ENHANCE DISASTER PREPARATION
AND RESPONSE WITH REMOTESENSING TECHNOLOGIES
Industry PerspectiveADVANCES IN DATA ANALYSIS,MANAGEMENT AND MACHINE LEARNING
ARE IMPROVING DISASTER MANAGEMENT
The recent proliferation of remote sensing platforms has presented those who prepare, respond and help
communities recover from natural disasters with unprecedented opportunities and challenges. Accurate, unbiased
situational awareness information is available faster than ever. But the enormous volume of raw data is creating
new problems related to the timely analysis, extraction and dissemination of useful information. NV5 Geospatial
has committed to creating product and service solutions that put actionable intelligence into the hands of disastermanagers both before and after a catastrophic event occurs.

On the evening of August 4, 2020, a Beirut
warehouse filled with ammonium nitrate
exploded with enough force to obliterate
the entire port facility and damage
buildings six miles away. There was an
immediate scramble of activity to try
and get intelligence as to what happened
and what the effects were. Remarkably,
just hours later in the dark of night, theEuropean Sentinel-1 satellite captured
synthetic aperture radar (SAR) data of thescene providing disaster response officials
their first synoptic overview ofthe destruction.
The Beirut explosion illustrates the value ofsatellite remote sensing as a critical sourceof information for disaster management.
Since the 1970s and 80s, we have reliedon Landsat and SPOT imagery to provide
coarse-resolution views of landscapes
devastated by disasters like floods, oilspills, tornadoes and wildfires. Most ofthese data sets, however, were collecteddays or weeks after the event due toinfrequent revisit cycles of the satellites,
usually limiting their applications to theclean-up and recovery phase ofdisaster response.

“Effective disaster response starts before the event occurs – inthe planning, preparation and prevention phases...this is where
remote sensing and geospatial technologies are increasingly
being applied,” said O’Connor.
In the past several years, however, the useof imaging satellites and other geospatial
technologies has evolved to support
emergency response in the immediate
aftermath of a disaster, not weeks later –an enormous leap forward. For response
agencies, speed is of the essence, andsituational awareness is key. They needto know which areas were hit the hardest
so they can deploy search-and-rescue
personnel along with water, food andmedical aid in a prioritized manner.
Remote sensing platforms continue toenhance the kinds of information that can
impact mitigation, preparation, response
and recovery for all types of disasters.
We are living in a Golden Age of remotesensing where at least one of a plethoraof new imaging satellites is likely to passover a trouble spot within hours of an
unplanned event. And more importantly,that satellite will probably be carrying theright sensor for the job. As witnessedin Beirut, Sentinel-1 is equipped with a
SAR sensor capable of seeing throughdarkness and lingering smoke, somethingan optical satellite could not accomplish.
The transition of geospatial technology
from the disaster recovery phase to theemergency preparation and response
phase is a huge achievement, according
to Amanda O’Connor, Industry Principal at
NV5 Geospatial. But it’s just part ofthe story.

NEW TECHNOLOGY: OPPORTUNITIES AND CHALLENGES
While satellites play a glamorous role in this Golden Age, they are accompanied by a supporting cast of manned
and unmanned aircraft capturing highly detailed data in localized situations. Airborne sensors have evolved rightalong with spaceborne devices, collecting diverse data sets faster and more economically than ever before.
Even on the ground, land-based sensors, part of the emerging Internet of Things, are enabling better emergency
management and response.
The abundance of sensors has been a net positive for disaster planning and situationalawareness, but it has also created new challenges related to dealing with all of this data.
As sensor platforms have multiplied and their collected data files have become larger andmore complex, incident commanders and others in the emergency response chain face atleast three distinct challenges:> Find the right data for a particular situation> Analyze raw data to extract situational information> Deliver actionable information to the front lines

Fortunately, NV5 Geospatial has
developed a series of products andservices to streamline the querying,
retrieval, exploitation and disseminationof actional intelligence for disaster
preparation and response.
Several geospatial data collection
technologies are the main drivers behindthe unprecedented opportunities andchallenges facing the disastermanagement community.

