subsequently gave way to a devastating post-fire mudslide

that killed 21 people and destroyed hundreds of homes. The

following map shows Montecito’s proximity to the Santa Ynez

Mountains and the city’s vulnerability based on the direction

of rainfall runoff. Also displayed are pre- and post-mudslide

satellite images.

APRIL 8, 2017 | WORLDVIEW-2 JANUARY 11, 2018 | GEOEYE-1

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SIGNATURE ANALYST MODEL: GREATEST SUSCEPTIBILITY TO WILDFIRE IGNITION (TOP-10%)

Analyzing Wildfire Risk and Causation through Statistical Geospatial ModelingAccording to recent research by the National Academy of

Sciences, humans are responsible for roughly 84 percent

of all wildfires across the United States. As such, analyzing

wildfires in the context of our human landscape is especially

valuable for classifying vulnerability and helping align

resources and preventative measures.

Maxar’s unique Signature Analyst tool was used to evaluate

wildfire ignition risks in Southern California. Through

statistical and geospatial modeling, Signature Analyst

predicts where events are likely to occur in the future

based on the geospatial signatures of previous events.

The Signature Analyst model incorporated 48 human

landscape data layers and over 2.1 million features specific

to California. The model also included a fire fuel rank index

compiled by the California Department of Forestry and

Fire Protection. (The fuel rank index evaluates potential

fire behavior by aggregating variables like vegetation type,

Top-10% Susceptibility

High Highest

Santa Barbara

brush density, tree density and slope.) The points of origin

for 10 non-lightning-related wildfires were used to train the

model. The Signature Analyst output highlights areas with

the greatest assessed wildfire ignition risks. Top factors

include a “very high” fuel ranking, followed by close proximity

to tracks, trails and service roads.

Conclusion: Fire-prevention strategies should be made a

priority. Over the past several years, the costs of fighting

wildfires has gone from consuming 15 percent of the US

Forest Service’s budget to 55 percent. With increasing

fire suppression costs and a flat budget, prevention and

preparedness efforts have suffered. Without comprehensive

fire management, wildfire risk and associated costs will

continue to rise.

This article demonstrates how Maxar’s unique tools and

geospatial applications can be used to analyze wildfire risk

factors like vegetation health and density, as well as predict

future risk through statistical geospatial modeling.

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