SMALLER SATELLITES
The miniaturization of propulsion,
stabilization and power subsystems has
reduced the size of satellites to the point
where entire constellations can be built
and launched for less than the cost of asingle legacy platform like Landsat and
SPOT. Multi-satellite constellations havebecome the norm for new commercial
Earth observation systems.
The upside to so many satellites inorbit is that nearly every spot on the
globe can be imaged several times
throughout the day. This means thedestruction from an explosion, storm or
earthquake can usually be mapped within
hours. Even more importantly, multiple
satellite passes throughout the day andfollowing days means dynamic ongoingincidents like floods and fires can betracked continuously.

As noted, the downside is that the enormous volumes of data being captured so quickly
from different types of sensors make it difficult to find and analyze the right image for agiven situation.

ADVANCED OPTICAL SENSORS
Imaging sensors have gotten smaller, less expensive and more sophisticated allowing them to fly on small satellites, aircraft anddrones. While optical sensors by their nature can operate only in daylight, the major technical improvement is spatial resolution,
which enables details as small as 30 cm to be seen from space and objects just a few centimeters to be discerned from the sky.
Even minor damage to infrastructure and buildings can be mapped accurately in the wake of a disaster – think being able to detecta large rope tethered to a sunken boat. The advantages of higher spatial resolution are infinite, but the downside is enormous files
that are difficult to archive and search.

SPECIALTY SENSORSPROFESSIONAL UAVS

The two most important specialty sensors
being deployed for disaster management
today are Hyperspectral and Thermal
Infrared scanners.

The cost of unmanned aerial vehicles(UAVs), or drones, with the stability andpower to carry mapping-grade imaging
sensors and laser scanners (LiDAR) has
transformed the remote sensing industry.
UAVs are now performing imaging andelevation mapping missions over small
areas of interest at a cost and speed not
possible for manned aircraft.

The wait for commercial hyperspectral
sensors has been long, but they are now
being flown on satellites, manned space
craft like the International Space Station(ISS), aircraft and drones. These passive
instruments measure reflected energy
typically in 30 or more discrete wavelengths
spanning the visible (red, green, blue), nearinfrared and short-wave
infrared. This data contains rich, often
invisible insights into objects on the ground,especially vegetation, allowing for subtle
differentiation by type or condition.
In disaster management, hyperspectral
imagery is now being used to classify
land cover vegetation in areas prone towildfires. Some species burn more readily
than others, and this helps planners map
sites requiring mitigation. Additionally,
FEMA has begun experimenting withhyperspectral data to classify types ofpost-storm debris piles to facilitate cleanup efforts. The main challenge of thisvaluable data today is that it is still so
new few geospatial professionals haveexpertisein processing it.
The applications of thermal sensors,
mostly flown on manned aircraft anddrones, are more straight forward. These
optical instruments measure emissionsof heat from sources. Today, their mostcommon application is to map hot spots
and perimeters of wildfires. A significant
advantage is their ability to capture thermal
data even through heavy smoke.

SYNTHETIC APERTURE RADAR(SAR)
Unlike passive optical sensors, SAR
systems have their own power sources
allowing them to capture data day or night,
through clouds, light smoke and in mostweather conditions. For decades, thepower required to operate these systems
limited their use to large satellites oraircraft. In just the past two years,
technology breakthroughs have put SAR
sensors in small satellites – most notably
the ICEYE and Capella systems – and insmaller manned aircraft.
Active all-weather imaging makes SAR
ideal for capturing disaster information atnighttime, during disasters that cause large
amounts of smoke like fires or volcanoes,in cloudy equatorial regions and in polar
darkness. Radar also precisely delineateswater/land boundaries, providing
extremely accurate flood extent mapping.
It can also pinpoint oil spills on water. On
land, SAR can mitigate geohazards related
to ground movement by spotting minuteshifts in landform positions and elevations.
The traditional challenge for operational
SAR use has been the complexity of thedata which historically required expensive
and complicated processing softwarethat even the most experienced geospatial
professionals were not trained to utilize. The
data and tools are now more approachable
to the geospatial community.

For disaster applications, they can fly
much closer to the surface of objects,
getting incredibly detailed views ofdamage, often from many perspectives.
In addition, some drones are so
inexpensive they are considereddisposable, capturing data in conditions
too hazardous for manned aircraft.
In terms of disaster mitigation, LiDARequipped drones are increasingly being
used in powerline corridors to monitor
vegetation encroachment that could
spark a fire.
The current challenge for UAV use inchaotic situations is the lack of standards
for data collection. Multiple drones may
be flying over a site simultaneously, each
capturing data at a different resolution froma different altitude. Syncing such a variety
of data sets for a consistent viewof the situation is a major challenge.
Complementing these improvements
in data collection are equally criticalbreakthroughs in data processing andexploitation driven by advances incomputer power, automation of image
processing algorithms and the integrationof Artificial Intelligence into workflows.

MANAGING THE DATA TSUNAMI
For decades, NV5 has developed solutions for the U.S. military, and has been at the front lines of addressing
challenges of enormous volumes of raw data coming in too fast for it to be processed, analyzed and digested
by commanders. In the last decade, NV5 has pivoted to customizing many of these same data management
remedies as solutions to similar problems facing civilian and private sector organizations.
As noted, the overarching data
management problem in chaotic disaster
scenarios is finding the right data set,
processing it into understandable
information and disseminating that
information to responders in the field.
To accomplish data query and informationdelivery, NV5 Geospatial offers Jagwire™,
a web-based software product providing an
end-to-end geospatial data management
solution and delivering near real-timesearch and discovery, image exploitationand information dissemination. Jagwire iscapable of ingesting enormous volumesof incoming geospatial data, including live
video feeds, satellite imagery, LiDAR point
clouds and GIS vector layers.
“Jagwire is designed to deliver imagery
and other geospatial files so they are
understandable to personnel with no GIS
or image processing training,” said Joey
Griebel, NV5 Geospatial North American
Sales Manager. “Even different typesof video or imagery streamed in realtime from a UAV can be presented as acommon operating picture to many incident
commanders at different locations.”

During multiple California wildfires in the summer of 2020, NV5 supported fire
perimeter mapping activities. Throughout the disasters, the CA Air National Guard
flew drones equipped with thermal infrared video cameras. These video feeds were
downlinked to a third-party processing hub on the ground that applied AI algorithms to
extract wildfire perimeters from the thermal infrared data. In seconds, this information
was transmitted to Jagwire, which streamed the annotated videos to mobile devices in
the field and laptop computers in other incident command centers.

SIMPLIFYING INFORMATION EXTRACTION
Data management is critical, but it is only the first step in dealing with the torrent of incoming geospatial data.
The next step is extracting meaningful information from the wide variety of complex data sets now available.
ENVI Modeler is frequently used in disaster
preparation to set up complex information
extraction workflows to be performed
repeatedly in an automated fashion as
new imagery is collected and received. The
workflow, for example, could continuously
monitor vegetative class changes likegrasses to shrubbery in hyperspectral
imagery and trigger a warning when certain
dangerous thresholds are exceeded.

NV5 offers an information extraction
solution in the form of its commerciallyavailable ENVI® software. ENVI is a robust
geospatial data analysis software thatallows anyone with minimal training to
extract meaningful information from alltypes of imagery – optical, multispectral,
hyperspectral, thermal, LiDAR and SAR.
“ENVI users don’t have to be remotesensing scientists or big data analysis
experts to derive meaningful informationfrom raw data sets,” said O’Connor.
NV5 has developed science-based image
analysis algorithms and integrated theminto automated workflows in ENVI. This
enables users to derive answers to themost complicated geospatial queries
from enormous data sets with a few
clicks of the mouse. Moreover, ENVI gives
more experienced users the means tocreate algorithms and build customized
automated analysis processes of their own
via creating visual models or coding.
To facilitate certain image analysis
applications, ENVI bundles complex
workflows into more than 200 automatedmodules or tools (often called ‘Tasks’).
These span many functional areas
including georeferencing, calibration,
spectral analysis, morphological filters,
classification, object detection, terrain
modeling, point cloud analysis, SAR
analysis, time series operations, machinelearning / deep learning and many more.

Change detection is among the disastermanager’s most powerful tools. Images
acquired over the same area before andafter an event are loaded into ENVI, andwith the push of a few buttons, the software
identifies where change – or damage – hasoccurred over time.
The ENVI Feature Extraction module
puts the power of the computer to work
identifying features based on their spatial,
spectral and textural characteristics.
Rivers, lakes, fields, trees and coastlines
can automatically be mapped.
ENVI feature extraction algorithms
have become so sophisticated, theycan even differentiate objects by theircondition in multispectral imagery. For
example, stressed plants can be isolated
automatically from healthy ones in farm
fields or forests. This information is used tofind and remediate disease or infestation
before they expand into widespread crop
failures which can wreak havoc on foodsecurity and economic stability.

In addition, ENVI is easily extendable via its
scripting language, IDL®. This enables ENVI
users to create customized algorithms,
workflows and analytics that suit theirneeds. For example, NASA techniciansused IDL to create 3D visualizations ofrain and snowfall data captured by its
Global Precipitation Measurement (GPM)
mission. GPM gathers precipitation
data from several satellites carrying
radar instruments for use in a variety of
applications. Among the most importantis modeling precipitation volumes in nearreal time as a predictor of dangerous
flooding and related rainfall events.

AUTOMATING ANALYTICS WITH DEEP LEARNING
The identification of specific objects
in data sets has been greatly facilitatedby automated recognition with the
ENVI FX module. There are numerous otherfeatures specific to identifying the destruction and damage such as spectral,
textural and special qualities related
to various catastrophes that disastermanagers need to find andmap in remotely sensed data sets.
NV5 has a solution for this challenge
as well – ENVI Deep Learning. A formof Artificial Intelligence, Deep Learning,enables the user to ‘teach’ the software
how to recognize nearly any feature orcondition in the data by its spectral,
situational and neighboring features.
Where earlier forms of Machine Learningalgorithms required the user to ‘show’ thecomputer hundreds of examples of thefeature to be identified, Deep Learning
uses iterative algorithms that can learn
from just a dozen examples in some cases.
“As with the other ENVI modules, you
don’t have to be an Artificial Intelligenceexpert or programmer to use the ENVI
Deep Learning module,” said Griebel.
“The module has intuitive tools andworkflows that enable users to easilylabel data and generate models with theclick of a button.”
Disaster planners have embraced the
ENVI Deep Learning module for thesignificant savings in time it provides
them, compared with manual processes.

“Finding and counting all the damaged houses, and differentiatingthem from rubble piles, in the imagery would have taken days
with the naked eye” said Griebel.
In the aftermath of a March 2020 tornado that tore through a suburb of Nashville, Tenn.,
NV5 obtained aerial imagery collected after the deadly incident. The image resolution was
sufficient to clearly identify damage to homes and other structures. In less than twohours, a technician used the ENVI Deep Learning module to teach the computer
to find damaged structures on its own. The actual mapping of destruction in theimagery took just a few minutes. “Finding and counting all the damaged houses, and differentiating them from rubble piles, in the imagery would have taken days with the nakedeye,” said Griebel.

EXTRACTING INFORMATION FROM
RADAR AND HYPERSPECTRAL DATA
Image data sets are becoming more complex, and SAR and Hyperspectral are the two leading examples.
Their size makes them particularly difficult to automate and exploit. Again, NV5 has leveraged the analyticalcapabilities in ENVI to automate the information extraction functions from these two data sets.
To make exploiting SAR data moreuser friendly, NV5 has introduced ENVI
SARscape ® Analytics, an analysis modulethat integrates multiple processing functions
and algorithms into simplified, automatedworkflows. As with the other ENVI modules,
ENVI SARscape puts powerful scientificcapabilities into the hands of end users
to quickly and easily extract meaningful
results from SAR data sets.
In the Beirut port explosion cited earlier,NV5 technicians used ENVI SARscapeAnalytics to process the Sentinel-1 satellite
SAR data and then overlay it on an archived
optical image of the same area to performa change detection analysis. The result wasa detailed map highlighting any feature that
had been altered by the blast, including
structural damage or covering by debris.
With hyperspectral imagery more readily
available thanks to new sources of data
(Satellites, ISS hosted systems, aerial
and drone systems), disaster planners
and managers have greater access to therich information that they contain. NV5
Geospatial has continued its tradition ofcreating simplified push button workflows
for hyperspectral data sets.

The most popular spectral workflows are:

> Anomaly Detection> Atmospheric Correction> Change Detection

NV5 Geospatial applied deep learning technology to airborne images captured over Nashville, Tennessee, following a deadly March 2020 tornado. Using
the ENVI Deep Learning module, which is offered as an extension to ENVI geospatial analysis software, technicians identified where building damage
had been inflicted by the tornado and charted its precise path of destruction from the aerial photos – in less than two hours.

TAPPING THE INTERNET OF THINGS (IOT)
Not all remote sensing devices fly on satellites and aircraft. Data-gathering sensors are all around us,
and efforts are being made to federate these ground observing sensors for the good of society.
MONITORING AND ANALYSISSERVICES BECOMING POPULAR

One of the areas where NV5 has madea valuable impact is in hyper-local
weather monitoring. Despite billions
spent on Doppler radar and satellite
assets, meteorologists still struggle togain insights into weather conditions
that occur on a hyper-local scale and inreal-time. Ground-based sensors candeliver complimentary information incontext not before considered especiallyto drivers on the highway and can save
lives. Fortunately, an extensive camera
sensor array capable of capturing local
conditions, already exists in the UnitedStates and across the world in the form ofexisting traffic and surveillance cameras.
NV5 has tapped into a worldwide setof 50,000 highway CCTVs to develop
a ground-level situational awarenesssystem called Helios®, which uses AI
technology to monitor the incoming video
streams from traffic cameras in real time.
Helios algorithms continuously analyze
the video from thousands of cameras
to detect specific conditions such as
precipitation, fog, wet/icy pavement
and stopped traffic. The algorithms
differentiate between snow, and rain
and their intensity.

“Fog kills more people than flooding in
Florida,” said Eric Dixon, Helios ProductManager at NV5 Geospatial. “The state
wants to know precisely when and where
fog is occurring and heavy rains can bea predictor of flooding, just as snow canprecede slick roads,” said Dixon.
Helios, which is offered to state andfederal agencies, as well as privatecompanies, as a subscription service, cantrigger alerts when certain environmental
thresholds are exceeded. Localized
heavy rain information can be sent tometeorologists to adjust their forecasts,
and transportation agencies can better
manage slick roads or stopped traffic.
This information, in the context of a driver
can be disseminated to the public anddirectly to drivers in the affected areas.
Dangerous conditions detected by Helios
can even trigger warnings to be broadcast
on highway signs.
“Helios fills the gap in localized weather
information that could tell us what is
occurring locally during a big storm,
which is important information fordisaster managers to have,” said Dixon.

While the increase in data variety,volume and revisit frequency of remotesensing devices is a net positive for all
consumers of geospatial information,
the negative impacts are starting to befelt as well. Specifically, the growing
variety of data types and the sheer
volume of the massive data sets iscreating havoc for some end users.
Disaster management professionals
simply don’t have time to become
subject matter experts on all thedifferent image data sets available tothem. And many organizations can’t
afford to buy and maintain the computing
infrastructure now required to process
massive remote sensing data files.
“The majority of our clients are buying
ENVI software and doing the work
themselves, but there is definitely apivot toward contracting for services,
especially related to ongoing monitoringof specific areas of interest,” said Griebel.
In response, NV5 has rolled out
geospatial solutions services customized
to the needs of specific applications.
The client does not have to know which
type of imagery or analysis algorithms touse. All the client does is describe what
disaster management questions need
to be answered, and NV5 takes care of
the rest – from choosing the right data
set and creating a practical analytical
workflow to providing the computerhorsepower and formatting the answer inan easy-to-understand format – tailoredfor disaster managers, first responders
and others.

Disaster Response and Remote Sensing© 2023 NV5 Geospatial Solutions, Inc. | 11/23 AL
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NV5 is a leading provider of technology, conformity assessment, and consulting solutions for public and private
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